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Benhabib H, Brandenberger D, Lajkosz K, Demicco EG, Tsoi KM, Wunder JS, Ferguson PC, Griffin AM, Naraghi A, Haider MA, White LM. MRI Radiomics Analysis in the Diagnostic Differentiation of Malignant Soft Tissue Myxoid Sarcomas From Benign Soft Tissue Musculoskeletal Myxomas. J Magn Reson Imaging 2025; 61:2630-2641. [PMID: 39843987 PMCID: PMC12063761 DOI: 10.1002/jmri.29691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2024] [Revised: 12/11/2024] [Accepted: 12/12/2024] [Indexed: 01/24/2025] Open
Abstract
BACKGROUND Differentiation of benign myxomas and malignant myxoid sarcomas can be difficult with an overlapping spectrum of morphologic MR findings. PURPOSE To assess the diagnostic utility of MRI radiomics in the differentiation of musculoskeletal myxomas and myxoid sarcomas. STUDY TYPE Retrospective. POPULATION A total of 523 patients were included; histologically proven myxomas (N = 201) and myxoid sarcomas (N = 322), randomly divided (70:30) into training:test subsets. SEQUENCE/FIELD STRENGTH T1-weighted (T1W), T2-weighted fat-suppressed (fluid-sensitive), and T1-weighted post-contrast (T1W + C) sequences at 1.0 T, 1.5 T, or 3.0 T. ASSESSMENT Seven semantic (qualitative) tumor features were assessed in each case. Manual 3D tumor segmentations performed with radiomics features extracted from T1W, fluid-sensitive, and T1W + C acquisitions. Models were constructed based on radiomic features from individual sequences and from their combination, both with and without the addition of qualitative tumor features. STATISTICAL TESTS Intraclass correlation evaluated in 60 cases segmented by three readers. Features with intraclass correlation <0.7 excluded from further analysis. Boruta feature selection and Random Forest modeling performed using the training-dataset, with resultant models used to assess class discrimination (myxoma vs. myxoid sarcoma) in the test dataset. Radiomics score defined as probability class = myxoma. Logistic regression modeling employed to estimate performance of the radiomics score. Area under the receiver operating characteristic curve (AUC) was used to assess diagnostic performance, and DeLong's test to assess performance between constructed models. A P-value <0.05 was considered significant. RESULTS Four qualitative semantic features showed significant predictive power in class discrimination. Radiomic models demonstrated excellent differentiation of myxomas from myxoid sarcomas: AUC of 0.9271 (T1W), 0.9049 (fluid-sensitive), and 0.9179 (T1W + C). Incorporation of multiparametric data or semantic features did not significantly improve model performance (P ≥ 0.08) compared to radiomic models derived from any individual MRI sequence alone. DATA CONCLUSION MRI radiomics appears to be accurate in the differentiation of myxomas from myxoid sarcomas. Classification performance did not improve when incorporating qualitative features or multiparametric imaging data. PLAIN LANGUAGE SUMMARY Accurately distinguishing between benign soft tissue myxomas and malignant myxoid sarcomas is essential for guiding appropriate management but remains challenging with conventional MRI interpretation. This study utilized radiomics, a method that extracts quantitative mathematically derived features from images, to develop predictive models based on routine MRI examination. Analyzing over 500 cases, MRI radiomics demonstrated excellent diagnostic accuracy in differentiating between benign myxomas and malignant myxoid sarcomas, highlighting the potential of the technique, as a powerful non-invasive tool that could complement current diagnostic approaches, and enhance clinical decision-making in patients with soft tissue myxoid tumors of the musculoskeletal system. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Hadas Benhabib
- Department of Medical ImagingUniversity of TorontoTorontoOntarioCanada
- Joint Department of Medical ImagingUniversity Health Network, Sinai Health System, Women's College Hospital, Mount Sinai HospitalTorontoOntarioCanada
| | - Daniel Brandenberger
- Department of Medical ImagingUniversity of TorontoTorontoOntarioCanada
- Joint Department of Medical ImagingUniversity Health Network, Sinai Health System, Women's College Hospital, Mount Sinai HospitalTorontoOntarioCanada
- Institut für Radiologie und NuklearmedizinKantonsspital BasellandLiestalSwitzerland
| | - Katherine Lajkosz
- Department of BiostatisticsUniversity Health NetworkTorontoOntarioCanada
| | - Elizabeth G. Demicco
- Department of Pathology and Laboratory MedicineMount Sinai HospitalTorontoOntarioCanada
- Department of Laboratory Medicine and PathobiologyUniversity of TorontoTorontoOntarioCanada
| | - Kim M. Tsoi
- Department of Pathology and Laboratory MedicineMount Sinai HospitalTorontoOntarioCanada
- Department of Laboratory Medicine and PathobiologyUniversity of TorontoTorontoOntarioCanada
- Department of SurgeryUniversity of TorontoTorontoOntarioCanada
| | - Jay S. Wunder
- Department of SurgeryUniversity of TorontoTorontoOntarioCanada
- University Musculoskeletal Oncology Unit, Division of Orthopedic SurgeryMount Sinai HospitalTorontoOntarioCanada
| | - Peter C. Ferguson
- Department of SurgeryUniversity of TorontoTorontoOntarioCanada
- University Musculoskeletal Oncology Unit, Division of Orthopedic SurgeryMount Sinai HospitalTorontoOntarioCanada
| | - Anthony M. Griffin
- University Musculoskeletal Oncology Unit, Division of Orthopedic SurgeryMount Sinai HospitalTorontoOntarioCanada
| | - Ali Naraghi
- Department of Medical ImagingUniversity of TorontoTorontoOntarioCanada
- Joint Department of Medical ImagingUniversity Health Network, Sinai Health System, Women's College Hospital, Mount Sinai HospitalTorontoOntarioCanada
| | - Masoom A. Haider
- Department of Medical ImagingUniversity of TorontoTorontoOntarioCanada
- Joint Department of Medical ImagingUniversity Health Network, Sinai Health System, Women's College Hospital, Mount Sinai HospitalTorontoOntarioCanada
| | - Lawrence M. White
- Department of Medical ImagingUniversity of TorontoTorontoOntarioCanada
- Joint Department of Medical ImagingUniversity Health Network, Sinai Health System, Women's College Hospital, Mount Sinai HospitalTorontoOntarioCanada
- Department of SurgeryUniversity of TorontoTorontoOntarioCanada
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Noortman WA, Vriens D, Bussink J, Meijer TWH, Aarntzen EHJG, Deroose CM, Lhommel R, Aide N, Le Tourneau C, de Koster EJ, Oyen WJG, Triemstra L, Ruurda JP, Vegt E, de Geus-Oei LF, van Velden FHP. Multicollinearity and redundancy of the PET radiomic feature set. Eur Radiol 2025:10.1007/s00330-025-11637-7. [PMID: 40332568 DOI: 10.1007/s00330-025-11637-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2024] [Revised: 04/02/2025] [Accepted: 04/07/2025] [Indexed: 05/08/2025]
Abstract
INTRODUCTION The aim of this study was to map multicollinearity of the radiomic feature set in five independent [18F]FDG-PET cohorts with different tumour types and identify generalizable non-redundant features. METHODS Five [18F]FDG-PET radiomic cohorts were analysed: non-small cell lung carcinomas (N = 35), pheochromocytomas and paragangliomas (N = 40), head and neck squamous cell carcinomas (N = 54), [18F]FDG-positive thyroid nodules with indeterminate cytology (N = 84), and gastric carcinomas (N = 206). Lesions were delineated, and 105 radiomic features were extracted using PyRradiomics. In every cohort, Spearman's rank correlation coefficient (ρ) matrices of features were calculated to determine which features showed (very) strong (ρ > 0.7 and ρ > 0.9) correlations with any other feature in all five cohorts. Cluster analysis of an averaged correlation matrix for all cohorts was performed at a threshold of ρ = 0.7 and ρ = 0.9. For each cluster, a representative, non-redundant feature was selected. RESULTS Seventy-two and 90 out of 105 features showed a (very) strong correlation with another feature in the correlation matrix in all five cohorts. Cluster analysis resulted in 35 and 15 non-redundant features at thresholds of ρ = 0.9 and ρ = 0.7, including 6 and 3 shape features, 4 and 2 intensity features, and 25 and 10 texture features, respectively. Seventy or 90 redundant features could be omitted at these thresholds, respectively. CONCLUSION At least two-thirds of the radiomic feature set could be omitted because of strong multicollinearity in multiple independent cohorts. More redundant features could be identified using a less conservative threshold. Future research should indicate whether multicollinearity of the radiomic feature set is similar for other radiopharmaceuticals and imaging modalities. KEY POINTS Question Radiomic feature sets contain many strongly correlating features, which results in statistical challenges. Findings Analysis of the correlation matrices showed that the same radiomic features were strongly correlated in five independent [18F]FDG-PET cohorts with different tumour types. Clinical relevance At least two-thirds of the radiomic feature set could be omitted, because of strong multicollinearity. More redundant features could be identified using a less conservative threshold.
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Affiliation(s)
- Wyanne A Noortman
- Department of Radiology, Section of Nuclear Medicine, Leiden University Medical Center, Leiden, The Netherlands.
- Department of Medical Oncology, University Medical Center Groningen, Groningen, The Netherlands.
| | - Dennis Vriens
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Johan Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Tineke W H Meijer
- Department of Radiation Oncology, University Medical Center Groningen, Groningen, The Netherlands
| | - Erik H J G Aarntzen
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands
- Department of Nuclear Medicine, Eberhard Karls University, Tuebingen, Germany
| | | | - Renaud Lhommel
- Division of Nuclear Medicine and Institut de Recherche Clinique, Cliniques Universitaires Saint Luc (UCLouvain), Brussels, Belgium
| | - Nicolas Aide
- INSERM ANTICIPE U1086, François Baclesse Cancer Centre, Caen, France
| | - Christophe Le Tourneau
- Department of Drug Development and Innovation, Institut Curie, Paris-Saclay University, Paris, France
| | - Elizabeth J de Koster
- Department of Radiology, Section of Nuclear Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Wim J G Oyen
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Radiology and Nuclear Medicine, Rijnstate Hospital, Arnhem, The Netherlands
- Department of Biomedical Sciences and Humanitas Clinical and Research Centre, Department of Nuclear Medicine, Humanitas University, Milan, Italy
| | - Lianne Triemstra
- Department of Radiology, Section of Nuclear Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Surgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jelle P Ruurda
- Department of Surgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Erik Vegt
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Lioe-Fee de Geus-Oei
- Department of Radiology, Section of Nuclear Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Biomedical Photonic Imaging Group, University of Twente, Enschede, The Netherlands
| | - Floris H P van Velden
- Department of Radiology, Section of Nuclear Medicine, Leiden University Medical Center, Leiden, The Netherlands
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Lewis JD, Miran AA, Stoopler M, Branson HM, Danguecan A, Raghu K, Ly LG, Cizmeci MN, Kalish BT. Automated Neuroprognostication Via Machine Learning in Neonates with Hypoxic-Ischemic Encephalopathy. Ann Neurol 2025; 97:791-802. [PMID: 39655476 PMCID: PMC11889534 DOI: 10.1002/ana.27154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 10/09/2024] [Accepted: 11/25/2024] [Indexed: 01/03/2025]
Abstract
OBJECTIVES Neonatal hypoxic-ischemic encephalopathy is a serious neurologic condition associated with death or neurodevelopmental impairments. Magnetic resonance imaging (MRI) is routinely used for neuroprognostication, but there is substantial subjectivity and uncertainty about neurodevelopmental outcome prediction. We sought to develop an objective and automated approach for the analysis of newborn brain MRI to improve the accuracy of prognostication. METHODS We created an anatomic MRI template from a sample of 286 infants treated with therapeutic hypothermia, and labeled the deep gray-matter structures. We extracted quantitative information, including shape-related information, and information represented by complex patterns (radiomic measures), from each of these structures in all infants. We then trained an elastic net model to use either only these measures, only the infants' demographic and laboratory data, or both, to predict neurodevelopmental outcomes, as measured by the Bayley Scales of Infant and Toddler Development at 18 months of age. RESULTS Among those infants for whom Bayley scores were available for cognitive, language, and motor outcomes, we found sets of MRI-based measures that could predict their Bayley scores with correlations that were greater than the correlations based on only the demographic and laboratory data, explained more of the variance in the observed scores, and generated a smaller error; predictions based on the combination of the demographic-laboratory and MRI-based measures were similar or marginally better. INTERPRETATION Our findings show that machine learning models using MRI-based measures can predict neurodevelopmental outcomes in neonates with hypoxic-ischemic encephalopathy across all neurodevelopmental domains and across the full spectrum of outcomes. ANN NEUROL 2025;97:791-802.
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Affiliation(s)
- John D. Lewis
- Program in Neuroscience and Mental Health, SickKids Research InstituteTorontoCanada
| | - Atiyeh A. Miran
- Division of Neonatology, Department of PaediatricsThe Hospital for Sick Children, University of TorontoTorontoCanada
| | - Michelle Stoopler
- Department of PaediatricsThe Hospital for Sick Children, University of TorontoTorontoCanada
| | - Helen M. Branson
- Department of Diagnostic and Interventional RadiologyThe Hospital for Sick Children, University of TorontoTorontoCanada
| | - Ashley Danguecan
- Division of Neonatology, Department of PaediatricsThe Hospital for Sick Children, University of TorontoTorontoCanada
- Department of PsychologyThe Hospital for Sick Children, University of TorontoTorontoCanada
| | - Krishna Raghu
- Division of Neonatology, Department of PaediatricsThe Hospital for Sick Children, University of TorontoTorontoCanada
| | - Linh G. Ly
- Division of Neonatology, Department of PaediatricsThe Hospital for Sick Children, University of TorontoTorontoCanada
| | - Mehmet N. Cizmeci
- Division of Neonatology, Department of PaediatricsThe Hospital for Sick Children, University of TorontoTorontoCanada
| | - Brian T. Kalish
- Program in Neuroscience and Mental Health, SickKids Research InstituteTorontoCanada
- Division of Neonatology, Department of PaediatricsThe Hospital for Sick Children, University of TorontoTorontoCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoCanada
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Dan Y, Cai A, Ma J, Zhong Y, Mahmoud SS, Fang Q. A Novel Approach for Aphasia Evaluation Based on ROI-Based Features From Structural Magnetic Resonance Image. IEEE J Biomed Health Inform 2025; 29:2772-2783. [PMID: 39495687 DOI: 10.1109/jbhi.2024.3492072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2024]
Abstract
Aphasia, affecting one-third of stroke survivors, impairs language comprehension and speech production, leading to challenges in daily interactions, social isolation, and economic losses. Assessing aphasia is crucial for effective rehabilitation and recovery in patients. However, the conventional behavioral-based evaluation, reliant on speech pathologists, is susceptible to individual variability, resulting in high labor costs, time-consuming processes, and low robustness. To address these limitations, this study introduces a novel evaluation method based on medical image processing and artificial intelligence. Magnetic resonance imaging (MRI) provides exceptional spatial resolution while mitigating the impact of individual variability. The image processing techniques were employed to extract pathological features, specifically region-of-interest (ROI)-based features. Subsequently, the evaluation models were trained using ROI-based features which initially identify the occurrence of aphasia and then categorize the type of aphasia, aiding clinicians in tailoring treatment to various therapeutic approaches and intensities. The evaluation models also predict the severity and generate scores for four types of language function: spontaneous speech, auditory comprehension, naming, and repetition. Both aphasia occurrence detection and aphasia type classification attain impressive accuracy rates of 100.00 $\pm$ 0.00% and 85.00 $\pm$ 13.23%, respectively. The severity prediction yields the lowest root mean square error (RMSE) of 17.03 $\pm$ 2.75, while the assessment of four language functions achieves the best RMSE of 1.27 $\pm$ 0.82. Utilising the advantages of a medical imaging-based automation approach, the proposed aphasia evaluation method provides a comprehensive procedure and generates rather accurate results. Hence it could assist the aphasia rehabilitation and substantially reduce clinicians' workload.
