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Gonzalez R, Saha A, Campbell CJ, Nejat P, Lokker C, Norgan AP. Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities. J Pathol Inform 2024; 15:100347. [PMID: 38162950 PMCID: PMC10755052 DOI: 10.1016/j.jpi.2023.100347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/06/2023] [Accepted: 11/01/2023] [Indexed: 01/03/2024] Open
Abstract
This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Systems" with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation strategies: those that need innovative approaches, time, or future technological capabilities and those that require a conceptual reappraisal from a critical perspective. Then, a novel opportunity to support "Learning Health Systems" by integrating hidden information extracted by ML models from digitalized histopathology slides with other healthcare big data is presented.
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Affiliation(s)
- Ricardo Gonzalez
- DeGroote School of Business, McMaster University, Hamilton, Ontario, Canada
- Division of Computational Pathology and Artificial Intelligence, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
| | - Ashirbani Saha
- Department of Oncology, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
- Escarpment Cancer Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Ontario, Canada
| | - Clinton J.V. Campbell
- William Osler Health System, Brampton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Peyman Nejat
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Cynthia Lokker
- Health Information Research Unit, Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Andrew P. Norgan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
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Wang Z, Peng H, Wan J, Song A. Identification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm. Med Mol Morphol 2024:10.1007/s00795-024-00399-8. [PMID: 39088070 DOI: 10.1007/s00795-024-00399-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 07/15/2024] [Indexed: 08/02/2024]
Abstract
The aim of this study is to establish a deep learning (DL) model to predict the pathological type of gastric adenocarcinoma cancer based on whole-slide images(WSIs). We downloaded 356 histopathological images of gastric adenocarcinoma (STAD) patients from The Cancer Genome Atlas database and randomly divided them into the training set, validation set and test set (8:1:1). Additionally, 80 H&E-stained WSIs of STAD were collected for external validation. The CLAM tool was used to cut the WSIs and further construct the model by DL algorithm, achieving an accuracy of over 90% in identifying and predicting histopathological subtypes. External validation results demonstrated the model had a certain generalization ability. Moreover, DL features were extracted from the model to further investigate the differences in immune infiltration and patient prognosis between the two subtypes. The DL model can accurately predict the pathological classification of STAD patients, and provide certain reference value for clinical diagnosis. The nomogram combining DL-signature, gene-signature and clinical features can be used as a prognostic classifier for clinical decision-making and treatment.
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Affiliation(s)
- Zhihui Wang
- Department of Ultrasound Imaging, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430101, Hubei, China
| | - Hui Peng
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430101, Hubei, China
| | - Jie Wan
- Department of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430101, Hubei, China
| | - Anping Song
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430101, Hubei, China.
- Department of Oncology, Tongji Hospital Sino-French New City Branch, Caidian District, No.288 Xintian Avenue, Wuhan, 430101, Hubei, China.
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Jiang L, Huang S, Luo C, Zhang J, Chen W, Liu Z. An improved multi-scale gradient generative adversarial network for enhancing classification of colorectal cancer histological images. Front Oncol 2023; 13:1240645. [PMID: 38023227 PMCID: PMC10679330 DOI: 10.3389/fonc.2023.1240645] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 10/23/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Deep learning-based solutions for histological image classification have gained attention in recent years due to their potential for objective evaluation of histological images. However, these methods often require a large number of expert annotations, which are both time-consuming and labor-intensive to obtain. Several scholars have proposed generative models to augment labeled data, but these often result in label uncertainty due to incomplete learning of the data distribution. Methods To alleviate these issues, a method called InceptionV3-SMSG-GAN has been proposed to enhance classification performance by generating high-quality images. Specifically, images synthesized by Multi-Scale Gradients Generative Adversarial Network (MSG-GAN) are selectively added to the training set through a selection mechanism utilizing a trained model to choose generated images with higher class probabilities. The selection mechanism filters the synthetic images that contain ambiguous category information, thus alleviating label uncertainty. Results Experimental results show that compared with the baseline method which uses InceptionV3, the proposed method can significantly improve the performance of pathological image classification from 86.87% to 89.54% for overall accuracy. Additionally, the quality of generated images is evaluated quantitatively using various commonly used evaluation metrics. Discussion The proposed InceptionV3-SMSG-GAN method exhibited good classification ability, where histological image could be divided into nine categories. Future work could focus on further refining the image generation and selection processes to optimize classification performance.
