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Zhu L, Pan J, Mou W, Deng L, Zhu Y, Wang Y, Pareek G, Hyams E, Carneiro BA, Hadfield MJ, El-Deiry WS, Yang T, Tan T, Tong T, Ta N, Zhu Y, Gao Y, Lai Y, Cheng L, Chen R, Xue W. Harnessing artificial intelligence for prostate cancer management. Cell Rep Med 2024; 5:101506. [PMID: 38593808 DOI: 10.1016/j.xcrm.2024.101506] [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: 08/30/2023] [Revised: 01/05/2024] [Accepted: 03/19/2024] [Indexed: 04/11/2024]
Abstract
Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging enable the application of artificial intelligence (AI) in pathology. This review highlights the success of AI in detecting and grading PCa, predicting patient outcomes, and identifying molecular subtypes. We propose that AI-based methods could collaborate with pathologists to reduce workload and assist clinicians in formulating treatment recommendations. We also introduce the general process and challenges in developing AI pathology models for PCa. Importantly, we summarize publicly available datasets and open-source codes to facilitate the utilization of existing data and the comparison of the performance of different models to improve future studies.
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Affiliation(s)
- Lingxuan Zhu
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Changping Laboratory, Beijing, China
| | - Jiahua Pan
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Weiming Mou
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Longxin Deng
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China
| | - Yinjie Zhu
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Yanqing Wang
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Gyan Pareek
- Department of Surgery (Urology), Brown University Warren Alpert Medical School, Providence, RI, USA; Minimally Invasive Urology Institute, Providence, RI, USA
| | - Elias Hyams
- Department of Surgery (Urology), Brown University Warren Alpert Medical School, Providence, RI, USA; Minimally Invasive Urology Institute, Providence, RI, USA
| | - Benedito A Carneiro
- The Legorreta Cancer Center at Brown University, Lifespan Cancer Institute, Providence, RI, USA
| | - Matthew J Hadfield
- The Legorreta Cancer Center at Brown University, Lifespan Cancer Institute, Providence, RI, USA
| | - Wafik S El-Deiry
- The Legorreta Cancer Center at Brown University, Laboratory of Translational Oncology and Experimental Cancer Therapeutics, Department of Pathology & Laboratory Medicine, The Warren Alpert Medical School of Brown University, The Joint Program in Cancer Biology, Brown University and Lifespan Health System, Division of Hematology/Oncology, The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Tao Yang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tao Tan
- Faculty of Applied Sciences, Macao Polytechnic University, Address: R. de Luís Gonzaga Gomes, Macao, China
| | - Tong Tong
- College of Physics and Information Engineering, Fuzhou University, Fujian 350108, China
| | - Na Ta
- Department of Pathology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China
| | - Yan Zhu
- Department of Pathology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China
| | - Yisha Gao
- Department of Pathology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China
| | - Yancheng Lai
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Liang Cheng
- Department of Surgery (Urology), Brown University Warren Alpert Medical School, Providence, RI, USA; Department of Pathology and Laboratory Medicine, Department of Surgery (Urology), Brown University Warren Alpert Medical School, Lifespan Health, and the Legorreta Cancer Center at Brown University, Providence, RI, USA.
| | - Rui Chen
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.
| | - Wei Xue
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.
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Zhang X, Yu X, Liang W, Zhang Z, Zhang S, Xu L, Zhang H, Feng Z, Song M, Zhang J, Feng S. Deep learning-based accurate diagnosis and quantitative evaluation of microvascular invasion in hepatocellular carcinoma on whole-slide histopathology images. Cancer Med 2024; 13:e7104. [PMID: 38488408 PMCID: PMC10941532 DOI: 10.1002/cam4.7104] [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] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 12/13/2023] [Accepted: 03/03/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Microvascular invasion (MVI) is an independent prognostic factor that is associated with early recurrence and poor survival after resection of hepatocellular carcinoma (HCC). However, the traditional pathology approach is relatively subjective, time-consuming, and heterogeneous in the diagnosis of MVI. The aim of this study was to develop a deep-learning model that could significantly improve the efficiency and accuracy of MVI diagnosis. MATERIALS AND METHODS We collected H&E-stained slides from 753 patients with HCC at the First Affiliated Hospital of Zhejiang University. An external validation set with 358 patients was selected from The Cancer Genome Atlas database. The deep-learning model was trained by simulating the method used by pathologists to diagnose MVI. Model performance was evaluated with accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve. RESULTS We successfully developed a MVI artificial intelligence diagnostic model (MVI-AIDM) which achieved an accuracy of 94.25% in the independent external validation set. The MVI positive detection rate of MVI-AIDM was significantly higher than the results of pathologists. Visualization results demonstrated the recognition of micro MVIs that were difficult to differentiate by the traditional pathology. Additionally, the model provided automatic quantification of the number of cancer cells and spatial information regarding MVI. CONCLUSIONS We developed a deep learning diagnostic model, which performed well and improved the efficiency and accuracy of MVI diagnosis. The model provided spatial information of MVI that was essential to accurately predict HCC recurrence after surgery.
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Affiliation(s)
- Xiuming Zhang
- Department of Pathology, The First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouP. R. China
| | - Xiaotian Yu
- Department of Computer Science and TechnologyZhejiang UniversityHangzhouP. R. China
| | - Wenjie Liang
- Department of Radiology, The First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouP. R. China
| | - Zhongliang Zhang
- School of ManagementHangzhou Dianzi UniversityHangzhouP. R. China
| | - Shengxuming Zhang
- Department of Computer Science and TechnologyZhejiang UniversityHangzhouP. R. China
| | - Linjie Xu
- Department of Pathology, The First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouP. R. China
| | - Han Zhang
- Department of Pathology, The First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouP. R. China
| | - Zunlei Feng
- Department of Computer Science and TechnologyZhejiang UniversityHangzhouP. R. China
| | - Mingli Song
- Department of Computer Science and TechnologyZhejiang UniversityHangzhouP. R. China
| | - Jing Zhang
- Department of Pathology, The First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouP. R. China
| | - Shi Feng
- Department of Pathology, The First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouP. R. China
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Schumann Y, Dottermusch M, Schweizer L, Krech M, Lempertz T, Schüller U, Neumann P, Neumann JE. Morphology-based molecular classification of spinal cord ependymomas using deep neural networks. Brain Pathol 2024:e13239. [PMID: 38205683 DOI: 10.1111/bpa.13239] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 12/31/2023] [Indexed: 01/12/2024] Open
Abstract
Based on DNA-methylation, ependymomas growing in the spinal cord comprise two major molecular types termed spinal (SP-EPN) and myxopapillary ependymomas (MPE(-A/B)), which differ with respect to their clinical features and prognosis. Due to the existing discrepancy between histomorphogical diagnoses and classification using methylation data, we asked whether deep neural networks can predict the DNA methylation class of spinal cord ependymomas from hematoxylin and eosin stained whole-slide images. Using explainable AI, we further aimed to prospectively improve the consistency of histology-based diagnoses with DNA methylation profiling by identifying and quantifying distinct morphological patterns of these molecular ependymoma types. We assembled a case series of 139 molecularly characterized spinal cord ependymomas (nMPE = 84, nSP-EPN = 55). Self-supervised and weakly-supervised neural networks were used for classification. We employed attention analysis and supervised machine-learning methods for the discovery and quantification of morphological features and their correlation to the diagnoses of experienced neuropathologists. Our best performing model predicted the DNA methylation class with 98% test accuracy and used self-supervised learning to outperform pretrained encoder-networks (86% test accuracy). In contrast, the diagnoses of neuropathologists matched the DNA methylation class in only 83% of cases. Domain-adaptation techniques improved model generalization to an external validation cohort by up to 22%. Statistically significant morphological features were identified per molecular type and quantitatively correlated to human diagnoses. The approach was extended to recently defined subtypes of myxopapillary ependymomas (MPE-(A/B), 80% test accuracy). In summary, we demonstrated the accurate prediction of the DNA methylation class of spinal cord ependymomas (SP-EPN, MPE(-A/B)) using hematoxylin and eosin stained whole-slide images. Our approach may prospectively serve as a supplementary resource for integrated diagnostics and may even help to establish a standardized, high-quality level of histology-based diagnostics across institutions-in particular in low-income countries, where expensive DNA-methylation analyses may not be readily available.