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Hao L, Zhang ZN, Han S, Li SS, Lin SX, Miao YD. New frontiers in hepatocellular carcinoma: Precision imaging for microvascular invasion prediction. World J Gastroenterol 2025; 31:102224. [PMID: 40062334 PMCID: PMC11886512 DOI: 10.3748/wjg.v31.i8.102224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2024] [Revised: 01/02/2025] [Accepted: 01/10/2025] [Indexed: 01/23/2025] Open
Abstract
This paper highlights the innovative approach and findings of the recently published study by Xu et al, which underscores the integration of radiomics and clinicoradiological factors to enhance the preoperative prediction of microvascular invasion in patients with hepatitis B virus-related hepatocellular carcinoma (HBV-HCC). The study's use of contrast-enhanced computed tomography radiomics to construct predictive models offers a significant advancement in the surgical planning and management of HBV-HCC, potentially transforming patient outcomes through more personalized treatment strategies. This editorial commends the study's contribution to precision medicine and discusses its implications for future research and clinical practice.
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Affiliation(s)
- Liang Hao
- Cancer Center, Yantai Affiliated Hospital of Binzhou Medical University, The 2nd Medical College of Binzhou Medical University, Yantai 264100, Shandong Province, China
| | - Zhao-Nan Zhang
- Cancer Center, Yantai Affiliated Hospital of Binzhou Medical University, The 2nd Medical College of Binzhou Medical University, Yantai 264100, Shandong Province, China
| | - Shuang Han
- Cancer Center, Yantai Affiliated Hospital of Binzhou Medical University, The 2nd Medical College of Binzhou Medical University, Yantai 264100, Shandong Province, China
| | - Shan-Shan Li
- Cancer Center, Yantai Affiliated Hospital of Binzhou Medical University, The 2nd Medical College of Binzhou Medical University, Yantai 264100, Shandong Province, China
| | - Si-Xiang Lin
- Cancer Center, Yantai Affiliated Hospital of Binzhou Medical University, The 2nd Medical College of Binzhou Medical University, Yantai 264100, Shandong Province, China
| | - Yan-Dong Miao
- Cancer Center, Yantai Affiliated Hospital of Binzhou Medical University, The 2nd Medical College of Binzhou Medical University, Yantai 264100, Shandong Province, China
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Okunlola FO, Adetuyi TG, Olajide PA, Okunlola AR, Adetuyi BO, Adeyemo-Eleyode VO, Akomolafe AA, Yunana N, Baba F, Nwachukwu KC, Oyewole OA, Adetunji CO, Shittu OB, Ginikanwa EG. Biomedical image characterization and radio genomics using machine learning techniques. MINING BIOMEDICAL TEXT, IMAGES AND VISUAL FEATURES FOR INFORMATION RETRIEVAL 2025:397-421. [DOI: 10.1016/b978-0-443-15452-2.00019-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
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Müller D, Voran JC, Macedo M, Hartmann D, Lind C, Frank D, Schreiweis B, Kramer F, Ulrich H. Assessing Patient Health Dynamics by Comparative CT Analysis: An Automatic Approach to Organ and Body Feature Evaluation. Diagnostics (Basel) 2024; 14:2760. [PMID: 39682668 DOI: 10.3390/diagnostics14232760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2024] [Revised: 11/24/2024] [Accepted: 12/05/2024] [Indexed: 12/18/2024] Open
Abstract
Background/Objectives: The integration of machine learning into the domain of radiomics has revolutionized the approach to personalized medicine, particularly in oncology. Our research presents RadTA (RADiomics Trend Analysis), a novel framework developed to facilitate the automatic analysis of quantitative imaging biomarkers (QIBs) from time-series CT volumes. Methods: RadTA is designed to bridge a technical gap for medical experts and enable sophisticated radiomic analyses without deep learning expertise. The core of RadTA includes an automated command line interface, streamlined image segmentation, comprehensive feature extraction, and robust evaluation mechanisms. RadTA utilizes advanced segmentation models, specifically TotalSegmentator and Body Composition Analysis (BCA), to accurately delineate anatomical structures from CT scans. These models enable the extraction of a wide variety of radiomic features, which are subsequently processed and compared to assess health dynamics across timely corresponding CT series. Results: The effectiveness of RadTA was tested using the HNSCC-3DCT-RT dataset, which includes CT scans from oncological patients undergoing radiation therapy. The results demonstrate significant changes in tissue composition and provide insights into the physical effects of the treatment. Conclusions: RadTA demonstrates a step of clinical adoption in the field of radiomics, offering a user-friendly, robust, and effective tool for the analysis of patient health dynamics. It can potentially also be used for other medical specialties.
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Affiliation(s)
- Dominik Müller
- IT-Infrastructure for Translational Medical Research, University of Augsburg, 86159 Augsburg, Germany
- Institute for Digital Medicine, University Hospital Augsburg, 86156 Augsburg, Germany
- Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, 24105 Kiel, Germany
| | - Jakob Christoph Voran
- Department of Cardiology, Kiel University and University Hospital Schleswig-Holstein, 24105 Kiel, Germany
- German Centre for Cardiovascular Research, Partner Site Hamburg/Kiel/Lübeck, 24103 Kiel, Germany
| | - Mário Macedo
- Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, 24105 Kiel, Germany
- Medical Data Integration Center, University Hospital Schleswig-Holstein, 24105 Kiel, Germany
| | - Dennis Hartmann
- IT-Infrastructure for Translational Medical Research, University of Augsburg, 86159 Augsburg, Germany
| | - Charlotte Lind
- Department of Cardiology, Kiel University and University Hospital Schleswig-Holstein, 24105 Kiel, Germany
| | - Derk Frank
- Department of Cardiology, Kiel University and University Hospital Schleswig-Holstein, 24105 Kiel, Germany
- German Centre for Cardiovascular Research, Partner Site Hamburg/Kiel/Lübeck, 24103 Kiel, Germany
| | - Björn Schreiweis
- Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, 24105 Kiel, Germany
- Medical Data Integration Center, University Hospital Schleswig-Holstein, 24105 Kiel, Germany
| | - Frank Kramer
- IT-Infrastructure for Translational Medical Research, University of Augsburg, 86159 Augsburg, Germany
| | - Hannes Ulrich
- Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, 24105 Kiel, Germany
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Coppes RP, van Dijk LV. Future of Team-based Basic and Translational Science in Radiation Oncology. Semin Radiat Oncol 2024; 34:370-378. [PMID: 39271272 DOI: 10.1016/j.semradonc.2024.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
To further optimize radiotherapy, a more personalized treatment towards individual patient's risk profiles, dissecting both patient-specific tumor and normal tissue response to multimodality treatments is needed. Novel developments in radiobiology, using in vitro patient-specific complex tissue resembling 3D models and multiomics approaches at a spatial single-cell level, may provide unprecedented insight into the radiation responses of tumors and normal tissue. Here, we describe the necessary team effort, including all disciplines in radiation oncology, to integrate such data into clinical prediction models and link the relatively "big data" from the clinical practice, allowing accurate patient stratification for personalized treatment approaches.
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Affiliation(s)
- R P Coppes
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.; Department of Biomedical Sciences, University Medical Center Groningen, University of Groningen, Groningen, Netherlands..
| | - L V van Dijk
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
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Illini O, Fabikan H, Fischer E, Lang-Stöberl AS, Krenbek D, Jarius C, Azarnia-Medan S, Gasser S, Hochmair MJ, Weinlinger C, Valipour A, Watzka S. Adenosquamous Carcinoma of the Lung: Survival, Radiologic Findings, PD-L1, and Driver Mutations. J Clin Med 2024; 13:5711. [PMID: 39407771 PMCID: PMC11476650 DOI: 10.3390/jcm13195711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2024] [Revised: 09/17/2024] [Accepted: 09/19/2024] [Indexed: 10/20/2024] Open
Abstract
Background: Adenosquamous carcinoma of the lung (ASC) is a rare non-small-cell lung cancer (NSCLC) subtype combining components of squamous cell carcinoma (SCC) and adenocarcinoma (AC). Data on ASC, particularly in Caucasian populations, are limited. Methods: We reviewed clinicopathological and radiological characteristics of ASC patients diagnosed between 1996 and 2023. Patients were classified into AC-predominant ASC (AC-ASC) and SCC-predominant ASC (SCC-ASC) groups for analysis. Results: Among the 66 patients included, the median overall survival was 41.7 (95% CI, 25.0-54.4), while it was 48.1 (95% CI, 27.3-88.0) in patients treated with curative surgery (n = 44) and 15.3 (95% CI, 6.5-42.6) months for palliative patients (n = 22). The five-year survival rates were 39% and 26%, respectively. Recurrence occurred in 43% of stage I patients and was associated with worse survival (HR 3.303 (95% CI, 1.10-9.89) p = 0.033). AC-ASCs (n = 17) more frequently showed air-bronchogram (p = 0.002) and pleural effusions (p = 0.054) compared to SCC-ASCs (n = 26). SCC-ASCs exhibited more vascular invasion (p = 0.006) and PD-L1 values between 1 and 49% (TPS) (p = 0.032). The subtype did not influence survival. EGFR and ALK alterations were found in 17% and 2% of patients, respectively. Conclusions: Despite early-stage disease, ASC patients had a high recurrence rate, associated with worse survival. Clinicopathologic differences between AC-ASCs and SCC-ASCs did not influence survival.
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Affiliation(s)
- Oliver Illini
- Department of Respiratory and Critical Care Medicine, Clinic Floridsdorf, Vienna Healthcare Group, Bruenner Straße 68, 1210 Vienna, Austria
- Karl Landsteiner Institute of Lung Research and Pulmonary Oncology, Clinic Floridsdorf, Bruenner Straße 68, 1210 Vienna, Austria
| | - Hannah Fabikan
- Karl Landsteiner Institute of Lung Research and Pulmonary Oncology, Clinic Floridsdorf, Bruenner Straße 68, 1210 Vienna, Austria
| | - Eva Fischer
- Department of Anesthesiology and Intensive Care, Clinic Donaustadt, Vienna Healthcare Group, Landobardenstraße 122, 1220 Vienna, Austria
| | - Anna Sophie Lang-Stöberl
- Karl Landsteiner Institute of Lung Research and Pulmonary Oncology, Clinic Floridsdorf, Bruenner Straße 68, 1210 Vienna, Austria
| | - Dagmar Krenbek
- Department of Pathology, Clinic Floridsdorf, Vienna Healthcare Group, Bruenner Straße 68, 1210 Vienna, Austria
| | - Christa Jarius
- Department of Pathology, Clinic Floridsdorf, Vienna Healthcare Group, Bruenner Straße 68, 1210 Vienna, Austria
| | | | - Stefan Gasser
- Unit for Interventional Radiology, Institute for Diagnostic and Interventional Radiology, KABEG Klinikum Klagenfurt am Wörthersee, Feschnigstraße 11, 9020 Klagenfurt, Austria
| | - Maximilian Johannes Hochmair
- Department of Respiratory and Critical Care Medicine, Clinic Floridsdorf, Vienna Healthcare Group, Bruenner Straße 68, 1210 Vienna, Austria
- Karl Landsteiner Institute of Lung Research and Pulmonary Oncology, Clinic Floridsdorf, Bruenner Straße 68, 1210 Vienna, Austria
| | - Christoph Weinlinger
- Karl Landsteiner Institute of Lung Research and Pulmonary Oncology, Clinic Floridsdorf, Bruenner Straße 68, 1210 Vienna, Austria
| | - Arschang Valipour
- Department of Respiratory and Critical Care Medicine, Clinic Floridsdorf, Vienna Healthcare Group, Bruenner Straße 68, 1210 Vienna, Austria
- Karl Landsteiner Institute of Lung Research and Pulmonary Oncology, Clinic Floridsdorf, Bruenner Straße 68, 1210 Vienna, Austria
| | - Stefan Watzka
- Department of Thoracic Surgery, Clinic Floridsdorf, Vienna Healthcare Group, Bruenner Straße 68, 1210 Vienna, Austria;
- Karl Landsteiner Institute for Clinical and Translational Thoracic Surgical Research, Clinic Floridsdorf, Bruenner Straße 68, 1210 Vienna, Austria
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Wang B, Bao C, Wang X, Wang Z, Zhang Y, Liu Y, Wang R, Han X. Inter-equipment validation of PET-based radiomics for predicting EGFR mutation statuses in patients with non-small cell lung cancer. Clin Radiol 2024; 79:571-578. [PMID: 38821756 DOI: 10.1016/j.crad.2023.12.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 10/03/2023] [Accepted: 12/31/2023] [Indexed: 06/02/2024]
Abstract
AIM To validate the inter-equipment generality of the radiomics based on PET images to predict the EGFR mutation status of patients with non-small cell lung cancer. MATERIALS AND METHODS Patients were retrospectively collected in the departments of nuclear medicine of Heyi branch (Siemens equipment) and East branch (General Electric (GE) equipment) of the first affiliated hospital of Zhengzhou university. 5 predicting logistic regression models were established. The 1st one was trained and tested by the GE dataset; The 2nd one was trained and tested by the Siemens dataset; The 3rd one was trained and tested by the mixed dataset consisting of GE and Siemens. The 4th one was trained by GE and tested by Siemens; The 5th one was trained by Siemens and tested by GE. RESULTS For the 1st ∼ 5th models, the mean values of AUCs for training/testing datasets were 0.78/0.73, 0.74/0.72, 0.75/0.70, 0.74/0.65 and 0.68/0.63, respectively. CONCLUSION The AUCs of the models trained and tested on the datasets from the same equipment were higher than those for different equipment. The inter-equipment generality of the radiomics was not good enough in clinical practice.
