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Yuan X, Ma C, Hu M, Qiu RLJ, Salari E, Martini R, Yang X. Machine learning in image-based outcome prediction after radiotherapy: A review. J Appl Clin Med Phys 2025; 26:e14559. [PMID: 39556691 PMCID: PMC11712300 DOI: 10.1002/acm2.14559] [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] [Received: 04/30/2024] [Revised: 07/25/2024] [Accepted: 08/14/2024] [Indexed: 11/20/2024] Open
Abstract
The integration of machine learning (ML) with radiotherapy has emerged as a pivotal innovation in outcome prediction, bringing novel insights amid unique challenges. This review comprehensively examines the current scope of ML applications in various treatment contexts, focusing on treatment outcomes such as patient survival, disease recurrence, and treatment-induced toxicity. It emphasizes the ascending trajectory of research efforts and the prominence of survival analysis as a clinical priority. We analyze the use of several common medical imaging modalities in conjunction with clinical data, highlighting the advantages and complexities inherent in this approach. The research reflects a commitment to advancing patient-centered care, advocating for expanded research on abdominal and pancreatic cancers. While data collection, patient privacy, standardization, and interpretability present significant challenges, leveraging ML in radiotherapy holds remarkable promise for elevating precision medicine and improving patient care outcomes.
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Affiliation(s)
- Xiaohan Yuan
- Department of Biomedical EngineeringEmory University and Georgia Institute of TechnologyAtlantaGeorgiaUSA
| | - Chaoqiong Ma
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Mingzhe Hu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Richard L. J. Qiu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Elahheh Salari
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Reema Martini
- Emory School of MedicineEmory UniversityAtlantaGeorgiaUSA
| | - Xiaofeng Yang
- Department of Biomedical EngineeringEmory University and Georgia Institute of TechnologyAtlantaGeorgiaUSA
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
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2
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Poursaeed R, Mohammadzadeh M, Safaei AA. Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review. BMC Cancer 2024; 24:1581. [PMID: 39731064 DOI: 10.1186/s12885-024-13320-4] [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: 03/09/2024] [Accepted: 12/10/2024] [Indexed: 12/29/2024] Open
Abstract
Glioblastoma Multiforme (GBM), classified as a grade IV glioma by the World Health Organization (WHO), is a prevalent and notably aggressive form of brain tumor derived from glial cells. It stands as one of the most severe forms of primary brain cancer in humans. The median survival time of GBM patients is only 12-15 months, making it the most lethal type of brain tumor. Every year, about 200,000 people worldwide succumb to this disease. GBM is also highly heterogeneous, meaning that its characteristics and behavior vary widely among different patients. This leads to different outcomes and survival times for each individual. Predicting the survival of GBM patients accurately can have multiple benefits. It can enable optimal and personalized treatment planning based on the patient's condition and prognosis. It can also support the patients and their families to cope with the possible outcomes and make informed decisions about their care and quality of life. Furthermore, it can assist the researchers and scientists to discover the most relevant biomarkers, features, and mechanisms of the disease and to design more effective and personalized therapies. Artificial intelligence methods, such as machine learning and deep learning, have been widely applied to survival prediction in various fields, such as breast cancer, lung cancer, gastric cancer, cervical cancer, liver cancer, prostate cancer, and covid 19. This systematic review summarizes the current state-of-the-art methods for predicting glioblastoma survival using different types of input data, such as clinical features, molecular markers, imaging features, radiomics features, omics data or a combination of them. Following PRISMA guidelines, we searched databases from 2015 to 2024, reviewing 107 articles meeting our criteria. We analyzed the data sources, methods, performance metrics and outcomes of the studies. We found that random forest was the most popular method, and a combination of radiomics and clinical data was the most common input data.
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Affiliation(s)
- Roya Poursaeed
- Department of Data Science, Faculty of Interdisciplinary Science and Technology, Tarbiat Modares University, Tehran, Iran
| | - Mohsen Mohammadzadeh
- Department of Data Science, Faculty of Interdisciplinary Science and Technology, Tarbiat Modares University, Tehran, Iran.
- Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran.
| | - Ali Asghar Safaei
- Department of Data Science, Faculty of Interdisciplinary Science and Technology, Tarbiat Modares University, Tehran, Iran.
- Department of Medical Informatics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
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3
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Chen W, Dhawan M, Liu J, Ing D, Mehta K, Tran D, Lawrence D, Ganhewa M, Cirillo N. Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review. Clin Exp Dent Res 2024; 10:e70035. [PMID: 39600121 PMCID: PMC11599430 DOI: 10.1002/cre2.70035] [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] [Received: 03/19/2024] [Revised: 09/19/2024] [Accepted: 10/20/2024] [Indexed: 11/29/2024] Open
Abstract
OBJECTIVES Artificial intelligence (AI) is an emerging field in dentistry. AI is gradually being integrated into dentistry to improve clinical dental practice. The aims of this scoping review were to investigate the application of AI in image analysis for decision-making in clinical dentistry and identify trends and research gaps in the current literature. MATERIAL AND METHODS This review followed the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). An electronic literature search was performed through PubMed and Scopus. After removing duplicates, a preliminary screening based on titles and abstracts was performed. A full-text review and analysis were performed according to predefined inclusion criteria, and data were extracted from eligible articles. RESULTS Of the 1334 articles returned, 276 met the inclusion criteria (consisting of 601,122 images in total) and were included in the qualitative synthesis. Most of the included studies utilized convolutional neural networks (CNNs) on dental radiographs such as orthopantomograms (OPGs) and intraoral radiographs (bitewings and periapicals). AI was applied across all fields of dentistry - particularly oral medicine, oral surgery, and orthodontics - for direct clinical inference and segmentation. AI-based image analysis was use in several components of the clinical decision-making process, including diagnosis, detection or classification, prediction, and management. CONCLUSIONS A variety of machine learning and deep learning techniques are being used for dental image analysis to assist clinicians in making accurate diagnoses and choosing appropriate interventions in a timely manner.