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Affiliation(s)
- Yannis Schumann
- Chair for High Performance Computing, Helmut-Schmidt-University Hamburg, Hamburg, Germany
| | - Matthias Dottermusch
- Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Institute of Neuropathology, UKE, Hamburg, Germany
| | - Leonille Schweizer
- Institute of Neurology (Edinger Institute), University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany
| | - Maja Krech
- Institute for Neuropathology, Charité Berlin, Berlin, Germany
| | - Tasja Lempertz
- Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Ulrich Schüller
- Institute of Neuropathology, UKE, Hamburg, Germany
- Research Institute Children's Cancer Center Hamburg, UKE, Hamburg, Germany
- Department of Pediatric Hematology and Oncology, UKE, Hamburg, Germany
| | - Philipp Neumann
- Chair for High Performance Computing, Helmut-Schmidt-University Hamburg, Hamburg, Germany
| | - Julia E Neumann
- Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Institute of Neuropathology, UKE, Hamburg, Germany
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Diao S, Chen P, Showkatian E, Bandyopadhyay R, Rojas FR, Zhu B, Hong L, Aminu M, Saad MB, Salehjahromi M, Muneer A, Sujit SJ, Behrens C, Gibbons DL, Heymach JV, Kalhor N, Wistuba II, Solis Soto LM, Zhang J, Qin W, Wu J. Automated Cellular-Level Dual Global Fusion of Whole-Slide Imaging for Lung Adenocarcinoma Prognosis. Cancers (Basel) 2023; 15:4824. [PMID: 37835518 PMCID: PMC10571722 DOI: 10.3390/cancers15194824] [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: 09/08/2023] [Revised: 09/24/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023] Open
Abstract
Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient prognosis and effectively extracting informative survival-specific representations from WSI with highly compounded gigapixels. In this study, we present a fully automated cellular-level dual global fusion pipeline for survival prediction. Specifically, the proposed method first describes the composition of different cell populations on WSI. Then, it generates dimension-reduced WSI-embedded maps, allowing for efficient investigation of the tumor microenvironment. In addition, we introduce a novel dual global fusion network to incorporate global and inter-patch features of cell distribution, which enables the sufficient fusion of different types and locations of cells. We further validate the proposed pipeline using The Cancer Genome Atlas lung adenocarcinoma dataset. Our model achieves a C-index of 0.675 (±0.05) in the five-fold cross-validation setting and surpasses comparable methods. Further, we extensively analyze embedded map features and survival probabilities. These experimental results manifest the potential of our proposed pipeline for applications using WSI in lung adenocarcinoma and other malignancies.
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Affiliation(s)
- Songhui Diao
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Pingjun Chen
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Eman Showkatian
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rukhmini Bandyopadhyay
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Frank R. Rojas
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Bo Zhu
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Lingzhi Hong
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Muhammad Aminu
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Maliazurina B. Saad
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Morteza Salehjahromi
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Amgad Muneer
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Sheeba J. Sujit
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Carmen Behrens
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Don L. Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - John V. Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Neda Kalhor
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ignacio I. Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Luisa M. Solis Soto
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Wenjian Qin
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jia Wu
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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5
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Fogarty R, Goldgof D, Hall L, Lopez A, Johnson J, Gadara M, Stoyanova R, Punnen S, Pollack A, Pow-Sang J, Balagurunathan Y. Classifying Malignancy in Prostate Glandular Structures from Biopsy Scans with Deep Learning. Cancers (Basel) 2023; 15:cancers15082335. [PMID: 37190264 DOI: 10.3390/cancers15082335] [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: 02/13/2023] [Revised: 04/07/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023] Open
Abstract
Histopathological classification in prostate cancer remains a challenge with high dependence on the expert practitioner. We develop a deep learning (DL) model to identify the most prominent Gleason pattern in a highly curated data cohort and validate it on an independent dataset. The histology images are partitioned in tiles (14,509) and are curated by an expert to identify individual glandular structures with assigned primary Gleason pattern grades. We use transfer learning and fine-tuning approaches to compare several deep neural network architectures that are trained on a corpus of camera images (ImageNet) and tuned with histology examples to be context appropriate for histopathological discrimination with small samples. In our study, the best DL network is able to discriminate cancer grade (GS3/4) from benign with an accuracy of 91%, F1-score of 0.91 and AUC 0.96 in a baseline test (52 patients), while the cancer grade discrimination of the GS3 from GS4 had an accuracy of 68% and AUC of 0.71 (40 patients).
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Affiliation(s)
- Ryan Fogarty
- Department of Machine Learning, H. Lee Moffitt Cancer Center, Tampa, FL 33612, USA
- Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA
| | - Dmitry Goldgof
- Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA
| | - Lawrence Hall
- Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA
| | - Alex Lopez
- Tissue Core Facility, H. Lee Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Joseph Johnson
- Analytic Microscopy Core Facility, H. Lee Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Manoj Gadara
- Anatomic Pathology Division, H. Lee Moffitt Cancer Center, Tampa, FL 33612, USA
- Quest Diagnostics, Tampa, FL 33612, USA
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Sanoj Punnen
- Desai Sethi Urology Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Julio Pow-Sang
- Genitourinary Cancers, H. Lee Moffitt Cancer Center, Tampa, FL 33612, USA
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6
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Che Y, Ren F, Zhang X, Cui L, Wu H, Zhao Z. Immunohistochemical HER2 Recognition and Analysis of Breast Cancer Based on Deep Learning. Diagnostics (Basel) 2023; 13:diagnostics13020263. [PMID: 36673073 PMCID: PMC9858188 DOI: 10.3390/diagnostics13020263] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/05/2023] [Accepted: 01/06/2023] [Indexed: 01/13/2023] Open
Abstract
Breast cancer is one of the common malignant tumors in women. It seriously endangers women's life and health. The human epidermal growth factor receptor 2 (HER2) protein is responsible for the division and growth of healthy breast cells. The overexpression of the HER2 protein is generally evaluated by immunohistochemistry (IHC). The IHC evaluation criteria mainly includes three indexes: staining intensity, circumferential membrane staining pattern, and proportion of positive cells. Manually scoring HER2 IHC images is an error-prone, variable, and time-consuming work. To solve these problems, this study proposes an automated predictive method for scoring whole-slide images (WSI) of HER2 slides based on a deep learning network. A total of 95 HER2 pathological slides from September 2021 to December 2021 were included. The average patch level precision and f1 score were 95.77% and 83.09%, respectively. The overall accuracy of automated scoring for slide-level classification was 97.9%. The proposed method showed excellent specificity for all IHC 0 and 3+ slides and most 1+ and 2+ slides. The evaluation effect of the integrated method is better than the effect of using the staining result only.
