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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: 2] [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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Affiliation(s)
- Megan Schuurmans
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
- Correspondence: (M.S.); (N.A.)
| | - Natália Alves
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
- Correspondence: (M.S.); (N.A.)
| | - Pierpaolo Vendittelli
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
| | - Henkjan Huisman
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
| | - John Hermans
- Department of Medical Imaging, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands;
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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: 11] [Impact Index Per Article: 5.5] [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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Affiliation(s)
- Boris V Janssen
- Department of Surgery, Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
- Department of Pathology, Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Severano Verhoef
- Department of Surgery, Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Nina J Wesdorp
- Department of Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | | | - Onno J de Boer
- Department of Pathology, Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Henk Marquering
- Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jaap Stoker
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Geert Kazemier
- Department of Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Marc G Besselink
- Department of Surgery, Amsterdam UMC, University of Amsterdam, Cancer Center Amsterdam, Amsterdam, The Netherlands
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Wang E, Li Y, Ming R, Wei J, Du P, Zhou P, Zong S, Xiao H. The Prognostic Value and Immune Landscapes of a m 6A/m 5C/m 1A-Related LncRNAs Signature in Head and Neck Squamous Cell Carcinoma. Front Cell Dev Biol 2021; 9:718974. [PMID: 34917609 PMCID: PMC8670092 DOI: 10.3389/fcell.2021.718974] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 11/05/2021] [Indexed: 12/17/2022] Open
Abstract
Background: N6-methyladenosine (m6A), 5-methylcytosine (m5C) and N1-methyladenosine (m1A) are the main RNA methylation modifications involved in the progression of cancer. However, it is still unclear whether m6A/m5C/m1A-related long non-coding RNAs (lncRNAs) affect the prognosis of head and neck squamous cell carcinoma (HNSCC). Methods: We summarized 52 m6A/m5C/m1A-related genes, downloaded 44 normal samples and 501 HNSCC tumor samples with RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) database, and then searched for m6A/m5C/m1A-related genes co-expressed lncRNAs. We adopt the least absolute shrinkage and selection operator (LASSO) Cox regression to obtain m6A/m5C/m1A-related lncRNAs to construct a prognostic signature of HNSCC. Results: This prognostic signature is based on six m6A/m5C/m1A-related lncRNAs (AL035587.1, AC009121.3, AF131215.5, FMR1-IT1, AC106820.5, PTOV1-AS2). It was found that the high-risk subgroup has worse overall survival (OS) than the low-risk subgroup. Moreover, the results showed that most immune checkpoint genes were significantly different between the two risk groups (p < 0.05). Immunity microenvironment analysis showed that the contents of NK cell resting, macrophages M2, and neutrophils in samples of low-risk group were significantly lower than those of high-risk group (p < 0.05), while the contents of B cells navie, plasma cells, and T cells regulatory (Tregs) were on the contrary (p < 0.05). In addition, patients with high tumor mutational burden (TMB) had the worse overall survival than those with low tumor mutational burden. Conclusion: Our study elucidated how m6A/m5C/m1A-related lncRNAs are related to the prognosis, immune microenvironment, and TMB of HNSCC. In the future, these m6A/m5C/m1A-related lncRNAs may become a new choice for immunotherapy of HNSCC.
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Affiliation(s)
- Enhao Wang
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Li
- Department of Stomatology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Ruijie Ming
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiahui Wei
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Peiyu Du
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Peng Zhou
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shimin Zong
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongjun Xiao
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Chen W, Zhang T, Xu L, Zhao L, Liu H, Gu LR, Wang DZ, Zhang M. Radiomics Analysis of Contrast-Enhanced CT for Hepatocellular Carcinoma Grading. Front Oncol 2021; 11:660509. [PMID: 34150628 PMCID: PMC8212783 DOI: 10.3389/fonc.2021.660509] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 05/11/2021] [Indexed: 01/03/2023] Open
Abstract
Objectives To investigate the value of contrast-enhanced computer tomography (CT)-based on radiomics in discriminating high-grade and low-grade hepatocellular carcinoma (HCC) before surgery. Methods The retrospective study including 161 consecutive subjects with HCC which was approved by the institutional review board, and the patients were divided into a training group (n = 112) and test group (n = 49) from January 2013 to January 2018. The least absolute shrinkage and selection operator (LASSO) was used to select the most valuable features to build a support vector machine (SVM) model. The performance of the predictive model was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity. Results The SVM model showed an acceptable ability to differentiate high-grade from low-grade HCC, with an AUC of 0.904 in the training dataset and 0.937 in the test dataset, accuracy (92.2% versus 95.7%), sensitivity(82.5% versus 88.0%), and specificity (92.7% versus 95.8%), respectively. Conclusion The machine learning-based radiomics reflects a better evaluating performance in differentiating HCC between low-grade and high-grade, which may contribute to personalized treatment.
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Affiliation(s)
- Wen Chen
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Tao Zhang
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Lin Xu
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Liang Zhao
- Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | | | - Liang Rui Gu
- Department of Radiology, Shanghai Sixth People's Hospital, Shanghai, China
| | - Dai Zhong Wang
- Department of Pathology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Ming Zhang
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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Karmazanovsky G, Schima W, Amosova E. Comments to Letter regarding "Nonhypervascular pancreatic neuroendocrine tumors: Spectrum of MDCT imaging findings and differentiation from pancreatic ductal adenocarcinoma". Eur J Radiol 2020; 132:109328. [PMID: 33068838 DOI: 10.1016/j.ejrad.2020.109328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 09/29/2020] [Indexed: 11/19/2022]
Affiliation(s)
| | - Wolfgang Schima
- Department of Diagnostic and Interventional Radiology, Goettlicher Heiland Krankenhaus, Barmherzige Schwestern Krankenhaus, Sankt Josef Krankenhaus, Vinzenzgruppe, Vienna, Austria
| | - Elena Amosova
- Department of Radiology, Ilyinskaya Hospital, Moscow, Russia.
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