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Affiliation(s)
- Wei Chen
- Melbourne Dental SchoolThe University of MelbourneCarltonVictoriaAustralia
| | - Monisha Dhawan
- Melbourne Dental SchoolThe University of MelbourneCarltonVictoriaAustralia
| | - Jonathan Liu
- Melbourne Dental SchoolThe University of MelbourneCarltonVictoriaAustralia
| | - Damie Ing
- Melbourne Dental SchoolThe University of MelbourneCarltonVictoriaAustralia
| | - Kruti Mehta
- Melbourne Dental SchoolThe University of MelbourneCarltonVictoriaAustralia
| | - Daniel Tran
- Melbourne Dental SchoolThe University of MelbourneCarltonVictoriaAustralia
| | | | - Max Ganhewa
- CoTreatAI, CoTreat Pty Ltd.MelbourneVictoriaAustralia
| | - Nicola Cirillo
- Melbourne Dental SchoolThe University of MelbourneCarltonVictoriaAustralia
- CoTreatAI, CoTreat Pty Ltd.MelbourneVictoriaAustralia
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4
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Tagliabue M, Ruju F, Mossinelli C, Gaeta A, Raimondi S, Volpe S, Zaffaroni M, Isaksson LJ, Garibaldi C, Cremonesi M, Rapino A, Chiocca S, Pietrobon G, Alterio D, Trisolini G, Morbini P, Rampinelli V, Grammatica A, Petralia G, Jereczek-Fossa BA, Preda L, Ravanelli M, Maroldi R, Piazza C, Benazzo M, Ansarin M. The prognostic role of MRI-based radiomics in tongue carcinoma: a multicentric validation study. LA RADIOLOGIA MEDICA 2024; 129:1369-1381. [PMID: 39096355 PMCID: PMC11379741 DOI: 10.1007/s11547-024-01859-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 07/17/2024] [Indexed: 08/05/2024]
Abstract
PURPOSE Radiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC). MATERIALS AND METHODS Retrospective multicentric study on OTSCC surgically treated from 2010 to 2019. All patients performed preoperative MRI, including contrast-enhanced T1-weighted (CE-T1), diffusion-weighted sequences and apparent diffusion coefficient map. We evaluated overall survival (OS), locoregional recurrence-free survival (LRRFS), cause-specific mortality (CSM). We elaborated different models based on clinical and radiomic data. C-indexes assessed the prediction accuracy of the models. RESULTS We collected 112 consecutive independent patients from three Italian Institutions to validate the previously published MRI radiomic models based on 79 different patients. The C-indexes for the hybrid clinical-radiomic models in the validation cohort were lower than those in the training cohort but remained > 0.5 in most cases. CE-T1 sequence provided the best fit to the models: the C-indexes obtained were 0.61, 0.59, 0.64 (pretreatment model) and 0.65, 0.69, 0.70 (posttreatment model) for OS, LRRFS and CSM, respectively. CONCLUSION Our clinical-radiomic models retain a potential to predict OS, LRRFS and CSM in heterogeneous cohorts across different centers. These findings encourage further research, aimed at overcoming current limitations, due to the variability of imaging acquisition, processing and tumor volume delineation.
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Affiliation(s)
- Marta Tagliabue
- Division of Otolaryngology and Head and Neck Surgery, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Francesca Ruju
- Division of Radiology, European Institute of Oncology IRCCS, Milan, Italy
| | - Chiara Mossinelli
- Division of Otolaryngology and Head and Neck Surgery, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy.