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Affiliation(s)
- B Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe East Road, Zhengzhou 450000, Henan, China
| | - C Bao
- Department of Nuclear Medicine, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe East Road, Zhengzhou 450000, Henan, China
| | - X Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe East Road, Zhengzhou 450000, Henan, China
| | - Z Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe East Road, Zhengzhou 450000, Henan, China
| | - Y Zhang
- Department of Nuclear Medicine, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe East Road, Zhengzhou 450000, Henan, China
| | - Y Liu
- Department of Nuclear Medicine, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe East Road, Zhengzhou 450000, Henan, China
| | - R Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe East Road, Zhengzhou 450000, Henan, China
| | - X Han
- Department of Nuclear Medicine, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe East Road, Zhengzhou 450000, Henan, China; Henan Medical Key Laboratory of Molecular Imaging, No.1 Jianshe East Road, Zhengzhou 450000, Henan, China.
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Chakrabarty N, Mahajan A. Imaging Analytics using Artificial Intelligence in Oncology: A Comprehensive Review. Clin Oncol (R Coll Radiol) 2024; 36:498-513. [PMID: 37806795 DOI: 10.1016/j.clon.2023.09.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 08/09/2023] [Accepted: 09/21/2023] [Indexed: 10/10/2023]
Abstract
The present era has seen a surge in artificial intelligence-related research in oncology, mainly using deep learning, because of powerful computer hardware, improved algorithms and the availability of large amounts of data from open-source domains and the use of transfer learning. Here we discuss the multifaceted role of deep learning in cancer care, ranging from risk stratification, the screening and diagnosis of cancer, to the prediction of genomic mutations, treatment response and survival outcome prediction, through the use of convolutional neural networks. Another role of artificial intelligence is in the generation of automated radiology reports, which is a boon in high-volume centres to minimise report turnaround time. Although a validated and deployable deep-learning model for clinical use is still in its infancy, there is ongoing research to overcome the barriers for its universal implementation and we also delve into this aspect. We also briefly describe the role of radiomics in oncoimaging. Artificial intelligence can provide answers pertaining to cancer management at baseline imaging, saving cost and time. Imaging biobanks, which are repositories of anonymised images, are also briefly described. We also discuss the commercialisation and ethical issues pertaining to artificial intelligence. The latest generation generalist artificial intelligence model is also briefly described at the end of the article. We believe this article will not only enrich knowledge, but also promote research acumen in the minds of readers to take oncoimaging to another level using artificial intelligence and also work towards clinical translation of such research.
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Affiliation(s)
- N Chakrabarty
- Department of Radiodiagnosis, Advanced Centre for Treatment, Research and Education in Cancer, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Parel, Mumbai, Maharashtra, India.
| | - A Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK.
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Han X, Wang Y, Jia X, Zheng Y, Ding C, Zhang X, Zhang K, Cao Y, Li Y, Xia L, Zheng C, Huang J, Shi H. Predictive value of delta-radiomic features for prognosis of advanced non-small cell lung cancer patients undergoing immune checkpoint inhibitor therapy. Transl Lung Cancer Res 2024; 13:1247-1263. [PMID: 38973966 PMCID: PMC11225045 DOI: 10.21037/tlcr-24-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 04/29/2024] [Indexed: 07/09/2024]
Abstract
Background No robust predictive biomarkers exist to identify non-small cell lung cancer (NSCLC) patients likely to benefit from immune checkpoint inhibitor (ICI) therapies. The aim of this study was to explore the role of delta-radiomics features in predicting the clinical outcomes of patients with advanced NSCLC who received ICI therapy. Methods Data of 179 patients with advanced NSCLC (stages IIIB-IV) from two institutions (Database 1 =133; Database 2 =46) were retrospectively analyzed. Patients in the Database 1 were randomly assigned into training and validation dataset, with a ratio of 8:2. Patients in Database 2 were allocated into testing dataset. Features were selected from computed tomography (CT) images before and 6-8 weeks after ICI therapy. For each lesion, a total of 1,037 radiomic features were extracted. Lowly reliable [intraclass correlation coefficient (ICC) <0.8] and redundant (r>0.8) features were excluded. The delta-radiomics features were defined as the relative net change of radiomics features between two time points. Prognostic models for progression-free survival (PFS) and overall survival (OS) were established using the multivariate Cox regression based on selected delta-radiomics features. A clinical model and a pre-treatment radiomics model were established as well. Results The median PFS (after therapy) was 7.0 [interquartile range (IQR): 3.4, 9.1] (range, 1.4-13.2) months. To predict PFS, the model established based on the five most contributing delta-radiomics features yielded Harrell's concordance index (C-index) values of 0.708, 0.688, and 0.603 in the training, validation, and testing databases, respectively. The median survival time was 12 (IQR: 8.7, 15.8) (range, 2.9-23.3) months. To predict OS, a promising prognostic performance was confirmed with the corresponding C-index values of 0.810, 0.762, and 0.697 in the three datasets based on the seven most contributing delta-radiomics features, respectively. Furthermore, compared with clinical and pre-treatment radiomics models, the delta-radiomics model had the highest area under the curve (AUC) value and the best patients' stratification ability. Conclusions The delta-radiomics model showed a good performance in predicting therapeutic outcomes in advanced NSCLC patients undergoing ICI therapy. It provides a higher predictive value than clinical and the pre-treatment radiomics models.
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Affiliation(s)
- Xiaoyu Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yujin Wang
- Departments of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xi Jia
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yuting Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | | | - Xiaohui Zhang
- Clinical Solution, Philips Healthcare, Shanghai, China
| | - Kailu Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yunkun Cao
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Yumin Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Liming Xia
- Departments of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Jing Huang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Heshui Shi
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
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Luo W, Xiu Z, Wang X, McGarry R, Allen J. A Novel Method for Evaluating Early Tumor Response Based on Daily CBCT Images for Lung SBRT. Cancers (Basel) 2023; 16:20. [PMID: 38201447 PMCID: PMC10778260 DOI: 10.3390/cancers16010020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/07/2023] [Accepted: 12/11/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND We aimed to develop a new tumor response assessment method for lung SBRT. METHODS In total, 132 lung cancer patients with 134 tumors who received SBRT treatment with daily CBCT were included in this study. The information about tumor size (area), contrast (contrast-to-noise ratio (CNR)), and density/attenuation (μ) was derived from the CBCT images for the first and the last fractions. The ratios of tumor area, CNR, and μ (RA, RCNR, Rμ) between the last and first fractions were calculated for comparison. The product of the three rations was defined as a new parameter (R) for assessment. Tumor response was independently assessed by a radiologist based on a comprehensive analysis of the CBCT images. RESULTS R ranged from 0.27 to 1.67 with a mean value of 0.95. Based on the radiologic assessment results, a receiver operation characteristic (ROC) curve with the area under the curve (AUC) of 95% was obtained and the optimal cutoff value (RC) was determined as 1.1. The results based on RC achieved a 94% accuracy, 94% specificity, and 90% sensitivity. CONCLUSION The results show that R was correlated with early tumor response to lung SBRT and that using R for evaluating tumor response to SBRT would be viable and efficient.
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Affiliation(s)
- Wei Luo
- Department of Radiation Medicine, University of Kentucky, 800 Rose Street, Lexington, KY 40536, USA; (Z.X.); (R.M.)
| | - Zijian Xiu
- Department of Radiation Medicine, University of Kentucky, 800 Rose Street, Lexington, KY 40536, USA; (Z.X.); (R.M.)
| | - Xiaoqin Wang
- Department of Radiology, University of Kentucky, 800 Rose Street, Lexington, KY 40536, USA;
| | - Ronald McGarry
- Department of Radiation Medicine, University of Kentucky, 800 Rose Street, Lexington, KY 40536, USA; (Z.X.); (R.M.)
| | - Joshua Allen
- AdventHealth, 2501 N Orange Ave, Orlando, FL 32804, USA;
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14
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Trivizakis E, Koutroumpa NM, Souglakos J, Karantanas A, Zervakis M, Marias K. Radiotranscriptomics of non-small cell lung carcinoma for assessing high-level clinical outcomes using a machine learning-derived multi-modal signature. Biomed Eng Online 2023; 22:125. [PMID: 38102586 PMCID: PMC10724973 DOI: 10.1186/s12938-023-01190-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Multi-omics research has the potential to holistically capture intra-tumor variability, thereby improving therapeutic decisions by incorporating the key principles of precision medicine. The purpose of this study is to identify a robust method of integrating features from different sources, such as imaging, transcriptomics, and clinical data, to predict the survival and therapy response of non-small cell lung cancer patients. METHODS 2996 radiomics, 5268 transcriptomics, and 8 clinical features were extracted from the NSCLC Radiogenomics dataset. Radiomics and deep features were calculated based on the volume of interest in pre-treatment, routine CT examinations, and then combined with RNA-seq and clinical data. Several machine learning classifiers were used to perform survival analysis and assess the patient's response to adjuvant chemotherapy. The proposed analysis was evaluated on an unseen testing set in a k-fold cross-validation scheme. Score- and concatenation-based multi-omics were used as feature integration techniques. RESULTS Six radiomics (elongation, cluster shade, entropy, variance, gray-level non-uniformity, and maximal correlation coefficient), six deep features (NasNet-based activations), and three transcriptomics (OTUD3, SUCGL2, and RQCD1) were found to be significant for therapy response. The examined score-based multi-omic improved the AUC up to 0.10 on the unseen testing set (0.74 ± 0.06) and the balance between sensitivity and specificity for predicting therapy response for 106 patients, resulting in less biased models and improving upon the either highly sensitive or highly specific single-source models. Six radiomics (kurtosis, GLRLM- and GLSZM-based non-uniformity from images with no filtering, biorthogonal, and daubechies wavelets), seven deep features (ResNet-based activations), and seven transcriptomics (ELP3, ZZZ3, PGRMC2, TRAK1, ATIC, USP7, and PNPLA2) were found to be significant for the survival analysis. Accordingly, the survival analysis for 115 patients was also enhanced up to 0.20 by the proposed score-based multi-omics in terms of the C-index (0.79 ± 0.03). CONCLUSIONS Compared to single-source models, multi-omics integration has the potential to improve prediction performance, increase model stability, and reduce bias for both treatment response and survival analysis.
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Affiliation(s)
- Eleftherios Trivizakis
- Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013, Heraklion, Greece.
- Medical School, University of Crete, 71003, Heraklion, Greece.
| | - Nikoletta-Maria Koutroumpa
- Medical School, University of Crete, 71003, Heraklion, Greece
- School of Electrical and Computer Engineering, Technical University of Crete, 73100, Chania, Greece
| | - John Souglakos
- Laboratory of Translational Oncology, Medical School, University of Crete, 71003, Heraklion, Greece
- Department of Medical Oncology, University Hospital of Heraklion, 71500, Heraklion, Greece
| | - Apostolos Karantanas
- Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013, Heraklion, Greece
- Department of Radiology, Medical School, University of Crete, 71003, Heraklion, Greece
| | - Michalis Zervakis
- School of Electrical and Computer Engineering, Technical University of Crete, 73100, Chania, Greece
| | - Kostas Marias
- Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013, Heraklion, Greece
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410, Heraklion, Greece
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15
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Huang S, Xu F, Zhu W, Xie D, Lou K, Huang D, Hu H. Multi-dimensional radiomics analysis to predict visceral pleural invasion in lung adenocarcinoma of ≤3 cm maximum diameter. Clin Radiol 2023; 78:e847-e855. [PMID: 37607844 DOI: 10.1016/j.crad.2023.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 06/20/2023] [Accepted: 07/21/2023] [Indexed: 08/24/2023]
Abstract
AIM To explore the value of radiomics analysis in preoperatively predicting visceral pleural invasion (VPI) of lung adenocarcinoma (LAC) with ≤3 cm maximum diameter and to compare the performance of two-dimensional (2D) and three-dimensional (3D) computed tomography (CT) radiomics models. MATERIALS AND METHODS A total of 391 LAC patients were enrolled retrospectively, of whom 142 were VPI (+) and 249 were VPI (-). Radiomics features were extracted from 2D and 3D regions of interest (ROIs) of tumours in CT images. 2D and 3D radiomics models were developed combining the optimal radiomics features by using the logistic regression machine-learning method and radiomics scores (rad-scores) were calculated. Nomograms were constructed by integrating independent risk factors and rad-scores. The performance of each model was evaluated by using the receiver operator characteristic (ROC) curve, decision curve analysis (DCA), clinical impact curve (CIC), and calculating the area under the curve (AUC). RESULTS There was no difference in the VPI prediction between 2D and 3D radiomics models (training group: 2D AUC=0.835, 3D AUC=0.836, p=0.896; validation group: 2D AUC=0.803, 3D AUC=0.794, p=0.567). The 2D and 3D nomograms performed similarly regarding discrimination (training group: 2D AUC=0.867, 3D AUC=0.862, p=0.409, validation group: 2D AUC=0.835, 3D AUC=0.827, p=0.558), and outperformed their corresponding radiomics models and the clinical model. DCA and CIC revealed that the 2D nomogram had slightly better clinical utility. CONCLUSION The 2D radiomics model has a similar discrimination capability compared with the 3D radiomics model. The 2D nomogram performs slightly better for individual VPI prediction in LAC.
