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Metzcar J, Jutzeler CR, Macklin P, Köhn-Luque A, Brüningk SC. A review of mechanistic learning in mathematical oncology. Front Immunol 2024; 15:1363144. [PMID: 38533513 PMCID: PMC10963621 DOI: 10.3389/fimmu.2024.1363144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 02/20/2024] [Indexed: 03/28/2024] Open
Abstract
Mechanistic learning refers to the synergistic combination of mechanistic mathematical modeling and data-driven machine or deep learning. This emerging field finds increasing applications in (mathematical) oncology. This review aims to capture the current state of the field and provides a perspective on how mechanistic learning may progress in the oncology domain. We highlight the synergistic potential of mechanistic learning and point out similarities and differences between purely data-driven and mechanistic approaches concerning model complexity, data requirements, outputs generated, and interpretability of the algorithms and their results. Four categories of mechanistic learning (sequential, parallel, extrinsic, intrinsic) of mechanistic learning are presented with specific examples. We discuss a range of techniques including physics-informed neural networks, surrogate model learning, and digital twins. Example applications address complex problems predominantly from the domain of oncology research such as longitudinal tumor response predictions or time-to-event modeling. As the field of mechanistic learning advances, we aim for this review and proposed categorization framework to foster additional collaboration between the data- and knowledge-driven modeling fields. Further collaboration will help address difficult issues in oncology such as limited data availability, requirements of model transparency, and complex input data which are embraced in a mechanistic learning framework.
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Affiliation(s)
- John Metzcar
- Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Bloomington, IN, United States
- Informatics, Luddy School of Informatics, Computing, and Engineering, Bloomington, IN, United States
| | - Catherine R. Jutzeler
- Department of Health Sciences and Technology (D-HEST), Eidgenössische Technische Hochschule Zürich (ETH), Zürich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Paul Macklin
- Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Bloomington, IN, United States
| | - Alvaro Köhn-Luque
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital, Oslo, Norway
| | - Sarah C. Brüningk
- Department of Health Sciences and Technology (D-HEST), Eidgenössische Technische Hochschule Zürich (ETH), Zürich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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2
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Bae K, Jeon YS, Hwangbo Y, Yoo CW, Han N, Feng M. Data-Efficient Computational Pathology Platform for Faster and Cheaper Breast Cancer Subtype Identifications: Development of a Deep Learning Model. JMIR Cancer 2023; 9:e45547. [PMID: 37669090 PMCID: PMC10509735 DOI: 10.2196/45547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 07/07/2023] [Accepted: 07/21/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND Breast cancer subtyping is a crucial step in determining therapeutic options, but the molecular examination based on immunohistochemical staining is expensive and time-consuming. Deep learning opens up the possibility to predict the subtypes based on the morphological information from hematoxylin and eosin staining, a much cheaper and faster alternative. However, training the predictive model conventionally requires a large number of histology images, which is challenging to collect by a single institute. OBJECTIVE We aimed to develop a data-efficient computational pathology platform, 3DHistoNet, which is capable of learning from z-stacked histology images to accurately predict breast cancer subtypes with a small sample size. METHODS We retrospectively examined 401 cases of patients with primary breast carcinoma diagnosed between 2018 and 2020 at the Department of Pathology, National Cancer Center, South Korea. Pathology slides of the patients with breast carcinoma were prepared according to the standard protocols. Age, gender, histologic grade, hormone receptor (estrogen receptor [ER], progesterone receptor [PR], and androgen receptor [AR]) status, erb-B2 receptor tyrosine kinase 2 (HER2) status, and Ki-67 index were evaluated by reviewing medical charts and pathological records. RESULTS The area under the receiver operating characteristic curve and decision curve were analyzed to evaluate the performance of our 3DHistoNet platform for predicting the ER, PR, AR, HER2, and Ki67 subtype biomarkers with 5-fold cross-validation. We demonstrated that 3DHistoNet can predict all clinically important biomarkers (ER, PR, AR, HER2, and Ki67) with performance exceeding the conventional multiple instance learning models by a considerable margin (area under the receiver operating characteristic curve: 0.75-0.91 vs 0.67-0.8). We further showed that our z-stack histology scanning method can make up for insufficient training data sets without any additional cost incurred. Finally, 3DHistoNet offered an additional capability to generate attention maps that reveal correlations between Ki67 and histomorphological features, which renders the hematoxylin and eosin image in higher fidelity to the pathologist. CONCLUSIONS Our stand-alone, data-efficient pathology platform that can both generate z-stacked images and predict key biomarkers is an appealing tool for breast cancer diagnosis. Its development would encourage morphology-based diagnosis, which is faster, cheaper, and less error-prone compared to the protein quantification method based on immunohistochemical staining.