| | - Aurora Gaeta
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Via Bicocca Degli Arcimboldi, Milan, Italy
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
| | - Sara Raimondi
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
| | - Stefania Volpe
- Division of Radiation Oncology, European Institute of Oncology, IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Mattia Zaffaroni
- Division of Radiation Oncology, European Institute of Oncology, IRCCS, Milan, Italy
| | - Lars Johannes Isaksson
- Division of Radiation Oncology, European Institute of Oncology, IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Cristina Garibaldi
- Unit of Radiation Research, IEO European Institute of Oncology, IRCCS, Milan, Italy
| | - Marta Cremonesi
- Unit of Radiation Research, IEO European Institute of Oncology, IRCCS, Milan, Italy
| | - Anna Rapino
- Postgraduate School of Radiodiagnostic, University of Milan, Milan, Italy
| | - Susanna Chiocca
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
| | - Giacomo Pietrobon
- Division of Otolaryngology and Head and Neck Surgery, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Daniela Alterio
- Division of Radiation Oncology, European Institute of Oncology, IRCCS, Milan, Italy
| | - Giuseppe Trisolini
- Department of Otorhinolaryngology and Skull Base Microsurgery-Neurosciences, ASST Ospedale Papa Giovanni XXIII, Bergamo, Italy
| | | | - Vittorio Rampinelli
- Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, ASST Spedali Civili of Brescia, University of Brescia, 25123, Brescia, Italy
| | - Alberto Grammatica
- Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, ASST Spedali Civili of Brescia, University of Brescia, 25123, Brescia, Italy
| | - Giuseppe Petralia
- Division of Radiology, European Institute of Oncology IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiation Oncology, European Institute of Oncology, IRCCS, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Lorenzo Preda
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, Italy
- Radiology Institute, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Marco Ravanelli
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, School of Medicine, Brescia, Italy
| | - Roberto Maroldi
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, School of Medicine, Brescia, Italy
| | - Cesare Piazza
- Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, ASST Spedali Civili of Brescia, University of Brescia, 25123, Brescia, Italy
| | - Marco Benazzo
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, Italy
- Department of Otorhinolaryngology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Mohssen Ansarin
- Division of Otolaryngology and Head and Neck Surgery, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
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Zhang L, Shi R, Youssefi N. Oral cancer diagnosis based on gated recurrent unit networks optimized by an improved version of Northern Goshawk optimization algorithm. Heliyon 2024; 10:e32077. [PMID: 38912510 PMCID: PMC11190545 DOI: 10.1016/j.heliyon.2024.e32077] [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: 02/06/2024] [Revised: 05/12/2024] [Accepted: 05/28/2024] [Indexed: 06/25/2024] Open
Abstract
Oral cancer early diagnosis is a critical task in the field of medical science, and one of the most necessary things is to develop sound and effective strategies for early detection. The current research investigates a new strategy to diagnose an oral cancer based upon combination of effective learning and medical imaging. The current research investigates a new strategy to diagnose an oral cancer using Gated Recurrent Unit (GRU) networks optimized by an improved model of the NGO (Northern Goshawk Optimization) algorithm. The proposed approach has several advantages over existing methods, including its ability to analyze large and complex datasets, its high accuracy, as well as its capacity to detect oral cancer at the very beginning stage. The improved NGO algorithm is utilized to improve the GRU network that helps to improve the performance of the network and increase the accuracy of the diagnosis. The paper describes the proposed approach and evaluates its performance using a dataset of oral cancer patients. The findings of the study demonstrate the efficiency of the suggested approach in accurately diagnosing oral cancer.
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Affiliation(s)
- Lei Zhang
- Department of Stomatology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250033, Shandong, China
| | - Rongji Shi
- Department of Stomatology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250033, Shandong, China
| | - Naser Youssefi
- Islamic Azad University, Science and Research Branch, Tehran, Iran
- College of Technical Engineering, The Islamic University, Najaf, Iraq
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6
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Gomes RFT, Schmith J, de Figueiredo RM, Freitas SA, Machado GN, Romanini J, Almeida JD, Pereira CT, Rodrigues JDA, Carrard VC. Convolutional neural network misclassification analysis in oral lesions: an error evaluation criterion by image characteristics. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 137:243-252. [PMID: 38161085 DOI: 10.1016/j.oooo.2023.10.003] [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: 05/01/2023] [Revised: 10/02/2023] [Accepted: 10/04/2023] [Indexed: 01/03/2024]
Abstract
OBJECTIVE This retrospective study analyzed the errors generated by a convolutional neural network (CNN) when performing automated classification of oral lesions according to their clinical characteristics, seeking to identify patterns in systemic errors in the intermediate layers of the CNN. STUDY DESIGN A cross-sectional analysis nested in a previous trial in which automated classification by a CNN model of elementary lesions from clinical images of oral lesions was performed. The resulting CNN classification errors formed the dataset for this study. A total of 116 real outputs were identified that diverged from the estimated outputs, representing 7.6% of the total images analyzed by the CNN. RESULTS The discrepancies between the real and estimated outputs were associated with problems relating to image sharpness, resolution, and focus; human errors; and the impact of data augmentation. CONCLUSIONS From qualitative analysis of errors in the process of automated classification of clinical images, it was possible to confirm the impact of image quality, as well as identify the strong impact of the data augmentation process. Knowledge of the factors that models evaluate to make decisions can increase confidence in the high classification potential of CNNs.
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Affiliation(s)
- Rita Fabiane Teixeira Gomes
- Department of Oral Pathology, Faculdade de Odontologia-Federal University of Rio Grande do Sul-UFRGS, Porto Alegre, Brazil.