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Affiliation(s)
- S Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Department of Radiology, Ningbo Medical Center LiHuili Hospital, Ningbo, Zhejiang, China
| | - F Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - W Zhu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - D Xie
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Department of Radiology, Shaoxing Second Hospital, Shaoxing, Zhejiang, China
| | - K Lou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - D Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - H Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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Pellegrino S, Fonti R, Hakkak Moghadam Torbati A, Bologna R, Morra R, Damiano V, Matano E, De Placido S, Del Vecchio S. Heterogeneity of Glycolytic Phenotype Determined by 18F-FDG PET/CT Using Coefficient of Variation in Patients with Advanced Non-Small Cell Lung Cancer. Diagnostics (Basel) 2023; 13:2448. [PMID: 37510192 PMCID: PMC10378511 DOI: 10.3390/diagnostics13142448] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/13/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
We investigated the role of Coefficient of Variation (CoV), a first-order texture parameter derived from 18F-FDG PET/CT, in the prognosis of Non-Small Cell Lung Cancer (NSCLC) patients. Eighty-four patients with advanced NSCLC who underwent 18F-FDG PET/CT before therapy were retrospectively studied. SUVmax, SUVmean, CoV, total Metabolic Tumor Volume (MTVTOT) and whole-body Total Lesion Glycolysis (TLGWB) were determined by an automated contouring program (SUV threshold at 2.5). We analyzed 194 lesions: primary tumors (n = 84), regional (n = 48) and non-regional (n = 17) lymph nodes and metastases in liver (n = 9), bone (n = 23) and other sites (n = 13); average CoVs were 0.36 ± 0.13, 0.36 ± 0.14, 0.42 ± 0.18, 0.30 ± 0.14, 0.37 ± 0.17, 0.34 ± 0.13, respectively. No significant differences were found between the CoV values among the different lesion categories. Survival analysis included age, gender, histology, stage, MTVTOT, TLGWB and imaging parameters derived from primary tumors. At univariate analysis, CoV (p = 0.0184), MTVTOT (p = 0.0050), TLGWB (p = 0.0108) and stage (p = 0.0041) predicted Overall Survival (OS). At multivariate analysis, age, CoV, MTVTOT and stage were retained in the model (p = 0.0001). Patients with CoV > 0.38 had significantly better OS than those with CoV ≤ 0.38 (p = 0.0143). Patients with MTVTOT ≤ 89.5 mL had higher OS than those with MTVTOT > 89.5 mL (p = 0.0063). Combining CoV and MTVTOT, patients with CoV ≤ 0.38 and MTVTOT > 89.5 mL had the worst prognosis. CoV, by reflecting the heterogeneity of glycolytic phenotype, can predict clinical outcomes in NSCLC patients.
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Affiliation(s)
- Sara Pellegrino
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Rosa Fonti
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | | | - Roberto Bologna
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Rocco Morra
- Department of Clinical Medicine and Surgery, University of Naples "Federico II", 80131 Naples, Italy
| | - Vincenzo Damiano
- Department of Clinical Medicine and Surgery, University of Naples "Federico II", 80131 Naples, Italy
| | - Elide Matano
- Department of Clinical Medicine and Surgery, University of Naples "Federico II", 80131 Naples, Italy
| | - Sabino De Placido
- Department of Clinical Medicine and Surgery, University of Naples "Federico II", 80131 Naples, Italy
| | - Silvana Del Vecchio
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
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Liu Y. Comparison of Magnetic Resonance Imaging-Based Radiomics Features with Nomogram for Prediction of Prostate Cancer Invasion. Int J Gen Med 2023; 16:3043-3051. [PMID: 37485455 PMCID: PMC10361087 DOI: 10.2147/ijgm.s419039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 07/10/2023] [Indexed: 07/25/2023] Open
Abstract
Objective To explore the value of the magnetic resonance imaging (MRI) radiomics model in predicting prostate cancer (PCa) invasion. Methods Clinical data of 86 pathologically confirmed PCa patients in our hospital were collected, including 44 cases in the invasive group and 42 cases in the non-invasive group. All patients underwent MRI examinations, and the same parameters were used. The lesion area was manually delineated and the radiomics features were extracted from T2WI. The radiomics signature based on LASSO regression was established. Besides, logistic regression was used to identify independent clinical predictors, and a combined model incorporating the radiomics signature and independent clinical risk factor was constructed. Finally, the receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA) was performed to compare the prediction efficiency and clinical benefit of each model. Results A total of 867 radiomics features were obtained, and six of them were incorporated into the radiomics model. Multivariate logistic regression analysis exhibited the Gleason score as an independent clinical risk factor for PCa invasion. ROC results showed that the performance of the radiomics model was comparable to that of the clinical-radiomics model in predicting PCa invasion, and it was better than that of the single Gleason score. DCA also confirmed the considerable clinical application value of the radiomics and the clinical-radiomics models. Conclusion As a simple, non-invasive, and efficient method, the radiomics model has important predictive value for PCa invasion.
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Affiliation(s)
- Yang Liu
- X-Ray Department, The No.1 People’s Hospital of Huzhou, Huzhou, Zhejiang, 313000, People’s Republic of China
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18
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Lastwika KJ, Wu W, Zhang Y, Ma N, Zečević M, Pipavath SNJ, Randolph TW, Houghton AM, Nair VS, Lampe PD, Kinahan PE. Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment. Cancers (Basel) 2023; 15:3418. [PMID: 37444527 PMCID: PMC10341085 DOI: 10.3390/cancers15133418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/23/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer in indeterminate pulmonary nodules. We analyzed 94 quantitative radiomic imaging features and 41 qualitative semantic imaging variables with molecular biomarkers from blood derived from an antibody-based microarray platform that determines protein, cancer-specific glycan, and autoantibody-antigen complex content with high sensitivity. From these datasets, we created a PSR (plasma, semantic, radiomic) risk prediction model comprising nine blood-based and imaging biomarkers with an area under the receiver operating curve (AUROC) of 0.964 that when tested in a second, independent cohort yielded an AUROC of 0.846. Incorporating known clinical risk factors (age, gender, and smoking pack years) for lung cancer into the PSR model improved the AUROC to 0.897 in the second cohort and was more accurate than a well-characterized clinical risk prediction model (AUROC = 0.802). Our findings support the use of a multi-omics approach to guide the clinical management of indeterminate pulmonary nodules.
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Affiliation(s)
- Kristin J. Lastwika
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (K.J.L.); (N.M.); (A.M.H.); (V.S.N.)
- Translational Research Program, Public Health Sciences Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Wei Wu
- Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA; (W.W.); (M.Z.); (S.N.J.P.)
| | - Yuzheng Zhang
- Program in Biostatistics and Biomathematics, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (Y.Z.); (T.W.R.)
| | - Ningxin Ma
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (K.J.L.); (N.M.); (A.M.H.); (V.S.N.)
| | - Mladen Zečević
- Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA; (W.W.); (M.Z.); (S.N.J.P.)
| | - Sudhakar N. J. Pipavath
- Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA; (W.W.); (M.Z.); (S.N.J.P.)
- Division of Pulmonary, Critical Care & Sleep Medicine, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Timothy W. Randolph
- Program in Biostatistics and Biomathematics, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (Y.Z.); (T.W.R.)
| | - A. McGarry Houghton
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (K.J.L.); (N.M.); (A.M.H.); (V.S.N.)
- Division of Pulmonary, Critical Care & Sleep Medicine, University of Washington School of Medicine, Seattle, WA 98195, USA
- Human Biology Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Viswam S. Nair
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; (K.J.L.); (N.M.); (A.M.H.); (V.S.N.)
- Division of Pulmonary, Critical Care & Sleep Medicine, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Paul D. Lampe
- Translational Research Program, Public Health Sciences Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Human Biology Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Paul E. Kinahan
- Department of Radiology, University of Washington School of Medicine, Seattle, WA 98109, USA; (W.W.); (M.Z.); (S.N.J.P.)
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Jin Y, Arimura H, Cui Y, Kodama T, Mizuno S, Ansai S. CT Image-Based Biopsy to Aid Prediction of HOPX Expression Status and Prognosis for Non-Small Cell Lung Cancer Patients. Cancers (Basel) 2023; 15:2220. [PMID: 37190150 PMCID: PMC10136849 DOI: 10.3390/cancers15082220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 05/17/2023] Open
Abstract
This study aimed to elucidate a computed tomography (CT) image-based biopsy with a radiogenomic signature to predict homeodomain-only protein homeobox (HOPX) gene expression status and prognosis in patients with non-small cell lung cancer (NSCLC). Patients were labeled as HOPX-negative or positive based on HOPX expression and were separated into training (n = 92) and testing (n = 24) datasets. In correlation analysis between genes and image features extracted by Pyradiomics for 116 patients, eight significant features associated with HOPX expression were selected as radiogenomic signature candidates from the 1218 image features. The final signature was constructed from eight candidates using the least absolute shrinkage and selection operator. An imaging biopsy model with radiogenomic signature was built by a stacking ensemble learning model to predict HOPX expression status and prognosis. The model exhibited predictive power for HOPX expression with an area under the receiver operating characteristic curve of 0.873 and prognostic power in Kaplan-Meier curves (p = 0.0066) in the test dataset. This study's findings implied that the CT image-based biopsy with a radiogenomic signature could aid physicians in predicting HOPX expression status and prognosis in NSCLC.
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Affiliation(s)
- Yu Jin
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan
| | - Hidetaka Arimura
- Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan
| | - YunHao Cui
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan
| | - Takumi Kodama
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan
| | - Shinichi Mizuno
- Division of Medical Technology, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan
| | - Satoshi Ansai
- Laboratory of Genome Editing Breeding, Graduate School of Agriculture, Kyoto University, Kyoto 606-8520, Japan
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Chen C, Geng Q, Song G, Zhang Q, Wang Y, Sun D, Zeng Q, Dai Z, Wang G. A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules. Front Oncol 2023; 13:1066360. [PMID: 37007065 PMCID: PMC10064794 DOI: 10.3389/fonc.2023.1066360] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 02/13/2023] [Indexed: 03/09/2023] Open
Abstract
ObjectiveTo establish a nomogram based on non-enhanced computed tomography(CT) imaging radiomics and clinical features for use in predicting the malignancy of sub-centimeter solid nodules (SCSNs).Materials and methodsRetrospective analysis was performed of records for 198 patients with SCSNs that were surgically resected and examined pathologically at two medical institutions between January 2020 and June 2021. Patients from Center 1 were included in the training cohort (n = 147), and patients from Center 2 were included in the external validation cohort (n = 52). Radiomic features were extracted from chest CT images. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomic feature extraction and computation of radiomic scores. Clinical features, subjective CT findings, and radiomic scores were used to build multiple predictive models. Model performance was examined by evaluating the area under the receiver operating characteristic curve (AUC). The best model was selected for efficacy evaluation in a validation cohort, and column line plots were created.ResultsPulmonary malignant nodules were significantly associated with vascular alterations in both the training (p < 0.001) and external validation (p < 0.001) cohorts. Eleven radiomic features were selected after a dimensionality reduction to calculate the radiomic scores. Based on these findings, three prediction models were constructed: subjective model (Model 1), radiomic score model (Model 2), and comprehensive model (Model 3), with AUCs of 0.672, 0.888, and 0.930, respectively. The optimal model with an AUC of 0.905 was applied to the validation cohort, and decision curve analysis indicated that the comprehensive model column line plot was clinically useful.ConclusionPredictive models constructed based on CT-based radiomics with clinical features can help clinicians diagnose pulmonary nodules and guide clinical decision making.
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Affiliation(s)
- Chengyu Chen
- Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Department of Thoracic Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Qun Geng
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Gesheng Song
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical Unversity, Jinan, China
| | - Qian Zhang
- Department of General Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Youruo Wang
- Elite Class of 2017, Shandong First Medical University, Jinan, China
| | - Dongfeng Sun
- Department of Thoracic Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Qingshi Zeng
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical Unversity, Jinan, China
| | - Zhengjun Dai
- Scientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, China
| | - Gongchao Wang
- Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- *Correspondence: Gongchao Wang,
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Dovrou A, Bei E, Sfakianakis S, Marias K, Papanikolaou N, Zervakis M. Synergies of Radiomics and Transcriptomics in Lung Cancer Diagnosis: A Pilot Study. Diagnostics (Basel) 2023; 13:738. [PMID: 36832225 PMCID: PMC9955510 DOI: 10.3390/diagnostics13040738] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 02/10/2023] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
Radiotranscriptomics is an emerging field that aims to investigate the relationships between the radiomic features extracted from medical images and gene expression profiles that contribute in the diagnosis, treatment planning, and prognosis of cancer. This study proposes a methodological framework for the investigation of these associations with application on non-small-cell lung cancer (NSCLC). Six publicly available NSCLC datasets with transcriptomics data were used to derive and validate a transcriptomic signature for its ability to differentiate between cancer and non-malignant lung tissue. A publicly available dataset of 24 NSCLC-diagnosed patients, with both transcriptomic and imaging data, was used for the joint radiotranscriptomic analysis. For each patient, 749 Computed Tomography (CT) radiomic features were extracted and the corresponding transcriptomics data were provided through DNA microarrays. The radiomic features were clustered using the iterative K-means algorithm resulting in 77 homogeneous clusters, represented by meta-radiomic features. The most significant differentially expressed genes (DEGs) were selected by performing Significance Analysis of Microarrays (SAM) and 2-fold change. The interactions among the CT imaging features and the selected DEGs were investigated using SAM and a Spearman rank correlation test with a False Discovery Rate (FDR) of 5%, leading to the extraction of 73 DEGs significantly correlated with radiomic features. These genes were used to produce predictive models of the meta-radiomics features, defined as p-metaomics features, by performing Lasso regression. Of the 77 meta-radiomic features, 51 can be modeled in terms of the transcriptomic signature. These significant radiotranscriptomics relationships form a reliable basis to biologically justify the radiomics features extracted from anatomic imaging modalities. Thus, the biological value of these radiomic features was justified via enrichment analysis on their transcriptomics-based regression models, revealing closely associated biological processes and pathways. Overall, the proposed methodological framework provides joint radiotranscriptomics markers and models to support the connection and complementarities between the transcriptome and the phenotype in cancer, as demonstrated in the case of NSCLC.
