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Wang YL, Gao S, Xiao Q, Li C, Grzegorzek M, Zhang YY, Li XH, Kang Y, Liu FH, Huang DH, Gong TT, Wu QJ. Role of artificial intelligence in digital pathology for gynecological cancers. Comput Struct Biotechnol J 2024; 24:205-212. [PMID: 38510535 PMCID: PMC10951449 DOI: 10.1016/j.csbj.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/08/2024] [Accepted: 03/09/2024] [Indexed: 03/22/2024] Open
Abstract
The diagnosis of cancer is typically based on histopathological sections or biopsies on glass slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract quantitative information from digital histopathology images as a rapid growth in oncology data. Gynecological cancers are major diseases affecting women's health worldwide. They are characterized by high mortality and poor prognosis, underscoring the critical importance of early detection, treatment, and identification of prognostic factors. This review highlights the various clinical applications of AI in gynecological cancers using digitized histopathology slides. Particularly, deep learning models have shown promise in accurately diagnosing, classifying histopathological subtypes, and predicting treatment response and prognosis. Furthermore, the integration with transcriptomics, proteomics, and other multi-omics techniques can provide valuable insights into the molecular features of diseases. Despite the considerable potential of AI, substantial challenges remain. Further improvements in data acquisition and model optimization are required, and the exploration of broader clinical applications, such as the biomarker discovery, need to be explored.
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Affiliation(s)
- Ya-Li Wang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Information Center, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Song Gao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qian Xiao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Chen Li
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Marcin Grzegorzek
- Institute for Medical Informatics, University of Luebeck, Luebeck, Germany
| | - Ying-Ying Zhang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiao-Han Li
- Department of Pathology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ye Kang
- Department of Pathology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Fang-Hua Liu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Dong-Hui Huang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ting-Ting Gong
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qi-Jun Wu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
- NHC Key Laboratory of Advanced Reproductive Medicine and Fertility (China Medical University), National Health Commission, Shenyang, China
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Cannarozzi AL, Massimino L, Latiano A, Parigi TL, Giuliani F, Bossa F, Di Brina AL, Ungaro F, Biscaglia G, Danese S, Perri F, Palmieri O. Artificial intelligence: A new tool in the pathologist's armamentarium for the diagnosis of IBD. Comput Struct Biotechnol J 2024; 23:3407-3417. [PMID: 39345902 PMCID: PMC11437746 DOI: 10.1016/j.csbj.2024.09.003] [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: 07/20/2024] [Revised: 09/06/2024] [Accepted: 09/06/2024] [Indexed: 10/01/2024] Open
Abstract
Inflammatory bowel diseases (IBD) are classified into two entities, namely Crohn's disease (CD) and ulcerative colitis (UC), which differ in disease trajectories, genetics, epidemiological, clinical, endoscopic, and histopathological aspects. As no single golden standard modality for diagnosing IBD exists, the differential diagnosis among UC, CD, and non-IBD involves a multidisciplinary approach, considering professional groups that include gastroenterologists, endoscopists, radiologists, and pathologists. In this context, histological examination of endoscopic or surgical specimens plays a fundamental role. Nevertheless, in differentiating IBD from non-IBD colitis, the histopathological evaluation of the morphological lesions is limited by sampling and subjective human judgment, leading to potential diagnostic discrepancies. To overcome these limitations, artificial intelligence (AI) techniques are emerging to enable automated analysis of medical images with advantages in accuracy, precision, and speed of investigation, increasing interest in the histological analysis of gastrointestinal inflammation. This review aims to provide an overview of the most recent knowledge and advances in AI methods, summarizing its applications in the histopathological analysis of endoscopic biopsies from IBD patients, and discussing its strengths and limitations in daily clinical practice.
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Affiliation(s)
- Anna Lucia Cannarozzi
- Division of Gastroenterology, Fondazione IRCCS - Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Luca Massimino
- Gastroenterology and Digestive Endoscopy Department, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Anna Latiano
- Division of Gastroenterology, Fondazione IRCCS - Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Tommaso Lorenzo Parigi
- Gastroenterology and Digestive Endoscopy Department, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Francesco Giuliani
- Innovation & Research Unit, Fondazione IRCCS "Casa Sollievo della Sofferenza", San Giovanni Rotondo, Italy
| | - Fabrizio Bossa
- Division of Gastroenterology, Fondazione IRCCS - Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Anna Laura Di Brina
- Division of Gastroenterology, Fondazione IRCCS - Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Federica Ungaro
- Gastroenterology and Digestive Endoscopy Department, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Giuseppe Biscaglia
- Division of Gastroenterology, Fondazione IRCCS - Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Silvio Danese
- Faculty of Medicine, Università Vita-Salute San Raffaele, Milan, Italy
| | - Francesco Perri
- Division of Gastroenterology, Fondazione IRCCS - Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Orazio Palmieri
- Division of Gastroenterology, Fondazione IRCCS - Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
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Xu L, Cao F, Wang L, Liu W, Gao M, Zhang L, Hong F, Lin M. Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients. Ren Fail 2024; 46:2324071. [PMID: 38494197 PMCID: PMC10946267 DOI: 10.1080/0886022x.2024.2324071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024] Open
Abstract
INTRODUCTION The study presented here aimed to establish a predictive model for heart failure (HF) and all-cause mortality in peritoneal dialysis (PD) patients with machine learning (ML) algorithm. METHODS We retrospectively included 1006 patients who initiated PD from 2010 to 2016. XGBoost, random forest (RF), and AdaBoost were used to train models for assessing risk for 1-year and 5-year HF hospitalization and mortality. The performance was validated using fivefold cross-validation. The optimal ML algorithm was used to construct the models to predictive the risk of the HF and all-cause mortality. The prediction performance of ML methods and Cox regression was compared. RESULTS Over a median follow-up of 49 months. Two hundred and ninety-eight patients developed HF required hospitalization; 199 patients died during the follow-up. The RF model (AUC = 0.853) was the best performing model for predicting HF, and the XGBoost model (AUC = 0.871) was the best model for predicting mortality. Baseline moderate or severe renal disease, systolic blood pressure (SBP), body mass index (BMI), age, Charlson Comorbidity Index (CCI) score were strongly associated with HF hospitalization, whereas age, CCI score, creatinine, age, high-density lipoprotein cholesterol (HDL-C), total cholesterol, baseline estimated glomerular filtration rate (eGFR) were the most significant predictors of mortality. For all the above endpoints, the ML models demonstrated better discrimination than Cox regression. CONCLUSIONS We developed and validated a novel method to predict the risk factors of HF and all-cause mortality that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among PD patients.