| | - Jean Schmith
- Polytechnic School, University of Vale do Rio dos Sinos-UNISINOS, São Leopoldo, Brazil; Technology in Automation and Electronics Laboratory-TECAE Lab, University of Vale do Rio dos Sinos-UNISINOS, São Leopoldo, Brazil
| | - Rodrigo Marques de Figueiredo
- Polytechnic School, University of Vale do Rio dos Sinos-UNISINOS, São Leopoldo, Brazil; Technology in Automation and Electronics Laboratory-TECAE Lab, University of Vale do Rio dos Sinos-UNISINOS, São Leopoldo, Brazil
| | - Samuel Armbrust Freitas
- Department of Applied Computing, University of Vale do Rio dos Sinos-UNISINOS, São Leopoldo, Brazil
| | | | - Juliana Romanini
- Oral Medicine, Otorhynolaringology Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Rio Grande do Sul, Brazil
| | - Janete Dias Almeida
- Department of Biosciences and Oral Diagnostics, São Paulo State University, Campus São José dos Campos, São Paulo, Brazil
| | | | - Jonas de Almeida Rodrigues
- Department of Surgery and Orthopaedics, Faculdade de Odontologia-Federal University of Rio Grande do Sul-UFRGS, Porto Alegre, Brazil
| | - Vinicius Coelho Carrard
- Department of Oral Pathology, Faculdade de Odontologia-Federal University of Rio Grande do Sul-UFRGS, Porto Alegre, Brazil; TelessaudeRS-UFRGS, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil; Oral Medicine, Otorhynolaringology Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Rio Grande do Sul, Brazil
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Chandra P, Deshmukh SP, Kendre A, Gupta M. Novel Scoring Formula to Predict Survival in Patients of Primary Tongue Cancer Belonging to Tobacco Chewing Population. Indian J Surg Oncol 2023; 14:928-934. [PMID: 38187857 PMCID: PMC10767176 DOI: 10.1007/s13193-023-01799-5] [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: 04/03/2023] [Accepted: 07/04/2023] [Indexed: 01/09/2024] Open
Abstract
Worldwide and in India head and neck malignancies are a major contributor to cancer mortality and morbidity. Tongue cancer predominates oral cavity cancers worldwide but in India it comes next to buccal mucosa. OPD patients after completing treatment tend to ask about the prognosis of their disease where they want an objective answer to "How long will I live?" His scoring system is intended to answer this question and guide patients for adjuvant therapy. This study enrolled all patients between 20 and 85 years old with a history of tobacco chewing at least for the last 1 year before diagnosis. Patients should have primary tongue cancer amenable to surgical resection. For survival calculation, date of diagnosis was taken as reference time. Using Kaplan-Meier survival analysis, clinicopathological factors significantly associated with survival were ascertained. Then using logit regression, a scoring system predicting patient survival in years based on clinicopathological risk factors was formulated and internal validation was done. A total 241 were enrolled and there were 69 cancer-related deaths. T stage, N stage, LVSI, and DOI were found to be significantly associated with cancer-related survival in tongue cancer patients. Another factor affecting survival was defaulting adjuvant radiation therapy. Using these variables, a survival predicting score was developed. On internal validation and regression, the score was found 80% accurate with error limits ± 6 months. It is a concise comprehensive score applicable on Indian population with history of tobacco chewing. It will not only help clinicians to tell patients about their survival expectancy but also help to counsel them for adjuvant therapy. However, external validation and if required recalibration incorporating other factors need to be done for this score.
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Affiliation(s)
- Prasant Chandra
- Surgical Oncology, DY Patil Medical College and Research Centre, Sant Tukaram Nagar, Pimpri, Pune, Maharashtra 411018 India
| | - Sanjay P. Deshmukh
- Surgical Oncology, Ruby Hall Clinic, 40, Sassoon Rd, Sangamvadi, Pune, Maharashtra 411001 India
| | - Ajita Kendre
- Aditya Birla Memorial Hospital, Aditya Birla Hospital Marg, Thergaon, Pimpri-Chinchwad, Maharashtra 411033 India
| | - Moulik Gupta
- Surgical Oncology, Ruby Hall Clinic, 40, Sassoon Rd, Sangamvadi, Pune, Maharashtra 411001 India
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Wang Z, Li VR, Chu FI, Yu V, Lee A, Low D, Moghanaki D, Lee P, Qi XS. Predicting Overall Survival for Patients with Malignant Mesothelioma Following Radiotherapy via Interpretable Machine Learning. Cancers (Basel) 2023; 15:3916. [PMID: 37568732 PMCID: PMC10416916 DOI: 10.3390/cancers15153916] [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: 06/19/2023] [Revised: 07/20/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
PURPOSE/OBJECTIVES Malignant pleural mesothelioma (MPM) is a rare but aggressive cancer arising from the cells of the thoracic pleura with a poor prognosis. We aimed to develop a model, via interpretable machine learning (ML) methods, predicting overall survival for MPM following radiotherapy based on dosimetric metrics as well as patient characteristics. MATERIALS/METHODS Sixty MPM (37 right, 23 left) patients treated on a Tomotherapy unit between 2013 and 2018 were retrospectively analyzed. All patients received 45 Gy (25 fractions). The multivariable Cox regression (Cox PH) model and Survival Support Vector Machine (sSVM) were applied to build predictive models of overall survival (OS) based on clinical, dosimetric, and combined variables. RESULTS Significant differences in dosimetric endpoints for critical structures, i.e., the lung, heart, liver, kidney, and stomach, were observed according to target laterality. The OS was found to be insignificantly different (p = 0.18) between MPM patients who tested left- and right-sided, with 1-year OS of 77.3% and 75.0%, respectively. With Cox PH regression, considering dosimetric variables for right-sided patients alone, an increase in PTV_Min, Total_Lung_PTV_Mean, Contra_Lung_Volume, Contra_Lung_V20, Esophagus_Mean, and Heart_Volume had a greater hazard to all-cause death, while an increase in Total_Lung_PTV_V20, Contra_Lung_V5, and Esophagus_Max had a lower hazard to all-cause death. Considering clinical variables alone, males and increases in N stage had greater hazard to all-cause death; considering both clinical and dosimetric variables, increases in N stage, PTV_Mean, PTV_Min, and esophagus_Mean had greater hazard to all-cause death, while increases in T stage and Heart_V30 had lower hazard to all-cause-death. In terms of C-index, the Cox PH model and sSVM performed similarly and fairly well when considering clinical and dosimetric variables independently or jointly. CONCLUSIONS Clinical and dosimetric variables may predict the overall survival of mesothelioma patients, which could guide personalized treatment planning towards a better treatment response. The identified predictors and their impact on survival offered additional value for translational application in clinical practice.