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Affiliation(s)
- Aikaterini Dovrou
- Digital Image and Signal Processing Laboratory, School of Electrical and Computer Engineering (ECE), Technical University of Crete, GR-73100 Chania, Greece
| | - Ekaterini Bei
- Digital Image and Signal Processing Laboratory, School of Electrical and Computer Engineering (ECE), Technical University of Crete, GR-73100 Chania, Greece
| | - Stelios Sfakianakis
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, GR-70013 Heraklion, Greece
| | - Kostas Marias
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, GR-70013 Heraklion, Greece
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, GR-71410 Heraklion, Greece
| | - Nickolas Papanikolaou
- Computational Clinical Imaging Group, Champalimaud Clinical Centre, Champalimaud Foundation, Avenida Brasilia, 1400-038 Lisbon, Portugal
| | - Michalis Zervakis
- Digital Image and Signal Processing Laboratory, School of Electrical and Computer Engineering (ECE), Technical University of Crete, GR-73100 Chania, Greece
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22
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An ordinal radiomic model to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma based on low-dose computed tomography in lung cancer screening. Eur Radiol 2023; 33:3072-3082. [PMID: 36790469 DOI: 10.1007/s00330-023-09453-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 11/16/2022] [Accepted: 01/18/2023] [Indexed: 02/16/2023]
Abstract
OBJECTIVES To construct a radiomic model of low-dose CT (LDCT) to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma (IPA) and compare its diagnostic performance with quantitative-semantic model and radiologists. METHODS A total of 682 pulmonary nodules were divided into the primary cohort (181 grade 1; 254 grade 2; 64 grade 3) and validation cohort (69 grade 1; 99 grade 2; 15 grade 3) according to scanners. The radiomic and quantitative-semantic models were built using ordinal logistic regression. The diagnostic performance of the models and radiologists was assessed by the area under the curve (AUC) of the receiver operating characteristic curve and accuracy. RESULTS The radiomic model demonstrated excellent diagnostic performance in the validation cohort (AUC, 0.900 (95%CI: 0.847-0.939) for Grade 1 vs. Grade 2/Grade 3; AUC, 0.929 (95%CI: 0.882-0.962) for Grade 1/Grade 2 vs. Grade 3; accuracy, 0.803 (95%CI: 0.737-0.857)). No significant difference in diagnostic performance was found between the radiomic model and radiological expert (AUC, 0.840 (95%CI: 0.779-0.890) for Grade 1 vs. Grade 2/Grade 3, p = 0.130; AUC, 0.852 (95%CI: 0.793-0.900) for Grade 1/Grade 2 vs. Grade 3, p = 0.170; accuracy, 0.743 (95%CI: 0.673-0.804), p = 0.079), but the radiomic model outperformed the quantitative-semantic model and inexperienced radiologists (all p < 0.05). CONCLUSIONS The radiomic model of LDCT can be used to predict the differentiation grade of IPA in lung cancer screening, and its diagnostic performance is comparable to that of radiological expert. KEY POINTS • Early identifying the novel differentiation grade of invasive non-mucinous pulmonary adenocarcinoma may provide guidance for further surveillance, surgical strategy, or more adjuvant treatment. • The diagnostic performance of the radiomic model is comparable to that of a radiological expert and superior to that of the quantitative-semantic model and inexperienced radiologists. • The radiomic model of low-dose CT can be used to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma in lung cancer screening.
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23
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Shen J, Du H, Wang Y, Du L, Yang D, Wang L, Zhu R, Zhang X, Wu J. A novel nomogram model combining CT texture features and urine energy metabolism to differentiate single benign from malignant pulmonary nodule. Front Oncol 2022; 12:1035307. [PMID: 36591441 PMCID: PMC9798090 DOI: 10.3389/fonc.2022.1035307] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022] Open
Abstract
Objective To investigate a novel diagnostic model for benign and malignant pulmonary nodule diagnosis based on radiomic and clinical features, including urine energy metabolism index. Methods A total of 107 pulmonary nodules were prospectively recruited and pathologically confirmed as malignant in 86 cases and benign in 21 cases. A chest CT scan and urine energy metabolism test were performed in all cases. A nomogram model was established in combination with radiomic and clinical features, including urine energy metabolism levels. The nomogram model was compared with the radiomic model and the clinical feature model alone to test its diagnostic validity, and receiver operating characteristic (ROC) curves were plotted to assess diagnostic validity. Results The nomogram was established using a logistic regression algorithm to combine radiomic features and clinical characteristics including urine energy metabolism results. The predictive performance of the nomogram was evaluated using the area under the ROC and calibration curve, which showed the best performance, area under the curve (AUC) = 0.982, 95% CI = 0.940-1.000, compared to clinical and radiomic models in the testing cohort. The clinical benefit of the model was assessed using the decision curve analysis (DCA) and using the nomogram for benign and malignant pulmonary nodules, and preoperative prediction of benign and malignant pulmonary nodules using nomograms showed better clinical benefit. Conclusion This study shows that a coupled model combining CT imaging features and clinical features (including urine energy metabolism) in combination with the nomogram model has higher diagnostic performance than the radiomic and clinical models alone, suggesting that the combination of both methods is more advantageous in identifying benign and malignant pulmonary nodules.
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Affiliation(s)
- Jing Shen
- Graduate School, Tianjin Medical University, Tianjin, China,Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Hai Du
- Graduate School, Tianjin Medical University, Tianjin, China,Department of Radiology, Ordos Central Hospital, Ordos Inner Mongolia, China
| | - Yadong Wang
- School of Medicine, Dalian University, Dalian, China,Department of Research, Dalian Detecsen Biomedical Co., LTD, Dalian, China
| | - Lina Du
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China,Graduate School, Dalian Medical University, Dalian, China
| | - Dong Yang
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China,Graduate School, Dalian University, Dalian, China
| | - Lingwei Wang
- Department of Cardio-Thoracic Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Ruiping Zhu
- Department of Pathology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Xiaohui Zhang
- College of Environment and Chemical Engineering, Dalian University, Dalian, China,*Correspondence: Jianlin Wu, ; Xiaohui Zhang,
| | - Jianlin Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China,*Correspondence: Jianlin Wu, ; Xiaohui Zhang,
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24
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Ferrante M, Rinaldi L, Botta F, Hu X, Dolp A, Minotti M, De Piano F, Funicelli G, Volpe S, Bellerba F, De Marco P, Raimondi S, Rizzo S, Shi K, Cremonesi M, Jereczek-Fossa BA, Spaggiari L, De Marinis F, Orecchia R, Origgi D. Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models. J Clin Med 2022; 11:7334. [PMID: 36555950 PMCID: PMC9784875 DOI: 10.3390/jcm11247334] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/05/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
Radiomics investigates the predictive role of quantitative parameters calculated from radiological images. In oncology, tumour segmentation constitutes a crucial step of the radiomic workflow. Manual segmentation is time-consuming and prone to inter-observer variability. In this study, a state-of-the-art deep-learning network for automatic segmentation (nnU-Net) was applied to computed tomography images of lung tumour patients, and its impact on the performance of survival radiomic models was assessed. In total, 899 patients were included, from two proprietary and one public datasets. Different network architectures (2D, 3D) were trained and tested on different combinations of the datasets. Automatic segmentations were compared to reference manual segmentations performed by physicians using the DICE similarity coefficient. Subsequently, the accuracy of radiomic models for survival classification based on either manual or automatic segmentations were compared, considering both hand-crafted and deep-learning features. The best agreement between automatic and manual contours (DICE = 0.78 ± 0.12) was achieved averaging 2D and 3D predictions and applying customised post-processing. The accuracy of the survival classifier (ranging between 0.65 and 0.78) was not statistically different when using manual versus automatic contours, both with hand-crafted and deep features. These results support the promising role nnU-Net can play in automatic segmentation, accelerating the radiomic workflow without impairing the models' accuracy. Further investigations on different clinical endpoints and populations are encouraged to confirm and generalise these findings.
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Affiliation(s)
- Matteo Ferrante
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Lisa Rinaldi
- Radiation Research Unit, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Francesca Botta
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Xiaobin Hu
- Department of Informatics, Technical University of Munich, Arcisstraße 21, 80333 Munich, Germany
| | - Andreas Dolp
- Department of Informatics, Technical University of Munich, Arcisstraße 21, 80333 Munich, Germany
| | - Marta Minotti
- Division of Radiology, IEO, European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Francesca De Piano
- Division of Radiology, IEO, European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Gianluigi Funicelli
- Division of Radiology, IEO, European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Stefania Volpe
- Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy
| | - Federica Bellerba
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Paolo De Marco
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Sara Raimondi
- Department of Experimental Oncology, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Stefania Rizzo
- Clinica di Radiologia EOC, Istituto Imaging della Svizzera Italiana (IIMSI), via Tesserete 46, 6900 Lugano, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana (USI), via G. Buffi 13, 6900 Lugano, Switzerland
| | - Kuangyu Shi
- Chair for Computer-Aided Medical Procedures, Department of Informatics, Technical University of Munich, Arcisstraße 21, 80333 Munich, Germany
- Department of Nuclear Medicine, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010 Bern, Switzerland
| | - Marta Cremonesi
- Radiation Research Unit, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Barbara A. Jereczek-Fossa
- Division of Radiation Oncology, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy
| | - Lorenzo Spaggiari
- Department of Oncology and Hemato-Oncology, University of Milan, via Festa del Perdono 7, 20122 Milan, Italy
- Division of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Filippo De Marinis
- Division of Thoracic Oncology, IEO, European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Roberto Orecchia
- Division of Radiology, IEO, European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
- Scientific Direction, IEO, European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
| | - Daniela Origgi
- Medical Physics Unit, IEO European Institute of Oncology IRCCS, via Ripamonti 435, 20141 Milan, Italy
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Iliadou V, Kakkos I, Karaiskos P, Kouloulias V, Platoni K, Zygogianni A, Matsopoulos GK. Early Prediction of Planning Adaptation Requirement Indication Due to Volumetric Alterations in Head and Neck Cancer Radiotherapy: A Machine Learning Approach. Cancers (Basel) 2022; 14:cancers14153573. [PMID: 35892831 PMCID: PMC9331795 DOI: 10.3390/cancers14153573] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/14/2022] [Accepted: 07/20/2022] [Indexed: 11/16/2022] Open
Abstract
Background: During RT cycles, the tumor response pattern could affect tumor coverage and may lead to organs at risk of overdose. As such, early prediction of significant volumetric changes could therefore reduce potential radiation-related adverse effects. Nevertheless, effective machine learning approaches based on the radiomic features of the clinically used CBCT images to determine the tumor volume variations due to RT not having been implemented so far. Methods: CBCT images from 40 HN cancer patients were collected weekly during RT treatment. From the obtained images, the Clinical Target Volume (CTV) and Parotid Glands (PG) regions of interest were utilized to calculate 104 delta-radiomics features. These features were fed on a feature selection and classification procedure for the early prediction of significant volumetric alterations. Results: The proposed framework was able to achieve 0.90 classification performance accuracy while detecting a small subset of discriminative characteristics from the 1st week of RT. The selected features were further analyzed regarding their effects on temporal changes in anatomy and tumor response modeling. Conclusion: The use of machine learning algorithms offers promising perspectives for fast and reliable early prediction of large volumetric deviations as a result of RT treatment, exploiting hidden patterns in the overall anatomical characteristics.
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Affiliation(s)
- Vasiliki Iliadou
- School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Athens, Greece; (I.K.); (G.K.M.)
- Correspondence: ; Tel.: +30-21-0772-3577
| | - Ioannis Kakkos
- School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Athens, Greece; (I.K.); (G.K.M.)
- Department of Biomedical Engineering, University of West Attica, 122 43 Athens, Greece
| | - Pantelis Karaiskos
- Medical Physics Laboratory, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece;
| | - Vassilis Kouloulias
- 2nd Department of Radiology, Radiotherapy Unit, ATTIKON University Hospital, 124 62 Athens, Greece; (V.K.); (K.P.)
| | - Kalliopi Platoni
- 2nd Department of Radiology, Radiotherapy Unit, ATTIKON University Hospital, 124 62 Athens, Greece; (V.K.); (K.P.)
| | - Anna Zygogianni
- 1st Department of Radiology, Radiotherapy Unit, ARETAIEION University Hospital, 115 28 Athens, Greece;
| | - George K. Matsopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Athens, Greece; (I.K.); (G.K.M.)
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26
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Witkam RL, Buckens CF, van Goethem JWM, Vissers KCP, Henssen DJHA. The current role and future directions of imaging in failed back surgery syndrome patients: an educational review. Insights Imaging 2022; 13:117. [PMID: 35838802 PMCID: PMC9287525 DOI: 10.1186/s13244-022-01246-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 06/02/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Failed back surgery syndrome (FBSS) is an umbrella term referring to painful sensations experienced by patients after spinal surgery, mostly of neuropathic nature. Adequate treatment of FBSS is challenging, as its etiology is believed to be multifactorial and still not fully clarified. Accurate identification of the source of pain is difficult but pivotal to establish the most appropriate treatment strategy. Although the clinical utility of imaging in FBSS patients is still contentious, objective parameters are highly warranted to map different phenotypes of FBSS and tailor each subsequent therapy. MAIN BODY Since technological developments have weakened the applicability of prior research, this educational review outlined the recent evidence (i.e., from January 2005 onwards) after a systematic literature search. The state of the art on multiple imaging modalities in FBSS patients was reviewed. Future directions related to functional MRI and the development of imaging biomarkers have also been discussed. CONCLUSION Besides the fact that more imaging studies correlated with symptomatology in the postoperative setting are warranted, the current educational review outlined that contrast-enhanced MRI and MR neurography have been suggested as valuable imaging protocols to assess alterations in the spine of FBSS patients. The use of imaging biomarkers to study correlations between imaging features and symptomatology might hold future potential; however, more research is required before any promising hypotheses can be drawn.
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Affiliation(s)
- Richard L Witkam
- Department of Anaesthesiology, Pain and Palliative Medicine, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands. .,Department of Neurosurgery, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Constantinus F Buckens
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Johan W M van Goethem
- Department of Medical and Molecular Imaging, General Hospital Nikolaas, Sint-Niklaas, Belgium
| | - Kris C P Vissers
- Department of Anaesthesiology, Pain and Palliative Medicine, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
| | - Dylan J H A Henssen
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
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The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization. Cancers (Basel) 2022; 14:cancers14143349. [PMID: 35884409 PMCID: PMC9321521 DOI: 10.3390/cancers14143349] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 07/07/2022] [Accepted: 07/08/2022] [Indexed: 12/10/2022] Open
Abstract
Simple Summary Modern, personalized therapy approaches are increasingly changing advanced cancer into a chronic disease. Compared to imaging, novel omics methodologies in molecular biology have already achieved an individual characterization of cancerous lesions. With quantitative imaging biomarkers, analyzed by radiomics or deep learning, an imaging-based assessment of tumoral biology can be brought into clinical practice. Combining these with other non-invasive methods, e.g., liquid profiling, could allow for more individual decision making regarding therapies and applications. Abstract Similar to the transformation towards personalized oncology treatment, emerging techniques for evaluating oncologic imaging are fostering a transition from traditional response assessment towards more comprehensive cancer characterization via imaging. This development can be seen as key to the achievement of truly personalized and optimized cancer diagnosis and treatment. This review gives a methodological introduction for clinicians interested in the potential of quantitative imaging biomarkers, treating of radiomics models, texture visualization, convolutional neural networks and automated segmentation, in particular. Based on an introduction to these methods, clinical evidence for the corresponding imaging biomarkers—(i) dignity and etiology assessment; (ii) tumoral heterogeneity; (iii) aggressiveness and response; and (iv) targeting for biopsy and therapy—is summarized. Further requirements for the clinical implementation of these imaging biomarkers and the synergistic potential of personalized molecular cancer diagnostics and liquid profiling are discussed.
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Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine. Cancers (Basel) 2022; 14:cancers14122860. [PMID: 35740526 PMCID: PMC9220825 DOI: 10.3390/cancers14122860] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 06/03/2022] [Accepted: 06/07/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Recently, radiogenomics has played a significant role and offered a new understanding of cancer’s biology and behavior in response to standard therapy. It also provides a more precise prognosis, investigation, and analysis of the patient’s cancer. Over the years, Artificial Intelligence (AI) has provided a significant strength in radiogenomics. In this paper, we offer computational and oncological prospects of the role of AI in radiogenomics, as well as its offers, achievements, opportunities, and limitations in the current clinical practices. Abstract Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.