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Affiliation(s)
- Liping Xu
- Department of Nephrology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, China
| | - Fang Cao
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
- Department of Nursing, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Lian Wang
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Weihua Liu
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Meizhu Gao
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Li Zhang
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Fuyuan Hong
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Miao Lin
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
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Li Y, Xiong X, Liu X, Xu M, Yang B, Li X, Li Y, Lin B, Xu B. Predicting BRCA mutation and stratifying targeted therapy response using multimodal learning: a multicenter study. Ann Med 2024; 56:2399759. [PMID: 39258876 PMCID: PMC11391871 DOI: 10.1080/07853890.2024.2399759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 07/29/2024] [Accepted: 07/30/2024] [Indexed: 09/12/2024] Open
Abstract
BACKGROUND The status of BRCA1/2 genes plays a crucial role in the treatment decision-making process for multiple cancer types. However, due to high costs and limited resources, a demand for BRCA1/2 genetic testing among patients is currently unmet. Notably, not all patients with BRCA1/2 mutations achieve favorable outcomes with poly (ADP-ribose) polymerase inhibitors (PARPi), indicating the necessity for risk stratification. In this study, we aimed to develop and validate a multimodal model for predicting BRCA1/2 gene status and prognosis with PARPi treatment. METHODS We included 1695 slides from 1417 patients with ovarian, breast, prostate, and pancreatic cancers across three independent cohorts. Using a self-attention mechanism, we constructed a multi-instance attention model (MIAM) to detect BRCA1/2 gene status from hematoxylin and eosin (H&E) pathological images. We further combined tissue features from the MIAM model, cell features, and clinical factors (the MIAM-C model) to predict BRCA1/2 mutations and progression-free survival (PFS) with PARPi therapy. Model performance was evaluated using area under the curve (AUC) and Kaplan-Meier analysis. Morphological features contributing to MIAM-C were analyzed for interpretability. RESULTS Across the four cancer types, MIAM-C outperformed the deep learning-based MIAM in identifying the BRCA1/2 genotype. Interpretability analysis revealed that high-attention regions included high-grade tumors and lymphocytic infiltration, which correlated with BRCA1/2 mutations. Notably, high lymphocyte ratios appeared characteristic of BRCA1/2 mutations. Furthermore, MIAM-C predicted PARPi therapy response (log-rank p < 0.05) and served as an independent prognostic factor for patients with BRCA1/2-mutant ovarian cancer (p < 0.05, hazard ratio:0.4, 95% confidence interval: 0.16-0.99). CONCLUSIONS The MIAM-C model accurately detected BRCA1/2 gene status and effectively stratified prognosis for patients with BRCA1/2 mutations.