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Affiliation(s)
- Yuxuan Che
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 101408, China
- Jinfeng Laboratory, Chongqing 401329, China
| | - Fei Ren
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Xueyuan Zhang
- Beijing Zhijian Life Technology Co., Ltd., Beijing 100036, China
| | - Li Cui
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Huanwen Wu
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
- Correspondence: (H.W.); (Z.Z.)
| | - Ze Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- Correspondence: (H.W.); (Z.Z.)
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7
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Zheng Q, Yang R, Ni X, Yang S, Jiao P, Wu J, Xiong L, Wang J, Jian J, Jiang Z, Wang L, Chen Z, Liu X. Quantitative Assessment of Tumor-Infiltrating Lymphocytes Using Machine Learning Predicts Survival in Muscle-Invasive Bladder Cancer. J Clin Med 2022; 11:jcm11237081. [PMID: 36498655 PMCID: PMC9739988 DOI: 10.3390/jcm11237081] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 11/25/2022] [Accepted: 11/25/2022] [Indexed: 12/05/2022] Open
Abstract
(1) Purpose: Although assessment of tumor-infiltrating lymphocytes (TILs) has been acknowledged to have important predictive prognostic value in muscle-invasive bladder cancer (MIBC), it is limited by inter- and intra-observer variability, hampering widespread clinical application. We aimed to evaluate the prognostic value of quantitative TILs score based on a machine learning (ML) algorithm to identify MIBC patients who might benefit from immunotherapy or the de-escalation of therapy. (2) Methods: We constructed an artificial neural network classifier for tumor cells, lymphocytes, stromal cells, and “ignore” cells from hematoxylin-and-eosin-stained slide images using the QuPath open source software. We defined four unique TILs variables based on ML to analyze TILs measurements. Pathological slide images from 133 MIBC patients were retrospectively collected as the discovery set to determine the optimal association of ML-read TILs variables with patient survival outcomes. For validation, we evaluated an independent external validation set consisting of 247 MIBC patients. (3) Results: We found that all four TILs variables had significant prognostic associations with survival outcomes in MIBC patients (p < 0.001 for all comparisons), with higher TILs score being associated with better prognosis. Univariate and multivariate Cox regression analyses demonstrated that electronic TILs (eTILs) variables were independently associated with overall survival after adjustment for clinicopathological factors including age, sex, and pathological stage (p < 0.001 for all analyses). Results analyzed in different subgroups showed that the eTILs variable was a strong prognostic factor that was not redundant with pre-existing clinicopathological features (p < 0.05 for all analyses). (4) Conclusion: ML-driven cell classifier-defined TILs variables were robust and independent prognostic factors in two independent cohorts of MIBC patients. eTILs have the potential to identify a subset of high-risk stage II or stage III-IV MIBC patients who might benefit from adjuvant immunotherapy.
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Affiliation(s)
- Qingyuan Zheng
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Rui Yang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Xinmiao Ni
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Song Yang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Panpan Jiao
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jiejun Wu
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lin Xiong
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jingsong Wang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Jun Jian
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Zhengyu Jiang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Lei Wang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Zhiyuan Chen
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Correspondence: (Z.C.); (X.L.)
| | - Xiuheng Liu
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Correspondence: (Z.C.); (X.L.)
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Xu H, Cha YJ, Clemenceau JR, Choi J, Lee SH, Kang J, Hwang TH. Spatial analysis of tumor-infiltrating lymphocytes in histological sections using deep learning techniques predicts survival in colorectal carcinoma. J Pathol Clin Res 2022; 8:327-339. [PMID: 35484698 PMCID: PMC9161341 DOI: 10.1002/cjp2.273] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/22/2022] [Accepted: 04/01/2022] [Indexed: 11/17/2022]
Abstract
This study aimed to explore the prognostic impact of spatial distribution of tumor‐infiltrating lymphocytes (TILs) quantified by deep learning (DL) approaches based on digitalized whole‐slide images stained with hematoxylin and eosin in patients with colorectal cancer (CRC). The prognostic impact of spatial distributions of TILs in patients with CRC was explored in the Yonsei cohort (n = 180) and validated in The Cancer Genome Atlas (TCGA) cohort (n = 268). Two experienced pathologists manually measured TILs at the most invasive margin (IM) as 0–3 by the Klintrup–Mäkinen (KM) grading method and this was compared to DL approaches. Inter‐rater agreement for TILs was measured using Cohen's kappa coefficient. On multivariate analysis of spatial TIL features derived by DL approaches and clinicopathological variables including tumor stage, microsatellite instability, and KRAS mutation, TIL densities within 200 μm of the IM (f_im200) remained the most significant prognostic factor for progression‐free survival (PFS) (hazard ratio [HR] 0.004 [95% confidence interval, CI, 0.0001–0.15], p = 0.0028) in the Yonsei cohort. On multivariate analysis using the TCGA dataset, f_im200 retained prognostic significance for PFS (HR 0.031 [95% CI 0.001–0.645], p = 0.024). Inter‐rater agreement of manual KM grading was insignificant in the Yonsei (κ = 0.109) and the TCGA (κ = 0.121) cohorts. The survival analysis based on KM grading showed statistically significant different PFS in the TCGA cohort, but not the Yonsei cohort. Automatic quantification of TILs at the IM based on DL approaches shows prognostic utility to predict PFS, and could provide robust and reproducible TIL density measurement in patients with CRC.
