Reference Citation Analysis: Find an Article, Find a Category, Find a Journal, Find a Scholar

For: Kaissis GA, Ziegelmayer S, Lohöfer FK, Harder FN, Jungmann F, Sasse D, Muckenhuber A, Yen HY, Steiger K, Siveke J, Friess H, Schmid R, Weichert W, Makowski MR, Braren RF. Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma. J Clin Med 2020;9:jcm9030724. [PMID: 32155990 PMCID: PMC7141256 DOI: 10.3390/jcm9030724] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 03/02/2020] [Accepted: 03/04/2020] [Indexed: 12/19/2022] Open

For:	Kaissis GA, Ziegelmayer S, Lohöfer FK, Harder FN, Jungmann F, Sasse D, Muckenhuber A, Yen HY, Steiger K, Siveke J, Friess H, Schmid R, Weichert W, Makowski MR, Braren RF. Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma. J Clin Med 2020;9:jcm9030724. [PMID: 32155990 PMCID: PMC7141256 DOI: 10.3390/jcm9030724] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 03/02/2020] [Accepted: 03/04/2020] [Indexed: 12/19/2022] Open

Number

Cited by Other Article(s)

Dogru GD, Tugcu AO, Dursun CU. Enhancing diagnostic frameworks in pancreatic cancer imaging: A critical appraisal. World J Radiol 2025;17:104818. [PMID: 40176956 PMCID: PMC11959621 DOI: 10.4329/wjr.v17.i3.104818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2025] [Revised: 03/04/2025] [Accepted: 03/20/2025] [Indexed: 03/27/2025] Open

Vitale F, Zileri Dal Verme L, Paratore M, Negri M, Nista EC, Ainora ME, Esposto G, Mignini I, Borriello R, Galasso L, Alfieri S, Gasbarrini A, Zocco MA, Nicoletti A. The Past, Present, and Future of Biomarkers for the Early Diagnosis of Pancreatic Cancer. Biomedicines 2024;12:2840. [PMID: 39767746 PMCID: PMC11673965 DOI: 10.3390/biomedicines12122840] [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: 11/01/2024] [Revised: 11/30/2024] [Accepted: 12/11/2024] [Indexed: 01/04/2025] Open

Affiliation(s)

Federica Vitale CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (F.V.); (L.Z.D.V.); (M.P.); (M.N.); (E.C.N.); (M.E.A.); (G.E.); (I.M.); (R.B.); (L.G.); (A.G.); (A.N.)
Lorenzo Zileri Dal Verme CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (F.V.); (L.Z.D.V.); (M.P.); (M.N.); (E.C.N.); (M.E.A.); (G.E.); (I.M.); (R.B.); (L.G.); (A.G.); (A.N.)
Mattia Paratore CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (F.V.); (L.Z.D.V.); (M.P.); (M.N.); (E.C.N.); (M.E.A.); (G.E.); (I.M.); (R.B.); (L.G.); (A.G.); (A.N.)
Marcantonio Negri CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (F.V.); (L.Z.D.V.); (M.P.); (M.N.); (E.C.N.); (M.E.A.); (G.E.); (I.M.); (R.B.); (L.G.); (A.G.); (A.N.)
Enrico Celestino Nista CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (F.V.); (L.Z.D.V.); (M.P.); (M.N.); (E.C.N.); (M.E.A.); (G.E.); (I.M.); (R.B.); (L.G.); (A.G.); (A.N.)
Maria Elena Ainora CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (F.V.); (L.Z.D.V.); (M.P.); (M.N.); (E.C.N.); (M.E.A.); (G.E.); (I.M.); (R.B.); (L.G.); (A.G.); (A.N.)
Giorgio Esposto CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (F.V.); (L.Z.D.V.); (M.P.); (M.N.); (E.C.N.); (M.E.A.); (G.E.); (I.M.); (R.B.); (L.G.); (A.G.); (A.N.)
Irene Mignini CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (F.V.); (L.Z.D.V.); (M.P.); (M.N.); (E.C.N.); (M.E.A.); (G.E.); (I.M.); (R.B.); (L.G.); (A.G.); (A.N.)
Raffaele Borriello CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (F.V.); (L.Z.D.V.); (M.P.); (M.N.); (E.C.N.); (M.E.A.); (G.E.); (I.M.); (R.B.); (L.G.); (A.G.); (A.N.)
Linda Galasso CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (F.V.); (L.Z.D.V.); (M.P.); (M.N.); (E.C.N.); (M.E.A.); (G.E.); (I.M.); (R.B.); (L.G.); (A.G.); (A.N.)
Sergio Alfieri Centro Pancreas, Chirurgia Digestiva, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy;
Antonio Gasbarrini CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (F.V.); (L.Z.D.V.); (M.P.); (M.N.); (E.C.N.); (M.E.A.); (G.E.); (I.M.); (R.B.); (L.G.); (A.G.); (A.N.)
Maria Assunta Zocco CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (F.V.); (L.Z.D.V.); (M.P.); (M.N.); (E.C.N.); (M.E.A.); (G.E.); (I.M.); (R.B.); (L.G.); (A.G.); (A.N.)
Alberto Nicoletti CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (F.V.); (L.Z.D.V.); (M.P.); (M.N.); (E.C.N.); (M.E.A.); (G.E.); (I.M.); (R.B.); (L.G.); (A.G.); (A.N.)

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Zaccaria GM, Berloco F, Buongiorno D, Brunetti A, Altini N, Bevilacqua V. A time-dependent explainable radiomic analysis from the multi-omic cohort of CPTAC-Pancreatic Ductal Adenocarcinoma. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024;257:108408. [PMID: 39342876 DOI: 10.1016/j.cmpb.2024.108408] [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: 05/22/2024] [Revised: 07/05/2024] [Accepted: 09/04/2024] [Indexed: 10/01/2024]

Abstract

BACKGROUND AND OBJECTIVE

In Pancreatic Ductal Adenocarcinoma (PDA), multi-omic models are emerging to answer unmet clinical needs to derive novel quantitative prognostic factors. We realized a pipeline that relies on survival machine-learning (SML) classifiers and explainability based on patients' follow-up (FU) to stratify prognosis from the public-available multi-omic datasets of the CPTAC-PDA project.

MATERIALS AND METHODS

Analyzed datasets included tumor-annotated radiologic images, clinical, and mutational data. A feature selection was based on univariate (UV) and multivariate (MV) survival analyses according to Overall Survival (OS) and recurrence (REC). In this study, we considered seven multi-omic datasets and compared four SML classifiers: Cox, survival random forest, generalized boosted, and support vector machines (SVM). For each classifier, we assessed the concordance (C) index on the validation set. The best classifiers for the validation set on both OS and REC underwent explainability analyses using SurvSHAP(t), which extends SHapley Additive exPlanations (SHAP).

RESULTS

According to OS, after UV and MV analyses we selected 18/37 and 10/37 multi-omic features, respectively. According to REC, based on UV and MV analyses we selected 10/35 and 5/35 determinants, respectively. Generally, SML classifiers including radiomics outperformed those modelled on clinical or mutational predictors. For OS, the Cox model encompassing radiomic, clinical, and mutational features reached 75 % of C index, outperforming other classifiers. On the other hand, for REC, the SVM model including only radiomics emerged as the best-performing, with 68 % of C index. For OS, SurvSHAP(t) identified the first order Median Gray Level (GL) intensities, the gender, the tumor grade, the Joint Energy GL Co-occurrence Matrix (GLCM), and the GLCM Informational Measures of Correlations of type 1 as the most important features. For REC, the first order Median GL intensities, the GL size zone matrix Small Area Low GL Emphasis, and first order variance of GL intensities emerged as the most discriminative.