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Affiliation(s)
- Yi Li
- School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
| | - Xiaomin Xiong
- School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
| | - Xiaohua Liu
- Bioengineering College of Chongqing University, Chongqing, China
| | - Mengke Xu
- Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
| | - Boping Yang
- Department of General Gynecology, Women and Children’s Hospital of Chongqing Medical University, Chongqing Health Center for Women and Children, Chongqing, China
| | - Xiaoju Li
- Department of Pathology, Chongqing University Cancer Hospital and School of Medicine, Chongqing University, Chongqing, China
| | - Yu Li
- Department of Pathology, Chongqing University Cancer Hospital and School of Medicine, Chongqing University, Chongqing, China
| | - Bo Lin
- Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
| | - Bo Xu
- School of Medicine, Chongqing University, Chongqing, China
- Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer, Chongqing University Cancer Hospital, Chongqing, China
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Jiang Y, Chen Y, Cheng Q, Lu W, Li Y, Zuo X, Wu Q, Wang X, Zhang F, Wang D, Wang Q, Lv T, Song Y, Zhan P. A random survival forest-based pathomics signature classifies immunotherapy prognosis and profiles TIME and genomics in ES-SCLC patients. Cancer Immunol Immunother 2024; 73:241. [PMID: 39358575 PMCID: PMC11448477 DOI: 10.1007/s00262-024-03829-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2024] [Accepted: 09/06/2024] [Indexed: 10/04/2024]
Abstract
BACKGROUND Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine tumor with high mortality, and only a limited subset of extensive-stage SCLC (ES-SCLC) patients demonstrate prolonged survival under chemoimmunotherapy, which warrants the exploration of reliable biomarkers. Herein, we built a machine learning-based model using pathomics features extracted from hematoxylin and eosin (H&E)-stained images to classify prognosis and explore its potential association with genomics and TIME. METHODS We retrospectively recruited ES-SCLC patients receiving first-line chemoimmunotherapy at Nanjing Jinling Hospital between April 2020 and August 2023. Digital H&E-stained whole-slide images were acquired, and targeted next-generation sequencing, programmed death ligand-1 staining, and multiplex immunohistochemical staining for immune cells were performed on a subset of patients. A random survival forest (RSF) model encompassing clinical and pathomics features was established to predict overall survival. The function of putative genes was assessed via single-cell RNA sequencing. RESULTS AND CONCLUSION During the median follow-up period of 12.12 months, 118 ES-SCLC patients receiving first-line immunotherapy were recruited. The RSF model utilizing three pathomics features and liver metastases, bone metastases, smoking status, and lactate dehydrogenase, could predict the survival of first-line chemoimmunotherapy in patients with ES-SCLC with favorable discrimination and calibration. Underlyingly, the higher RSF-Score potentially indicated more infiltration of CD8+ T cells in the stroma as well as a greater probability of MCL-1 amplification and EP300 mutation. At the single-cell level, MCL-1 was associated with TNFA-NFKB signaling and apoptosis-related processes. Hopefully, this noninvasive model could act as a biomarker for immunotherapy, potentially facilitating precision medicine in the management of ES-SCLC.
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Affiliation(s)
- Yuxin Jiang
- School of Medicine, Southeast University, Nanjing, 210000, China
| | - Yueying Chen
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Medical School, Jinling Hospital, Nanjing University, Nanjing, 210002, China
| | - Qinpei Cheng
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Medical School, Jinling Hospital, Nanjing University, Nanjing, 210002, China
| | - Wanjun Lu
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Medical School, Jinling Hospital, Nanjing University, Nanjing, 210002, China
| | - Yu Li
- Department of Respiratory and Critical Care Medicine, Jinling Hospital, Affiliated Hospital of Nanjing Medical School, Nanjing, 210002, China
| | - Xueying Zuo
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Medical School, Jinling Hospital, Nanjing University, Nanjing, 210002, China
| | - Qiuxia Wu
- Jinling Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210002, China
| | - Xiaoxia Wang
- Department of Pathology, Affiliated Hospital of Medical School, Jinling Hospital, Nanjing University, Nanjing, 210002, China
| | - Fang Zhang
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Medical School, Jinling Hospital, Nanjing University, Nanjing, 210002, China
- Department of Respiratory and Critical Care Medicine, Jinling Hospital, Affiliated Hospital of Nanjing Medical School, Nanjing, 210002, China
- Department of Respiratory and Critical Care Medicine, School of Medicine, Jinling Hospital, Southeast University, 305 Zhongshan East Road, Nanjing, 210002, China
| | - Dong Wang
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Medical School, Jinling Hospital, Nanjing University, Nanjing, 210002, China
- Department of Respiratory and Critical Care Medicine, Jinling Hospital, Affiliated Hospital of Nanjing Medical School, Nanjing, 210002, China
- Department of Respiratory and Critical Care Medicine, School of Medicine, Jinling Hospital, Southeast University, 305 Zhongshan East Road, Nanjing, 210002, China
| | - Qin Wang
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Medical School, Jinling Hospital, Nanjing University, Nanjing, 210002, China.
| | - Tangfeng Lv
- School of Medicine, Southeast University, Nanjing, 210000, China.
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Medical School, Jinling Hospital, Nanjing University, Nanjing, 210002, China.
- Department of Respiratory and Critical Care Medicine, Jinling Hospital, Affiliated Hospital of Nanjing Medical School, Nanjing, 210002, China.
- Department of Respiratory and Critical Care Medicine, School of Medicine, Jinling Hospital, Southeast University, 305 Zhongshan East Road, Nanjing, 210002, China.
| | - Yong Song
- School of Medicine, Southeast University, Nanjing, 210000, China.
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Medical School, Jinling Hospital, Nanjing University, Nanjing, 210002, China.
- Department of Respiratory and Critical Care Medicine, Jinling Hospital, Affiliated Hospital of Nanjing Medical School, Nanjing, 210002, China.
- Department of Respiratory and Critical Care Medicine, School of Medicine, Jinling Hospital, Southeast University, 305 Zhongshan East Road, Nanjing, 210002, China.
| | - Ping Zhan
- School of Medicine, Southeast University, Nanjing, 210000, China.
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Medical School, Jinling Hospital, Nanjing University, Nanjing, 210002, China.
- Department of Respiratory and Critical Care Medicine, Jinling Hospital, Affiliated Hospital of Nanjing Medical School, Nanjing, 210002, China.
- Department of Respiratory and Critical Care Medicine, School of Medicine, Jinling Hospital, Southeast University, 305 Zhongshan East Road, Nanjing, 210002, China.