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Affiliation(s)
- Hongming Xu
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, PR China
| | - Yoon Jin Cha
- Department of Pathology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jean R Clemenceau
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA
| | - Jinhwan Choi
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA
| | - Sung Hak Lee
- Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jeonghyun Kang
- Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Tae Hyun Hwang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA
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9
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Ou YC, Tsao TY, Chang MC, Lin YS, Yang WL, Hang JF, Li CB, Lee CM, Yeh CH, Liu TJ. Evaluation of an artificial intelligence algorithm for assisting the Paris System in reporting urinary cytology: A pilot study. Cancer Cytopathol 2022; 130:872-880. [PMID: 35727052 DOI: 10.1002/cncy.22615] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [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: 03/24/2022] [Revised: 05/30/2022] [Accepted: 06/02/2022] [Indexed: 11/12/2022]
Abstract
BACKGROUND The Paris System for Reporting Urinary Cytology (TPS) has been shown to improve bladder cancer diagnosis. Advances in artificial intelligence (AI) may assist and improve the clinical workflow by applying TPS in routine diagnostic services. METHODS A deep-learning-based algorithm was developed to identify urothelial cancer candidate cells using whole-slide images (WSIs). In the testing cohort, 131 urine cytology slides were retrospectively retrieved and analyzed using this AI algorithm. The authors compared the performance of one cytopathologist and two cytotechnologists using AI-assisted digital urine cytology. Then, the AI-assisted WSIs were evaluated in the clinical workflow. The cytopathologist first made a diagnosis by reviewing the AI-inferred WSIs and quantitative data (nuclear-to-cytoplasmic ratio and nuclear size) for each sample. After a washout period, the same cytopathologist made a diagnosis for the same samples using direct microscopy. All diagnosis results were compared with the expert panel consensus. RESULTS The AI-assisted diagnosis by the two cytotechnologists and the one cytopathologist demonstrated performance results that were comparable to the expert panel consensus (sensitivity, 79.5% and 82.1% vs. 92.3%, respectively; specificity, 100% and 98.9% vs. 100%, respectively). Furthermore, the performance of the AI-assisted WSIs compared with the microscopic diagnosis by the cytopathologist demonstrated superior sensitivity (92.3% vs. 87.2%) and negative predictive value (96.8% vs. 94.8%). In addition, the AI-assisted reporting demonstrated near perfect agreement with the expert panel consensus (κ = 0.944) and the microscopic diagnosis (κ = 0.862). CONCLUSIONS The AI algorithm developed by the authors effectively assisted TPS-based reporting by providing AI-inferred WSIs and quantitative data.
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Affiliation(s)
- Yen-Chuan Ou
- Division of Urology, Department of Surgery, Tung's Taichung MetroHarbor Hospital, Taichung City, Taiwan
| | - Tang-Yi Tsao
- Department of Pathology, Tung's Taichung MetroHarbor Hospital, Taichung City, Taiwan
| | - Ming-Chen Chang
- Department of Pathology, Tung's Taichung MetroHarbor Hospital, Taichung City, Taiwan
| | - Yi-Sheng Lin
- Division of Urology, Department of Surgery, Tung's Taichung MetroHarbor Hospital, Taichung City, Taiwan
| | | | - Jen-Fan Hang
- Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.,School of Medicine and Institution of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chi-Bin Li
- AIxMed, Inc., Santa Clara, California, USA
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10
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Forest T, Aeffner F, Bangari DS, Bawa B, Carter J, Fikes J, High W, Hayashi SM, Jacobsen M, McKinney L, Rudmann D, Steinbach T, Schumacher V, Turner O, Ward JM, Willson CJ. Scientific and Regulatory Policy Committee Points to Consider: Primary Digital Histopathology Evaluation and Peer Review for Good Laboratory Practice (GLP) Nonclinical Toxicology Studies. Toxicol Pathol 2022; 50:531-543. [PMID: 35657014 DOI: 10.1177/01926233221099273] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The Society of Toxicologic Pathology's Scientific and Regulatory Policy Committee formed a working group to consider the present and future use of digital pathology in toxicologic pathology in general and specifically its use in primary evaluation and peer review in Good Laboratory Practice (GLP) environments. Digital histopathology systems can save costs by reducing travel, enhancing organizational flexibility, decreasing slide handling, improving collaboration, increasing access to historical images, and improving quality and efficiency through integration with laboratory information management systems. However, the resources to implement and operate a digital pathology system can be significant. Given the magnitude and risks involved in the decision to adopt digital histopathology, this working group used pertinent previously published survey results and its members' expertise to create a Points-to-Consider article to assist organizations with building and implementing digital pathology workflows. With the aim of providing a comprehensive perspective, the current publication summarizes aspects of digital whole-slide imaging relevant to nonclinical histopathology evaluations, and then presents points to consider applicable to both primary digital histopathology evaluation and digital peer review in GLP toxicology studies. The Supplemental Appendices provide additional tabulated resources.
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Affiliation(s)
| | | | | | | | | | | | - Wanda High
- High Preclinical Pathology Consulting, Rochester, New York, USA
| | - Shim-Mo Hayashi
- Laboratory of Veterinary Pathology, Tokyo University of Agriculture and Technology, Fuchu, Tokyo, Japan
- Division of Food Additives, National Institute of Health Sciences, Kawasaki, Kanagawa, Japan
| | - Matthew Jacobsen
- Regulatory Safety Centre of Excellence, Clinical Pharmacology & Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - LuAnn McKinney
- Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Daniel Rudmann
- Charles River Laboratories International, Inc., Wilmington, Massachusetts, USA
| | - Thomas Steinbach
- Experimental Pathology Laboratories, Inc., Research Triangle Park, North Carolina, USA
| | | | | | | | - Cynthia J Willson
- Integrated Laboratory Systems, Research Triangle Park, North Carolina, USA
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11
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Forest T, Aeffner F, Bangari DS, Bawa B, Carter J, Fikes J, High WB, Hayashi SM, Jacobsen M, McKinney L, Rudmann D, Steinbach T, Schumacher V, Turner OC, Ward JM, Willson CJ. Scientific and Regulatory Policy Committee Brief Communication: 2019 Survey on Use of Digital Histopathology Systems in Nonclinical Toxicology Studies. Toxicol Pathol 2022; 50:397-401. [PMID: 35321602 DOI: 10.1177/01926233221084621] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Histopathologic evaluation and peer review using digital whole-slide images (WSIs) is a relatively new medium for assessing nonclinical toxicology studies in Good Laboratory Practice (GLP) environments. To better understand the present and future use of digital pathology in nonclinical toxicology studies, the Society of Toxicologic Pathology (STP) formed a working group to survey STP members with the goal of creating recommendations for implementation. The survey was administered in December 2019, immediately before the COVID-19 pandemic, and the results suggested that the use of digital histopathology for routine GLP histopathology assessment was not widespread. Subsequently, in follow-up correspondence during the pandemic, many responding institutions either began investigating or adopting digital WSI systems to reduce employee exposure to COVID-19. Therefore, the working group presents the survey results as a pre-pandemic baseline data set. Recommendations for use of WSI systems in GLP environments will be the subject of a separate publication.