CONCLUSIONS

In this work, radiomics showed the potential for improving patients' risk stratification in PDA. Furthermore, a deeper understanding of how radiomics can contribute to prognosis in PDA was achieved with a time-dependent explainability of the top multi-omic predictors.

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Jiang L, Ye Y, Feng Z, Liu W, Cao Y, Zhao X, Zhu X, Zhang H. Stereotactic body radiation therapy for the primary tumor and oligometastases versus the primary tumor alone in patients with metastatic pancreatic cancer. Radiat Oncol 2024;19:111. [PMID: 39160547 PMCID: PMC11334573 DOI: 10.1186/s13014-024-02493-8] [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: 01/10/2024] [Accepted: 07/19/2024] [Indexed: 08/21/2024] Open

Nicoletti A, Paratore M, Vitale F, Negri M, Quero G, Esposto G, Mignini I, Alfieri S, Gasbarrini A, Zocco MA, Zileri Dal Verme L. Understanding the Conundrum of Pancreatic Cancer in the Omics Sciences Era. Int J Mol Sci 2024;25:7623. [PMID: 39062863 PMCID: PMC11276793 DOI: 10.3390/ijms25147623] [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: 05/01/2024] [Revised: 07/03/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024] Open

Abstract

Pancreatic cancer (PC) is an increasing cause of cancer-related death, with a dismal prognosis caused by its aggressive biology, the lack of clinical symptoms in the early phases of the disease, and the inefficacy of treatments. PC is characterized by a complex tumor microenvironment. The interaction of its cellular components plays a crucial role in tumor development and progression, contributing to the alteration of metabolism and cellular hyperproliferation, as well as to metastatic evolution and abnormal tumor-associated immunity. Furthermore, in response to intrinsic oncogenic alterations and the influence of the tumor microenvironment, cancer cells undergo a complex oncogene-directed metabolic reprogramming that includes changes in glucose utilization, lipid and amino acid metabolism, redox balance, and activation of recycling and scavenging pathways. The advent of omics sciences is revolutionizing the comprehension of the pathogenetic conundrum of pancreatic carcinogenesis. In particular, metabolomics and genomics has led to a more precise classification of PC into subtypes that show different biological behaviors and responses to treatments. The identification of molecular targets through the pharmacogenomic approach may help to personalize treatments. Novel specific biomarkers have been discovered using proteomics and metabolomics analyses. Radiomics allows for an earlier diagnosis through the computational analysis of imaging. However, the complexity, high expertise required, and costs of the omics approach are the main limitations for its use in clinical practice at present. In addition, the studies of extracellular vesicles (EVs), the use of organoids, the understanding of host-microbiota interactions, and more recently the advent of artificial intelligence are helping to make further steps towards precision and personalized medicine. This present review summarizes the main evidence for the application of omics sciences to the study of PC and the identification of future perspectives.

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Affiliation(s)

Alberto Nicoletti CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (A.N.); (M.P.); (F.V.); (M.N.); (G.E.); (I.M.); (A.G.); (L.Z.D.V.)
Mattia Paratore CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (A.N.); (M.P.); (F.V.); (M.N.); (G.E.); (I.M.); (A.G.); (L.Z.D.V.)
Federica Vitale CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (A.N.); (M.P.); (F.V.); (M.N.); (G.E.); (I.M.); (A.G.); (L.Z.D.V.)
Marcantonio Negri CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (A.N.); (M.P.); (F.V.); (M.N.); (G.E.); (I.M.); (A.G.); (L.Z.D.V.)
Giuseppe Quero Centro Pancreas, Chirurgia Digestiva, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.Q.); (S.A.)
Giorgio Esposto CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (A.N.); (M.P.); (F.V.); (M.N.); (G.E.); (I.M.); (A.G.); (L.Z.D.V.)
Irene Mignini CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (A.N.); (M.P.); (F.V.); (M.N.); (G.E.); (I.M.); (A.G.); (L.Z.D.V.)
Sergio Alfieri Centro Pancreas, Chirurgia Digestiva, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (G.Q.); (S.A.)
Antonio Gasbarrini CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (A.N.); (M.P.); (F.V.); (M.N.); (G.E.); (I.M.); (A.G.); (L.Z.D.V.)
Maria Assunta Zocco CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (A.N.); (M.P.); (F.V.); (M.N.); (G.E.); (I.M.); (A.G.); (L.Z.D.V.)
Lorenzo Zileri Dal Verme CEMAD Centro Malattie dell’Apparato Digerente, Medicina Interna e Gastroenterologia, Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, 00168 Rome, Italy; (A.N.); (M.P.); (F.V.); (M.N.); (G.E.); (I.M.); (A.G.); (L.Z.D.V.)

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Jiang W, Pan X, Luo Q, Huang S, Liang Y, Zhong X, Zhang X, Deng W, Lv Y, Chen L. Radiomics analysis of pancreas based on dual-energy computed tomography for the detection of type 2 diabetes mellitus. Front Med (Lausanne) 2024;11:1328687. [PMID: 38707184 PMCID: PMC11069320 DOI: 10.3389/fmed.2024.1328687] [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: 10/27/2023] [Accepted: 04/03/2024] [Indexed: 05/07/2024] Open

Grewal M, Ahmed T, Javed AA. Current state of radiomics in hepatobiliary and pancreatic malignancies. ARTIFICIAL INTELLIGENCE SURGERY 2023;3:217-32. [DOI: 10.20517/ais.2023.28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2025]

Abstract Rising in incidence, hepatobiliary and pancreatic (HPB) cancers continue to exhibit dismal long-term survival. The overall poor prognosis of HPB cancers is reflective of the advanced stage at which most patients are diagnosed. Late diagnosis is driven by the often-asymptomatic nature of these diseases, as well as a dearth of screening modalities. Additionally, standard imaging modalities fall short of providing accurate and detailed information regarding specific tumor characteristics, which can better inform surgical planning and sequencing of systemic therapy. Therefore, precise therapeutic planning must be delayed until histopathological examination is performed at the time of resection. Given the current shortcomings in the management of HPB cancers, investigations of numerous noninvasive biomarkers, including circulating tumor cells and DNA, proteomics, immunolomics, and radiomics, are underway. Radiomics encompasses the extraction and analysis of quantitative imaging features. Along with summarizing the general framework of radiomics, this review synthesizes the state of radiomics in HPB cancers, outlining its role in various aspects of management, present limitations, and future applications for clinical integration. Current literature underscores the utility of radiomics in early detection, tumor characterization, therapeutic selection, and prognostication for HPB cancers. Seeing as single-center, small studies constitute the majority of radiomics literature, there is considerable heterogeneity with respect to steps of the radiomics workflow such as segmentation, or delineation of the region of interest on a scan. Nonetheless, the introduction of the radiomics quality score (RQS) demonstrates a step towards greater standardization and reproducibility in the young field of radiomics. Altogether, in the setting of continually improving artificial intelligence algorithms, radiomics represents a promising biomarker avenue for promoting enhanced and tailored management of HPB cancers, with the potential to improve long-term outcomes for patients. Collapse

Ramaekers M, Viviers CGA, Janssen BV, Hellström TAE, Ewals L, van der Wulp K, Nederend J, Jacobs I, Pluyter JR, Mavroeidis D, van der Sommen F, Besselink MG, Luyer MDP. Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions. J Clin Med 2023;12:4209. [PMID: 37445243 PMCID: PMC10342462 DOI: 10.3390/jcm12134209] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/08/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023] Open

Affiliation(s)