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Wang Z, Ma J, Gao Q, Bain C, Imoto S, Liò P, Cai H, Chen H, Song J. Dual-stream multi-dependency graph neural network enables precise cancer survival analysis. Med Image Anal 2024; 97:103252. [PMID: 38963973 DOI: 10.1016/j.media.2024.103252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 05/24/2024] [Accepted: 06/21/2024] [Indexed: 07/06/2024]
Abstract
Histopathology image-based survival prediction aims to provide a precise assessment of cancer prognosis and can inform personalized treatment decision-making in order to improve patient outcomes. However, existing methods cannot automatically model the complex correlations between numerous morphologically diverse patches in each whole slide image (WSI), thereby preventing them from achieving a more profound understanding and inference of the patient status. To address this, here we propose a novel deep learning framework, termed dual-stream multi-dependency graph neural network (DM-GNN), to enable precise cancer patient survival analysis. Specifically, DM-GNN is structured with the feature updating and global analysis branches to better model each WSI as two graphs based on morphological affinity and global co-activating dependencies. As these two dependencies depict each WSI from distinct but complementary perspectives, the two designed branches of DM-GNN can jointly achieve the multi-view modeling of complex correlations between the patches. Moreover, DM-GNN is also capable of boosting the utilization of dependency information during graph construction by introducing the affinity-guided attention recalibration module as the readout function. This novel module offers increased robustness against feature perturbation, thereby ensuring more reliable and stable predictions. Extensive benchmarking experiments on five TCGA datasets demonstrate that DM-GNN outperforms other state-of-the-art methods and offers interpretable prediction insights based on the morphological depiction of high-attention patches. Overall, DM-GNN represents a powerful and auxiliary tool for personalized cancer prognosis from histopathology images and has great potential to assist clinicians in making personalized treatment decisions and improving patient outcomes.
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Affiliation(s)
- Zhikang Wang
- Xiangya Hospital, Central South University, Changsha, China; Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Australia; Wenzhou Medical University-Monash Biomedicine Discovery Institute (BDI) Alliance in Clinical and Experimental Biomedicine, Wenzhou, China
| | - Jiani Ma
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Qian Gao
- Xiangya Hospital, Central South University, Changsha, China
| | - Chris Bain
- Faculty of Information Technology, Monash University, Melbourne, Australia
| | - Seiya Imoto
- Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Pietro Liò
- Department of Computer Science and Technology, The University of Cambridge, Cambridge, United Kingdom
| | - Hongmin Cai
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Hao Chen
- Department of Computer Science and Engineering and Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Australia; Wenzhou Medical University-Monash Biomedicine Discovery Institute (BDI) Alliance in Clinical and Experimental Biomedicine, Wenzhou, China.
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Alsaafin A, Nejat P, Shafique A, Khan J, Alfasly S, Alabtah G, Tizhoosh HR. Sequential Patching Lattice for Image Classification and Enquiry: Streamlining Digital Pathology Image Processing. THE AMERICAN JOURNAL OF PATHOLOGY 2024; 194:1898-1912. [PMID: 39032601 DOI: 10.1016/j.ajpath.2024.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 06/06/2024] [Accepted: 06/18/2024] [Indexed: 07/23/2024]
Abstract
Digital pathology and the integration of artificial intelligence (AI) models have revolutionized histopathology, opening new opportunities. With the increasing availability of whole-slide images (WSIs), demand is growing for efficient retrieval, processing, and analysis of relevant images from vast biomedical archives. However, processing WSIs presents challenges due to their large size and content complexity. Full computer digestion of WSIs is impractical, and processing all patches individually is prohibitively expensive. In this article, we propose an unsupervised patching algorithm, Sequential Patching Lattice for Image Classification and Enquiry (SPLICE). This novel approach condenses a histopathology WSI into a compact set of representative patches, forming a collage of WSI while minimizing redundancy. SPLICE prioritizes patch quality and uniqueness by sequentially analyzing a WSI and selecting nonredundant representative features. In search and match applications, SPLICE showed improved accuracy, reduced computation time, and storage requirements compared with existing state-of-the-art methods. As an unsupervised method, SPLICE effectively reduced storage requirements for representing tissue images by 50%. This reduction can enable numerous algorithms in computational pathology to operate much more efficiently, paving the way for accelerated adoption of digital pathology.
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Affiliation(s)
- Areej Alsaafin
- KIMIA Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Peyman Nejat
- KIMIA Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Abubakr Shafique
- KIMIA Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Jibran Khan
- KIMIA Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Saghir Alfasly
- KIMIA Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Ghazal Alabtah
- KIMIA Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Hamid R Tizhoosh
- KIMIA Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota.
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Mathur A, Arya N, Pasupa K, Saha S, Roy Dey S, Saha S. Breast cancer prognosis through the use of multi-modal classifiers: current state of the art and the way forward. Brief Funct Genomics 2024; 23:561-569. [PMID: 38688724 DOI: 10.1093/bfgp/elae015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 03/01/2024] [Accepted: 04/09/2024] [Indexed: 05/02/2024] Open
Abstract
We present a survey of the current state-of-the-art in breast cancer detection and prognosis. We analyze the evolution of Artificial Intelligence-based approaches from using just uni-modal information to multi-modality for detection and how such paradigm shift facilitates the efficacy of detection, consistent with clinical observations. We conclude that interpretable AI-based predictions and ability to handle class imbalance should be considered priority.