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Affiliation(s)
- Kideog Bae
- Healthcare AI Team, Healthcare Platform Center, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea
| | - Young Seok Jeon
- Institute of Data Science, National University of Singapore, Singapore, Singapore
| | - Yul Hwangbo
- Healthcare AI Team, Healthcare Platform Center, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea
- Department of Cancer AI & Digital Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea
| | - Chong Woo Yoo
- Department of Pathology, National Cancer Center Hospital, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea
| | - Nayoung Han
- Healthcare AI Team, Healthcare Platform Center, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea
- Department of Cancer AI & Digital Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea
- Department of Pathology, National Cancer Center Hospital, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea
| | - Mengling Feng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
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Hölscher DL, Bouteldja N, Joodaki M, Russo ML, Lan YC, Sadr AV, Cheng M, Tesar V, Stillfried SV, Klinkhammer BM, Barratt J, Floege J, Roberts ISD, Coppo R, Costa IG, Bülow RD, Boor P. Next-Generation Morphometry for pathomics-data mining in histopathology. Nat Commun 2023; 14:470. [PMID: 36709324 PMCID: PMC9884209 DOI: 10.1038/s41467-023-36173-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 01/16/2023] [Indexed: 01/29/2023] Open
Abstract
Pathology diagnostics relies on the assessment of morphology by trained experts, which remains subjective and qualitative. Here we developed a framework for large-scale histomorphometry (FLASH) performing deep learning-based semantic segmentation and subsequent large-scale extraction of interpretable, quantitative, morphometric features in non-tumour kidney histology. We use two internal and three external, multi-centre cohorts to analyse over 1000 kidney biopsies and nephrectomies. By associating morphometric features with clinical parameters, we confirm previous concepts and reveal unexpected relations. We show that the extracted features are independent predictors of long-term clinical outcomes in IgA-nephropathy. We introduce single-structure morphometric analysis by applying techniques from single-cell transcriptomics, identifying distinct glomerular populations and morphometric phenotypes along a trajectory of disease progression. Our study provides a concept for Next-generation Morphometry (NGM), enabling comprehensive quantitative pathology data mining, i.e., pathomics.
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Affiliation(s)
- David L Hölscher
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
| | - Nassim Bouteldja
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
| | - Mehdi Joodaki
- Institute for Computational Genomics, RWTH Aachen University Clinic, Aachen, Germany
| | | | - Yu-Chia Lan
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
| | | | - Mingbo Cheng
- Institute for Computational Genomics, RWTH Aachen University Clinic, Aachen, Germany
| | - Vladimir Tesar
- Department of Nephrology, 1st Faculty of Medicine and General University Hospital, Charles University, Prague, Czech Republic
| | | | | | - Jonathan Barratt
- John Walls Renal Unit, University Hospital of Leicester National Health Service Trust, Leicester, United Kingdom
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
| | - Jürgen Floege
- Department of Nephrology and Immunology, RWTH Aachen University Clinic, Aachen, Germany
| | - Ian S D Roberts
- Department of Cellular Pathology, Oxford University Hospitals National Health Services Foundation Trust, Oxford, United Kingdom
| | - Rosanna Coppo
- Fondazione Ricerca Molinette, Torino, Italy
- Regina Margherita Children's University Hospital, Torino, Italy
| | - Ivan G Costa
- Institute for Computational Genomics, RWTH Aachen University Clinic, Aachen, Germany
| | - Roman D Bülow
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Clinic, Aachen, Germany.
- Department of Nephrology and Immunology, RWTH Aachen University Clinic, Aachen, Germany.
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Nasser M, Yusof UK. Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction. Diagnostics (Basel) 2023; 13:diagnostics13010161. [PMID: 36611453 PMCID: PMC9818155 DOI: 10.3390/diagnostics13010161] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 12/19/2022] [Accepted: 12/19/2022] [Indexed: 01/06/2023] Open
Abstract
Breast cancer is one of the precarious conditions that affect women, and a substantive cure has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep learning techniques have been used effectively in breast cancer detection, facilitating early diagnosis and therefore increasing the chances of patients' survival. Compared to classical machine learning techniques, deep learning requires less human intervention for similar feature extraction. This study presents a systematic literature review on the deep learning-based methods for breast cancer detection that can guide practitioners and researchers in understanding the challenges and new trends in the field. Particularly, different deep learning-based methods for breast cancer detection are investigated, focusing on the genomics and histopathological imaging data. The study specifically adopts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), which offer a detailed analysis and synthesis of the published articles. Several studies were searched and gathered, and after the eligibility screening and quality evaluation, 98 articles were identified. The results of the review indicated that the Convolutional Neural Network (CNN) is the most accurate and extensively used model for breast cancer detection, and the accuracy metrics are the most popular method used for performance evaluation. Moreover, datasets utilized for breast cancer detection and the evaluation metrics are also studied. Finally, the challenges and future research direction in breast cancer detection based on deep learning models are also investigated to help researchers and practitioners acquire in-depth knowledge of and insight into the area.
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Iqbal MS, Ahmad W, Alizadehsani R, Hussain S, Rehman R. Breast Cancer Dataset, Classification and Detection Using Deep Learning. Healthcare (Basel) 2022; 10. [PMID: 36553919 DOI: 10.3390/healthcare10122395] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 11/24/2022] [Accepted: 11/25/2022] [Indexed: 12/05/2022] Open
Abstract
Incorporating scientific research into clinical practice via clinical informatics, which includes genomics, proteomics, bioinformatics, and biostatistics, improves patients' treatment. Computational pathology is a growing subspecialty with the potential to integrate whole slide images, multi-omics data, and health informatics. Pathology and laboratory medicine are critical to diagnosing cancer. This work will review existing computational and digital pathology methods for breast cancer diagnosis with a special focus on deep learning. The paper starts by reviewing public datasets related to breast cancer diagnosis. Additionally, existing deep learning methods for breast cancer diagnosis are reviewed. The publicly available code repositories are introduced as well. The paper is closed by highlighting challenges and future works for deep learning-based diagnosis.