Mark Ramaekers Department of Surgery, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands;
Christiaan G. A. Viviers Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands; (C.G.A.V.); (T.A.E.H.); (F.v.d.S.)
Boris V. Janssen Department of Surgery, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; (B.V.J.); (M.G.B.) Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands
Terese A. E. Hellström Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands; (C.G.A.V.); (T.A.E.H.); (F.v.d.S.)
Lotte Ewals Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands; (L.E.); (K.v.d.W.); (J.N.)
Kasper van der Wulp Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands; (L.E.); (K.v.d.W.); (J.N.)
Joost Nederend Department of Radiology, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands; (L.E.); (K.v.d.W.); (J.N.)
Igor Jacobs Department of Hospital Services and Informatics, Philips Research, 5656 AE Eindhoven, The Netherlands;
Jon R. Pluyter Department of Experience Design, Philips Design, 5656 AE Eindhoven, The Netherlands;
Dimitrios Mavroeidis Department of Data Science, Philips Research, 5656 AE Eindhoven, The Netherlands;
Fons van der Sommen Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands; (C.G.A.V.); (T.A.E.H.); (F.v.d.S.)
Marc G. Besselink Department of Surgery, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; (B.V.J.); (M.G.B.) Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands
Misha D. P. Luyer Department of Surgery, Catharina Cancer Institute, Catharina Hospital Eindhoven, 5623 EJ Eindhoven, The Netherlands;

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Taha A, Taha-Mehlitz S, Ortlieb N, Ochs V, Honaker MD, Rosenberg R, Lock JF, Bolli M, Cattin PC. Machine learning in pancreas surgery, what is new? literature review. Front Surg 2023;10:1142585. [PMID: 37383385 PMCID: PMC10293756 DOI: 10.3389/fsurg.2023.1142585] [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: 01/11/2023] [Accepted: 05/19/2023] [Indexed: 06/30/2023] Open

Jia X, Liang J, Ma X, Wang W, Lai C. Radiomic-based machine learning model for predicting the surgical risk in children with abdominal neuroblastoma. WORLD JOURNAL OF PEDIATRIC SURGERY 2023;6:e000531. [PMID: 37223779 PMCID: PMC10201264 DOI: 10.1136/wjps-2022-000531] [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: 11/18/2022] [Accepted: 03/27/2023] [Indexed: 05/25/2023] Open

Jan Z, El Assadi F, Abd-Alrazaq A, Jithesh PV. Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review. J Med Internet Res 2023;25:e44248. [PMID: 37000507 PMCID: PMC10131763 DOI: 10.2196/44248] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 02/21/2023] [Indexed: 04/01/2023] Open

Abstract

BACKGROUND

Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer.

OBJECTIVE

This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature.

METHODS

A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively.

RESULTS

Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms.

CONCLUSIONS

This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.

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Jan Z, El Assadi F, Abd-alrazaq A, Jithesh PV. Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review (Preprint).. [DOI: 10.2196/preprints.44248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]

Abstract

BACKGROUND

OBJECTIVE

This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature.

METHODS

RESULTS

CONCLUSIONS

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Marti-Bonmati L, Cerdá-Alberich L, Pérez-Girbés A, Díaz Beveridge R, Montalvá Orón E, Pérez Rojas J, Alberich-Bayarri A. Pancreatic cancer, radiomics and artificial intelligence. Br J Radiol 2022;95:20220072. [PMID: 35687700 PMCID: PMC10996946 DOI: 10.1259/bjr.20220072] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/19/2022] [Accepted: 05/27/2022] [Indexed: 11/05/2022] Open

Abstract

Patients with pancreatic ductal adenocarcinoma (PDAC) are generally classified into four categories based on contrast-enhanced CT at diagnosis: resectable, borderline resectable, unresectable, and metastatic disease. In the initial grading and staging of PDAC, structured radiological templates are useful but limited, as there is a need to define the aggressiveness and microscopic disease stage of these tumours to ensure adequate treatment allocation. Quantitative imaging analysis allows radiomics and dynamic imaging features to provide information of clinical outcomes, and to construct clinical models based on radiomics signatures or imaging phenotypes. These quantitative features may be used as prognostic and predictive biomarkers in clinical decision-making, enabling personalised management of advanced PDAC. Deep learning and convolutional neural networks also provide high level bioinformatics tools that can help define features associated with a given aspect of PDAC biology and aggressiveness, paving the way to define outcomes based on these features. Thus, the prediction of tumour phenotype, treatment response and patient prognosis may be feasible by using such comprehensive and integrated radiomics models. Despite these promising results, quantitative imaging is not ready for clinical implementation in PDAC. Limitations include the instability of metrics and lack of external validation. Large properly annotated datasets, including relevant semantic features (demographics, blood markers, genomics), image harmonisation, robust radiomics analysis, clinically significant tasks as outputs, comparisons with gold-standards (such as TNM or pretreatment classifications) and fully independent validation cohorts, will be required for the development of trustworthy radiomics and artificial intelligence solutions to predict PDAC aggressiveness in a clinical setting.

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Salinas-Miranda E, Healy GM, Grünwald B, Jain R, Deniffel D, O'Kane GM, Grant R, Wilson J, Knox J, Gallinger S, Fischer S, Khokha R, Haider MA. Correlation of transcriptional subtypes with a validated CT radiomics score in resectable pancreatic ductal adenocarcinoma. Eur Radiol 2022;32:6712-6722. [PMID: 36006427 DOI: 10.1007/s00330-022-09057-y] [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: 03/03/2022] [Revised: 06/14/2022] [Accepted: 07/24/2022] [Indexed: 11/04/2022]

Abstract

OBJECTIVES

Transcriptional classifiers (Bailey, Moffitt and Collison) are key prognostic factors of pancreatic ductal adenocarcinoma (PDAC). Among these classifiers, the squamous, basal-like, and quasimesenchymal subtypes overlap and have inferior survival. Currently, only an invasive biopsy can determine these subtypes, possibly resulting in treatment delay. This study aimed to investigate the association between transcriptional subtypes and an externally validated preoperative CT-based radiomic prognostic score (Rad-score).

METHODS

We retrospectively evaluated 122 patients who underwent resection for PDAC. All treatment decisions were determined at multidisciplinary tumor boards. Tumor Rad-score values from preoperative CT were dichotomized into high or llow categories. The primary endpoint was the correlation between the transcriptional subtypes and the Rad-score using multivariable linear regression, adjusting for clinical and histopathological variables (i.e., tumor size). Prediction of overall survival (OS) was secondary endpoint.

RESULTS

The Bailey transcriptional classifier significantly associated with the Rad-score (coefficient = 0.31, 95% confidence interval [CI]: 0.13-0.44, p = 0.001). Squamous subtype was associated with high Rad-scores while non-squamous subtype was associated with low Rad-scores (adjusted p = 0.03). Squamous subtype and high Rad-score were both prognostic for OS at multivariable analysis with hazard ratios (HR) of 2.79 (95% CI: 1.12-6.92, p = 0.03) and 4.03 (95% CI: 1.42-11.39, p = 0.01), respectively.

CONCLUSIONS

In patients with resectable PDAC, an externally validated prognostic radiomic model derived from preoperative CT is associated with the Bailey transcriptional classifier. Higher Rad-scores were correlated with the squamous subtype, while lower Rad-scores were associated with the less lethal subtypes (immunogenic, ADEX, pancreatic progenitor).

KEY POINTS

• The transcriptional subtypes of PDAC have been shown to have prognostic importance but they require invasive biopsy to be assessed. • The Rad-score radiomic biomarker, which is obtained non-invasively from preoperative CT, correlates with the Bailey squamous transcriptional subtype and both are negative prognostic biomarkers. • The Rad-score is a promising non-invasive imaging biomarker for personalizing neoadjuvant approaches in patients undergoing resection for PDAC, although additional validation studies are required.