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Affiliation(s)
- Archana Mathur
- Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Yelahanka, 560064, Karnataka, India
| | - Nikhilanand Arya
- School of Computer Engineering, Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneshwar, 751024, Odisha, India
| | - Kitsuchart Pasupa
- School of Information Technology, King Mongkut's Institute of Technology Ladkrabang, 1 Soi Chalongkrung 1, 10520, Bangkok, Thailand
| | - Sriparna Saha
- Computer Science and Engineering, Indian Institute of Technology Patna, Bihta, 801106, Bihar, India
| | - Sudeepa Roy Dey
- Department of Computer Science and Engineering, PES University, Hosur Road, 560100, Karnataka, India
| | - Snehanshu Saha
- CSIS and APPCAIR, BITS Pilani K.K Birla Goa Campus, Goa, 403726, Goa, India
- Div of AI Research, HappyMonk AI, Bangalore, 560078, Karnataka, India
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Todhunter ME, Jubair S, Verma R, Saqe R, Shen K, Duffy B. Artificial intelligence and machine learning applications for cultured meat. Front Artif Intell 2024; 7:1424012. [PMID: 39381621 PMCID: PMC11460582 DOI: 10.3389/frai.2024.1424012] [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: 04/26/2024] [Accepted: 08/21/2024] [Indexed: 10/10/2024] Open
Abstract
Cultured meat has the potential to provide a complementary meat industry with reduced environmental, ethical, and health impacts. However, major technological challenges remain which require time-and resource-intensive research and development efforts. Machine learning has the potential to accelerate cultured meat technology by streamlining experiments, predicting optimal results, and reducing experimentation time and resources. However, the use of machine learning in cultured meat is in its infancy. This review covers the work available to date on the use of machine learning in cultured meat and explores future possibilities. We address four major areas of cultured meat research and development: establishing cell lines, cell culture media design, microscopy and image analysis, and bioprocessing and food processing optimization. In addition, we have included a survey of datasets relevant to CM research. This review aims to provide the foundation necessary for both cultured meat and machine learning scientists to identify research opportunities at the intersection between cultured meat and machine learning.
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Affiliation(s)
| | - Sheikh Jubair
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Rikard Saqe
- Department of Biology, University of Waterloo, Waterloo, ON, Canada
| | - Kevin Shen
- Department of Mathematics, University of Waterloo, Waterloo, ON, Canada
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10
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de Lacy N, Lam WY, Ramshaw M. RiskPath : Explainable deep learning for multistep biomedical prediction in longitudinal data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.19.24313909. [PMID: 39371168 PMCID: PMC11451668 DOI: 10.1101/2024.09.19.24313909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/08/2024]
Abstract
Predicting individual and population risk for disease outcomes and identifying persons at elevated risk is a key prerequisite for targeting interventions to improve health. However, current risk stratification tools for the common, chronic diseases that develop over the lifecourse and represent the majority of disease morbidity, mortality and healthcare costs are aging and achieve only moderate predictive performance. In some common, highly morbid conditions such as mental illness no risk stratification tools are yet available. There is an urgent need to improve predictive performance for chronic diseases and understand how cumulative, multifactorial risks aggregate over time so that intervention programs can be targeted earlier and more effectively in the disease course. Chronic diseases are the end outcomes of multifactorial risks that increment over years and represent cumulative, temporally-sensitive risk pathways. However, tools in current clinical use were constructed in older data and utilize inputs from a single data collection step. Here, we present RiskPath, a multistep deep learning method for temporally-sensitive biomedical risk prediction tailored for the constraints and demands of biomedical practice that achieves very strong performance and full translational explainability. RiskPath delineates and quantifies cumulative multifactorial risk pathways and allows the user to explore performance-complexity tradeoffs and constrain models as required by clinical use cases. Our results highlight the potential for developing a new generation of risk stratification tools and risk pathway mapping in time-dependent diseases and health outcomes by leveraging powerful timeseries deep learning methods in the wealth of biomedical data now appearing in large, longitudinal open science datasets.
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11
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Yang D, Miao Y, Liu C, Zhang N, Zhang D, Guo Q, Gao S, Li L, Wang J, Liang S, Li P, Bai X, Zhang K. Advances in artificial intelligence applications in the field of lung cancer. Front Oncol 2024; 14:1449068. [PMID: 39309740 PMCID: PMC11412794 DOI: 10.3389/fonc.2024.1449068] [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: 06/14/2024] [Accepted: 08/19/2024] [Indexed: 09/25/2024] Open
Abstract
Lung cancer remains a leading cause of cancer-related deaths globally, with its incidence steadily rising each year, representing a significant threat to human health. Early detection, diagnosis, and timely treatment play a crucial role in improving survival rates and reducing mortality. In recent years, significant and rapid advancements in artificial intelligence (AI) technology have found successful applications in various clinical areas, especially in the diagnosis and treatment of lung cancer. AI not only improves the efficiency and accuracy of physician diagnosis but also aids in patient treatment and management. This comprehensive review presents an overview of fundamental AI-related algorithms and highlights their clinical applications in lung nodule detection, lung cancer pathology classification, gene mutation prediction, treatment strategies, and prognosis. Additionally, the rapidly advancing field of AI-based three-dimensional (3D) reconstruction in lung cancer surgical resection is discussed. Lastly, the limitations of AI and future prospects are addressed.