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Affiliation(s)
- Zitian Wang
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Vincent R. Li
- Department of Biology, University of Southern California Dornsife School of Arts and Sciences, Los Angeles, CA 90089, USA
| | - Fang-I Chu
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Victoria Yu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Alan Lee
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Daniel Low
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Drew Moghanaki
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Percy Lee
- Department of Radiation Oncology, City of Hope Orange County Lennar Foundation Cancer Center, Irvine, CA 92618, USA
| | - X. Sharon Qi
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, USA
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Gomes RFT, Schuch LF, Martins MD, Honório EF, de Figueiredo RM, Schmith J, Machado GN, Carrard VC. Use of Deep Neural Networks in the Detection and Automated Classification of Lesions Using Clinical Images in Ophthalmology, Dermatology, and Oral Medicine-A Systematic Review. J Digit Imaging 2023; 36:1060-1070. [PMID: 36650299 PMCID: PMC10287602 DOI: 10.1007/s10278-023-00775-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 01/19/2023] Open
Abstract
Artificial neural networks (ANN) are artificial intelligence (AI) techniques used in the automated recognition and classification of pathological changes from clinical images in areas such as ophthalmology, dermatology, and oral medicine. The combination of enterprise imaging and AI is gaining notoriety for its potential benefits in healthcare areas such as cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, and endoscopic. The present study aimed to analyze, through a systematic literature review, the application of performance of ANN and deep learning in the recognition and automated classification of lesions from clinical images, when comparing to the human performance. The PRISMA 2020 approach (Preferred Reporting Items for Systematic Reviews and Meta-analyses) was used by searching four databases of studies that reference the use of IA to define the diagnosis of lesions in ophthalmology, dermatology, and oral medicine areas. A quantitative and qualitative analyses of the articles that met the inclusion criteria were performed. The search yielded the inclusion of 60 studies. It was found that the interest in the topic has increased, especially in the last 3 years. We observed that the performance of IA models is promising, with high accuracy, sensitivity, and specificity, most of them had outcomes equivalent to human comparators. The reproducibility of the performance of models in real-life practice has been reported as a critical point. Study designs and results have been progressively improved. IA resources have the potential to contribute to several areas of health. In the coming years, it is likely to be incorporated into everyday life, contributing to the precision and reducing the time required by the diagnostic process.
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Affiliation(s)
- Rita Fabiane Teixeira Gomes
- Graduate Program in Dentistry, School of Dentistry, Federal University of Rio Grande Do Sul, Barcelos 2492/503, Bairro Santana, Porto Alegre, RS, CEP 90035-003, Brazil.
| | - Lauren Frenzel Schuch
- Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil
| | - Manoela Domingues Martins
- Graduate Program in Dentistry, School of Dentistry, Federal University of Rio Grande Do Sul, Barcelos 2492/503, Bairro Santana, Porto Alegre, RS, CEP 90035-003, Brazil
- Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil
| | | | - Rodrigo Marques de Figueiredo
- Technology in Automation and Electronics Laboratory - TECAE Lab, University of Vale Do Rio Dos Sinos - UNISINOS, São Leopoldo, Brazil
| | - Jean Schmith
- Technology in Automation and Electronics Laboratory - TECAE Lab, University of Vale Do Rio Dos Sinos - UNISINOS, São Leopoldo, Brazil
| | - Giovanna Nunes Machado
- Technology in Automation and Electronics Laboratory - TECAE Lab, University of Vale Do Rio Dos Sinos - UNISINOS, São Leopoldo, Brazil
| | - Vinicius Coelho Carrard
- Graduate Program in Dentistry, School of Dentistry, Federal University of Rio Grande Do Sul, Barcelos 2492/503, Bairro Santana, Porto Alegre, RS, CEP 90035-003, Brazil
- Department of Epidemiology, School of Medicine, TelessaúdeRS-UFRGS, Federal University of Rio Grande Do Sul, Porto Alegre, RS, Brazil
- Department of Oral Medicine, Otorhinolaryngology Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil
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Adeoye J, Hui L, Su YX. Data-centric artificial intelligence in oncology: a systematic review assessing data quality in machine learning models for head and neck cancer. JOURNAL OF BIG DATA 2023; 10:28. [DOI: 10.1186/s40537-023-00703-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 02/23/2023] [Indexed: 01/03/2025]
Abstract
AbstractMachine learning models have been increasingly considered to model head and neck cancer outcomes for improved screening, diagnosis, treatment, and prognostication of the disease. As the concept of data-centric artificial intelligence is still incipient in healthcare systems, little is known about the data quality of the models proposed for clinical utility. This is important as it supports the generalizability of the models and data standardization. Therefore, this study overviews the quality of structured and unstructured data used for machine learning model construction in head and neck cancer. Relevant studies reporting on the use of machine learning models based on structured and unstructured custom datasets between January 2016 and June 2022 were sourced from PubMed, EMBASE, Scopus, and Web of Science electronic databases. Prediction model Risk of Bias Assessment (PROBAST) tool was used to assess the quality of individual studies before comprehensive data quality parameters were assessed according to the type of dataset used for model construction. A total of 159 studies were included in the review; 106 utilized structured datasets while 53 utilized unstructured datasets. Data quality assessments were deliberately performed for 14.2% of structured datasets and 11.3% of unstructured datasets before model construction. Class imbalance and data fairness were the most common limitations in data quality for both types of datasets while outlier detection and lack of representative outcome classes were common in structured and unstructured datasets respectively. Furthermore, this review found that class imbalance reduced the discriminatory performance for models based on structured datasets while higher image resolution and good class overlap resulted in better model performance using unstructured datasets during internal validation. Overall, data quality was infrequently assessed before the construction of ML models in head and neck cancer irrespective of the use of structured or unstructured datasets. To improve model generalizability, the assessments discussed in this study should be introduced during model construction to achieve data-centric intelligent systems for head and neck cancer management.