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Affiliation(s)

Emmanuel Salinas-Miranda Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph & Wolf Lebovic Health Complex, 600 University Avenue, 6th Floor, Office 6 200, Toronto, ON, M5G 1X5, Canada.,Joint Department of Medical Imaging, University Health Network/Sinai Health System, 600 University Ave, 5th Floor, Toronto, ON, M5G1X5, Canada
Gerard M Healy Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph & Wolf Lebovic Health Complex, 600 University Avenue, 6th Floor, Office 6 200, Toronto, ON, M5G 1X5, Canada.,Joint Department of Medical Imaging, University Health Network/Sinai Health System, 600 University Ave, 5th Floor, Toronto, ON, M5G1X5, Canada.,Department of Medical Imaging, University of Toronto, 263 McCaul St 4th Floor, Toronto, ON, M5T 1W5, Canada
Barbara Grünwald Department of Pathology, University Health Network, 610 University Ave, Toronto, ON, M5G 2C1, Canada.,PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, ON, M5G 0A3, Canada
Rahi Jain Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, 610 University Ave, Toronto, ON, M5G 2C1, Canada
Dominik Deniffel Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph & Wolf Lebovic Health Complex, 600 University Avenue, 6th Floor, Office 6 200, Toronto, ON, M5G 1X5, Canada
Grainne M O'Kane PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, ON, M5G 0A3, Canada.,Department of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, 610 University Ave, Toronto, ON, M5G 2C1, Canada
Robert Grant PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, ON, M5G 0A3, Canada.,Department of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, 610 University Ave, Toronto, ON, M5G 2C1, Canada
Julie Wilson PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, ON, M5G 0A3, Canada
Jennifer Knox PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, ON, M5G 0A3, Canada.,Department of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, 610 University Ave, Toronto, ON, M5G 2C1, Canada
Steven Gallinger PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, ON, M5G 0A3, Canada.,Department of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, 610 University Ave, Toronto, ON, M5G 2C1, Canada.,Hepatobiliary Pancreatic Surgical Oncology Program, University Health Network, 190 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada
Sandra Fischer Department of Pathology, University Health Network, 610 University Ave, Toronto, ON, M5G 2C1, Canada
Rama Khokha Department of Medical Biophysics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, 610 University Ave, Toronto, ON, M5G 2C1, Canada
Masoom A Haider Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph & Wolf Lebovic Health Complex, 600 University Avenue, 6th Floor, Office 6 200, Toronto, ON, M5G 1X5, Canada. .,Joint Department of Medical Imaging, University Health Network/Sinai Health System, 600 University Ave, 5th Floor, Toronto, ON, M5G1X5, Canada. .,Department of Medical Imaging, University of Toronto, 263 McCaul St 4th Floor, Toronto, ON, M5T 1W5, Canada. .,PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, ON, M5G 0A3, Canada.

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Laino ME, Ammirabile A, Lofino L, Mannelli L, Fiz F, Francone M, Chiti A, Saba L, Orlandi MA, Savevski V. Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review. Healthcare (Basel) 2022;10:1511. [PMID: 36011168 PMCID: PMC9408381 DOI: 10.3390/healthcare10081511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/31/2022] [Accepted: 08/08/2022] [Indexed: 12/19/2022] Open

Schuurmans M, Alves N, Vendittelli P, Huisman H, Hermans J. Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging. Cancers (Basel) 2022;14:cancers14143498. [PMID: 35884559 PMCID: PMC9316850 DOI: 10.3390/cancers14143498] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/07/2022] [Accepted: 07/15/2022] [Indexed: 11/16/2022] Open

Abstract

Simple Summary

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide, associated with a 98% loss of life expectancy and a 30% increase in disability-adjusted life years. Image-based artificial intelligence (AI) can help improve outcomes for PDAC given that current clinical guidelines are non-uniform and lack evidence-based consensus. However, research on image-based AI for PDAC is too scattered and lacking in sufficient quality to be incorporated into clinical workflows. In this review, an international, multi-disciplinary team of the world’s leading experts in pancreatic cancer breaks down the patient pathway and pinpoints the current clinical touchpoints in each stage. The available PDAC imaging AI literature addressing each pathway stage is then rigorously analyzed, and current performance and pitfalls are identified in a comprehensive overview. Finally, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed.

Abstract

Pancreatic ductal adenocarcinoma (PDAC), estimated to become the second leading cause of cancer deaths in western societies by 2030, was flagged as a neglected cancer by the European Commission and the United States Congress. Due to lack of investment in research and development, combined with a complex and aggressive tumour biology, PDAC overall survival has not significantly improved the past decades. Cross-sectional imaging and histopathology play a crucial role throughout the patient pathway. However, current clinical guidelines for diagnostic workup, patient stratification, treatment response assessment, and follow-up are non-uniform and lack evidence-based consensus. Artificial Intelligence (AI) can leverage multimodal data to improve patient outcomes, but PDAC AI research is too scattered and lacking in quality to be incorporated into clinical workflows. This review describes the patient pathway and derives touchpoints for image-based AI research in collaboration with a multi-disciplinary, multi-institutional expert panel. The literature exploring AI to address these touchpoints is thoroughly retrieved and analysed to identify the existing trends and knowledge gaps. The results show absence of multi-institutional, well-curated datasets, an essential building block for robust AI applications. Furthermore, most research is unimodal, does not use state-of-the-art AI techniques, and lacks reliable ground truth. Based on this, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed.

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Eroğlu O, Eroğlu Y, Yıldırım M, Karlıdag T, Çınar A, Akyiğit A, Kaygusuz İ, Yıldırım H, Keleş E, Yalçın Ş. Is it useful to use computerized tomography image-based artificial intelligence modelling in the differential diagnosis of chronic otitis media with and without cholesteatoma? Am J Otolaryngol 2022;43:103395. [PMID: 35241288 DOI: 10.1016/j.amjoto.2022.103395] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/01/2022] [Accepted: 02/13/2022] [Indexed: 11/19/2022]

Abstract

OBJECTIVE

Cholesteatoma is an aggressive form of chronic otitis media (COM). For this reason, it is important to distinguish between COM with and without cholesteatoma. In this study, the role of artificial intelligence modelling in differentiating COM with and without cholesteatoma on computed tomography images was evaluated.

METHODS

The files of 200 patients who underwent mastoidectomy and/or tympanoplasty for COM in our clinic between January 2016 and January 2021 were retrospectively reviewed. According to the presence of cholesteatoma, the patients were divided into two groups as chronic otitis with cholesteatoma (n = 100) and chronic otitis without cholesteatoma (n = 100). The control group (n = 100) consisted of patients who did not have any previous ear disease and did not have any active complaints about the ear. Temporal bone computed tomography (CT) images of all patients were analyzed. The distinction between cholesteatoma and COM was evaluated by using 80% of the CT images obtained for the training of artificial intelligence modelling and the remaining 20% for testing purposes.

RESULTS

The accuracy rate obtained in the hybrid model we used in our study was 95.4%. The proposed model correctly predicted 2952 out of 3093 CT images, while it predicted 141 incorrectly. It correctly predicted 936 (93.78%) of 998 images in the COM group with cholesteatoma, 835 (92.77%) of 900 images in the COM group without cholesteatoma, and 1181 (98.82%) of 1195 images in the normal group.

CONCLUSION

In our study, it has been shown that the differentiation of COM with and without cholesteatoma with artificial intelligence modelling can be made with highly accurate diagnosis rates by using CT images. With the deep learning modelling we proposed, the highest correct diagnosis rate in the literature was obtained. According to the results of our study, we think that with the use of artificial intelligence in practice, the diagnosis of cholesteatoma can be made earlier, it will help in the selection of the most appropriate treatment approach, and the complications can be reduced.