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6
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Qiao Y, Zhao L, Luo C, Luo Y, Wu Y, Li S, Bu D, Zhao Y. Multi-modality artificial intelligence in digital pathology. Brief Bioinform 2022; 23:6702380. [PMID: 36124675 PMCID: PMC9677480 DOI: 10.1093/bib/bbac367] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/27/2022] [Accepted: 08/05/2022] [Indexed: 12/14/2022] Open
Abstract
In common medical procedures, the time-consuming and expensive nature of obtaining test results plagues doctors and patients. Digital pathology research allows using computational technologies to manage data, presenting an opportunity to improve the efficiency of diagnosis and treatment. Artificial intelligence (AI) has a great advantage in the data analytics phase. Extensive research has shown that AI algorithms can produce more up-to-date and standardized conclusions for whole slide images. In conjunction with the development of high-throughput sequencing technologies, algorithms can integrate and analyze data from multiple modalities to explore the correspondence between morphological features and gene expression. This review investigates using the most popular image data, hematoxylin-eosin stained tissue slide images, to find a strategic solution for the imbalance of healthcare resources. The article focuses on the role that the development of deep learning technology has in assisting doctors' work and discusses the opportunities and challenges of AI.
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Affiliation(s)
- Yixuan Qiao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lianhe Zhao
- Corresponding authors: Yi Zhao, Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences; Shandong First Medical University & Shandong Academy of Medical Sciences. Tel.: +86 10 6260 0822; Fax: +86 10 6260 1356; E-mail: ; Lianhe Zhao, Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences. Tel.: +86 18513983324; E-mail:
| | - Chunlong Luo
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yufan Luo
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Wu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Shengtong Li
- Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Dechao Bu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Yi Zhao
- Corresponding authors: Yi Zhao, Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences; Shandong First Medical University & Shandong Academy of Medical Sciences. Tel.: +86 10 6260 0822; Fax: +86 10 6260 1356; E-mail: ; Lianhe Zhao, Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences. Tel.: +86 18513983324; E-mail:
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Rehman KU, Li J, Pei Y, Yasin A. A review on machine learning techniques for the assessment of image grading in breast mammogram. INT J MACH LEARN CYB. [DOI: 10.1007/s13042-022-01546-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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8
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Nassif AB, Talib MA, Nasir Q, Afadar Y, Elgendy O. Breast cancer detection using artificial intelligence techniques: A systematic literature review. Artif Intell Med 2022; 127:102276. [DOI: 10.1016/j.artmed.2022.102276] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 10/18/2021] [Accepted: 03/04/2022] [Indexed: 02/07/2023]
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9
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Anand D, Yashashwi K, Kumar N, Rane S, Gann PH, Sethi A. Weakly supervised learning on unannotated hematoxylin and eosin stained slides predicts BRAF mutation in thyroid cancer with high accuracy. J Pathol 2021; 255:232-242. [PMID: 34346511 DOI: 10.1002/path.5773] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 06/18/2021] [Accepted: 07/28/2021] [Indexed: 11/08/2022]
Abstract
Deep neural networks (DNNs) that predict mutational status from H&E slides of cancers can enable inexpensive and timely precision oncology. Although expert knowledge is reliable for annotating regions informative of malignancy and other known histological patterns (strong supervision), it is unreliable for identifying regions informative of mutational status. This poses a serious impediment to obtaining higher prognostic accuracy and discovering new knowledge of pathobiology. We used a weakly supervised learning technique to train a DNN to predict BRAF V600E mutational status, determined using DNA testing, in H&E stained images of thyroid cancer tissue without regional annotations. Our discovery cohort was a tissue microarray of only 85 patients from a single hospital. Yet, on a large independent external cohort of 444 patients from other hospitals, the trained model gave an AUC = 0.98 (95% CI: 0.97-1.00), which is much higher than the previously reported results for detecting any mutation using H&E by DNNs trained using strong supervision. We also developed a visualization technique that can automatically highlight regions the DNN found most informative for predicting mutational status. Our visualization is spatially granular and highly specific in highlighting regions with strong negative and positive regions and move us towards explainable artificial intelligence. Using t-tests, we confirmed that the proportions of follicular or papillary histology and oncocytic cytology, as noted for each patient by a pathologist who was blinded to the mutational status, were significantly different between mutated and wildtype patients. However, based solely on these features noted by the pathologist, a logistic regression classifier gave an average AUC = 0.78 in 5-fold CV, which is much lower than that obtained using the DNN. These results highlight the potential of weakly supervised learning for training DNN models for problems where the informative visual patterns and their locations are not known a priori. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Deepak Anand
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, MH, India
| | - Kumar Yashashwi
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, MH, India
| | - Neeraj Kumar
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada.,Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada
| | - Swapnil Rane
- Department of Pathology, Tata Memorial Centre-ACTREC, HBNI, Mumbai, MH, India
| | - Peter H Gann
- Department of Pathology, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Amit Sethi
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, MH, India.,Department of Pathology, University of Illinois at Chicago, Chicago, Illinois, USA
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Rehman KU, Li J, Pei Y, Yasin A, Ali S, Mahmood T. Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network. Sensors (Basel) 2021; 21:s21144854. [PMID: 34300597 PMCID: PMC8309805 DOI: 10.3390/s21144854] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/12/2021] [Accepted: 07/12/2021] [Indexed: 01/21/2023]
Abstract
Microcalcification clusters in mammograms are one of the major signs of breast cancer. However, the detection of microcalcifications from mammograms is a challenging task for radiologists due to their tiny size and scattered location inside a denser breast composition. Automatic CAD systems need to predict breast cancer at the early stages to support clinical work. The intercluster gap, noise between individual MCs, and individual object’s location can affect the classification performance, which may reduce the true-positive rate. In this study, we propose a computer-vision-based FC-DSCNN CAD system for the detection of microcalcification clusters from mammograms and classification into malignant and benign classes. The computer vision method automatically controls the noise and background color contrast and directly detects the MC object from mammograms, which increases the classification performance of the neural network. The breast cancer classification framework has four steps: image preprocessing and augmentation, RGB to grayscale channel transformation, microcalcification region segmentation, and MC ROI classification using FC-DSCNN to predict malignant and benign cases. The proposed method was evaluated on 3568 DDSM and 2885 PINUM mammogram images with automatic feature extraction, obtaining a score of 0.97 with a 2.35 and 0.99 true-positive ratio with 2.45 false positives per image, respectively. Experimental results demonstrated that the performance of the proposed method remains higher than the traditional and previous approaches.