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Affiliation(s)
- Liwen Jiang
- Department of Pathology, Affiliated Cancer Hospital and Institution of Guangzhou Medical University, Guangzhou, China
| | - Shuting Huang
- School of Information Engineering, Guangdong University of Technology, Guangzhou, China
| | - Chaofan Luo
- School of Information Engineering, Guangdong University of Technology, Guangzhou, China
| | - Jiangyu Zhang
- Department of Pathology, Affiliated Cancer Hospital and Institution of Guangzhou Medical University, Guangzhou, China
| | - Wenjing Chen
- Department of Pathology, Guangdong Women and Children Hospital, Guangzhou, China
| | - Zhenyu Liu
- School of Information Engineering, Guangdong University of Technology, Guangzhou, China
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Wang Z, Zhang X, Wang X, Li J, Zhang Y, Zhang T, Xu S, Jiao W, Niu H. Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends. Front Oncol 2023; 13:1152622. [PMID: 37727213 PMCID: PMC10505614 DOI: 10.3389/fonc.2023.1152622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 08/11/2023] [Indexed: 09/21/2023] Open
Abstract
This study summarizes the latest achievements, challenges, and future research directions in deep learning technologies for the diagnosis of renal cell carcinoma (RCC). This is the first review of deep learning in RCC applications. This review aims to show that deep learning technologies hold great promise in the field of RCC diagnosis, and we look forward to more research results to meet us for the mutual benefit of renal cell carcinoma patients. Medical imaging plays an important role in the early detection of renal cell carcinoma (RCC), as well as in the monitoring and evaluation of RCC during treatment. The most commonly used technologies such as contrast enhanced computed tomography (CECT), ultrasound and magnetic resonance imaging (MRI) are now digitalized, allowing deep learning to be applied to them. Deep learning is one of the fastest growing fields in the direction of medical imaging, with rapidly emerging applications that have changed the traditional medical treatment paradigm. With the help of deep learning-based medical imaging tools, clinicians can diagnose and evaluate renal tumors more accurately and quickly. This paper describes the application of deep learning-based imaging techniques in RCC assessment and provides a comprehensive review.
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Affiliation(s)
- Zijie Wang
- Department of Vascular Intervention, ShengLi Oilfield Center Hospital, Dongying, China
| | - Xiaofei Zhang
- Department of Education and Training, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xinning Wang
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jianfei Li
- Extenics Specialized Committee, Chinese Association of Artificial Intelligence (ESCCAAI), Beijing, China
| | - Yuhao Zhang
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Tianwei Zhang
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Shang Xu
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wei Jiao
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Haitao Niu
- Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
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Zhang H, He Y, Wu X, Huang P, Qin W, Wang F, Ye J, Huang X, Liao Y, Chen H, Guo L, Shi X, Luo L. PathNarratives: Data annotation for pathological human-AI collaborative diagnosis. Front Med (Lausanne) 2023; 9:1070072. [PMID: 36777158 PMCID: PMC9908590 DOI: 10.3389/fmed.2022.1070072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 12/22/2022] [Indexed: 01/27/2023] Open
Abstract
Pathology is the gold standard of clinical diagnosis. Artificial intelligence (AI) in pathology becomes a new trend, but it is still not widely used due to the lack of necessary explanations for pathologists to understand the rationale. Clinic-compliant explanations besides the diagnostic decision of pathological images are essential for AI model training to provide diagnostic suggestions assisting pathologists practice. In this study, we propose a new annotation form, PathNarratives, that includes a hierarchical decision-to-reason data structure, a narrative annotation process, and a multimodal interactive annotation tool. Following PathNarratives, we recruited 8 pathologist annotators to build a colorectal pathological dataset, CR-PathNarratives, containing 174 whole-slide images (WSIs). We further experiment on the dataset with classification and captioning tasks to explore the clinical scenarios of human-AI-collaborative pathological diagnosis. The classification tasks show that fine-grain prediction enhances the overall classification accuracy from 79.56 to 85.26%. In Human-AI collaboration experience, the trust and confidence scores from 8 pathologists raised from 3.88 to 4.63 with providing more details. Results show that the classification and captioning tasks achieve better results with reason labels, provide explainable clues for doctors to understand and make the final decision and thus can support a better experience of human-AI collaboration in pathological diagnosis. In the future, we plan to optimize the tools for the annotation process, and expand the datasets with more WSIs and covering more pathological domains.
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Affiliation(s)
- Heyu Zhang
- College of Engineering, Peking University, Beijing, China
| | - Yan He
- Department of Pathology, Longgang Central Hospital of Shenzhen, Shenzhen, China
| | - Xiaomin Wu
- College of Engineering, Peking University, Beijing, China
| | - Peixiang Huang
- College of Engineering, Peking University, Beijing, China
| | - Wenkang Qin
- College of Engineering, Peking University, Beijing, China
| | - Fan Wang
- College of Engineering, Peking University, Beijing, China
| | - Juxiang Ye
- Department of Pathology, School of Basic Medical Science, Peking University Health Science Center, Peking University Third Hospital, Beijing, China
| | - Xirui Huang
- Department of Pathology, Longgang Central Hospital of Shenzhen, Shenzhen, China
| | - Yanfang Liao
- Department of Pathology, Longgang Central Hospital of Shenzhen, Shenzhen, China
| | - Hang Chen
- College of Engineering, Peking University, Beijing, China
| | - Limei Guo
- Department of Pathology, School of Basic Medical Science, Peking University Health Science Center, Peking University Third Hospital, Beijing, China,*Correspondence: Limei Guo,
| | - Xueying Shi
- Department of Pathology, School of Basic Medical Science, Peking University Health Science Center, Peking University Third Hospital, Beijing, China,Xueying Shi,
| | - Lin Luo
- College of Engineering, Peking University, Beijing, China,Lin Luo,
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