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Meng Y, Zhang H, Li Q, Liu F, Fang X, Li J, Yu J, Feng X, Lu J, Bian Y, Shao C. Magnetic Resonance Radiomics and Machine-learning Models: An Approach for Evaluating Tumor-stroma Ratio in Patients with Pancreatic Ductal Adenocarcinoma. Acad Radiol 2022;29:523-535. [PMID: 34563443 DOI: 10.1016/j.acra.2021.08.013] [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: 05/07/2021] [Revised: 07/14/2021] [Accepted: 08/09/2021] [Indexed: 12/12/2022]

Tharmaseelan H, Hertel A, Tollens F, Rink J, Woźnicki P, Haselmann V, Ayx I, Nörenberg D, Schoenberg SO, Froelich MF. Identification of CT Imaging Phenotypes of Colorectal Liver Metastases from Radiomics Signatures-Towards Assessment of Interlesional Tumor Heterogeneity. Cancers (Basel) 2022;14:cancers14071646. [PMID: 35406418 PMCID: PMC8997087 DOI: 10.3390/cancers14071646] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 03/17/2022] [Accepted: 03/21/2022] [Indexed: 02/06/2023] Open

Affiliation(s)

Hishan Tharmaseelan Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany; (H.T.); (A.H.); (F.T.); (J.R.); (P.W.); (I.A.); (D.N.); (S.O.S.)
Alexander Hertel Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany; (H.T.); (A.H.); (F.T.); (J.R.); (P.W.); (I.A.); (D.N.); (S.O.S.)
Fabian Tollens Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany; (H.T.); (A.H.); (F.T.); (J.R.); (P.W.); (I.A.); (D.N.); (S.O.S.)
Johann Rink Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany; (H.T.); (A.H.); (F.T.); (J.R.); (P.W.); (I.A.); (D.N.); (S.O.S.)
Piotr Woźnicki Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany; (H.T.); (A.H.); (F.T.); (J.R.); (P.W.); (I.A.); (D.N.); (S.O.S.)
Verena Haselmann Institute of Clinical Chemistry, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany;
Isabelle Ayx Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany; (H.T.); (A.H.); (F.T.); (J.R.); (P.W.); (I.A.); (D.N.); (S.O.S.)
Dominik Nörenberg Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany; (H.T.); (A.H.); (F.T.); (J.R.); (P.W.); (I.A.); (D.N.); (S.O.S.)
Stefan O. Schoenberg Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany; (H.T.); (A.H.); (F.T.); (J.R.); (P.W.); (I.A.); (D.N.); (S.O.S.)
Matthias F. Froelich Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany; (H.T.); (A.H.); (F.T.); (J.R.); (P.W.); (I.A.); (D.N.); (S.O.S.) Correspondence:

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Janssen BV, Verhoef S, Wesdorp NJ, Huiskens J, de Boer OJ, Marquering H, Stoker J, Kazemier G, Besselink MG. Imaging-based Machine-learning Models to Predict Clinical Outcomes and Identify Biomarkers in Pancreatic Cancer: A Scoping Review. Ann Surg 2022;275:560-567. [PMID: 34954758 DOI: 10.1097/sla.0000000000005349] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]

Abstract

OBJECTIVE

To perform a scoping review of imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in patients with PDAC.

SUMMARY OF BACKGROUND DATA

Patients with PDAC could benefit from better selection for systemic and surgical therapy. Imaging-based machine-learning models may improve treatment selection.

METHODS

A scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses-scoping review guidelines in the PubMed and Embase databases (inception-October 2020). The review protocol was prospectively registered (open science framework registration: m4cyx). Included were studies on imaging-based machine-learning models for predicting clinical outcomes and identifying biomarkers for PDAC. The primary outcome was model performance. An area under the curve (AUC) of ≥0.75, or a P-value of ≤0.05, was considered adequate model performance. Methodological study quality was assessed using the modified radiomics quality score.

RESULTS

After screening 1619 studies, 25 studies with 2305 patients fulfilled the eligibility criteria. All but 1 study was published in 2019 and 2020. Overall, 23/25 studies created models using radiomics features, 1 study quantified vascular invasion on computed tomography, and one used histopathological data. Nine models predicted clinical outcomes with AUC measures of 0.78-0.95, and C-indices of 0.65-0.76. Seventeen models identified biomarkers with AUC measures of 0.68-0.95. Adequate model performance was reported in 23/25 studies. The methodological quality of the included studies was suboptimal, with a median modified radiomics quality score score of 7/36.

CONCLUSIONS

The use of imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in patients with PDAC is increasingly rapidly. Although these models mostly have good performance scores, their methodological quality should be improved.

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Yu J, Li Q, Zhang H, Meng Y, Liu YF, Jiang H, Ma C, Liu F, Fang X, Li J, Feng X, Shao C, Bian Y, Lu J. Contrast-enhanced computed tomography radiomics and multilayer perceptron network classifier: an approach for predicting CD20⁺ B cells in patients with pancreatic ductal adenocarcinoma. Abdom Radiol (NY) 2022;47:242-253. [PMID: 34708252 DOI: 10.1007/s00261-021-03285-4] [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: 06/19/2021] [Revised: 09/11/2021] [Accepted: 09/11/2021] [Indexed: 11/24/2022]

Casà C, Piras A, D’Aviero A, Preziosi F, Mariani S, Cusumano D, Romano A, Boskoski I, Lenkowicz J, Dinapoli N, Cellini F, Gambacorta MA, Valentini V, Mattiucci GC, Boldrini L. The impact of radiomics in diagnosis and staging of pancreatic cancer. Ther Adv Gastrointest Endosc 2022;15:26317745221081596. [PMID: 35342883 PMCID: PMC8943316 DOI: 10.1177/26317745221081596] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 02/02/2022] [Indexed: 02/05/2023] Open

Affiliation(s)

Calogero Casà UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
Antonio Piras UO Radioterapia Oncologica, Palermo, Italy
Andrea D’Aviero UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
Francesco Preziosi Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
Silvia Mariani Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
Davide Cusumano UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
Angela Romano UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
Ivo Boskoski Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCSS, Rome, Italy
Jacopo Lenkowicz UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
Nicola Dinapoli UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
Francesco Cellini UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
Maria Antonietta Gambacorta UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
Vincenzo Valentini UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
Gian Carlo Mattiucci UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
Luca Boldrini UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy

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Hayashi H, Uemura N, Matsumura K, Zhao L, Sato H, Shiraishi Y, Yamashita YI, Baba H. Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma. World J Gastroenterol 2021;27:7480-7496. [PMID: 34887644 PMCID: PMC8613738 DOI: 10.3748/wjg.v27.i43.7480] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 08/02/2021] [Accepted: 11/15/2021] [Indexed: 02/06/2023] Open

Goyal H, Sherazi SAA, Mann R, Gandhi Z, Perisetti A, Aziz M, Chandan S, Kopel J, Tharian B, Sharma N, Thosani N. Scope of Artificial Intelligence in Gastrointestinal Oncology. Cancers (Basel) 2021;13:5494. [PMID: 34771658 PMCID: PMC8582733 DOI: 10.3390/cancers13215494] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 10/27/2021] [Indexed: 12/12/2022] Open

Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021;27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open

Gutiérrez ML, Muñoz-Bellvís L, Orfao A. Genomic Heterogeneity of Pancreatic Ductal Adenocarcinoma and Its Clinical Impact. Cancers (Basel) 2021;13:4451. [PMID: 34503261 PMCID: PMC8430663 DOI: 10.3390/cancers13174451] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/30/2021] [Accepted: 08/31/2021] [Indexed: 02/07/2023] Open

Knolle M, Kaissis G, Jungmann F, Ziegelmayer S, Sasse D, Makowski M, Rueckert D, Braren R. Efficient, high-performance semantic segmentation using multi-scale feature extraction. PLoS One 2021;16:e0255397. [PMID: 34411138 PMCID: PMC8375977 DOI: 10.1371/journal.pone.0255397] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 07/15/2021] [Indexed: 11/19/2022] Open