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Affiliation(s)
| | | | | | | | | | | | - Wanda B High
- High Preclinical Pathology Consulting, Rochester, New York, USA
| | - Shim-Mo Hayashi
- Tokyo University of Agriculture and Technology, Fuchu, Japan.,National Institute of Health Sciences, Kawasaki, Japan
| | | | - LuAnn McKinney
- US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Daniel Rudmann
- Charles River Laboratories International, Inc., Wilmington, Massachusetts, USA
| | - Thomas Steinbach
- Experimental Pathology Laboratories, Inc., Research Triangle Park, North Carolina, USA
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12
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Tsuneki M, Abe M, Kanavati F. A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning. Diagnostics (Basel) 2022; 12:768. [PMID: 35328321 DOI: 10.3390/diagnostics12030768] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/08/2022] [Accepted: 03/18/2022] [Indexed: 02/04/2023] Open
Abstract
The histopathological diagnosis of prostate adenocarcinoma in needle biopsy specimens is of pivotal importance for determining optimum prostate cancer treatment. Since diagnosing a large number of cases containing 12 core biopsy specimens by pathologists using a microscope is time-consuming manual system and limited in terms of human resources, it is necessary to develop new techniques that can rapidly and accurately screen large numbers of histopathological prostate needle biopsy specimens. Computational pathology applications that can assist pathologists in detecting and classifying prostate adenocarcinoma from whole-slide images (WSIs) would be of great benefit for routine pathological practice. In this paper, we trained deep learning models capable of classifying needle biopsy WSIs into adenocarcinoma and benign (non-neoplastic) lesions. We evaluated the models on needle biopsy, transurethral resection of the prostate (TUR-P), and The Cancer Genome Atlas (TCGA) public dataset test sets, achieving an ROC-AUC up to 0.978 in needle biopsy test sets and up to 0.9873 in TCGA test sets for adenocarcinoma.
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13
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Bulloni M, Sandrini G, Stacchiotti I, Barberis M, Calabrese F, Carvalho L, Fontanini G, Alì G, Fortarezza F, Hofman P, Hofman V, Kern I, Maiorano E, Maragliano R, Marchiori D, Metovic J, Papotti M, Pezzuto F, Pisa E, Remmelink M, Serio G, Marzullo A, Trabucco SMR, Pennella A, De Palma A, Marulli G, Fassina A, Maffeis V, Nesi G, Naheed S, Rea F, Ottensmeier CH, Sessa F, Uccella S, Pelosi G, Pattini L. Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms. Cancers (Basel) 2021; 13:4875. [PMID: 34638359 DOI: 10.3390/cancers13194875] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/09/2021] [Accepted: 09/23/2021] [Indexed: 12/01/2022] Open
Abstract
Simple Summary Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome, particularly for the intermediate domains of adenocarcinomas and large-cell neuroendocrine carcinomas. Moreover, subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. The aim of this study was to design and evaluate an objective and reproducible approach to the grading of lung NENs, potentially extendable to other NENs, by exploring a completely new perspective of interpreting the well-recognised proliferation marker Ki-67. We designed an automated pipeline to harvest quantitative information from the spatial distribution of Ki-67-positive cells, analysing its heterogeneity in the entire extent of tumour tissue—which currently represents the main weakness of Ki-67—and employed machine learning techniques to predict prognosis based on this information. Demonstrating the efficacy of the proposed framework would hint at a possible path for the future of grading and classification of NENs. Abstract Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome. Subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. Here, we propose a machine learning framework for tumour prognosis assessment based on a quantitative, automated and repeatable evaluation of the spatial distribution of cells immunohistochemically positive for the proliferation marker Ki-67, performed on the entire extent of high-resolution whole slide images. Combining features from the fields of graph theory, fractality analysis, stochastic geometry and information theory, we describe the topology of replicating cells and predict prognosis in a histology-independent way. We demonstrate how our approach outperforms the well-recognised prognostic role of Ki-67 Labelling Index on a multi-centre dataset comprising the most controversial lung NENs. Moreover, we show that our system identifies arrangement patterns in the cells positive for Ki-67 that appear independently of tumour subtyping. Strikingly, the subset of these features whose presence is also independent of the value of the Labelling Index and the density of Ki-67-positive cells prove to be especially relevant in discerning prognostic classes. These findings disclose a possible path for the future of grading and classification of NENs.
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14
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Ramaswamy V, Tejaswini BN, Uthaiah SB. Remote Reporting During a Pandemic Using Digital Pathology Solution: Experience from a Tertiary Care Cancer Center. J Pathol Inform 2021; 12:20. [PMID: 34267985 PMCID: PMC8274304 DOI: 10.4103/jpi.jpi_109_20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 12/09/2020] [Revised: 12/28/2020] [Accepted: 03/01/2021] [Indexed: 12/23/2022] Open
Abstract
Background: Remote reporting in anatomic pathology is an important advantage of digital pathology that has not been much explored. The COVID-19 pandemic has provided an opportunity to explore this important application of digital pathology system in a tertiary care cancer center to ensure patient care and staff safety. Regulatory guidelines have been described for remote reporting following the pandemic. Herein, we describe our experience of validation of digital pathology workflow for remote reporting to encourage pathologists to utilize this facility which opens door for multiple, multidisciplinary collaborations. Objective: To demonstrate the validation and the operational feasibility of remote reporting using a digital pathology system. Materials and Methods: Our retrospective validation included whole-slide images (WSIs) of 60 cases of histopathology and 20 cases each of frozen sections and a digital image-based breast algorithm after a washout period of 3 months. Three pathologists with different models of consumer-grade laptops reviewed the cases remotely to assess the diagnostic concordance and operational feasibility of the modified workflow. The slides were digitized on a USFDA-approved Philips UFS 300 scanner at ×40 resolution (0.25 μm/pixel) and viewed on the Image Management System through a web browser. All the essential parameters were reported for each case. After successful validation, 886 cases were reported remotely from March 29, 2020, to June 30, 2020, prospectively. Light microscopy formed the gold standard reference in remote reporting. Results: 100% major diagnostic concordance was observed in the validation of remote reporting in the retrospective and prospective studies using consumer-grade laptops. The deferral rate was 0.34%. 97.6% of histopathology and 100% of frozen sections were signed out within the turnaround time. Network speed and a lack of virtual private network did not significantly affect the study. Conclusion: This study of validation and reporting of complete pathology cases remotely, including their operational feasibility during a public health emergency, proves that remote sign-out using a digital pathology system is not inferior to WSIs on medical-grade monitors and light microscopy. Such studies on remote reporting open the door for the use of digital pathology for interinstitutional consultation and collaboration: Its main intended use.