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Dubuc A, Zitouni A, Thomas C, Kémoun P, Cousty S, Monsarrat P, Laurencin S. Improvement of Mucosal Lesion Diagnosis with Machine Learning Based on Medical and Semiological Data: An Observational Study. J Clin Med 2022; 11:jcm11216596. [PMID: 36362822 PMCID: PMC9654969 DOI: 10.3390/jcm11216596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 11/01/2022] [Accepted: 11/02/2022] [Indexed: 11/09/2022] Open
Abstract
Despite artificial intelligence used in skin dermatology diagnosis is booming, application in oral pathology remains to be developed. Early diagnosis and therefore early management, remain key points in the successful management of oral mucosa cancers. The objective was to develop and evaluate a machine learning algorithm that allows the prediction of oral mucosa lesions diagnosis. This cohort study included patients followed between January 2015 and December 2020 in the oral mucosal pathology consultation of the Toulouse University Hospital. Photographs and demographic and medical data were collected from each patient to constitute clinical cases. A machine learning model was then developed and optimized and compared to 5 models classically used in the field. A total of 299 patients representing 1242 records of oral mucosa lesions were used to train and evaluate machine learning models. Our model reached a mean accuracy of 0.84 for diagnostic prediction. The specificity and sensitivity range from 0.89 to 1.00 and 0.72 to 0.92, respectively. The other models were proven to be less efficient in performing this task. These results suggest the utility of machine learning-based tools in diagnosing oral mucosal lesions with high accuracy. Moreover, the results of this study confirm that the consideration of clinical data and medical history, in addition to the lesion itself, appears to play an important role.
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Affiliation(s)
- Antoine Dubuc
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- Center for Epidemiology and Research in POPulation Health (CERPOP), UMR 1295, Paul Sabatier University, 31062 Toulouse, France
| | - Anissa Zitouni
- Oral Surgery and Oral Medicine Department, CHU Limoges, 87000 Limoges, France
| | - Charlotte Thomas
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- InCOMM, I2MC, UMR 1297, Paul Sabatier University, 31062 Toulouse, France
| | - Philippe Kémoun
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- RESTORE Research Center, Université de Toulouse, INSERM, CNRS, EFS, ENVT, Université P. Sabatier, CHU de Toulouse, 31300 Toulouse, France
| | - Sarah Cousty
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- LAPLACE, UMR 5213 CNRS, Paul Sabatier University, 31062 Toulouse, France
| | - Paul Monsarrat
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- RESTORE Research Center, Université de Toulouse, INSERM, CNRS, EFS, ENVT, Université P. Sabatier, CHU de Toulouse, 31300 Toulouse, France
- Artificial and Natural Intelligence Toulouse Institute ANITI, 31013 Toulouse, France
| | - Sara Laurencin
- School of Dental Medicine and CHU de Toulouse—Toulouse Institute of Oral Medicine and Science, 31062 Toulouse, France
- Center for Epidemiology and Research in POPulation Health (CERPOP), UMR 1295, Paul Sabatier University, 31062 Toulouse, France
- Correspondence:
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Rasteau S, Ernenwein D, Savoldelli C, Bouletreau P. Artificial intelligence for oral and maxillo-facial surgery: A narrative review. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2022; 123:276-282. [PMID: 35091121 DOI: 10.1016/j.jormas.2022.01.010] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 01/23/2022] [Indexed: 12/24/2022]
Abstract
Artificial Intelligence (AI) is a set of technologies that simulate human cognition in order to address a specific problem. The improvement in computing speed, the exponential production and the routine collection of data have led to the rapid development of AI in the health sector. In this review, we propose to provide surgeons with the essential technical elements to help them understand the possibilities offered by AI and to review the current applications of AI for oral and maxillofacial surgery (OMFS). The review of the literature reveals a real research boom of AI in all fields in OMFS. The algorithms used are related to machine learning, with a strong representation of the convolutional neural networks specific to deep learning. The complex architecture of these networks gives them the capacity to extract and process the elementary characteristics of an image, and they are therefore particularly used for diagnostic purposes on medical imagery or facial photography. We identified representative articles dealing with AI algorithms providing assistance in diagnosis, therapeutic decision, preoperative planning, or prediction and evaluation of the outcomes. Thanks to their learning, classification, prediction and detection capabilities, AI algorithms complement human skills while limiting their imperfections. However, these algorithms should be subject to rigorous clinical evaluation, and ethical reflection on data protection should be systematically conducted.
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Affiliation(s)
- Simon Rasteau
- Maxillo-Facial Surgery, Facial Plastic Surgery, Stomatology and Oral Surgery, Hospices Civils de Lyon, Lyon-Sud Hospital - Claude-Bernard Lyon 1 University, 165 Chemin du Grand-Revoyet, Pierre-Bénite 69310, France.