Abstract

The success of deep learning in recent years has arguably been driven by the availability of large datasets for training powerful predictive algorithms. In medical applications however, the sensitive nature of the data limits the collection and exchange of large-scale datasets. Privacy-preserving and collaborative learning systems can enable the successful application of machine learning in medicine. However, collaborative protocols such as federated learning require the frequent transfer of parameter updates over a network. To enable the deployment of such protocols to a wide range of systems with varying computational performance, efficient deep learning architectures for resource-constrained environments are required. Here we present MoNet, a small, highly optimized neural-network-based segmentation algorithm leveraging efficient multi-scale image features. MoNet is a shallow, U-Net-like architecture based on repeated, dilated convolutions with decreasing dilation rates. We apply and test our architecture on the challenging clinical tasks of pancreatic segmentation in computed tomography (CT) images as well as brain tumor segmentation in magnetic resonance imaging (MRI) data. We assess our model's segmentation performance and demonstrate that it provides performance on par with compared architectures while providing superior out-of-sample generalization performance, outperforming larger architectures on an independent validation set, while utilizing significantly fewer parameters. We furthermore confirm the suitability of our architecture for federated learning applications by demonstrating a substantial reduction in serialized model storage requirement as a surrogate for network data transfer. Finally, we evaluate MoNet's inference latency on the central processing unit (CPU) to determine its utility in environments without access to graphics processing units. Our implementation is publicly available as free and open-source software.

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Wang S, Zheng Y, Yang F, Zhu L, Zhu XQ, Wang ZF, Wu XL, Zhou CH, Yan JY, Hu BY, Kong B, Fu DL, Bruns C, Zhao Y, Qin LX, Dong QZ. The molecular biology of pancreatic adenocarcinoma: translational challenges and clinical perspectives. Signal Transduct Target Ther 2021;6:249. [PMID: 34219130 PMCID: PMC8255319 DOI: 10.1038/s41392-021-00659-4] [Citation(s) in RCA: 187] [Impact Index Per Article: 46.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 04/27/2021] [Accepted: 05/26/2021] [Indexed: 02/06/2023] Open

Affiliation(s)

Shun Wang Department of General Surgery, Huashan Hospital, Cancer Metastasis Institute, Fudan University, Shanghai, China
Yan Zheng Department of General Surgery, Huashan Hospital, Cancer Metastasis Institute, Fudan University, Shanghai, China
Feng Yang Department of Pancreatic Surgery, Pancreatic Disease Institute, Huashan Hospital, Fudan University, Shanghai, China
Le Zhu Department of General Surgery, Huashan Hospital, Cancer Metastasis Institute, Fudan University, Shanghai, China
Xiao-Qiang Zhu School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
Zhe-Fang Wang General, Visceral and Cancer Surgery, University Hospital of Cologne, Cologne, Germany
Xiao-Lin Wu General, Visceral and Cancer Surgery, University Hospital of Cologne, Cologne, Germany
Cheng-Hui Zhou General, Visceral and Cancer Surgery, University Hospital of Cologne, Cologne, Germany
Jia-Yan Yan General, Visceral and Cancer Surgery, University Hospital of Cologne, Cologne, Germany Department of Biliary-Pancreatic Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
Bei-Yuan Hu Department of General Surgery, Huashan Hospital, Cancer Metastasis Institute, Fudan University, Shanghai, China
Bo Kong Department of Surgery, Klinikum rechts der Isar, School of Medicine, Technical University of Munich (TUM), Munich, Germany
De-Liang Fu Department of Pancreatic Surgery, Pancreatic Disease Institute, Huashan Hospital, Fudan University, Shanghai, China
Christiane Bruns General, Visceral and Cancer Surgery, University Hospital of Cologne, Cologne, Germany
Yue Zhao General, Visceral and Cancer Surgery, University Hospital of Cologne, Cologne, Germany.
Lun-Xiu Qin Department of General Surgery, Huashan Hospital, Cancer Metastasis Institute, Fudan University, Shanghai, China.
Qiong-Zhu Dong Department of General Surgery, Huashan Hospital, Cancer Metastasis Institute, Fudan University, Shanghai, China. Key laboratory of whole-period monitoring and precise intervention of digestive cancer, Shanghai Municipal Health Commission (SMHC), Shanghai, China.

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Arendt CT, Leithner D, Mayerhoefer ME, Gibbs P, Czerny C, Arnoldner C, Burck I, Leinung M, Tanyildizi Y, Lenga L, Martin SS, Vogl TJ, Schernthaner RE. Radiomics of high-resolution computed tomography for the differentiation between cholesteatoma and middle ear inflammation: effects of post-reconstruction methods in a dual-center study. Eur Radiol 2021;31:4071-4078. [PMID: 33277670 PMCID: PMC8128805 DOI: 10.1007/s00330-020-07564-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 11/07/2020] [Accepted: 11/25/2020] [Indexed: 11/27/2022]

Abstract

OBJECTIVES

To evaluate the performance of radiomic features extracted from high-resolution computed tomography (HRCT) for the differentiation between cholesteatoma and middle ear inflammation (MEI), and to investigate the impact of post-reconstruction harmonization and data resampling.

METHODS

One hundred patients were included in this retrospective dual-center study: 48 with histology-proven cholesteatoma (center A: 23; center B: 25) and 52 with MEI (A: 27; B: 25). Radiomic features (co-occurrence and run-length matrix, absolute gradient, autoregressive model, Haar wavelet transform) were extracted from manually defined 2D-ROIs. The ten best features for lesion differentiation were selected using probability of error and average correlation coefficients. A multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used for radiomics-based classification, with histopathology serving as the reference standard (70% of cases for training, 30% for validation). The analysis was performed five times each on (a) unmodified data and on data that were (b) resampled to the same matrix size, and (c) corrected for acquisition protocol differences using ComBat harmonization.

RESULTS

Using unmodified data, the MLP-ANN classification yielded an overall median area under the receiver operating characteristic curve (AUC) of 0.78 (0.72-0.84). Using original data from center A and resampled data from center B, an overall median AUC of 0.88 (0.82-0.99) was yielded, while using ComBat harmonized data, an overall median AUC of 0.89 (0.79-0.92) was revealed.

CONCLUSION

Radiomic features extracted from HRCT differentiate between cholesteatoma and MEI. When using multi-centric data obtained with differences in CT acquisition parameters, data resampling and ComBat post-reconstruction harmonization clearly improve radiomics-based lesion classification.

KEY POINTS

• Unenhanced high-resolution CT coupled with radiomics analysis may be useful for the differentiation between cholesteatoma and middle ear inflammation. • Pooling of data extracted from inhomogeneous CT datasets does not appear meaningful without further post-processing. • When using multi-centric CT data obtained with differences in acquisition parameters, post-reconstruction harmonization and data resampling clearly improve radiomics-based soft-tissue differentiation.

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Affiliation(s)

Christophe T Arendt Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
Doris Leithner Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
Marius E Mayerhoefer Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Peter Gibbs Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
Christian Czerny Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
Christoph Arnoldner Department of Otorhinolaryngology, Head Neck Surgery, Medical University of Vienna, Vienna, Austria
Iris Burck Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
Martin Leinung Department of Otolaryngology, Head and Neck Surgery, University Hospital Frankfurt, Frankfurt am Main, Germany
Yasemin Tanyildizi Department of Neuroradiology, University Medical Center of the Johannes Gutenberg-University, Mainz, Germany
Lukas Lenga Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
Simon S Martin Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
Thomas J Vogl Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
Ruediger E Schernthaner Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria

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Xie XJ, Liu SY, Chen JY, Zhao Y, Jiang J, Wu L, Zhang XW, Wu Y, Duan H, He B, Luo H, Han D. Development of unenhanced CT-based imaging signature for BAP1 mutation status prediction in malignant pleural mesothelioma: Consideration of 2D and 3D segmentation. Lung Cancer 2021;157:30-39. [PMID: 34052706 DOI: 10.1016/j.lungcan.2021.04.023] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 04/22/2021] [Accepted: 04/24/2021] [Indexed: 01/07/2023]

Abstract

OBJECTIVES

We aimed to explore the feasibility of 2D and 3D radiomics signature based on the unenhanced computed tomography (CT) images to predict BRCA1-associated protein 1 (BAP1) gene mutation status for malignant pleural mesothelioma (MPM) patients.