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Affiliation(s)
- Di Yang
- Clinical Medical College of Hebei University, Affiliated Hospital of Hebei University, Baoding, China
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
| | - Yafei Miao
- Clinical Medical College of Hebei University, Affiliated Hospital of Hebei University, Baoding, China
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
| | - Changjiang Liu
- Thoracic Surgery Department, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Nan Zhang
- Thoracic Surgery Department, Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Duo Zhang
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
| | - Qiang Guo
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
| | - Shuo Gao
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Information center, Affiliated Hospital of Hebei University, Baoding, China
| | - Linqian Li
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
- 3D Image and 3D Printing Center, Affiliated Hospital of Hebei University, Baoding, China
| | - Jianing Wang
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
| | - Si Liang
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
| | - Peng Li
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
| | - Xuan Bai
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
| | - Ke Zhang
- Thoracic Surgery Department, Affiliated Hospital of Hebei University, Baoding, China
- Basic Research Key Laboratory of General Surgery for Digital Medicine, Affiliated Hospital of Hebei University, Baoding, China
- Institute of Life Science and Green Development, Hebei University, Baoding, China
- 3D Image and 3D Printing Center, Affiliated Hospital of Hebei University, Baoding, China
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12
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Wang X, Zhao J, Marostica E, Yuan W, Jin J, Zhang J, Li R, Tang H, Wang K, Li Y, Wang F, Peng Y, Zhu J, Zhang J, Jackson CR, Zhang J, Dillon D, Lin NU, Sholl L, Denize T, Meredith D, Ligon KL, Signoretti S, Ogino S, Golden JA, Nasrallah MP, Han X, Yang S, Yu KH. A pathology foundation model for cancer diagnosis and prognosis prediction. Nature 2024:10.1038/s41586-024-07894-z. [PMID: 39232164 DOI: 10.1038/s41586-024-07894-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 08/01/2024] [Indexed: 09/06/2024]
Abstract
Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task1,2. Although such methods have achieved some success, they often have limited generalizability to images generated by different digitization protocols or samples collected from different populations3. Here, to address this challenge, we devised the Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, a general-purpose weakly supervised machine learning framework to extract pathology imaging features for systematic cancer evaluation. CHIEF leverages two complementary pretraining methods to extract diverse pathology representations: unsupervised pretraining for tile-level feature identification and weakly supervised pretraining for whole-slide pattern recognition. We developed CHIEF using 60,530 whole-slide images spanning 19 anatomical sites. Through pretraining on 44 terabytes of high-resolution pathology imaging datasets, CHIEF extracted microscopic representations useful for cancer cell detection, tumour origin identification, molecular profile characterization and prognostic prediction. We successfully validated CHIEF using 19,491 whole-slide images from 32 independent slide sets collected from 24 hospitals and cohorts internationally. Overall, CHIEF outperformed the state-of-the-art deep learning methods by up to 36.1%, showing its ability to address domain shifts observed in samples from diverse populations and processed by different slide preparation methods. CHIEF provides a generalizable foundation for efficient digital pathology evaluation for patients with cancer.
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Affiliation(s)
- Xiyue Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Junhan Zhao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Eliana Marostica
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Health Sciences and Technology, Harvard-Massachusetts Institute of Technology, Boston, MA, USA
| | - Wei Yuan
- College of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Jietian Jin
- Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jiayu Zhang
- College of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Hongping Tang
- Department of Pathology, Shenzhen Maternity & Child Healthcare Hospital, Shenzhen, China
| | - Kanran Wang
- Department of Radiation Oncology, Chongqing University Cancer Hospital, Chongqing, China
| | - Yu Li
- Department of Pathology, Chongqing University Cancer Hospital, Chongqing, China
| | - Fang Wang
- Department of Pathology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Yulong Peng
- Department of Pathology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Junyou Zhu
- Department of Burn, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jing Zhang
- College of Biomedical Engineering, Sichuan University, Chengdu, China
| | - Christopher R Jackson
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Pathology and Laboratory Medicine, Pennsylvania State University, Hummelstown, PA, USA
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Deborah Dillon
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Nancy U Lin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Lynette Sholl
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Pathology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Thomas Denize
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Pathology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - David Meredith
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Keith L Ligon
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Pathology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sabina Signoretti
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Pathology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Shuji Ogino
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jeffrey A Golden
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Pathology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - MacLean P Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | | | - Sen Yang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
- Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA.
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13
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Kaur J, Gupta RK, Kumar A. Electrocatalytic ethanol oxidation reaction: recent progress, challenges, and future prospects. DISCOVER NANO 2024; 19:137. [PMID: 39225940 PMCID: PMC11371986 DOI: 10.1186/s11671-024-04067-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 07/16/2024] [Indexed: 09/04/2024]
Abstract
Direct ethanol fuel cells (DEFCs) have been widely considered as a feasible power conversion technology for portable and mobile applications. The economic feasibility of DEFCs relies on two conditions: a notable reduction in the expensive nature of precious metal electrocatalysts and a simultaneous remarkable improvement in the anode's long-term performance. Despite the considerable progress achieved in recent decades in Pt nanoengineering to reduce its loading in catalyst ink with enhanced mass activity, attempts to tackle these problems have yet to be successful. During the ethanol oxidation reaction (EOR) at the anode surface, Pt electrocatalysts lose their electrocatalytic activity rapidly due to poisoning by surface-adsorbed reaction intermediates like CO. This phenomenon leads to a significant loss in electrocatalytic performance within a relatively short time. This review provides an overview of the mechanistic approaches during the EOR of noble metal-based anode materials. Additionally, we emphasized the significance of many essential factors that govern the EOR activity of the electrode surface. Furthermore, we provided a comprehensive examination of the challenges and potential advancements in electrocatalytic EOR.