| | - Didier Ernenwein
- Department of Pediatric Oral & Maxillofacial & Plastic Surgery, Children's Hospital Robert-Debré, Paris-Diderot University, Paris, France
| | - Charles Savoldelli
- University Institute of the Face and Neck, Côte d'Azur University, Nice University Hospital, 31 Avenue de Valombrose, Nice 06100, France
| | - Pierre Bouletreau
- Maxillo-Facial Surgery, Facial Plastic Surgery, Stomatology and Oral Surgery, Hospices Civils de Lyon, Lyon-Sud Hospital - Claude-Bernard Lyon 1 University, 165 Chemin du Grand-Revoyet, Pierre-Bénite 69310, France
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Bhandari A, Tripathy BK, Jawad K, Bhatia S, Rahmani MKI, Mashat A. Cancer Detection and Prediction Using Genetic Algorithms. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1871841. [PMID: 35615545 PMCID: PMC9126682 DOI: 10.1155/2022/1871841] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 04/08/2022] [Accepted: 04/21/2022] [Indexed: 01/07/2023]
Abstract
Cancer is a wide category of diseases that is caused by the abnormal, uncontrollable growth of cells, and it is the second leading cause of death globally. Screening, early diagnosis, and prediction of recurrence give patients the best possible chance for successful treatment. However, these tests can be expensive and invasive and the results have to be interpreted by experts. Genetic algorithms (GAs) are metaheuristics that belong to the class of evolutionary algorithms. GAs can find the optimal or near-optimal solutions in huge, difficult search spaces and are widely used for search and optimization. This makes them ideal for detecting cancer by creating models to interpret the results of tests, especially noninvasive. In this article, we have comprehensively reviewed the existing literature, analyzed them critically, provided a comparative analysis of the state-of-the-art techniques, and identified the future challenges in the development of such techniques by medical professionals.
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Affiliation(s)
| | | | - Khurram Jawad
- College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia
| | - Surbhi Bhatia
- Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al Hasa, Saudi Arabia
| | | | - Arwa Mashat
- Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh 21911, Saudi Arabia
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Yan KX, Liu L, Li H. Application of machine learning in oral and maxillofacial surgery. Artif Intell Med Imaging 2021; 2:104-114. [DOI: 10.35711/aimi.v2.i6.104] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/20/2021] [Accepted: 12/28/2021] [Indexed: 02/06/2023] Open
Abstract
Oral and maxillofacial anatomy is extremely complex, and medical imaging is critical in the diagnosis and treatment of soft and bone tissue lesions. Hence, there exists accumulating imaging data without being properly utilized over the last decades. As a result, problems are emerging regarding how to integrate and interpret a large amount of medical data and alleviate clinicians’ workload. Recently, artificial intelligence has been developing rapidly to analyze complex medical data, and machine learning is one of the specific methods of achieving this goal, which is based on a set of algorithms and previous results. Machine learning has been considered useful in assisting early diagnosis, treatment planning, and prognostic estimation through extracting key features and building mathematical models by computers. Over the past decade, machine learning techniques have been applied to the field of oral and maxillofacial surgery and increasingly achieved expert-level performance. Thus, we hold a positive attitude towards developing machine learning for reducing the number of medical errors, improving the quality of patient care, and optimizing clinical decision-making in oral and maxillofacial surgery. In this review, we explore the clinical application of machine learning in maxillofacial cysts and tumors, maxillofacial defect reconstruction, orthognathic surgery, and dental implant and discuss its current problems and solutions.
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Affiliation(s)
- Kai-Xin Yan
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Lei Liu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Hui Li
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
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15
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English Feature Recognition Based on GA-BP Neural Network Algorithm and Data Mining. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:1890120. [PMID: 34504519 PMCID: PMC8423560 DOI: 10.1155/2021/1890120] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/04/2021] [Accepted: 08/16/2021] [Indexed: 11/19/2022]
Abstract
With the development of society and the promotion of science and technology, English, as the largest universal language in the world, is used by more and more people. In the life around us, there is information in English all the time. However, because the process of manual recognition of English letters is very labor-intensive and inefficient, the demand for computer recognition of English letters is increasing. This paper studies the influence of the parameters of BP neural network and genetic algorithm on the whole network, including the input, output, and number of hidden layer nodes. Finally, it improves and determines the settings and values of the relevant parameters. On this basis, it shows the rationality of the selected parameters through experiments. The results show that only GA-BP neural network and feature data mining algorithm can complete feature extraction and become the main function of feature classification at the same time. After enough initial data sample analysis training, the GA-BP neural network was found to have good data fault tolerance and feature recognition. The experimental results show that the genetic algorithm can find the best weights and thresholds and the weights and thresholds are given to the BP neural network. After training, the recognition of handwritten letters can be realized. Finally, the convergence of the two algorithms is compared through experiments, which shows that the overall performance of the BP neural network algorithm is improved after genetic algorithm optimization. It can be seen that the genetic algorithm has a good effect in improving the BP neural network and this method has a broad prospect in English feature recognition.
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Bruschini R, Maffini F, Chiesa F, Lepanto D, De Berardinis R, Chu F, Tagliabue M, Giugliano G, Ansarin M. Oral cancer: changing the aim of the biopsy in the age of precision medicine. A review. ACTA ACUST UNITED AC 2021; 41:108-119. [PMID: 34028455 PMCID: PMC8142729 DOI: 10.14639/0392-100x-n1056] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 02/03/2021] [Indexed: 01/15/2023]
Abstract
Oral cancer is a heterogeneous disease that develops through a complex, multi-step process. Precision medicine should help to better understand its molecular basis, integrate traditional classifications and have a positive impact on cancer management. To apply this information in clinical practice, we need to define its histology and identify biomarkers expressed by the tumour that provide useful information for planning tailored treatment. The most reliable information currently derives from evaluation of biomarkers on post-operative samples. To plan personalised treatment, oncologists need to assess these markers on biopsy samples. We reviewed the recent literature and identified 6 of 184 publications that compared markers measured on biopsy and post-operative samples or assessed their predictivity for the development of lymph node metastases. Data from these studies suggest that markers measured on biopsy samples can provide useful indications for tailoring treatments. However, due to their heterogeneity and low level of evidence, these results need to be confirmed by clinical studies on a large population to standardise and validate biomarkers in biopsies and to assess their reliability in other diagnostic mini-invasive procedures such as radiomics and liquid biopsy.