MATERIALS AND METHODS

74 patients with MPM were retrospectively enrolled (22 mutant BAP1, 52 wild-type BAP1 demonstrated by Sanger sequencing). The radiomic features were extracted respectively from the 2D and 3D segmentation of unenhanced pre-treatment CT images, and the dataset was randomly divided into training (n = 51) and test (n = 23) sets for radiomics model development and internal validation. The synthetic minority over-sampling technique (SMOTE) was used for data balancing in the training set. 2D or 3D features were sequentially selected by ICC > 0.8, correlation analysis (cut-value 0.7), univariate analysis or univariate logistic regression (LR), which were involved into multivariate LR for LR model construction. Following the comparison of the 2D and 3D models by the ROC analysis and Delong test for AUC, the calibration and clinical utility of 2D and 3D models were evaluated.

RESULTS

3D radiomic features showed better ICCs compared with 2D in both intra- (P < 0.001) and inter-observer (P < 0.001) analysis. 3D radiomic model based on selected features developed from a balanced training dataset presented a favorable predictive performance with AUC of 0.786 and 0.768 in the training and test sets, respectively. The predictive performance of 3D model was superior to 2D model (1 feature) both in the training (AUC 0.786 vs. 0.683, P = 0.036) and the test (AUC 0.768 vs.0.652, P = 0.441) set. The calibration curve and decision curves also indicate a better BAP1 prediction performance and clinical benefit for 3D model than that of 2D model.

CONCLUSION

The developed unenhanced CT-based 3D radiomics signature is potential as a noninvasive marker for predicting BAP1 mutation status.

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Laoveeravat P, Abhyankar PR, Brenner AR, Gabr MM, Habr FG, Atsawarungruangkit A. Artificial intelligence for pancreatic cancer detection: Recent development and future direction. Artif Intell Gastroenterol 2021;2:56-68. [DOI: 10.35712/aig.v2.i2.56] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 03/31/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open

Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging. Cancers (Basel) 2021;13:cancers13092069. [PMID: 33922981 PMCID: PMC8123300 DOI: 10.3390/cancers13092069] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/17/2021] [Accepted: 04/21/2021] [Indexed: 12/19/2022] Open

Abstract

Simple Summary

Pancreatic ductal adenocarcinoma (PDAC) remains a devastating disease. However, variations in tumor biology influence individual patient outcomes greatly. We previously showed a strong association between magnetic resonance imaging-based tumor cell estimates and patient survival. In this study we aimed to transfer this finding to more broadly applied computed tomography (CT) imaging for non-invasive risk stratification. We correlated in vivo CT imaging with histopathological analyses and could show a strong association between regional Hounsfield Units (HU) and tumor cellularity. In conclusion, our study suggests CT-based tumor cell estimates as a widely applicable way of non-invasive tumor cellularity characterization in PDAC.

Abstract

Background: PDAC remains a tumor entity with poor prognosis and a 5-year survival rate below 10%. Recent research has revealed invasive biomarkers, such as distinct molecular subtypes, predictive for therapy response and patient survival. Non-invasive prediction of individual patient outcome however remains an unresolved task. Methods: Discrete cellularity regions of PDAC resection specimen (n = 43) were analyzed by routine histopathological work up. Regional tumor cellularity and CT-derived Hounsfield Units (HU, n = 66) as well as iodine concentrations were regionally matched. One-way ANOVA and pairwise t-tests were performed to assess the relationship between different cellularity level in conventional, virtual monoenergetic 40 keV (monoE 40 keV) and iodine map reconstructions. Results: A statistically significant negative correlation between regional tumor cellularity in histopathology and CT-derived HU from corresponding image regions was identified. Radiological differentiation was best possible in monoE 40 keV CT images. However, HU values differed significantly in conventional reconstructions as well, indicating the possibility of a broad clinical application of this finding. Conclusion: In this study we establish a novel method for CT-based prediction of tumor cellularity for in-vivo tumor characterization in PDAC patients.

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Gorris M, Hoogenboom SA, Wallace MB, van Hooft JE. Artificial intelligence for the management of pancreatic diseases. Dig Endosc 2021;33:231-241. [PMID: 33065754 PMCID: PMC7898901 DOI: 10.1111/den.13875] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/29/2020] [Accepted: 10/11/2020] [Indexed: 12/16/2022]

Zha HL, Zong M, Liu XP, Pan JZ, Wang H, Gong HY, Xia TS, Liu XA, Li CY. Preoperative ultrasound-based radiomics score can improve the accuracy of the Memorial Sloan Kettering Cancer Center nomogram for predicting sentinel lymph node metastasis in breast cancer. Eur J Radiol 2020;135:109512. [PMID: 33429302 DOI: 10.1016/j.ejrad.2020.109512] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 11/25/2020] [Accepted: 12/28/2020] [Indexed: 12/18/2022]

Abstract

PURPOSE

To develop a combined nomogram by incorporating the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram and ultrasound (US)-based radiomics score (Radscore) for predicting sentinel lymph node (SLN) metastasis in invasive breast cancer.

MATERIALS AND METHODS

This retrospective study was approved by the ethics committee of our institution, and written informed consent was waived. A total of 452 patients with invasive breast cancer who received SLN Biopsy in a single center were included between January 2016 and December 2019. The patients were divided into a training set (n = 318) and a validation set (n = 134). A total of 1216 features were extracted from the regions of interest (ROIs) of the tumors on conventional ultrasound. The maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to build the Radscore. Afterward, the diagnostic performance was assessed and validated. Comparison of receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were performed to evaluate the incremental value of the combined model.

RESULTS

Obtained from 18 features, the Radscore indicated a favorable discriminatory capability in the training set with an area under the curve (AUC) of 0.834, whereas a value of 0.770 was observed in the validation set. The AUC of the combined model was 0.901 (95 % confidence interval (95 % CI): 0.865-0.938) in the training set and 0.833 (95 % CI: 0.788-0.878) in the validation set. Both of them were superior to MSKCC or imaging Radscore alone (P < 0.05). DCA demonstrated that the combined model was superior to the others in terms of clinical practicability.

CONCLUSION

Preoperative US-based Radscore can improve the accuracy of clinical MSKCC nomogram for SLN metastasis prediction in breast cancer.