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Diniz de Lima E, Souza Paulino JA, Lira de Farias Freitas AP, Viana Ferreira JE, Barbosa JDS, Bezerra Silva DF, Bento PM, Araújo Maia Amorim AM, Melo DP. Artificial intelligence and infrared thermography as auxiliary tools in the diagnosis of temporomandibular disorder. Dentomaxillofac Radiol 2022; 51:20210318. [PMID: 34613829 PMCID: PMC8802706 DOI: 10.1259/dmfr.20210318] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
OBJECTIVE To assess three machine learning (ML) attribute extraction methods: radiomic, semantic and radiomic-semantic association on temporomandibular disorder (TMD) detection using infrared thermography (IT); and to determine which ML classifier, KNN, SVM and MLP, is the most efficient for this purpose. METHODS AND MATERIALS 78 patients were selected by applying the Fonseca questionnaire and RDC/TMD to categorize control patients (37) and TMD patients (41). IT lateral projections of each patient were acquired. The masseter and temporal muscles were selected as regions of interest (ROI) for attribute extraction. Three methods of extracting attributes were assessed: radiomic, semantic and radiomic-semantic association. For radiomic attribute extraction, 20 texture attributes were assessed using co-occurrence matrix in a standardized angulation of 0°. The semantic features were the ROI mean temperature and pain intensity data. For radiomic-semantic association, a single dataset composed of 28 features was assessed. The classification algorithms assessed were KNN, SVM and MLP. Hopkins's statistic, Shapiro-Wilk, ANOVA and Tukey tests were used to assess data. The significance level was set at 5% (p < 0.05). RESULTS Training and testing accuracy values differed statistically for the radiomic-semantic association (p = 0.003). MLP differed from the other classifiers for the radiomic-semantic association (p = 0.004). Accuracy, precision and sensitivity values of semantic and radiomic-semantic association differed statistically from radiomic features (p = 0.008, p = 0.016 and p = 0.013). CONCLUSION Semantic and radiomic-semantic-associated ML feature extraction methods and MLP classifier should be chosen for TMD detection using IT images and pain scale data. IT associated with ML presents promising results for TMD detection.
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Affiliation(s)
- Elisa Diniz de Lima
- Department of Dentistry, State University of Paraíba, Campina Grande, Paraíba, Brazil
| | | | | | | | | | | | - Patrícia Meira Bento
- Department of Dentistry, State University of Paraíba, Campina Grande, Paraíba, Brazil
| | | | - Daniela Pita Melo
- Department of Dentistry, State University of Paraíba, Campina Grande, Paraíba, Brazil
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Development and Validation of a CT-Based Signature for the Prediction of Distant Metastasis Before Treatment of Non-Small Cell Lung Cancer. Acad Radiol 2022; 29 Suppl 2:S62-S72. [PMID: 33402298 DOI: 10.1016/j.acra.2020.12.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/11/2020] [Accepted: 12/11/2020] [Indexed: 01/06/2023]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a radiomics model, a clinical-semantic model and a combined model by using standard methods for the pretreatment prediction of distant metastasis (DM) in patients with non-small-cell lung cancer (NSCLC) and to explore whether the combined model provides added value compared to the individual models. MATERIALS AND METHODS This retrospective study involved 356 patients with NSCLC. According to the image biomarker standardization initiative reference manual, we standardized the image processing and feature extraction using in-house software. Finally, 6692 radiomics features were extracted from each lesion based on contrast-enhanced chest CT images. The least absolute shrinkage selection operator and the recursive feature elimination algorithm were used to select features. The logistic regression classifier was used to build the model. Three models (radiomics model, clinical-semantic model and combined model) were constructed to predict DM in NSCLC. Area under the receiver operating characteristic curves were used to validate the ability of the three models to predict DM. A visual nomogram based on the combined model was developed for DM risk assessment in each patient. RESULTS The receiver operating characteristic curve showed predictive performance for DM of the radiomics model (area under the curve [AUC] values for training and validation were 0.76 [95% CI, 0.704 - 0.820] and 0.76 [95% CI, 0.653 - 0.858], respectively). The combined model had AUCs of 0.78 (95% CI, 0.723 - 0.835) and 0.77 (95% CI, 0.673 - 0.870) in the training and validation cohorts, respectively. Both the radiomics model and combined model performed better than the clinical-semantic model (0.70 [95% CI, 0.634 - 0.760] and 0.67 [95% CI, 0.554 - 0.787] in the training and validation cohorts, respectively). CONCLUSION The radiomics model and combined model may be useful for the prediction of DM in patients with NSCLC.
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Zhang Y, Liu F, Zhang H, Ma H, Sun J, Zhang R, Song L, Shi H. Diagnostic Value of Radiomics Analysis in Contrast-Enhanced Spectral Mammography for Identifying Triple-Negative Breast Cancer. Front Oncol 2022; 11:773196. [PMID: 35004294 PMCID: PMC8733550 DOI: 10.3389/fonc.2021.773196] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 12/02/2021] [Indexed: 11/29/2022] Open
Abstract
Purpose To evaluate the value of radiomics analysis in contrast-enhanced spectral mammography (CESM) for the identification of triple-negative breast cancer (TNBC). Method CESM images of 367 pathologically confirmed breast cancer patients (training set: 218, testing set: 149) were retrospectively analyzed. Cranial caudal (CC), mediolateral oblique (MLO), and combined models were built on the basis of the features extracted from subtracted images on CC, MLO, and the combination of CC and MLO, respectively, in the tumour region. The performance of the models was evaluated through receiver operating characteristic (ROC) curve analysis, the Hosmer-Lemeshow test, and decision curve analysis (DCA). The areas under ROC curves (AUCs) were compared through the DeLong test. Results The combined CC and MLO model had the best AUC and sensitivity of 0.90 (95% confidence interval: 0.85–0.96) and 0.97, respectively. The Hosmer–Lemeshow test yielded a non-significant statistic with p-value of 0.59. The clinical usefulness of the combined CC and MLO model was confirmed if the threshold was between 0.02 and 0.81 in the DCA. Conclusions Machine learning models based on subtracted images in CESM images were valuable for distinguishing TNBC and NTNBC. The model with the combined CC and MLO features had the best performance compared with models that used CC or MLO features alone.
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Affiliation(s)
- Yongxia Zhang
- Department of Medical Imaging, Shandong Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Fengjie Liu
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Han Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Jian Sun
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Ran Zhang
- Marketing Research Department, Huiying Medical Technology Co. Ltd, Beijing, China
| | - Lei Song
- Department of Geratology, Yantai Yuhuangding Hospital, Yantai, China
| | - Hao Shi
- Department of Medical Imaging, Shandong Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
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Liu Q, Hu P. Extendable and explainable deep learning for pan-cancer radiogenomics research. Curr Opin Chem Biol 2022; 66:102111. [PMID: 34999476 DOI: 10.1016/j.cbpa.2021.102111] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/06/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022]
Abstract
Radiogenomics is a field where medical images and genomic profiles are jointly analyzed to answer critical clinical questions. Specifically, people want to identify non-invasive imaging biomarkers that are associated with both genomic features and clinical outcomes. Deep learning is an advanced computer science technique that has been applied in many fields, including medical image and genomic data analysis. This review summarizes the current state of deep learning in pan-cancer radiogenomic research, discusses its limitations, and indicates the potential future directions. Traditional machine learning in radiomics, genomics, and radiogenomics have also been briefly discussed. We also summarize the main pan-cancer radiogenomic research resources. Two characteristics of deep learning are emphasized when discussing its application to pan-cancer radiogenomics, which are extendibility and explainability.
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Affiliation(s)
- Qian Liu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada; Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada; Department of Statistics, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada.
| | - Pingzhao Hu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada; Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, R3E 0W3, Canada.
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He Y, Liu P, Xie L, Zeng S, Lin H, Zhang B, Liu J. Construction and Verification of a Predictive Model for Risk Factors in Children With Severe Adenoviral Pneumonia. Front Pediatr 2022; 10:874822. [PMID: 35832584 PMCID: PMC9271770 DOI: 10.3389/fped.2022.874822] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 06/08/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE To construct and validate a predictive model for risk factors in children with severe adenoviral pneumonia based on chest low-dose CT imaging and clinical features. METHODS A total of 177 patients with adenoviral pneumonia who underwent low-dose CT examination were collected between January 2019 and August 2019. The assessment criteria for severe pneumonia were divided into mild group (N = 125) and severe group (N = 52). All cases divided into training cohort (N = 125) and validation cohort (N = 52). We constructed a prediction model by drawing a nomogram and verified the predictive efficacy of the model through the ROC curve, calibration curve and decision curve analysis. RESULTS The difference was statistically significant (P < 0.05) between the mild adenovirus pneumonia group and the severe adenovirus pneumonia group in gender, age, weight, body temperature, L/N ratio, LDH, ALT, AST, CK-MB, ADV DNA, bronchial inflation sign, emphysema, ground glass sign, bronchial wall thickening, bronchiectasis, pleural effusion, consolidation score, and lobular inflammation score. Multivariate logistic regression analysis showed that gender, LDH value, emphysema, consolidation score, and lobular inflammation score were severe independent risk factors for adenovirus pneumonia in children. Logistic regression was employed to construct clinical model, imaging semantic feature model, and combined model. The AUC values of the training sets of the three models were 0.85 (0.77-0.94), 0.83 (0.75-0.91), and 0.91 (0.85-0.97). The AUC of the validation set was 0.77 (0.64-0.91), 0.83 (0.71-0.94), and 0.85 (0.73-0.96), respectively. The calibration curve fit good of the three models. The clinical decision curve analysis demonstrates the clinical application value of the nomogram prediction model. CONCLUSION The prediction model based on chest low-dose CT image characteristics and clinical characteristics has relatively clear predictive value in distinguishing mild adenovirus pneumonia from severe adenovirus pneumonia in children and might provide a new method for early clinical prediction of the outcome of adenovirus pneumonia in children.
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Affiliation(s)
- Yaqiong He
- Department of Radiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Peng Liu
- Department of Radiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Leyun Xie
- Department of Pediatrics, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Saizhen Zeng
- Department of Pediatrics, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | | | - Bing Zhang
- Department of Pediatrics, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
| | - Jianbin Liu
- Department of Radiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China
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Reddy R, Reddy S. Trends in Imaging Patterns of Bronchogenic Carcinoma: Reality or a Statistical Variation? A Single-Center Cross-Sectional Analysis of Outcomes. Med Princ Pract 2022; 31:480-485. [PMID: 36195060 PMCID: PMC9801366 DOI: 10.1159/000527246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 09/25/2022] [Indexed: 01/02/2023] Open
Abstract
INTRODUCTION Bronchogenic carcinoma accounts for more cancer-related deaths than any other malignancy and is the most frequently diagnosed cancer in the world. Bronchogenic carcinoma is by far the leading cause of cancer death among both men and women, making up almost 25% of all cancer deaths. The objective of this study was to identify the changing trends, if any, in radiological patterns of bronchogenic carcinoma to document the various computed tomography (CT) appearances of bronchogenic carcinoma with histopathologic correlation. METHODS This was a single-center cross-sectional study on 162 patients with clinical or radiological suspicion of bronchogenic carcinoma with histopathological confirmation of diagnosis. RESULTS There was a male preponderance with bronchogenic carcinoma and smoking being the most common risk factor. Squamous cell carcinoma followed by adenocarcinoma and small cell carcinoma is the most common histologic subtype. Squamous cell carcinoma was noted to be present predominantly in the peripheral location (55.5%), and adenocarcinoma was noted to be present predominantly in the central location (68.4%). CONCLUSION CT is the imaging modality of choice for evaluating bronchogenic carcinoma and provides for precise characterization of the size, extent, and staging of the carcinoma. Among 162 bronchogenic carcinoma cases evaluated in the current study, a definite changing trend in the radiological pattern of squamous cell carcinoma and adenocarcinoma was observed. Squamous cell carcinoma was predominantly noted to be a peripheral tumor, and adenocarcinoma is predominantly noted to be a central tumor. Surveillance or restaging scans are recommended, considering the high mortality rate in patients with bronchogenic carcinoma.
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Affiliation(s)
- Ravikanth Reddy
- Department of Radiology, St. John's Hospital, Kattappana, India
| | - Sandeep Reddy
- Department of Radiology, St. John's Hospital, Bengaluru, India
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Goda J, Nayak P, Sinha S, Sahu A, Joshi K, Choudhary O, Mhatre R, Mummudi N, Agarwal J. Computerized tomography-based first order tumor texture features in non-small cell lung carcinoma treated with concurrent chemoradiation: A simplistic and potential surrogate imaging marker for survival. J Cancer Res Ther 2022. [DOI: 10.4103/jcrt.jcrt_2317_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Mao X, Mei R, Yu S, Shou L, Zhang W, Li K, Qiu Z, Xie T, Sui X. Emerging Technologies for the Detection of Cancer Micrometastasis. Technol Cancer Res Treat 2022; 21:15330338221100355. [PMID: 35903930 PMCID: PMC9340332 DOI: 10.1177/15330338221100355] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 06/13/2021] [Accepted: 07/23/2021] [Indexed: 11/18/2022] Open
Abstract
The most efficient way to treat tumors is through surgery. However, many cancer patients have a poor prognosis even when they undergo radical excision at an early stage. Micrometastasis is one of the most critical factors that induced this situation. Undetected micrometastasis can lead to the failure of initial treatment. Therefore, preoperative and intraoperative detection of micrometastasis could have a significant clinical influence on the prognosis and optimal therapy for cancer patients. Additionally, to achieve this goal, researchers have aimed to create more effective detection technologies. Herein, we classify the currently reported micrometastasis detection technologies, introduce some representative samples for each technology, including the limitations, and provide future directions to overcome the limitations.