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Affiliation(s)
- Veena Ramaswamy
- Department of Histopathology, Strand Life Sciences - Health Care Global Cancer Hospital, Bengaluru, Karnataka, India
| | - B N Tejaswini
- Department of Histopathology, Strand Life Sciences - Health Care Global Cancer Hospital, Bengaluru, Karnataka, India
| | - Sowmya B Uthaiah
- Department of Histopathology, Strand Life Sciences - Health Care Global Cancer Hospital, Bengaluru, Karnataka, India
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15
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Cheng J, Liu Y, Huang W, Hong W, Wang L, Zhan X, Han Z, Ni D, Huang K, Zhang J. Computational Image Analysis Identifies Histopathological Image Features Associated With Somatic Mutations and Patient Survival in Gastric Adenocarcinoma. Front Oncol 2021; 11:623382. [PMID: 33869007 PMCID: PMC8045755 DOI: 10.3389/fonc.2021.623382] [Citation(s) in RCA: 3] [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: 10/30/2020] [Accepted: 03/15/2021] [Indexed: 12/24/2022] Open
Abstract
Computational analysis of histopathological images can identify sub-visual objective image features that may not be visually distinguishable by human eyes, and hence provides better modeling of disease phenotypes. This study aims to investigate whether specific image features are associated with somatic mutations and patient survival in gastric adenocarcinoma (sample size = 310). An automated image analysis pipeline was developed to extract quantitative morphological features from H&E stained whole-slide images. We found that four frequently somatically mutated genes (TP53, ARID1A, OBSCN, and PIK3CA) were significantly associated with tumor morphological changes. A prognostic model built on the image features significantly stratified patients into low-risk and high-risk groups (log-rank test p-value = 2.6e-4). Multivariable Cox regression showed the model predicted risk index was an additional prognostic factor besides tumor grade and stage. Gene ontology enrichment analysis showed that the genes whose expressions mostly correlated with the contributing features in the prognostic model were enriched on biological processes such as cell cycle and muscle contraction. These results demonstrate that histopathological image features can reflect underlying somatic mutations and identify high-risk patients that may benefit from more precise treatment regimens. Both the image features and pipeline are highly interpretable to enable translational applications.
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Affiliation(s)
- Jun Cheng
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen University, Shenzhen, China.,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Marshall Laboratory of Biomedical Engineering Shenzhen University, Shenzhen, China
| | - Yuting Liu
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen University, Shenzhen, China.,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Wei Huang
- Department of Radiation Oncology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, China
| | - Wenhui Hong
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen University, Shenzhen, China.,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Lingling Wang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen University, Shenzhen, China.,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Xiaohui Zhan
- School of Basic Medicine, Chongqing Medical University, Chongqin, China
| | - Zhi Han
- Department of Medicine, Indiana University, School of Medicine, Indianapolis, IN, United States
| | - Dong Ni
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen University, Shenzhen, China.,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Marshall Laboratory of Biomedical Engineering Shenzhen University, Shenzhen, China
| | - Kun Huang
- Department of Medicine, Indiana University, School of Medicine, Indianapolis, IN, United States
| | - Jie Zhang
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
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16
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Cadwell CR, Bowman S, Laszik ZG, Pekmezci M. Loss of fidelity in scanned digital images compared to glass slides of brain tumors resected using cavitron ultrasonic surgical aspirator. Brain Pathol 2021; 31:e12938. [PMID: 33576118 PMCID: PMC8412125 DOI: 10.1111/bpa.12938] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 12/18/2020] [Accepted: 01/04/2021] [Indexed: 11/30/2022] Open
Abstract
Conversion of glass slides to digital images is necessary to capitalize on advances in computational pathology and could potentially transform our approach to primary diagnosis, research, and medical education. Most slide scanners have a limited maximum scannable area and utilize proprietary tissue detection algorithms to selectively scan regions that contain tissue, allowing for increased scanning speed and reduced file size compared to scanning the entire slide at high resolution. However, very small and faintly stained tissue fragments may not be recognized by these algorithms, leading to loss of fidelity in the digital image compared to the glass slides. Cavitron ultrasonic surgical aspirator (CUSA) is frequently used in brain tumor resections, resulting in highly fragmented specimens that are used for primary diagnosis. Here we evaluated the rate of loss of fidelity in 296 digital images from 40 CUSA-resected brain tumors scanned using a Philips Ultra Fast Scanner. Overall, 54% of the slides (at least one from every case) showed loss of fidelity, with at least one tissue fragment not scanned at high resolution. The majority of the missed tissue fragments were small (<0.5 mm), but rare slides were missing fragments greater than 5 mm in greatest dimension. In addition, 19% of the slides with missing tissue showed no indication of loss of fidelity in the digital image itself; the missing tissue could only be appreciated upon review of the glass slides. These results highlight a potential liability in the use of digital images for primary diagnosis in CUSA-resected brain tumor specimens.
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Affiliation(s)
- Cathryn R Cadwell
- Department of Pathology, University of California San Francisco, San Francisco, CA, USA
| | - Sarah Bowman
- Department of Pathology, University of California San Francisco, San Francisco, CA, USA
| | - Zoltan G Laszik
- Department of Pathology, University of California San Francisco, San Francisco, CA, USA
| | - Melike Pekmezci
- Department of Pathology, University of California San Francisco, San Francisco, CA, USA
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17
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Chen Y, Zee J, Smith A, Jayapandian C, Hodgin J, Howell D, Palmer M, Thomas D, Cassol C, Farris AB, Perkinson K, Madabhushi A, Barisoni L, Janowczyk A. Assessment of a computerized quantitative quality control tool for whole slide images of kidney biopsies. J Pathol 2021; 253:268-278. [PMID: 33197281 DOI: 10.1002/path.5590] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [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: 08/10/2020] [Revised: 10/30/2020] [Accepted: 11/11/2020] [Indexed: 12/16/2022]
Abstract
Inconsistencies in the preparation of histology slides and whole-slide images (WSIs) may lead to challenges with subsequent image analysis and machine learning approaches for interrogating the WSI. These variabilities are especially pronounced in multicenter cohorts, where batch effects (i.e. systematic technical artifacts unrelated to biological variability) may introduce biases to machine learning algorithms. To date, manual quality control (QC) has been the de facto standard for dataset curation, but remains highly subjective and is too laborious in light of the increasing scale of tissue slide digitization efforts. This study aimed to evaluate a computer-aided QC pipeline for facilitating a reproducible QC process of WSI datasets. An open source tool, HistoQC, was employed to identify image artifacts and compute quantitative metrics describing visual attributes of WSIs to the Nephrotic Syndrome Study Network (NEPTUNE) digital pathology repository. A comparison in inter-reader concordance between HistoQC aided and unaided curation was performed to quantify improvements in curation reproducibility. HistoQC metrics were additionally employed to quantify the presence of batch effects within NEPTUNE WSIs. Of the 1814 WSIs (458 H&E, 470 PAS, 438 silver, 448 trichrome) from n = 512 cases considered in this study, approximately 9% (163) were identified as unsuitable for subsequent computational analysis. The concordance in the identification of these WSIs among computational pathologists rose from moderate (Gwet's AC1 range 0.43 to 0.59 across stains) to excellent (Gwet's AC1 range 0.79 to 0.93 across stains) agreement when aided by HistoQC. Furthermore, statistically significant batch effects (p < 0.001) in the NEPTUNE WSI dataset were discovered. Taken together, our findings strongly suggest that quantitative QC is a necessary step in the curation of digital pathology cohorts. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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Affiliation(s)
- Yijiang Chen
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Jarcy Zee
- Arbor Research Collaborative for Health, Ann Arbor, MI, USA
| | - Abigail Smith
- Arbor Research Collaborative for Health, Ann Arbor, MI, USA
| | - Catherine Jayapandian