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Affiliation(s)
- Khalil ur Rehman
- The School of Software Engineering, Beijing University of Technology, Beijing 100024, China; (K.u.R.); (J.L.); (A.Y.); (S.A.); (T.M.)
| | - Jianqiang Li
- The School of Software Engineering, Beijing University of Technology, Beijing 100024, China; (K.u.R.); (J.L.); (A.Y.); (S.A.); (T.M.)
- Beijing Engineering Research Center for IoT Software and Systems, Beijing 100124, China
| | - Yan Pei
- Computer Science Division, University of Aizu, Aizuwakamatsu, Fukushima 965-8580, Japan
- Correspondence:
| | - Anaa Yasin
- The School of Software Engineering, Beijing University of Technology, Beijing 100024, China; (K.u.R.); (J.L.); (A.Y.); (S.A.); (T.M.)
| | - Saqib Ali
- The School of Software Engineering, Beijing University of Technology, Beijing 100024, China; (K.u.R.); (J.L.); (A.Y.); (S.A.); (T.M.)
| | - Tariq Mahmood
- The School of Software Engineering, Beijing University of Technology, Beijing 100024, China; (K.u.R.); (J.L.); (A.Y.); (S.A.); (T.M.)
- Division of Science and Technology, University of Education, Lahore 54000, Pakistan
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Vellal AD, Sirinukunwattan K, Kensler KH, Baker GM, Stancu AL, Pyle ME, Collins LC, Schnitt SJ, Connolly JL, Veta M, Eliassen AH, Tamimi RM, Heng YJ. Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer. JNCI Cancer Spectr 2021; 5:pkaa119. [PMID: 33644680 PMCID: PMC7898083 DOI: 10.1093/jncics/pkaa119] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 11/04/2020] [Accepted: 12/18/2020] [Indexed: 12/16/2022] Open
Abstract
Background New biomarkers of risk may improve breast cancer (BC) risk prediction. We developed a computational pathology method to segment benign breast disease (BBD) whole slide images into epithelium, fibrous stroma, and fat. We applied our method to the BBD BC nested case-control study within the Nurses' Health Studies to assess whether computer-derived tissue composition or a morphometric signature was associated with subsequent risk of BC. Methods Tissue segmentation and nuclei detection deep-learning networks were established and applied to 3795 whole slide images from 293 cases who developed BC and 1132 controls who did not. Percentages of each tissue region were calculated, and 615 morphometric features were extracted. Elastic net regression was used to create a BC morphometric signature. Associations between BC risk factors and age-adjusted tissue composition among controls were assessed using analysis of covariance. Unconditional logistic regression, adjusting for the matching factors, BBD histological subtypes, parity, menopausal status, and body mass index evaluated the relationship between tissue composition and BC risk. All statistical tests were 2-sided. Results Among controls, direction of associations between BBD subtypes, parity, and number of births with breast composition varied by tissue region; select regions were associated with childhood body size, body mass index, age of menarche, and menopausal status (all P < .05). A higher proportion of epithelial tissue was associated with increased BC risk (odds ratio = 1.39, 95% confidence interval = 0.91 to 2.14, for highest vs lowest quartiles, P trend = .047). No morphometric signature was associated with BC. Conclusions The amount of epithelial tissue may be incorporated into risk assessment models to improve BC risk prediction.
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Affiliation(s)
- Adithya D Vellal
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Korsuk Sirinukunwattan
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Engineering Science, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, UK
- Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford University NHS Foundation Trust, Oxford, UK
| | - Kevin H Kensler
- Division of Population Sciences, Dana Farber Cancer Institute, Boston, MA, USA
| | - Gabrielle M Baker
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Andreea L Stancu
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michael E Pyle
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Laura C Collins
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Stuart J Schnitt
- Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Dana-Farber Cancer Institute-Brigham and Women's Hospital, Boston, MA, USA
| | - James L Connolly
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Mitko Veta
- Medical Image Analysis Group, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Yujing J Heng
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
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12
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Naik N, Madani A, Esteva A, Keskar NS, Press MF, Ruderman D, Agus DB, Socher R. Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains. Nat Commun 2020; 11:5727. [PMID: 33199723 PMCID: PMC7670411 DOI: 10.1038/s41467-020-19334-3] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 09/25/2020] [Indexed: 12/09/2022] Open
Abstract
For newly diagnosed breast cancer, estrogen receptor status (ERS) is a key molecular marker used for prognosis and treatment decisions. During clinical management, ERS is determined by pathologists from immunohistochemistry (IHC) staining of biopsied tissue for the targeted receptor, which highlights the presence of cellular surface antigens. This is an expensive, time-consuming process which introduces discordance in results due to variability in IHC preparation and pathologist subjectivity. In contrast, hematoxylin and eosin (H&E) staining—which highlights cellular morphology—is quick, less expensive, and less variable in preparation. Here we show that machine learning can determine molecular marker status, as assessed by hormone receptors, directly from cellular morphology. We develop a multiple instance learning-based deep neural network that determines ERS from H&E-stained whole slide images (WSI). Our algorithm—trained strictly with WSI-level annotations—is accurate on a varied, multi-country dataset of 3,474 patients, achieving an area under the curve (AUC) of 0.92 for sensitivity and specificity. Our approach has the potential to augment clinicians’ capabilities in cancer prognosis and theragnosis by harnessing biological signals imperceptible to the human eye. Determination of estrogen receptor status (ERS) in breast cancer tissue requires immunohistochemistry, which is sensitive to the vagaries of sample processing and the subjectivity of pathologists. Here the authors present a deep learning model that determines ERS from H&E stained tissue, which could improve oncology decisions in under-resourced settings.