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Affiliation(s)
- Jasvinder Kaur
- Department of Chemistry, School of Sciences, IFTM University, Moradabad, Uttar Pradesh, 244102, India.
| | - Ram K Gupta
- Department of Chemistry, Pittsburg State University, Pittsburg, KS, 66762, USA
- National Institute of Material Advancement, Pittsburg, KS, 66762, USA
| | - Anuj Kumar
- Department of Chemistry, GLA University, Mathura, 281406, India.
- National Institute of Material Advancement, Pittsburg, KS, 66762, USA.
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Lan H, Chen P, Wang C, Chen C, Yao C, Jin F, Wan T, Lv X, Wang J. A Multiscale Connected UNet for the Segmentation of Lung Cancer Cells in Pathology Sections Stained Using Rapid On-Site Cytopathological Evaluation. THE AMERICAN JOURNAL OF PATHOLOGY 2024; 194:1712-1723. [PMID: 38897537 DOI: 10.1016/j.ajpath.2024.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 04/30/2024] [Accepted: 05/23/2024] [Indexed: 06/21/2024]
Abstract
Lung cancer is an increasingly serious health problem worldwide, and early detection and diagnosis are crucial for successful treatment. With the development of artificial intelligence and the growth of data volume, machine learning techniques can play a significant role in improving the accuracy of early detection in lung cancer. This study proposes a deep learning-based segmentation algorithm for rapid on-site cytopathological evaluation (ROSE) to enhance the diagnostic efficiency of endobronchial ultrasound-guided transbronchial needle aspiration biopsy (EBUS-TBNA) during surgery. By utilizing the CUNet3+ network model, cell clusters, including cancer cell clusters, can be accurately segmented in ROSE-stained pathological sections. The model demonstrated high accuracy, with an F1-score of 0.9604, recall of 0.9609, precision of 0.9654, and accuracy of 0.9834 on the internal testing data set. It also achieved an area under the receiver-operating characteristic curve of 0.9972 for cancer identification. The proposed algorithm saved time for on-site diagnosis, improved EBUS-TBNA efficiency, and outperformed classical segmentation algorithms in accurately identifying lung cancer cell clusters in ROSE-stained images. It effectively reduced over-segmentation, decreased network parameters, and enhanced computational efficiency, making it suitable for real-time patient evaluation during surgical procedures.
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Affiliation(s)
- Hongyi Lan
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Pei Chen
- Department of Pulmonary and Critical Care Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - ChenXi Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Chen Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Cuiping Yao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Fang Jin
- Department of Respiratory and Critical Care Medicine, Tianjin Medical University General Hospital, Tianjin, China
| | - Tao Wan
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xing Lv
- Department of Pulmonary and Critical Care Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
| | - Jing Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
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15
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Li Y, Long W, Zhou H, Tan T, Xie H. Revolutionizing breast cancer Ki-67 diagnosis: ultrasound radiomics and fully connected neural networks (FCNN) combination method. Breast Cancer Res Treat 2024; 207:453-468. [PMID: 38853220 DOI: 10.1007/s10549-024-07375-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 05/14/2024] [Indexed: 06/11/2024]
Abstract
PURPOSE This study aims to assess the diagnostic value of ultrasound habitat sub-region radiomics feature parameters using a fully connected neural networks (FCNN) combination method L2,1-norm in relation to breast cancer Ki-67 status. METHODS Ultrasound images from 528 cases of female breast cancer at the Affiliated Hospital of Xiangnan University and 232 cases of female breast cancer at the Affiliated Rehabilitation Hospital of Xiangnan University were selected for this study. We utilized deep learning methods to automatically outline the gross tumor volume and perform habitat clustering. Subsequently, habitat sub-regions were extracted to identify radiomics features and underwent feature engineering using the L1,2-norm. A prediction model for the Ki-67 status of breast cancer patients was then developed using a FCNN. The model's performance was evaluated using accuracy, area under the curve (AUC), specificity (Spe), positive predictive value (PPV), negative predictive value (NPV), Recall, and F1. In addition, calibration curves and clinical decision curves were plotted for the test set to visually assess the predictive accuracy and clinical benefit of the models. RESULT Based on the feature engineering using the L1,2-norm, a total of 9 core features were identified. The predictive model, constructed by the FCNN model based on these 9 features, achieved the following scores: ACC 0.856, AUC 0.915, Spe 0.843, PPV 0.920, NPV 0.747, Recall 0.974, and F1 0.890. Furthermore, calibration curves and clinical decision curves of the validation set demonstrated a high level of confidence in the model's performance and its clinical benefit. CONCLUSION Habitat clustering of ultrasound images of breast cancer is effectively supported by the combined implementation of the L1,2-norm and FCNN algorithms, allowing for the accurate classification of the Ki-67 status in breast cancer patients.
