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Klimov S, Xue Y, Gertych A, Graham RP, Jiang Y, Bhattarai S, Pandol SJ, Rakha EA, Reid MD, Aneja R. Predicting Metastasis Risk in Pancreatic Neuroendocrine Tumors Using Deep Learning Image Analysis. Front Oncol 2021; 10:593211. [PMID: 33718106 PMCID: PMC7946991 DOI: 10.3389/fonc.2020.593211] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 12/18/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The prognosis of patients with pancreatic neuroendocrine tumors (PanNET), the second most common type of pancreatic cancer, varies significantly, and up to 15% of patients develop metastasis. Although certain morphological characteristics of PanNETs have been associated with patient outcome, there are no available morphology-based prognostic markers. Given that current clinical histopathology markers are unable to identify high-risk PanNET patients, the development of accurate prognostic biomarkers is needed. Here, we describe a novel machine learning, multiclassification pipeline to predict the risk of metastasis using morphological information from whole tissue slides. METHODS Digital images from surgically resected tissues from 89 PanNET patients were used. Pathologist-annotated regions were extracted to train a convolutional neural network (CNN) to identify tiles consisting of PanNET, stroma, normal pancreas parenchyma, and fat. Computationally annotated cancer or stroma tiles and patient metastasis status were used to train CNN to calculate a region based metastatic risk score. Aggregation of the metastatic probability scores across the slide was performed to predict the risk of metastasis. RESULTS The ability of CNN to discriminate different tissues was high (per-tile accuracy >95%; whole slide cancer regions Jaccard index = 79%). Cancer and stromal tiles with high evaluated probability provided F1 scores of 0.82 and 0.69, respectively, when we compared tissues from patients who developed metastasis and those who did not. The final model identified low-risk (n = 76) and high-risk (n = 13) patients, as well as predicted metastasis-free survival (hazard ratio: 4.71) after adjusting for common clinicopathological variables, especially in grade I/II patients. CONCLUSION Using slides from surgically resected PanNETs, our novel, multiclassification, deep learning pipeline was able to predict the risk of metastasis in PanNET patients. Our results suggest the presence of prognostic morphological patterns in PanNET tissues, and that these patterns may help guide clinical decision making.
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Affiliation(s)
- Sergey Klimov
- Department of Biology, Georgia State University, Atlanta, GA, United States
- Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, United States
| | - Yue Xue
- Department of Pathology, Northwestern University, Chicago, IL, United States
| | - Arkadiusz Gertych
- Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
- Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland
| | - Rondell P Graham
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
| | - Yi Jiang
- Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, United States
| | - Shristi Bhattarai
- Department of Biology, Georgia State University, Atlanta, GA, United States
| | - Stephen J Pandol
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Emad A Rakha
- Department of Cellular Pathology, University of Nottingham, Nottingham, United Kingdom
| | - Michelle D Reid
- Department of Pathology, Emory University, Atlanta, GA, United States
| | - Ritu Aneja
- Department of Biology, Georgia State University, Atlanta, GA, United States
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Alvarez-Jimenez C, Barrera C, Munera N, Viswanath SE, Romero E. Differentiating Cancerous and Non-cancerous Prostate Tissue Using Multi-scale Texture Analysis on MRI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:2695-2698. [PMID: 31946451 DOI: 10.1109/embc.2019.8856927] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Prostate cancer (PCa) diagnosis is established by pathological examination via biopsies, which are associated with significant complications and false negatives. Using MRIs to identify locations with high probability of containing cancer could instead be used to guide the biopsy procedure. The present investigation aims to identify target regions within different prostatic zones on MRI with high probability of being cancerous for assisting in the decision of where and how to perform biopsy. Our approach involved extracting multi-scale texture features for capturing local patterns to distinguish cancer and healthy tissue in different T2W-MRI prostate zones. Three different classification models were fed by the proposed strategy, namely support vector machine (SVM), Adaboost, and Random Forest. SVM with a linear kernel showed the best classification performance, with AUC scores of 0.91 in the anterior fibromuscular stroma area, 0.85 in the peripheral zone, and 0.87 when classification is performed independently of the prostate zone. The proposed method demonstrated that discriminant multi-scale texture features can accurately identify regions of prostate cancer in a zone-specific fashion, via MRI.
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Carleton NM, Lee G, Madabhushi A, Veltri RW. Advances in the computational and molecular understanding of the prostate cancer cell nucleus. J Cell Biochem 2018; 119:7127-7142. [PMID: 29923622 PMCID: PMC6150831 DOI: 10.1002/jcb.27156] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 05/18/2018] [Indexed: 12/17/2022]
Abstract
Nuclear alterations are a hallmark of many types of cancers, including prostate cancer (PCa). Recent evidence shows that subvisual changes, ones that may not be visually perceptible to a pathologist, to the nucleus and its ultrastructural components can precede visual histopathological recognition of cancer. Alterations to nuclear features, such as nuclear size and shape, texture, and spatial architecture, reflect the complex molecular-level changes that occur during oncogenesis. Quantitative nuclear morphometry, a field that uses computational approaches to identify and quantify malignancy-induced nuclear changes, can enable a detailed and objective analysis of the PCa cell nucleus. Recent advances in machine learning-based approaches can now automatically mine data related to these changes to aid in the diagnosis, decision making, and prediction of PCa prognoses. In this review, we use PCa as a case study to connect the molecular-level mechanisms that underlie these nuclear changes to the machine learning computational approaches, bridging the gap between the clinical and computational understanding of PCa. First, we will discuss recent developments to our understanding of the molecular events that drive nuclear alterations in the context of PCa: the role of the nuclear matrix and lamina in size and shape changes, the role of 3-dimensional chromatin organization and epigenetic modifications in textural changes, and the role of the tumor microenvironment in altering nuclear spatial topology. We will then discuss the advances in the applications of machine learning algorithms to automatically segment nuclei in prostate histopathological images, extract nuclear features to aid in diagnostic decision making, and predict potential outcomes, such as biochemical recurrence and survival. Finally, we will discuss the challenges and opportunities associated with translation of the quantitative nuclear morphometry methodology into the clinical space. Ultimately, accurate identification and quantification of nuclear alterations can contribute to the field of nucleomics and has applications for computationally driven precision oncologic patient care.
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Affiliation(s)
- Neil M. Carleton
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
| | - George Lee
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106
| | - Robert W. Veltri
- The James Buchanan Brady Urological Institute, Department of Urology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287
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