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Affiliation(s)
- Nikhil Naik
- Salesforce Research, 575 High St, Palo Alto, CA, 94301, USA.
| | - Ali Madani
- Salesforce Research, 575 High St, Palo Alto, CA, 94301, USA
| | - Andre Esteva
- Salesforce Research, 575 High St, Palo Alto, CA, 94301, USA
| | | | - Michael F Press
- Department of Pathology, Keck School of Medicine, University of Southern California, 2011 Zonal Ave, Los Angeles, CA, 90033, USA
| | - Daniel Ruderman
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, 12414 Exposition Blvd, Los Angeles, CA, 90064, USA
| | - David B Agus
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, 12414 Exposition Blvd, Los Angeles, CA, 90064, USA
| | - Richard Socher
- Salesforce Research, 575 High St, Palo Alto, CA, 94301, USA
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13
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Schmauch B, Romagnoni A, Pronier E, Saillard C, Maillé P, Calderaro J, Kamoun A, Sefta M, Toldo S, Zaslavskiy M, Clozel T, Moarii M, Courtiol P, Wainrib G. A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nat Commun 2020; 11:3877. [PMID: 32747659 PMCID: PMC7400514 DOI: 10.1038/s41467-020-17678-4] [Citation(s) in RCA: 178] [Impact Index Per Article: 44.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 07/13/2020] [Indexed: 02/06/2023] Open
Abstract
Deep learning methods for digital pathology analysis are an effective way to address multiple clinical questions, from diagnosis to prediction of treatment outcomes. These methods have also been used to predict gene mutations from pathology images, but no comprehensive evaluation of their potential for extracting molecular features from histology slides has yet been performed. We show that HE2RNA, a model based on the integration of multiple data modes, can be trained to systematically predict RNA-Seq profiles from whole-slide images alone, without expert annotation. Through its interpretable design, HE2RNA provides virtual spatialization of gene expression, as validated by CD3- and CD20-staining on an independent dataset. The transcriptomic representation learned by HE2RNA can also be transferred on other datasets, even of small size, to increase prediction performance for specific molecular phenotypes. We illustrate the use of this approach in clinical diagnosis purposes such as the identification of tumors with microsatellite instability. RNA-sequencing of tumour tissue can provide important diagnostic and prognostic information but this is costly and not routinely performed in all clinical settings. Here, the authors show that whole slide histology slides—part of routine care—can be used to predict RNA-sequencing data and thus reduce the need for additional analyses.
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Affiliation(s)
| | | | | | | | - Pascale Maillé
- INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers", Créteil, France.,APHP, Department of Pathology, Hôpital Henri Mondor, Université Paris-Est, Créteil, France
| | - Julien Calderaro
- INSERM U955, Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers", Créteil, France.,APHP, Department of Pathology, Hôpital Henri Mondor, Université Paris-Est, Créteil, France
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14
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Anand D, Kurian NC, Dhage S, Kumar N, Rane S, Gann PH, Sethi A. Deep Learning to Estimate Human Epidermal Growth Factor Receptor 2 Status from Hematoxylin and Eosin-Stained Breast Tissue Images. J Pathol Inform 2020; 11:19. [PMID: 33033656 PMCID: PMC7513777 DOI: 10.4103/jpi.jpi_10_20] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 03/23/2020] [Accepted: 05/17/2020] [Indexed: 01/15/2023] Open
Abstract
Context: Several therapeutically important mutations in cancers are economically detected using immunohistochemistry (IHC), which highlights the overexpression of specific antigens associated with the mutation. However, IHC panels can be imprecise and relatively expensive in low-income settings. On the other hand, although hematoxylin and eosin (H&E) staining used to visualize the general tissue morphology is a routine and low cost, it does not highlight any specific antigen or mutation. Aims: Using the human epidermal growth factor receptor 2 (HER2) mutation in breast cancer as an example, we strengthen the case for cost-effective detection and screening of overexpression of HER2 protein in H&E-stained tissue. Settings and Design: We use computational methods that reliably detect subtle morphological changes associated with the over-expression of mutation-specific proteins directly from H&E images. Subjects and Methods: We trained a classification pipeline to determine HER2 overexpression status of H&E stained whole slide images. Our training dataset was derived from a single hospital containing 26 (11 HER2+ and 15 HER2–) cases. We tested the classification pipeline on 26 (8 HER2+ and 18 HER2–) held-out cases from the same hospital and 45 independent cases (23 HER2+ and 22 HER2–) from the TCGA-BRCA cohort. The pipeline was composed of a stain separation module and three deep neural network modules in tandem for robustness and interpretability. Statistical Analysis Used: We evaluate our trained model through area under the curve (AUC)-receiver operating characteristic. Results: Our pipeline achieved an AUC of 0.82 (confidence interval [CI]: 0.65–0.98) on held-out cases and an AUC of 0.76 (CI: 0.61–0.89) on the independent dataset from TCGA. We also demonstrate the region-level correspondence of HER2 overexpression between a patient's IHC and H&E serial sections. Conclusions: Our work strengthens the case for automatically quantifying the overexpression of mutation-specific proteins in H&E-stained digital pathology, and it highlights the importance of multi-stage machine learning pipelines for added robustness and interpretability.