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Affiliation(s)
- Xuqing Mao
- The Affiliated Hospital of Hangzhou Normal University, College of Medicine, Hangzhou Normal University, Hangzhou,
Zhejiang, China
- School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Ruyi Mei
- The Affiliated Hospital of Hangzhou Normal University, College of Medicine, Hangzhou Normal University, Hangzhou,
Zhejiang, China
- School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Shuxian Yu
- The Affiliated Hospital of Hangzhou Normal University, College of Medicine, Hangzhou Normal University, Hangzhou,
Zhejiang, China
- School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Lan Shou
- The Affiliated Hospital of Hangzhou Normal University, College of Medicine, Hangzhou Normal University, Hangzhou,
Zhejiang, China
- School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Wenzheng Zhang
- The Affiliated Hospital of Hangzhou Normal University, College of Medicine, Hangzhou Normal University, Hangzhou,
Zhejiang, China
- School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Keshuai Li
- The Affiliated Hospital of Hangzhou Normal University, College of Medicine, Hangzhou Normal University, Hangzhou,
Zhejiang, China
- School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Zejing Qiu
- The Affiliated Hospital of Hangzhou Normal University, College of Medicine, Hangzhou Normal University, Hangzhou,
Zhejiang, China
- School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Tian Xie
- The Affiliated Hospital of Hangzhou Normal University, College of Medicine, Hangzhou Normal University, Hangzhou,
Zhejiang, China
- School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang, China
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines,
Engineering Laboratory of Development and Application of Traditional Chinese
Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of
Zhejiang Province, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Xinbing Sui
- The Affiliated Hospital of Hangzhou Normal University, College of Medicine, Hangzhou Normal University, Hangzhou,
Zhejiang, China
- School of Pharmacy, Hangzhou Normal University, Hangzhou, Zhejiang, China
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines,
Engineering Laboratory of Development and Application of Traditional Chinese
Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of
Zhejiang Province, Hangzhou Normal University, Hangzhou, Zhejiang, China
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Williams TL, Saadat LV, Gonen M, Wei A, Do RKG, Simpson AL. Radiomics in surgical oncology: applications and challenges. Comput Assist Surg (Abingdon) 2021; 26:85-96. [PMID: 34902259 PMCID: PMC9238238 DOI: 10.1080/24699322.2021.1994014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
Surgery is a curative treatment option for many patients with malignant tumors. Increased attention has focused on the combination of surgery with chemotherapy, as multimodality treatment has been associated with promising results in certain cancer types. Despite these data, there remains clinical equipoise on optimal timing and patient selection for neoadjuvant or adjuvant strategies. Radiomics, an emerging field involving the extraction of advanced features from radiographic images, has the potential to revolutionize oncologic treatment and contribute to the advance of personalized therapy by helping predict tumor behavior and response to therapy. This review analyzes and summarizes studies that use radiomics with machine learning in patients who have received neoadjuvant and/or adjuvant chemotherapy to predict prognosis, recurrence, survival, and therapeutic response for various cancer types. While studies in both neoadjuvant and adjuvant settings demonstrate above average performance on ability to predict progression-free and overall survival, there remain many challenges and limitations to widespread implementation of this technology. The lack of standardization of common practices to analyze radiomics, limited data sharing, and absence of auto-segmentation have hindered the inclusion and rapid adoption of radiomics in prospective, clinical studies.
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Affiliation(s)
- Travis L. Williams
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lily V. Saadat
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mithat Gonen
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alice Wei
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Richard K. G. Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amber L. Simpson
- School of Computing, Queen’s University, Kingston, ON, Canada
- Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada
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Malafaia M, Pereira T, Silva F, Morgado J, Cunha A, Oliveira HP. Ensemble Strategies for EGFR Mutation Status Prediction in Lung Cancer. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3285-3288. [PMID: 34891942 DOI: 10.1109/embc46164.2021.9629755] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Lung cancer treatments that are accurate and effective are urgently needed. The diagnosis of advanced-stage patients accounts for the majority of the cases, being essential to provide a specialized course of treatment. One emerging course of treatment relies on target therapy through the testing of biomarkers, such as the Epidermal Growth Factor Receptor (EGFR) gene. Such testing can be obtained from invasive methods, namely through biopsy, which may be avoided by applying machine learning techniques to the imaging phenotypes extracted from Computerized Tomography (CT). This study aims to explore the contribution of ensemble methods when applied to the prediction of EGFR mutation status. The obtained results translate in a direct correlation between the semantic predictive model and the outcome of the combined ensemble methods, showing that the utilized features do not have a positive contribution to the predictive developed models.
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Song L, Xing T, Zhu Z, Han W, Fan G, Li J, Du H, Song W, Jin Z, Zhang G. Hybrid Clinical-Radiomics Model for Precisely Predicting the Invasiveness of Lung Adenocarcinoma Manifesting as Pure Ground-Glass Nodule. Acad Radiol 2021; 28:e267-e277. [PMID: 32534967 DOI: 10.1016/j.acra.2020.05.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 04/27/2020] [Accepted: 05/04/2020] [Indexed: 12/12/2022]
Abstract
RATIONALE AND OBJECTIVES To identify whether the radiomics features of computed tomography (CT) allowed for the preoperative discrimination of the invasiveness of lung adenocarcinomas manifesting as pure ground-glass nodules (pGGNs) and further to develop and compare different predictive models. MATERIALS AND METHODS We retrospectively included 187 lung adenocarcinomas presenting as pGGNs (66 preinvasive lesions and 121 invasive lesions), which were randomly divided into the training and test sets (8:2). Radiomics features were extracted from non-enhanced CT images. Clinical features, including patient's demographic characteristics, smoking status, and conventional CT features that reflect tumor's morphology and surrounding information were also collected. Intraclass correlation coefficient and ℓ2.1-norm minimization were used to identify influential feature subset which was then used to build three predictive models (clinical, radiomics, and clinical-radiomics models) with the gradient boosting regression tree classifier. The performances of the predictive models were evaluated using the area under the curve (AUC). RESULTS Of the 1409 radiomics features and 27 clinical feature subtypes, 102 features were selected to construct the hybrid clinical-radiomics model, which achieved the best discriminative power (AUC = 0.934 and 0.929 in training and test set). The radiomics model showed comparable predictive performance (AUC = 0.911 and 0.901 in training and test set) compared to the clinical model (AUC = 0.911 and 0.894 in training and test set). CONCLUSION The radiomics model showed good predictive performance in discriminating invasive lesions from preinvasive lesions for lung adenocarcinomas presenting as pGGNs. Its performance can be further improved by adding clinical features.
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Affiliation(s)
- Lan Song
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Tongtong Xing
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China; School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Zhenchen Zhu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China; 4+4 MD Program, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Wei Han
- Department of Epidemiology and Health Statistics, Institute of Basic Medicine Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China
| | - Guangda Fan
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China; School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Ji Li
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Huayang Du
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Wei Song
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
| | - Guanglei Zhang
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China; School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
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Salzillo TC, Taku N, Wahid KA, McDonald BA, Wang J, van Dijk LV, Rigert JM, Mohamed ASR, Wang J, Lai SY, Fuller CD. Advances in Imaging for HPV-Related Oropharyngeal Cancer: Applications to Radiation Oncology. Semin Radiat Oncol 2021; 31:371-388. [PMID: 34455992 DOI: 10.1016/j.semradonc.2021.05.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
While there has been an overall decline of tobacco and alcohol-related head and neck cancer in recent decades, there has been an increased incidence of HPV-associated oropharyngeal cancer (OPC). Recent research studies and clinical trials have revealed that the cancer biology and clinical progression of HPV-positive OPC is unique relative to its HPV-negative counterparts. HPV-positive OPC is associated with higher rates of disease control following definitive treatment when compared to HPV-negative OPC. Thus, these conditions should be considered unique diseases with regards to treatment strategies and survival. In order to sufficiently characterize HPV-positive OPC and guide treatment strategies, there has been a considerable effort to diagnose, prognose, and track the treatment response of HPV-associated OPC through advanced imaging research. Furthermore, HPV-positive OPC patients are prime candidates for radiation de-escalation protocols, which will ideally reduce toxicities associated with radiation therapy and has prompted additional imaging research to detect radiation-induced changes in organs at risk. This manuscript reviews the various imaging modalities and current strategies for tackling these challenges as well as provides commentary on the potential successes and suggested improvements for the optimal treatment of these tumors.
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Affiliation(s)
- Travis C Salzillo
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Nicolette Taku
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Kareem A Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Brigid A McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX; Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Jarey Wang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Lisanne V van Dijk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Jillian M Rigert
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX; Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Jihong Wang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Stephen Y Lai
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
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Oliveira C, Amstutz F, Vuong D, Bogowicz M, Hüllner M, Foerster R, Basler L, Schröder C, Eboulet EI, Pless M, Thierstein S, Peters S, Hillinger S, Tanadini-Lang S, Guckenberger M. Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging. EJNMMI Res 2021; 11:79. [PMID: 34417899 PMCID: PMC8380219 DOI: 10.1186/s13550-021-00809-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 07/08/2021] [Indexed: 12/25/2022] Open
Abstract
Background Radiomics is a promising tool for identifying imaging-based biomarkers. Radiomics-based models are often trained on single-institution datasets; however, multi-centre imaging datasets are preferred for external generalizability owing to the influence of inter-institutional scanning differences and acquisition settings. The study aim was to determine the value of preselection of robust radiomic features in routine clinical positron emission tomography (PET) images to predict clinical outcomes in locally advanced non-small cell lung cancer (NSCLC). Methods A total of 1404 primary tumour radiomic features were extracted from pre-treatment [18F]fluorodeoxyglucose (FDG)-PET scans of stage IIIA/N2 or IIIB NSCLC patients using a training cohort (n = 79; prospective Swiss multi-centre randomized phase III trial SAKK 16/00; 16 centres) and an internal validation cohort (n = 31; single centre). Robustness studies investigating delineation variation, attenuation correction and motion were performed (intraclass correlation coefficient threshold > 0.9). Two 12-/24-month event-free survival (EFS) and overall survival (OS) logistic regression models were trained using standardized imaging: (1) with robust features alone and (2) with all available features. Models were then validated using fivefold cross-validation, and validation on a separate single-centre dataset. Model performance was assessed using area under the receiver operating characteristic curve (AUC). Results Robustness studies identified 179 stable features (13%), with 25% stable features for 3D versus 4D acquisition, 31% for attenuation correction and 78% for delineation. Univariable analysis found no significant robust features predicting 12-/24-month EFS and 12-month OS (p value > 0.076). Prognostic models without robust preselection performed well for 12-month EFS in training (AUC = 0.73) and validation (AUC = 0.74). Patient stratification into two risk groups based on 12-month EFS was significant for training (p value = 0.02) and validation cohorts (p value = 0.03). Conclusions A PET-based radiomics model using a standardized, multi-centre dataset to predict EFS in locally advanced NSCLC was successfully established and validated with good performance. Prediction models with robust feature preselection were unsuccessful, indicating the need for a standardized imaging protocol. Supplementary Information The online version contains supplementary material available at 10.1186/s13550-021-00809-3.
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Affiliation(s)
- Carol Oliveira
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland. .,Division of Radiation Oncology, Cancer Center of Southeastern Ontario, Queen's University, Kingston, ON, Canada.
| | - Florian Amstutz
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Martin Hüllner
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Robert Foerster
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Lucas Basler
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Christina Schröder
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Eric I Eboulet
- Swiss Group for Clinical Cancer Research (SAKK) Coordinating Center, Bern, Switzerland
| | - Miklos Pless
- Department of Medical Oncology, Kantonsspital Winterthur, Winterthur, Switzerland
| | - Sandra Thierstein
- Swiss Group for Clinical Cancer Research (SAKK) Coordinating Center, Bern, Switzerland
| | - Solange Peters
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Sven Hillinger
- Department of Thoracic Surgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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van Dijk LV, Fuller CD. Artificial Intelligence and Radiomics in Head and Neck Cancer Care: Opportunities, Mechanics, and Challenges. Am Soc Clin Oncol Educ Book 2021; 41:1-11. [PMID: 33929877 DOI: 10.1200/edbk_320951] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The advent of large-scale high-performance computing has allowed the development of machine-learning techniques in oncologic applications. Among these, there has been substantial growth in radiomics (machine-learning texture analysis of images) and artificial intelligence (which uses deep-learning techniques for "learning algorithms"); however, clinical implementation has yet to be realized at scale. To improve implementation, opportunities, mechanics, and challenges, models of imaging-enabled artificial intelligence approaches need to be understood by clinicians who make the treatment decisions. This article aims to convey the basic conceptual premises of radiomics and artificial intelligence using head and neck cancer as a use case. This educational overview focuses on approaches for head and neck oncology imaging, detailing current research efforts and challenges to implementation.
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Affiliation(s)
- Lisanne V van Dijk
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX.,Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Clifton D Fuller
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX
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Cucchiara F, Petrini I, Romei C, Crucitta S, Lucchesi M, Valleggi S, Scavone C, Capuano A, De Liperi A, Chella A, Danesi R, Del Re M. Combining liquid biopsy and radiomics for personalized treatment of lung cancer patients. State of the art and new perspectives. Pharmacol Res 2021; 169:105643. [PMID: 33940185 DOI: 10.1016/j.phrs.2021.105643] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/22/2021] [Accepted: 04/22/2021] [Indexed: 12/11/2022]
Abstract
Lung cancer has become a paradigm for precision medicine in oncology, and liquid biopsy (LB) together with radiomics may have a great potential in this scenario. They are both minimally invasive, easy to perform, and can be repeated during patient's follow-up. Also, increasing evidence suggest that LB and radiomics may provide an efficient way to screen and diagnose tumors at an early stage, including the monitoring of any change in the tumor molecular profile. This could allow treatment optimization, improvement of patients' quality of life, and healthcare-related costs reduction. Latest reports on lung cancer patients suggest a combination of these two strategies, along with cutting-edge data analysis, to decode valuable information regarding tumor type, aggressiveness, progression, and response to treatment. The approach seems more compatible with clinical practice than the current standard, and provides new diagnostic companions being able to suggest the best treatment strategy compared to conventional methods. To implement radiomics and liquid biopsy directly into clinical practice, an artificial intelligence (AI)-based system could help to link patients' clinical data together with tumor molecular profiles and imaging characteristics. AI could also solve problems and limitations related to LB and radiomics methodologies. Further work is needed, including new health policies and the access to large amounts of high-quality and well-organized data, allowing a complementary and synergistic combination of LB and imaging, to provide an attractive choice e in the personalized treatment of lung cancer.