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Affiliation(s)
- Roberto Bruschini
- Division of Otolaryngology and Head & Neck Surgery, European Institute of Oncology IRCCS, Milan, Italy
| | - Fausto Maffini
- Division of Pathology, European Institute of Oncology IRCCS, Milan, Italy
| | - Fausto Chiesa
- Division of Otolaryngology and Head & Neck Surgery, European Institute of Oncology IRCCS, Milan, Italy
| | - Daniela Lepanto
- Division of Pathology, European Institute of Oncology IRCCS, Milan, Italy
| | - Rita De Berardinis
- Division of Otolaryngology and Head & Neck Surgery, European Institute of Oncology IRCCS, Milan, Italy
| | - Francesco Chu
- Division of Otolaryngology and Head & Neck Surgery, European Institute of Oncology IRCCS, Milan, Italy
| | - Marta Tagliabue
- Division of Otolaryngology and Head & Neck Surgery, European Institute of Oncology IRCCS, Milan, Italy
| | - Gioacchino Giugliano
- Division of Otolaryngology and Head & Neck Surgery, European Institute of Oncology IRCCS, Milan, Italy
| | - Mohssen Ansarin
- Division of Otolaryngology and Head & Neck Surgery, European Institute of Oncology IRCCS, Milan, Italy
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Rojas-López JA, Díaz Moreno RM, Venencia CD. Use of genetic algorithm for PTV optimization in single isocenter multiple metastases radiosurgery treatments with Brainlab Elements™. Phys Med 2021; 86:82-90. [PMID: 34062337 DOI: 10.1016/j.ejmp.2021.05.031] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 05/15/2021] [Accepted: 05/22/2021] [Indexed: 11/29/2022] Open
Abstract
PURPOSE To optimize PTV margins for single isocenter multiple metastases stereotactic radiosurgery through a genetic algorithm (GA) that determines the maximum effective displacement of each target (GTV) due to rotations. METHOD 10 plans were optimized. The plans were created with Elements Multiple Mets™ (Brainlab AG, Munchen, Germany) from a predefined template. The mean number of metastases per plan was 5 ± 2 [3,9] and the mean volume of GTV was 1.1 ± 1.3 cc [0.02, 5.1]. PTV margin criterion was based on GTV-isocenter distance and target dimensions. The effective displacement to perform specific rotational combination (roll, pitch, yaw) was optimized by GA. The original plans were re-calculated using the PTV optimized margin and new dosimetric variations were obtained. The Dmean, D99, Paddick conformity index (PCI), gradient index (GI) and dose variations in healthy brain were studied. RESULTS Regarding targets located shorter than 50 mm from the isocenter, the maximum calculated displacement was 2.5 mm. The differences between both PTV margin criteria were statistically significant for Dmean (p = 0.0163), D99 (p = 0.0439), PCI (p = 0.0242), GI (p = 0.0160) and for healthy brain V12 (p = 0.0218) and V10 (p = 0.0264). CONCLUSION The GA allows to determine an optimized PTV margin based on the maximum displacement. Optimized PTV margins reduce the detriment of dosimetric parameters. Greater PTV margins are associated with an increase in healthy brain volume.
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Ren R, Luo H, Su C, Yao Y, Liao W. Machine learning in dental, oral and craniofacial imaging: a review of recent progress. PeerJ 2021; 9:e11451. [PMID: 34046262 PMCID: PMC8136280 DOI: 10.7717/peerj.11451] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 04/22/2021] [Indexed: 02/05/2023] Open
Abstract
Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study.
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Affiliation(s)
- Ruiyang Ren
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China School of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Haozhe Luo
- School of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Chongying Su
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China School of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Yang Yao
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Wen Liao
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Orthodontics, Osaka Dental University, Hirakata, Osaka, Japan
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Peng Z, Wang Y, Wang Y, Jiang S, Fan R, Zhang H, Jiang W. Application of radiomics and machine learning in head and neck cancers. Int J Biol Sci 2021; 17:475-486. [PMID: 33613106 PMCID: PMC7893590 DOI: 10.7150/ijbs.55716] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 12/10/2020] [Indexed: 02/07/2023] Open
Abstract
With the continuous development of medical image informatics technology, more and more high-throughput quantitative data could be extracted from digital medical images, which has resulted in a new kind of omics-Radiomics. In recent years, in addition to genomics, proteomics and metabolomics, radiomic has attracted the interest of more and more researchers. Compared to other omics, radiomics can be perfectly integrated with clinical data, even with the pathology and molecular biomarker, so that the study can be closer to the clinical reality and more revealing of the tumor development. Mass data will also be generated in this process. Machine learning, due to its own characteristics, has a unique advantage in processing massive radiomic data. By analyzing mass amounts of data with strong clinical relevance, people can construct models that more accurately reflect tumor development and progression, thereby providing the possibility of personalized and sequential treatment of patients. As one of the cancer types whose treatment and diagnosis rely on imaging examination, radiomics has a very broad application prospect in head and neck cancers (HNC). Until now, there have been some notable results in HNC. In this review, we will introduce the concepts and workflow of radiomics and machine learning and their current applications in head and neck cancers, as well as the directions and applications of artificial intelligence in the treatment and diagnosis of HNC.
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Affiliation(s)
| | | | | | | | | | | | - Weihong Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
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