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Affiliation(s)
- Deepak Anand
- Department of Electrical Engineering, IIT Bombay, Mumbai, Maharashtra, India
| | | | - Shubham Dhage
- Department of Electrical Engineering, IIT Bombay, Mumbai, Maharashtra, India
| | - Neeraj Kumar
- Department of Computing Science, University of Alberta, Edmonton, Canada.,Alberta Machine Intelligence Institute, Edmonton, Canada
| | - Swapnil Rane
- Department of Pathology, Tata Memorial Centre - ACTREC, HBNI, Navi Mumbai, Maharashtra, India
| | - Peter H Gann
- Department of Pathology, University of Illinois, Chicago, USA
| | - Amit Sethi
- Department of Electrical Engineering, IIT Bombay, Mumbai, Maharashtra, India.,Department of Pathology, University of Illinois, Chicago, USA
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15
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Martino F, Varricchio S, Russo D, Merolla F, Ilardi G, Mascolo M, dell’Aversana GO, Califano L, Toscano G, De Pietro G, Frucci M, Brancati N, Fraggetta F, Staibano S. A Machine-learning Approach for the Assessment of the Proliferative Compartment of Solid Tumors on Hematoxylin-Eosin-Stained Sections. Cancers (Basel) 2020; 12:cancers12051344. [PMID: 32466184 PMCID: PMC7281627 DOI: 10.3390/cancers12051344] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 05/20/2020] [Indexed: 01/19/2023] Open
Abstract
We introduce a machine learning-based analysis to predict the immunohistochemical (IHC) labeling index for the cell proliferation marker Ki67/MIB1 on cancer tissues based on morphometrical features extracted from hematoxylin and eosin (H&E)-stained formalin-fixed, paraffin-embedded (FFPE) tumor tissue samples. We provided a proof-of-concept prediction of the Ki67/MIB1 IHC positivity of cancer cells through the definition and quantitation of single nuclear features. In the first instance, we set our digital framework on Ki67/MIB1-stained OSCC (oral squamous cell carcinoma) tissue sample whole slide images, using QuPath as a working platform and its integrated algorithms, and we built a classifier in order to distinguish tumor and stroma classes and, within them, Ki67-positive and Ki67-negative cells; then, we sorted the morphometric features of tumor cells related to their Ki67 IHC status. Among the evaluated features, nuclear hematoxylin mean optical density (NHMOD) presented as the best one to distinguish Ki67/MIB1 positive from negative cells. We confirmed our findings in a single-cell level analysis of H&E staining on Ki67-immunostained/H&E-decolored tissue samples. Finally, we tested our digital framework on a case series of oral squamous cell carcinomas (OSCC), arranged in tissue microarrays; we selected two consecutive sections of each OSCC FFPE TMA (tissue microarray) block, respectively stained with H&E and immuno-stained for Ki67/MIB1. We automatically detected tumor cells in H&E slides and generated a “false color map” (FCM) based on NHMOD through the QuPath measurements map tool. FCM nearly coincided with the actual immunohistochemical result, allowing the prediction of Ki67/MIB1 positive cells in a direct visual fashion. Our proposed approach provides the pathologist with a fast method of identifying the proliferating compartment of the tumor through a quantitative assessment of the nuclear features on H&E slides, readily appreciable by visual inspection. Although this technique needs to be fine-tuned and tested on larger series of tumors, the digital analysis approach appears to be a promising tool to quickly forecast the tumor’s proliferation fraction directly on routinely H&E-stained digital sections.
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Affiliation(s)
- Francesco Martino
- Department of Advanced Biomedical Sciences, Pathology Unit, University of Naples Federico II, 80131 Naples, Italy; (F.M.); (S.V.); (G.I.); (M.M.); (S.S.)
| | - Silvia Varricchio
- Department of Advanced Biomedical Sciences, Pathology Unit, University of Naples Federico II, 80131 Naples, Italy; (F.M.); (S.V.); (G.I.); (M.M.); (S.S.)
| | - Daniela Russo
- Department of Advanced Biomedical Sciences, Pathology Unit, University of Naples Federico II, 80131 Naples, Italy; (F.M.); (S.V.); (G.I.); (M.M.); (S.S.)
- Correspondence: (D.R.); (F.M.)
| | - Francesco Merolla
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy
- Correspondence: (D.R.); (F.M.)
| | - Gennaro Ilardi
- Department of Advanced Biomedical Sciences, Pathology Unit, University of Naples Federico II, 80131 Naples, Italy; (F.M.); (S.V.); (G.I.); (M.M.); (S.S.)
| | - Massimo Mascolo
- Department of Advanced Biomedical Sciences, Pathology Unit, University of Naples Federico II, 80131 Naples, Italy; (F.M.); (S.V.); (G.I.); (M.M.); (S.S.)
| | | | - Luigi Califano
- Department of Maxillofacial Surgery, University of Naples “Federico II”, 80131 Naples, Italy; (G.O.d’A.); (L.C.)
| | - Guglielmo Toscano
- Healthcare Informatics Services, A.O.U. Federico II, 80131 Naples, Italy;
| | - Giuseppe De Pietro
- Institute for High Performance Computing and Networking of National Research Council of Italy, ICAR-CNR, 80131 Naples, Italy; (G.D.P.); (M.F.); (N.B.)