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Affiliation(s)
- Federico Cucchiara
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
| | - Iacopo Petrini
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Chiara Romei
- Unit II of Radio-diagnostics, Department of Diagnostic and Imaging, University Hospital of Pisa, Pisa, Italy
| | - Stefania Crucitta
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
| | - Maurizio Lucchesi
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Simona Valleggi
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Cristina Scavone
- Department of Experimental Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Annalisa Capuano
- Department of Experimental Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Annalisa De Liperi
- Unit II of Radio-diagnostics, Department of Diagnostic and Imaging, University Hospital of Pisa, Pisa, Italy
| | - Antonio Chella
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Romano Danesi
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy.
| | - Marzia Del Re
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
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CT radiomics-based prediction of anaplastic lymphoma kinase and epidermal growth factor receptor mutations in lung adenocarcinoma. Eur J Radiol 2021; 139:109710. [PMID: 33862316 DOI: 10.1016/j.ejrad.2021.109710] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 03/27/2021] [Accepted: 04/06/2021] [Indexed: 12/29/2022]
Abstract
PURPOSE To develop and validate a CT-based radiomic model to simultaneously diagnose anaplastic lymphoma kinase (ALK) rearrangements and epidermal growth factor receptor (EGFR) mutation status of lung adenocarcinoma and to assess whether peritumoural radiomic features add value in the prediction of mutation status. METHODS 503 patients with pathologically proven lung adenocarcinoma containing information on the mutation status were retrospectively included. Intratumoural and peritumoural radiomic features of the primary lesion were extracted from CT. We proposed two-level stepwise binary radiomics-based classification models to diagnose ALK (step1) and EGFR mutation status (step2). The performance of proposed models and added value of peritumoural radiomic features were evaluated by using the areas under receiver operating characteristic curves (AUC) and Obuchowski index in the development and validation sets. RESULTS Regarding the prediction of ALK rearrangement, the diagnostic performance of the intratumoural radiomic model showed the AUC of 0.77 and 0.68 for the development and validation sets, respectively. As for EGFR mutation, the diagnostic performance of the intratumoural radiomic model showed the AUCs of 0.64 and 0.62 for the development and validation sets, respectively. The radiomics added value to the model based on clinical features (development set [radiomics + clinical model vs. clinical model]: Obuchowski index, 0.76 vs. 0.66, p < 0.001; validation set: 0.69 vs. 0.61, p = 0.075). Adding peritumoural features resulted in no improvement in terms of model performance. CONCLUSION The CT radiomics-based model allowed the simultaneous prediction of the presence of ALK and EGFR mutations while adding value to the clinical features.
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Lee G, Park H, Lee HY, Ahn JH, Sohn I, Lee SH, Kim J. Tumor Margin Contains Prognostic Information: Radiomic Margin Characteristics Analysis in Lung Adenocarcinoma Patients. Cancers (Basel) 2021; 13:1676. [PMID: 33918164 PMCID: PMC8037340 DOI: 10.3390/cancers13071676] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/28/2021] [Accepted: 03/29/2021] [Indexed: 01/15/2023] Open
Abstract
We aimed to investigate the relationship between tumor radiomic margin characteristics and prognosis in patients with lung cancer. We enrolled 334 patients who underwent complete resection for lung adenocarcinoma. A quantitative computed tomography analysis was performed, and 76 radiomic margin characteristics were extracted. The radiomic margin characteristics were correlated with overall survival. The selected clinical variables and radiomic margin characteristics were used to calculate a prognostic model with subsequent internal and external validation. Nearly all of the radiomic margin characteristics showed excellent reproducibility. The least absolute shrinkage and selection operator (LASSO) method was used to select eight radiomic margin characteristics. When compared to the model with clinical variables only (C-index = 0.738), the model incorporating clinical variables and radiomic margin characteristics (C-index = 0.753) demonstrated a higher C-index for predicting overall survival. In the model integrating both clinical variables and radiomic margin characteristics, convexity, a Laplace of Gaussian (LoG) kurtosis of 3, and the roundness factor were each independently predictive of overall survival. In addition, radiomic margin characteristics were also correlated with the micropapillary subtype, and the sphericity value was able to predict the presence of the micropapillary subtype. In conclusion, our study showed that radiomic margin characteristics helped predict overall survival in patients with lung adenocarcinomas, thus implying that the tumor margin contains prognostic information.
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Affiliation(s)
- Geewon Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
- Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan 49241, Korea
| | - Hyunjin Park
- School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon 16419, Korea;
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, Korea
| | - Ho Yun Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul 06355, Korea
| | - Joong Hyun Ahn
- Biostatistics and Clinical Epidemiology Center, Samsung Biomedical Research Institute, Seoul 06351, Korea; (J.H.A.); (I.S.)
| | - Insuk Sohn
- Biostatistics and Clinical Epidemiology Center, Samsung Biomedical Research Institute, Seoul 06351, Korea; (J.H.A.); (I.S.)
| | - Seung-Hak Lee
- Department of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea;
- Core Research and Development Center, Korean University Ansan Hospital, Ansan 15355, Korea
| | - Jhingook Kim
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
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Du D, Gu J, Chen X, Lv W, Feng Q, Rahmim A, Wu H, Lu L. Integration of PET/CT Radiomics and Semantic Features for Differentiation between Active Pulmonary Tuberculosis and Lung Cancer. Mol Imaging Biol 2021; 23:287-298. [PMID: 33030709 DOI: 10.1007/s11307-020-01550-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 09/25/2020] [Accepted: 09/29/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE We aim to accurately differentiate between active pulmonary tuberculosis (TB) and lung cancer (LC) based on radiomics and semantic features as extracted from pre-treatment positron emission tomography/X-ray computed tomography (PET/CT) images. PROCEDURES A total of 174 patients (77/97 pulmonary TB/LC as confirmed by pathology) were retrospectively selected, with 122 in the training cohort and 52 in the validation cohort. Four hundred eighty-seven radiomics features were initially extracted to quantify phenotypic characteristics of the lesion region in both PET and CT images. Eleven semantic features were additionally defined by two experienced nuclear medicine physicians. Feature selection was performed in 5 steps to enable derivation of robust and effective signatures. Multivariable logistic regression analysis was subsequently used to develop a radiomics nomogram. The calibration, discrimination, and clinical usefulness of the nomogram were evaluated in both the training and independent validation cohorts. RESULTS The individualized radiomics nomogram, which combined PET/CT radiomics signature with semantic features, demonstrated good calibration and significantly improved the diagnostic performance with respect to the semantic model alone or PET/CT signature alone in training cohort (AUC 0.97 vs. 0.94 or 0.91, p = 0.0392 or 0.0056), whereas did not significantly improve the performance in validation cohort (AUC 0.93 vs. 0.89 or 0.91, p = 0.3098 or 0.3323). CONCLUSION The radiomics nomogram showed potential for individualized differential diagnosis between solid active pulmonary TB and solid LC, although the improvement of performance was not significant relative to semantic model.
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Affiliation(s)
- Dongyang Du
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Jiamei Gu
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Xiaohui Chen
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Wenbing Lv
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Qianjin Feng
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, V6T 1Z1, Canada
- Department of Integrative Oncology, BC Cancer Research Centre, Vancouver, BC, V5Z 1L3, Canada
| | - Hubing Wu
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China.
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Ferreira Junior JR, Koenigkam-Santos M, Machado CVB, Faleiros MC, Correia NSC, Cipriano FEG, Fabro AT, de Azevedo-Marques PM. Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity. Radiol Bras 2021; 54:87-93. [PMID: 33854262 PMCID: PMC8029936 DOI: 10.1590/0100-3984.2019.0135] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Objective To determine whether the radiomic features of lung lesions on computed tomography correlate with overall survival in lung cancer patients. Materials and Methods This was a retrospective study involving 101 consecutive patients with malignant neoplasms confirmed by biopsy or surgery. On computed tomography images, the lesions were submitted to semi-automated segmentation and were characterized on the basis of 2,465 radiomic variables. The prognostic assessment was based on Kaplan-Meier analysis and log-rank tests, according to the median value of the radiomic variables. Results Of the 101 patients evaluated, 28 died (16 dying from lung cancer), and 73 were censored, with a mean overall survival time of 1,819.4 days (95% confidence interval [95% CI]: 1,481.2-2,157.5). One radiomic feature (the mean of the Fourier transform) presented a difference on Kaplan-Meier curves (p < 0.05). A high-risk group of patients was identified on the basis of high values for the mean of the Fourier transform. In that group, the mean survival time was 1,465.4 days (95% CI: 985.2-1,945.6), with a hazard ratio of 2.12 (95% CI: 1.01-4.48). We also identified a low-risk group, in which the mean of the Fourier transform was low (mean survival time of 2,164.8 days; 95% CI: 1,745.4-2,584.1). Conclusion A radiomic signature based on the Fourier transform correlates with overall survival, representing a prognostic biomarker for risk stratification in patients with lung cancer.
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Affiliation(s)
| | - Marcel Koenigkam-Santos
- Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (FMRP-USP), Ribeirão Preto, SP, Brazil
| | - Camila Vilas Boas Machado
- Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (FMRP-USP), Ribeirão Preto, SP, Brazil
| | - Matheus Calil Faleiros
- Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (FMRP-USP), Ribeirão Preto, SP, Brazil
| | | | | | - Alexandre Todorovic Fabro
- Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (FMRP-USP), Ribeirão Preto, SP, Brazil
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Liu Y, Wu M, Zhang Y, Luo Y, He S, Wang Y, Chen F, Liu Y, Yang Q, Li Y, Wei H, Zhang H, Jin C, Lu N, Li W, Wang S, Guo Y, Ye Z. Imaging Biomarkers to Predict and Evaluate the Effectiveness of Immunotherapy in Advanced Non-Small-Cell Lung Cancer. Front Oncol 2021; 11:657615. [PMID: 33816314 PMCID: PMC8017283 DOI: 10.3389/fonc.2021.657615] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 02/25/2021] [Indexed: 02/05/2023] Open
Abstract
Objective We aimed to identify imaging biomarkers to assess predictive capacity of radiomics nomogram regarding treatment response status (responder/non-responder) in patients with advanced NSCLC undergoing anti-PD1 immunotherapy. Methods 197 eligible patients with histologically confirmed NSCLC were retrospectively enrolled from nine hospitals. We carried out a radiomics characterization from target lesions (TL) approach and largest target lesion (LL) approach on baseline and first follow-up (TP1) CT imaging data. Delta-radiomics feature was calculated as the relative net change in radiomics feature between baseline and TP1. Minimum Redundancy Maximum Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression were applied for feature selection and radiomics signature construction. Results Radiomics signature at baseline did not show significant predictive value regarding response status for LL approach (P = 0.10), nor in terms of TL approach (P = 0.27). A combined Delta-radiomics nomogram incorporating Delta-radiomics signature with clinical factor of distant metastasis for target lesions had satisfactory performance in distinguishing responders from non-responders with AUCs of 0.83 (95% CI: 0.75–0.91) and 0.81 (95% CI: 0.68–0.95) in the training and test sets respectively, which was comparable with that from LL approach (P = 0.92, P = 0.97). Among a subset of those patients with available pretreatment PD-L1 expression status (n = 66), models that incorporating Delta-radiomics features showed superior predictive accuracy than that of PD-L1 expression status alone (P <0.001). Conclusion Early response assessment using combined Delta-radiomics nomograms have potential advantages to identify patients that were more likely to benefit from immunotherapy, and help oncologists modify treatments tailored individually to each patient under therapy.
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Affiliation(s)
- Ying Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Minghao Wu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Yuwei Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Yahong Luo
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Shuai He
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Yina Wang
- Department of Medical Oncology, 1st Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Feng Chen
- Department of Radiology, 1st Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yulin Liu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Yang
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yanying Li
- Department of Thoracic Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Zhang
- Department of Radiology, Tianjin Chest Hospital, Tianjin, China
| | - Chenwang Jin
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Nian Lu
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Guangzhou, China
| | - Wanhu Li
- Department of Medical Imaging, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Sicong Wang
- Prognostic Diagnosis, GE Healthcare China, Beijing, China
| | - Yan Guo
- Prognostic Diagnosis, GE Healthcare China, Beijing, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
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Tomaszewski MR, Gillies RJ. The Biological Meaning of Radiomic Features. Radiology 2021; 298:505-516. [PMID: 33399513 PMCID: PMC7924519 DOI: 10.1148/radiol.2021202553] [Citation(s) in RCA: 329] [Impact Index Per Article: 82.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 07/30/2020] [Accepted: 08/17/2020] [Indexed: 02/06/2023]
Abstract
An earlier incorrect version appeared online. This article was corrected on February 10, 2021.
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Affiliation(s)
- Michal R. Tomaszewski
- From the Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr, Tampa, FL 33612
| | - Robert J. Gillies
- From the Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr, Tampa, FL 33612
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Qiu J, Peng S, Yin J, Wang J, Jiang J, Li Z, Song H, Zhang W. A Radiomics Signature to Quantitatively Analyze COVID-19-Infected Pulmonary Lesions. Interdiscip Sci 2021; 13:61-72. [PMID: 33411162 PMCID: PMC7788548 DOI: 10.1007/s12539-020-00410-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 11/30/2020] [Accepted: 12/09/2020] [Indexed: 02/05/2023]
Abstract
Assessing pulmonary lesions using computed tomography (CT) images is of great significance to the severity diagnosis and treatment of coronavirus disease 2019 (COVID-19)-infected patients. Such assessment mainly depends on radiologists' subjective judgment, which is inefficient and presents difficulty for those with low levels of experience, especially in rural areas. This work focuses on developing a radiomics signature to quantitatively analyze whether COVID-19-infected pulmonary lesions are mild (Grade I) or moderate/severe (Grade II). We retrospectively analyzed 1160 COVID-19-infected pulmonary lesions from 16 hospitals. First, texture features were extracted from the pulmonary lesion regions of CT images. Then, feature preselection was performed and a radiomics signature was built using a stepwise logistic regression. The stepwise logistic regression also calculated the correlation between the radiomics signature and the grade of a pulmonary lesion. Finally, a logistic regression model was trained to classify the grades of pulmonary lesions. Given a significance level of α = 0.001, the stepwise logistic regression achieved an R (multiple correlation coefficient) of 0.70, which is much larger than Rα = 0.18 (the critical value of R). In the classification, the logistic regression model achieved an AUC of 0.87 on an independent test set. Overall, the radiomics signature is significantly correlated with the grade of a pulmonary lesion in COVID-19 infection. The classification model is interpretable and can assist radiologists in quickly and efficiently diagnosing pulmonary lesions. This work aims to develop a CT-based radiomics signature to quantitatively analyze whether COVID-19-infected pulmonary lesions are mild (Grade I) or moderate/severe (Grade II). The logistic regression model established based on this radiomics signature can assist radiologists to quickly and efficiently diagnose the grades of pulmonary lesions. The model calculates a radiomics score for a lesion and is interpretable and appropriate for clinical use.
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Affiliation(s)
- Jiajun Qiu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610000 China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering and National Supercomputing Centre in Changsha, Hunan University, Lushan Road (S), Yuelu District, Changsha, 410082 Hunan China
| | - Jin Yin
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610000 China
| | - Junren Wang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610000 China
| | - Jingwen Jiang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610000 China
| | - Zhenlin Li
- Department of Radiology, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610000 China
| | - Huan Song
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610000 China
| | - Wei Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610000 China
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