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Jeffrey Hodgin
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - David Howell
- Department of Pathology, Duke University, Durham, NC, USA
| | - Matthew Palmer
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, USA
| | - David Thomas
- Department of Pathology, Duke University, Durham, NC, USA.,Nephrocor, Memphis, TN, USA
| | - Clarissa Cassol
- Renal Pathology Division, Arkana Laboratories, Little Rock, AK, USA.,Department of Pathology - Renal Pathology Division, Ohio State University Medical Center, Columbus, OH, USA
| | - Alton B Farris
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA
| | | | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.,Louis Stokes VA Medical Center, Cleveland, OH, USA
| | - Laura Barisoni
- Department of Pathology, Duke University, Durham, NC, USA.,Department of Medicine, Division of Nephrology, Duke University, Durham, NC, USA
| | - Andrew Janowczyk
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.,Precision Oncology Center, University of Lausanne, Lausanne, Switzerland
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18
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Zhao K, Wu X, Li Z, Wang Y, Xu Z, Li Y, Wu L, Yao S, Huang Y, Liang C, Liu Z. Prognostic value of a modified Immunoscore in patients with stage I -III resectable colon cancer. Chin J Cancer Res 2021; 33:379-390. [PMID: 34321834 PMCID: PMC8286894 DOI: 10.21147/j.issn.1000-9604.2021.03.09] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 01/09/2021] [Accepted: 03/26/2021] [Indexed: 12/09/2022] Open
Abstract
Objective The Immunoscore method has proved fruitful for predicting prognosis in patients with colon cancer. However, there is still room for improvement in this scoring method to achieve further advances in its clinical translation. This study aimed to develop and validate a modified Immunoscore (IS-mod) system for predicting overall survival (OS) in patients with stage I-III colon cancer. Methods The IS-mod was proposed by counting CD3+ and CD8+ immune cells in regions of the tumor core and its invasive margin by drawing two lines of interest. A discovery cohort (N=212) and validation cohort (N=103) from two centers were used to evaluate the prognostic value of the IS-mod. Results In the discovery cohort, 5-year survival rates were 88.6% in the high IS-mod group and 60.7% in the low IS-mod group. Multivariate analysis confirmed that the IS-mod was an independent prognostic factor for OS [adjusted hazard ratio (HR)=0.36, 95% confidence interval (95% CI): 0.20-0.63]. With less annotation and computation cost, the IS-mod achieved performance comparable to that of the Immunoscore-like (IS-like) system (C-index, 0.676 vs. 0.661, P=0.231). The 2-category IS-mod using 47.5% as the threshold had a better prognostic value than that using a fixed threshold of 25% (C-index, 0.653 vs. 0.573, P=0.004). Similar results were confirmed in the validation cohort. Conclusions Our method simplifies the annotation and accelerates the calculation of Immunoscore method, thus making it easier for clinical implementation. The IS-mod achieved comparable prognostic performance when compared to the IS-like system in both cohorts. Besides, we further found that even with a small reference set (N≥120), the IS-mod still demonstrated a stable prognostic value. This finding may inspire other institutions to develop a local reference set of an IS-mod system for more accurate risk stratification of colon cancer.
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Affiliation(s)
- Ke Zhao
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.,School of Medicine, South China University of Technology, Guangzhou 510006, China
| | - Xiaomei Wu
- Department of Radiology, the Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou 510655, China
| | - Zhenhui Li
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China
| | - Yingyi Wang
- Department of Radiology, Zhuhai People's Hospital, Zhuhai Hospital Affiliated with Jinan University, Zhuhai 519000, China
| | - Zeyan Xu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Yajun Li
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Lin Wu
- Department of Pathology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China
| | - Su Yao
- Department of Pathology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Yanqi Huang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.,the Second School of Clinical Medicine, Southern Medical University, Guangzhou 510080, China
| | - Changhong Liang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
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Dong Y, Puttapirat P, Deng J, Zhang X, Li C. LibMI: An Open Source Library for Efficient Histopathological Image Processing. J Pathol Inform 2020; 11:26. [PMID: 33042605 PMCID: PMC7518208 DOI: 10.4103/jpi.jpi_11_20] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 04/20/2020] [Accepted: 06/25/2020] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Whole-slide images (WSIs) as a kind of image data are rapidly growing in the digital pathology domain. With unusual high resolution, these images make them hard to be supported by conventional tools or file formats. Thus, it obstructs data sharing and automated analysis. Here, we propose a library, LibMI, along with its open and standardized image file format. They can be used together to efficiently read, write, modify, and annotate large images. MATERIALS AND METHODS LibMI utilizes the concept of pyramid image structure and lazy propagation from a segment tree algorithm to support reading and modifying and to guarantee that both operations have linear time complexity. Further, a cache mechanism was introduced to speed up the program. RESULTS LibMI is an open and efficient library for histopathological image processing. To demonstrate its functions, we applied it to several tasks including image thresholding, microscopic color correction, and storing pixel-wise information on WSIs. The result shows that libMI is particularly suitable for modifying large images. Furthermore, compared with congeneric libraries and file formats, libMI and modifiable multiscale image (MMSI) run 18.237 times faster on read-only tasks. CONCLUSIONS The combination of libMI library and MMSI file format enables developers to efficiently read and modify WSIs, thus can assist in pixel-wise image processing on extremely large images to promote building image processing pipeline. The library together with the data schema is freely available on GitLab: https://gitlab.com/BioAI/libMI.
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Affiliation(s)
- Yuxin Dong
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Pargorn Puttapirat
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Jingyi Deng
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xiangrong Zhang
- Institute of Intelligent Information Processing, Xidian University, Xi'an, Shaanxi, China
| | - Chen Li
- School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China
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20
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Sturm B, Creytens D, Cook MG, Smits J, van Dijk MCRF, Eijken E, Kurpershoek E, Küsters-Vandevelde HVN, Ooms AHAG, Wauters C, Blokx WAM, van der Laak JAWM. Validation of Whole-slide Digitally Imaged Melanocytic Lesions: Does Z-Stack Scanning Improve Diagnostic Accuracy? J Pathol Inform 2019; 10:6. [PMID: 30972225 PMCID: PMC6415522 DOI: 10.4103/jpi.jpi_46_18] [Citation(s) in RCA: 5] [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: 07/11/2018] [Accepted: 12/17/2018] [Indexed: 01/21/2023] Open
Abstract
Background Accurate diagnosis of melanocytic lesions is challenging, even for expert pathologists. Nowadays, whole-slide imaging (WSI) is used for routine clinical pathology diagnosis in several laboratories. One of the limitations of WSI, as it is most often used, is the lack of a multiplanar focusing option. In this study, we aim to establish the diagnostic accuracy of WSI for melanocytic lesions and investigate the potential accuracy increase of z-stack scanning. Z-stack enables pathologists to use a software focus adjustment, comparable to the fine-focus knob of a conventional light microscope. Materials and Methods Melanocytic lesions (n = 102) were selected from our pathology archives: 35 nevi, 5 spitzoid tumors of unknown malignant potential, and 62 malignant melanomas, including 10 nevoid melanomas. All slides were scanned at a magnification comparable to use of a ×40 objective, in z-stack mode. A ground truth diagnosis was established on the glass slides by four academic dermatopathologists with a special interest in the diagnosis of melanoma. Six nonacademic surgical pathologists subspecialized in dermatopathology examined the cases by WSI. Results An expert consensus diagnosis was achieved in 99 (97%) of cases. Concordance rates between surgical pathologists and the ground truth varied between 75% and 90%, excluding nevoid melanoma cases. Concordance rates of nevoid melanoma varied between 10% and 80%. Pathologists used the software focusing option in 7%-28% of cases, which in 1 case of nevoid melanoma resulted in correcting a misdiagnosis after finding a dermal mitosis. Conclusion Diagnostic accuracy of melanocytic lesions based on glass slides and WSI is comparable with previous publications. A large variability in diagnostic accuracy of nevoid melanoma does exist. Our results show that z-stack scanning, in general, does not increase the diagnostic accuracy of melanocytic.