| | - Maria Frucci
- Institute for High Performance Computing and Networking of National Research Council of Italy, ICAR-CNR, 80131 Naples, Italy; (G.D.P.); (M.F.); (N.B.)
| | - Nadia Brancati
- Institute for High Performance Computing and Networking of National Research Council of Italy, ICAR-CNR, 80131 Naples, Italy; (G.D.P.); (M.F.); (N.B.)
| | - Filippo Fraggetta
- Pathology Unit, Azienda Ospedaliera per l’Emergenza Cannizzaro Hospital, 95126 Catania, Italy;
| | - Stefania Staibano
- Department of Advanced Biomedical Sciences, Pathology Unit, University of Naples Federico II, 80131 Naples, Italy; (F.M.); (S.V.); (G.I.); (M.M.); (S.S.)
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16
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Rawat RR, Ortega I, Roy P, Sha F, Shibata D, Ruderman D, Agus DB. Deep learned tissue "fingerprints" classify breast cancers by ER/PR/Her2 status from H&E images. Sci Rep 2020; 10:7275. [PMID: 32350370 PMCID: PMC7190637 DOI: 10.1038/s41598-020-64156-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 04/13/2020] [Indexed: 12/17/2022] Open
Abstract
Because histologic types are subjective and difficult to reproduce between pathologists, tissue morphology often takes a back seat to molecular testing for the selection of breast cancer treatments. This work explores whether a deep-learning algorithm can learn objective histologic H&E features that predict the clinical subtypes of breast cancer, as assessed by immunostaining for estrogen, progesterone, and Her2 receptors (ER/PR/Her2). Translating deep learning to this and related problems in histopathology presents a challenge due to the lack of large, well-annotated data sets, which are typically required for the algorithms to learn statistically significant discriminatory patterns. To overcome this limitation, we introduce the concept of “tissue fingerprints,” which leverages large, unannotated datasets in a label-free manner to learn H&E features that can distinguish one patient from another. The hypothesis is that training the algorithm to learn the morphological differences between patients will implicitly teach it about the biologic variation between them. Following this training internship, we used the features the network learned, which we call “fingerprints,” to predict ER, PR, and Her2 status in two datasets. Despite the discovery dataset being relatively small by the standards of the machine learning community (n = 939), fingerprints enabled the determination of ER, PR, and Her2 status from whole slide H&E images with 0.89 AUC (ER), 0.81 AUC (PR), and 0.79 AUC (Her2) on a large, independent test set (n = 2531). Tissue fingerprints are concise but meaningful histopathologic image representations that capture biological information and may enable machine learning algorithms that go beyond the traditional ER/PR/Her2 clinical groupings by directly predicting theragnosis.
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Affiliation(s)
- Rishi R Rawat
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, 12414 Exposition Blvd, Los Angeles, CA, 90064, USA
| | - Itzel Ortega
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, 12414 Exposition Blvd, Los Angeles, CA, 90064, USA
| | - Preeyam Roy
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, 12414 Exposition Blvd, Los Angeles, CA, 90064, USA
| | - Fei Sha
- DASH Center at USC, 1002 Childs Way, MCB 114, Los Angeles, CA, 90089-0005, USA
| | - Darryl Shibata
- Department of Pathology, University of Southern California Health Sciences Campus, NOR 1441 Eastlake Ave, Los Angeles, 90033, USA
| | - Daniel Ruderman
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, 12414 Exposition Blvd, Los Angeles, CA, 90064, USA.
| | - David B Agus
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, 12414 Exposition Blvd, Los Angeles, CA, 90064, USA
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17
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Prasanna P, Karnawat A, Ismail M, Madabhushi A, Tiwari P. Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging. J Med Imaging (Bellingham) 2019; 6:024005. [PMID: 31093517 DOI: 10.1117/1.jmi.6.2.024005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2018] [Accepted: 04/15/2019] [Indexed: 01/24/2023] Open
Abstract
Accurate segmentation of gliomas on routine magnetic resonance image (MRI) scans plays an important role in disease diagnosis, prognosis, and patient treatment planning. We present a fully automated approach, radiomics-based convolutional neural network (RadCNN), for segmenting both high- and low-grade gliomas using multimodal MRI volumes (T1c, T2w, and FLAIR). RadCNN incorporates radiomic texture features (i.e., Haralick, Gabor, and Laws) within DeepMedic [a deep 3-D convolutional neural network (CNN) segmentation framework that uses image intensities; a top performing method in the BraTS 2016 challenge] to further augment the performance of brain tumor subcompartment segmentation. We first identify textural radiomic representations that best separate the different subcompartments [enhancing tumor (ET), whole tumor (WT), and tumor core (TC)] on the training set, and then feed these representations as inputs to the CNN classifier for prediction of different subcompartments. We hypothesize that textural radiomic representations of lesion subcompartments will enhance the separation of subcompartment boundaries, and hence providing these features as inputs to the deep CNN, over and above raw intensity values alone, will improve the subcompartment segmentation. Using a training set of N = 241 patients, validation set of N = 44 , and test set of N = 46 patients, RadCNN method achieved Dice similarity coefficient (DSC) scores of 0.71, 0.89, and 0.73 for ET, WT, and TC, respectively. Compared to the DeepMedic model, RadCNN showed improvement in DSC scores for both ET and WT and demonstrated comparable results in segmenting the TC. Similarly, smaller Hausdorff distance measures were obtained with RadCNN as compared to the DeepMedic model across all the subcompartments. Following the segmentation of the different subcompartments, we extracted a set of subcompartment specific radiomic descriptors that capture lesion disorder and assessed their ability in separating patients into different survival cohorts (short-, mid- and long-term survival) based on their overall survival from the date of baseline diagnosis. Using a multilinear regression approach, we achieved accuracies of 0.57, 0.63, and 0.45 for the training, validation, and test cases, respectively.