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Ziegelmayer S, Kaissis G, Harder F, Jungmann F, Müller T, Makowski M, Braren R. Deep Convolutional Neural Network-Assisted Feature Extraction for Diagnostic Discrimination and Feature Visualization in Pancreatic Ductal Adenocarcinoma (PDAC) versus Autoimmune Pancreatitis (AIP). J Clin Med 2020;9:jcm9124013. [PMID: 33322559 PMCID: PMC7764649 DOI: 10.3390/jcm9124013] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 11/25/2020] [Accepted: 12/08/2020] [Indexed: 12/12/2022] Open

Abstract

The differentiation of autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma (PDAC) poses a relevant diagnostic challenge and can lead to misdiagnosis and consequently poor patient outcome. Recent studies have shown that radiomics-based models can achieve high sensitivity and specificity in predicting both entities. However, radiomic features can only capture low level representations of the input image. In contrast, convolutional neural networks (CNNs) can learn and extract more complex representations which have been used for image classification to great success. In our retrospective observational study, we performed a deep learning-based feature extraction using CT-scans of both entities and compared the predictive value against traditional radiomic features. In total, 86 patients, 44 with AIP and 42 with PDACs, were analyzed. Whole pancreas segmentation was automatically performed on CT-scans during the portal venous phase. The segmentation masks were manually checked and corrected if necessary. In total, 1411 radiomic features were extracted using PyRadiomics and 256 features (deep features) were extracted using an intermediate layer of a convolutional neural network (CNN). After feature selection and normalization, an extremely randomized trees algorithm was trained and tested using a two-fold shuffle-split cross-validation with a test sample of 20% (n = 18) to discriminate between AIP or PDAC. Feature maps were plotted and visual difference was noted. The machine learning (ML) model achieved a sensitivity, specificity, and ROC-AUC of 0.89 ± 0.11, 0.83 ± 0.06, and 0.90 ± 0.02 for the deep features and 0.72 ± 0.11, 0.78 ± 0.06, and 0.80 ± 0.01 for the radiomic features. Visualization of feature maps indicated different activation patterns for AIP and PDAC. We successfully trained a machine learning model using deep feature extraction from CT-images to differentiate between AIP and PDAC. In comparison to traditional radiomic features, deep features achieved a higher sensitivity, specificity, and ROC-AUC. Visualization of deep features could further improve the diagnostic accuracy of non-invasive differentiation of AIP and PDAC.

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Abunahel BM, Pontre B, Kumar H, Petrov MS. Pancreas image mining: a systematic review of radiomics. Eur Radiol 2020;31:3447-3467. [PMID: 33151391 DOI: 10.1007/s00330-020-07376-6] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 08/25/2020] [Accepted: 10/05/2020] [Indexed: 12/16/2022]

Abstract

OBJECTIVES

To systematically review published studies on the use of radiomics of the pancreas.

METHODS

The search was conducted in the MEDLINE database. Human studies that investigated the applications of radiomics in diseases of the pancreas were included. The radiomics quality score was calculated for each included study.

RESULTS

A total of 72 studies encompassing 8863 participants were included. Of them, 66 investigated focal pancreatic lesions (pancreatic cancer, precancerous lesions, or benign lesions); 4, pancreatitis; and 2, diabetes mellitus. The principal applications of radiomics were differential diagnosis between various types of focal pancreatic lesions (n = 19), classification of pancreatic diseases (n = 23), and prediction of prognosis or treatment response (n = 30). Second-order texture features were most useful for the purpose of differential diagnosis of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature), whereas filtered image features were most useful for the purpose of classification of diseases of the pancreas and prediction of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature). The median radiomics quality score of the included studies was 28%, with the interquartile range of 22% to 36%. The radiomics quality score was significantly correlated with the number of extracted radiomics features (r = 0.52, p < 0.001) and the study sample size (r = 0.34, p = 0.003).

CONCLUSIONS

Radiomics of the pancreas holds promise as a quantitative imaging biomarker of both focal pancreatic lesions and diffuse changes of the pancreas. The usefulness of radiomics features may vary depending on the purpose of their application. Standardisation of image acquisition protocols and image pre-processing is warranted prior to considering the use of radiomics of the pancreas in routine clinical practice.

KEY POINTS

• Methodologically sound studies on radiomics of the pancreas are characterised by a large sample size and a large number of extracted features. • Optimisation of the radiomics pipeline will increase the clinical utility of mineable pancreas imaging data. • Radiomics of the pancreas is a promising personalised medicine tool in diseases of the pancreas.

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Collisson EA. Distinctions with a Difference: RNA Subtyping and Clinical Outcome in Pancreatic Cancer. Clin Cancer Res 2020;26:4715-4716. [PMID: 32661068 DOI: 10.1158/1078-0432.ccr-20-1062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/12/2020] [Accepted: 07/08/2020] [Indexed: 11/16/2022]

Deng Y, Zhou T, Wu JL, Chen Y, Shen CY, Zeng M, Chen T, Zhang XM. The impact of molecular classification based on the transcriptome of pancreatic cancer: from bench to bedside. CHINESE JOURNAL OF ACADEMIC RADIOLOGY 2020;3:67-75. [DOI: 10.1007/s42058-020-00037-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 05/16/2020] [Accepted: 05/18/2020] [Indexed: 07/25/2024]

Kaissis GA, Jungmann F, Ziegelmayer S, Lohöfer FK, Harder FN, Schlitter AM, Muckenhuber A, Steiger K, Schirren R, Friess H, Schmid R, Weichert W, Makowski MR, Braren RF. Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic and Image-Derived Parameters. J Clin Med 2020;9:jcm9051250. [PMID: 32344944 PMCID: PMC7287805 DOI: 10.3390/jcm9051250] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 04/17/2020] [Accepted: 04/21/2020] [Indexed: 02/07/2023] Open

Abstract

Rationale: Pancreatic ductal adenocarcinoma (PDAC) remains a tumor entity of exceptionally poor prognosis, and several biomarkers are under current investigation for the prediction of patient prognosis. Many studies focus on promoting newly developed imaging biomarkers without a rigorous comparison to other established parameters. To assess the true value and leverage the potential of all efforts in this field, a multi-parametric evaluation of the available biomarkers for PDAC survival prediction is warranted. Here we present a multiparametric analysis to assess the predictive value of established parameters and the added contribution of newly developed imaging features such as biomarkers for overall PDAC patient survival. Methods: 103 patients with resectable PDAC were retrospectively enrolled. Clinical and histopathological data (age, sex, chemotherapy regimens, tumor size, lymph node status, grading and resection status), morpho-molecular and genetic data (tumor morphology, molecular subtype, tp53, kras, smad4 and p16 genetics), image-derived features and the combination of all parameters were tested for their prognostic strength based on the concordance index (CI) of multivariate Cox proportional hazards survival modelling after unsupervised machine learning preprocessing. Results: The average CIs of the out-of-sample data were: 0.63 for the clinical and histopathological features, 0.53 for the morpho-molecular and genetic features, 0.65 for the imaging features and 0.65 for the combined model including all parameters. Conclusions: Imaging-derived features represent an independent survival predictor in PDAC and enable the multiparametric, machine learning-assisted modelling of postoperative overall survival with a high performance compared to clinical and morpho-molecular/genetic parameters. We propose that future studies systematically include imaging-derived features to benchmark their additive value when evaluating biomarker-based model performance.

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Affiliation(s)

Georgios A. Kaissis Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany Department of Computing, Faculty of Engineering, Imperial College of Science, Technology and Medicine, London SW7 2BU, UK
Friederike Jungmann Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany
Sebastian Ziegelmayer Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany
Fabian K. Lohöfer Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany
Felix N. Harder Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany
Anna Melissa Schlitter Institute for Pathology, School of Medicine, Technical University of Munich, 81675 Munich, Germany German Cancer Consortium, Partner Site Technical University of Munich, D-69120 Heidelberg, Germany
Alexander Muckenhuber Institute for Pathology, School of Medicine, Technical University of Munich, 81675 Munich, Germany
Katja Steiger Institute for Pathology, School of Medicine, Technical University of Munich, 81675 Munich, Germany
Rebekka Schirren School of Medicine, Surgical Clinic and Policlinic, Technical University of Munich, 81675 Munich, Germany
Helmut Friess School of Medicine, Surgical Clinic and Policlinic, Technical University of Munich, 81675 Munich, Germany
Roland Schmid Department of Internal Medicine II, School of Medicine, Technical University of Munich, 81675 Munich, Germany
Wilko Weichert Institute for Pathology, School of Medicine, Technical University of Munich, 81675 Munich, Germany
Marcus R. Makowski Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany
Rickmer F. Braren Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, 81675 Munich, Germany Correspondence: ; Tel.: +49-89-4140-5627

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