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Affiliation(s)
- Bart Sturm
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - David Creytens
- Department of Pathology, Ghent University Hospital, Ghent, Belgium
| | - Martin G Cook
- Department of Histopathology, Royal Surrey County Hospital, Guildford, United Kingdom
| | - Jan Smits
- Pathan B.V., Rotterdam, The Netherlands
| | | | - Erik Eijken
- Laboratory for Pathology East Netherlands (LabPON), Hengelo, The Netherlands
| | | | | | | | - Carla Wauters
- Department of Pathology, Canisius Wilhelmina Hospital, Nijmegen, The Netherlands
| | - Willeke A M Blokx
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
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21
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Pantanowitz L, Sharma A, Carter AB, Kurc T, Sussman A, Saltz J. Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives. J Pathol Inform 2018; 9:40. [PMID: 30607307 PMCID: PMC6289005 DOI: 10.4103/jpi.jpi_69_18] [Citation(s) in RCA: 98] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 10/28/2018] [Indexed: 12/13/2022] Open
Abstract
Almost 20 years have passed since the commercial introduction of whole-slide imaging (WSI) scanners. During this time, the creation of various WSI devices with the ability to digitize an entire glass slide has transformed the field of pathology. Parallel advances in computational technology and storage have permitted rapid processing of large-scale WSI datasets. This article provides an overview of important past and present efforts related to WSI. An account of how the virtual microscope evolved from the need to visualize and manage satellite data for earth science applications is provided. The article also discusses important milestones beginning from the first WSI scanner designed by Bacus to the Food and Drug Administration approval of the first digital pathology system for primary diagnosis in surgical pathology. As pathology laboratories commit to going fully digitalize, the need has emerged to include WSIs into an enterprise-level vendor-neutral archive (VNA). The different types of VNAs available are reviewed as well as how best to implement them and how pathology can benefit from participating in this effort. Differences between traditional image algorithms that extract pixel-, object-, and semantic-level features versus deep learning methods are highlighted. The need for large-scale data management, analysis, and visualization in computational pathology is also addressed.
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Affiliation(s)
- Liron Pantanowitz
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University, GA, USA
| | - Alexis B. Carter
- Department of Pathology and Laboratory Medicine, Children's Healthcare of Atlanta, GA, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Alan Sussman
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
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22
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Mateevitsi V, Patel T, Leigh J, Levy B. Reimagining the microscope in the 21(st) century using the scalable adaptive graphics environment. J Pathol Inform 2015; 6:25. [PMID: 26110092 PMCID: PMC4470015 DOI: 10.4103/2153-3539.158042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [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: 03/03/2015] [Accepted: 03/24/2015] [Indexed: 11/18/2022] Open
Abstract
Background: Whole-slide imaging (WSI), while technologically mature, remains in the early adopter phase of the technology adoption lifecycle. One reason for this current situation is that current methods of visualizing and using WSI closely follow long-existing workflows for glass slides. We set out to “reimagine” the digital microscope in the era of cloud computing by combining WSI with the rich collaborative environment of the Scalable Adaptive Graphics Environment (SAGE). SAGE is a cross-platform, open-source visualization and collaboration tool that enables users to access, display and share a variety of data-intensive information, in a variety of resolutions and formats, from multiple sources, on display walls of arbitrary size. Methods: A prototype of a WSI viewer app in the SAGE environment was created. While not full featured, it enabled the testing of our hypothesis that these technologies could be blended together to change the essential nature of how microscopic images are utilized for patient care, medical education, and research. Results: Using the newly created WSI viewer app, demonstration scenarios were created in the patient care and medical education scenarios. This included a live demonstration of a pathology consultation at the International Academy of Digital Pathology meeting in Boston in November 2014. Conclusions: SAGE is well suited to display, manipulate and collaborate using WSIs, along with other images and data, for a variety of purposes. It goes beyond how glass slides and current WSI viewers are being used today, changing the nature of digital pathology in the process. A fully developed WSI viewer app within SAGE has the potential to encourage the wider adoption of WSI throughout pathology.
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Affiliation(s)
- Victor Mateevitsi
- Department of Computer Science (MC 152), University of Illinois at Chicago, IL 60607, USA
| | - Tushar Patel
- Department of Pathology (MC 847), University of Illinois at Chicago, IL 60607, USA
| | - Jason Leigh
- Laboratory for Advanced Visualizations and Applications, University of Hawai'i at Manoa, 309 Pacific Ocean Sciences and Technology Building (POST), Honolulu 96822, Hawaii, USA
| | - Bruce Levy
- Department of Pathology (MC 847), University of Illinois at Chicago, IL 60607, USA
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23
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Goode A, Gilbert B, Harkes J, Jukic D, Satyanarayanan M. OpenSlide: A vendor-neutral software foundation for digital pathology. J Pathol Inform 2013; 4:27. [PMID: 24244884 PMCID: PMC3815078 DOI: 10.4103/2153-3539.119005] [Citation(s) in RCA: 237] [Impact Index Per Article: 21.5] [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: 07/17/2013] [Accepted: 08/19/2013] [Indexed: 11/14/2022] Open
Abstract
Although widely touted as a replacement for glass slides and microscopes in pathology, digital slides present major challenges in data storage, transmission, processing and interoperability. Since no universal data format is in widespread use for these images today, each vendor defines its own proprietary data formats, analysis tools, viewers and software libraries. This creates issues not only for pathologists, but also for interoperability. In this paper, we present the design and implementation of OpenSlide, a vendor-neutral C library for reading and manipulating digital slides of diverse vendor formats. The library is extensible and easily interfaced to various programming languages. An application written to the OpenSlide interface can transparently handle multiple vendor formats. OpenSlide is in use today by many academic and industrial organizations world-wide, including many research sites in the United States that are funded by the National Institutes of Health.
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Affiliation(s)
- Adam Goode
- School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States ; Google, Pittsburgh, PA, USA
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