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Affiliation(s)
- Prateek Prasanna
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Ayush Karnawat
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Marwa Ismail
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Anant Madabhushi
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States.,Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio, United States
| | - Pallavi Tiwari
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
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18
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Montalto MC, Edwards R. And They Said It Couldn't Be Done: Predicting Known Driver Mutations From H&E Slides. J Pathol Inform 2019; 10:17. [PMID: 31149368 PMCID: PMC6537629 DOI: 10.4103/jpi.jpi_91_18] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 01/16/2019] [Indexed: 12/17/2022] Open
Affiliation(s)
- Michael C Montalto
- Department of Translational Medicine, Bristol-Myers Squibb, Princeton, NJ 08543, USA
| | - Robin Edwards
- Department of Translational Medicine, Bristol-Myers Squibb, Princeton, NJ 08543, USA
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19
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Fritz AJ, Gillis NE, Gerrard DL, Rodriguez PD, Hong D, Rose JT, Ghule PN, Bolf EL, Gordon JA, Tye CE, Boyd JR, Tracy KM, Nickerson JA, van Wijnen AJ, Imbalzano AN, Heath JL, Frietze SE, Zaidi SK, Carr FE, Lian JB, Stein JL, Stein GS. Higher order genomic organization and epigenetic control maintain cellular identity and prevent breast cancer. Genes Chromosomes Cancer 2019; 58:484-499. [PMID: 30873710 DOI: 10.1002/gcc.22731] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 01/07/2019] [Accepted: 01/07/2019] [Indexed: 12/24/2022] Open
Abstract
Cells establish and sustain structural and functional integrity of the genome to support cellular identity and prevent malignant transformation. In this review, we present a strategic overview of epigenetic regulatory mechanisms including histone modifications and higher order chromatin organization (HCO) that are perturbed in breast cancer onset and progression. Implications for dysfunctions that occur in hormone regulation, cell cycle control, and mitotic bookmarking in breast cancer are considered, with an emphasis on epithelial-to-mesenchymal transition and cancer stem cell activities. The architectural organization of regulatory machinery is addressed within the contexts of translating cancer-compromised genomic organization to advances in breast cancer risk assessment, diagnosis, prognosis, and identification of novel therapeutic targets with high specificity and minimal off target effects.
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Affiliation(s)
- A J Fritz
- Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, Vermont.,University of Vermont Cancer Center, Burlington, Vermont
| | - N E Gillis
- University of Vermont Cancer Center, Burlington, Vermont.,Department of Pharmacology, Larner college of Medicine, University of Vermont, Burlington, Vermont
| | - D L Gerrard
- Cellular Molecular Biomedical Sciences Program, University of Vermont, Burlington, Vermont.,Department of Biomedical and Health Sciences, University of Vermont, Burlington, Vermont
| | - P D Rodriguez
- Cellular Molecular Biomedical Sciences Program, University of Vermont, Burlington, Vermont.,Department of Biomedical and Health Sciences, University of Vermont, Burlington, Vermont
| | - D Hong
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, Massachusetts
| | - J T Rose
- Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, Vermont.,University of Vermont Cancer Center, Burlington, Vermont
| | - P N Ghule
- Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, Vermont.,University of Vermont Cancer Center, Burlington, Vermont
| | - E L Bolf
- University of Vermont Cancer Center, Burlington, Vermont.,Department of Pharmacology, Larner college of Medicine, University of Vermont, Burlington, Vermont
| | - J A Gordon
- Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, Vermont.,University of Vermont Cancer Center, Burlington, Vermont
| | - C E Tye
- Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, Vermont.,University of Vermont Cancer Center, Burlington, Vermont
| | - J R Boyd
- Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, Vermont.,University of Vermont Cancer Center, Burlington, Vermont
| | - K M Tracy
- Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, Vermont.,University of Vermont Cancer Center, Burlington, Vermont
| | - J A Nickerson
- Division of Genes and Development of the Department of Pediatrics, University of Massachusetts Medical School, Worcester, Massachusetts
| | - A J van Wijnen
- Orthopedic Surgery and Biochemistry and Molecular Biology, Mayo Clinic Minnesota, Rochester, Minnesota
| | - A N Imbalzano
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - J L Heath
- Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, Vermont.,University of Vermont Cancer Center, Burlington, Vermont.,Department of Pediatrics, Larner College of Medicine, University of Vermont, Burlington, Vermont
| | - S E Frietze
- Cellular Molecular Biomedical Sciences Program, University of Vermont, Burlington, Vermont.,Department of Biomedical and Health Sciences, University of Vermont, Burlington, Vermont
| | - S K Zaidi
- Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, Vermont.,University of Vermont Cancer Center, Burlington, Vermont
| | - F E Carr
- Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, Vermont.,University of Vermont Cancer Center, Burlington, Vermont.,Department of Pharmacology, Larner college of Medicine, University of Vermont, Burlington, Vermont
| | - J B Lian
- Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, Vermont.,University of Vermont Cancer Center, Burlington, Vermont
| | - J L Stein
- Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, Vermont.,University of Vermont Cancer Center, Burlington, Vermont
| | - G S Stein
- Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, Vermont.,University of Vermont Cancer Center, Burlington, Vermont
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