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Affiliation(s)
- Yanfeng Li
- Department of Interventional Vascular Surgery, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, People's Republic of China
| | - Wengxing Long
- Department of Interventional Vascular Surgery, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, People's Republic of China
| | - Hongda Zhou
- Department of Oncology, Affiliated Hospital of Xiangnan University, Chenzhou, 423000, Hunan, People's Republic of China
| | - Tao Tan
- Faulty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China
| | - Hui Xie
- Department of Oncology, Affiliated Hospital of Xiangnan University, Chenzhou, 423000, Hunan, People's Republic of China.
- Faulty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China.
- Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, People's Republic of China.
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16
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Ji J, Liu Y, Bao Y, Men Y, Hui Z. Network analysis of histopathological image features and genomics data improving prognosis performance in clear cell renal cell carcinoma. Urol Oncol 2024; 42:249.e1-249.e11. [PMID: 38653593 DOI: 10.1016/j.urolonc.2024.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/25/2024] [Accepted: 03/18/2024] [Indexed: 04/25/2024]
Abstract
INTRODUCTION Clear cell renal cell carcinoma is the most common type of kidney cancer, but the prediction of prognosis remains a challenge. METHODS We collected whole-slide histopathological images, corresponding clinical and genetic information from the The Cancer Imaging Archive and The Cancer Genome Atlas databases and randomly divided patients into training (n = 197) and validation (n = 84) cohorts. After feature extraction by CellProfiler, we used 2 different machine learning techniques (Least Absolute Shrinkage and Selector Operation-regularized Cox and Support Vector Machine-Recursive Feature Elimination) and weighted gene co-expression network analysis to select prognosis-related image features and genes, respectively. These features and genes were integrated into a joint model using random forest and used to create a nomogram that combines other predictive indicators. RESULTS A total of 4 overlapped features were identified, represented by the computed histopathological risk score in the random forest model, and showed predictive value for overall survival (test set: 1-year area under the curves (AUC) = 0.726, 3-year AUC = 0.727, and 5-year AUC = 0.764). The histopathological-genetic risk score (HGRS) integrating the genetic information computed performed better than the model that used image features only (test set: 1-year AUC = 0.682, 3-year AUC = 0.734, and 5-year AUC = 0.78). The nomogram (gender, stage, and HGRS) achieved the highest net benefit according to decision curve analysis compared to HGRS or clinical model. CONCLUSION This study developed a histopathological-genetic-related nomogram by combining histopathological features and clinical predictors, providing a more comprehensive prognostic assessment for clear cell renal cell carcinoma patients.
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Affiliation(s)
- Jianrui Ji
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yunsong Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yongxing Bao
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Men
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Department of VIP Medical Services, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhouguang Hui
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Department of VIP Medical Services, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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17
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Chen H, Chen Q, Chen J, Mao Y, Duan L, Ye D, Cheng W, Chen J, Gao X, Lin R, Lin W, Zhang M, Qi Y. Deciphering the Effects of the PYCR Family on Cell Function, Prognostic Value, Immune Infiltration in ccRCC and Pan-Cancer. Int J Mol Sci 2024; 25:8096. [PMID: 39125668 PMCID: PMC11311831 DOI: 10.3390/ijms25158096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 07/17/2024] [Accepted: 07/19/2024] [Indexed: 08/12/2024] Open
Abstract
Pyrroline-5-carboxylate reductase (PYCR) is pivotal in converting pyrroline-5-carboxylate (P5C) to proline, the final step in proline synthesis. Three isoforms, PYCR1, PYCR2, and PYCR3, existed and played significant regulatory roles in tumor initiation and progression. In this study, we first assessed the molecular and immune characteristics of PYCRs by a pan-cancer analysis, especially focusing on their prognostic relevance. Then, a kidney renal clear cell carcinoma (KIRC)-specific prognostic model was established, incorporating pathomics features to enhance predictive capabilities. The biological functions and regulatory mechanisms of PYCR1 and PYCR2 were investigated by in vitro experiments in renal cancer cells. The PYCRs' expressions were elevated in diverse tumors, correlating with unfavorable clinical outcomes. PYCRs were enriched in cancer signaling pathways, significantly correlating with immune cell infiltration, tumor mutation burden (TMB), and microsatellite instability (MSI). In KIRC, a prognostic model based on PYCR1 and PYCR2 was independently validated statistically. Leveraging features from H&E-stained images, a pathomics feature model reliably predicted patient prognosis. In vitro experiments demonstrated that PYCR1 and PYCR2 enhanced the proliferation and migration of renal carcinoma cells by activating the mTOR pathway, at least in part. This study underscores PYCRs' pivotal role in various tumors, positioning them as potential prognostic biomarkers and therapeutic targets, particularly in malignancies like KIRC. The findings emphasize the need for a broader exploration of PYCRs' implications in pan-cancer contexts.
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MESH Headings
- Humans
- Pyrroline Carboxylate Reductases/metabolism
- Pyrroline Carboxylate Reductases/genetics
- Carcinoma, Renal Cell/immunology
- Carcinoma, Renal Cell/pathology
- Carcinoma, Renal Cell/genetics
- Carcinoma, Renal Cell/metabolism
- Prognosis
- Kidney Neoplasms/immunology
- Kidney Neoplasms/pathology
- Kidney Neoplasms/genetics
- Kidney Neoplasms/metabolism
- Biomarkers, Tumor/metabolism
- Biomarkers, Tumor/genetics
- Cell Line, Tumor
- Gene Expression Regulation, Neoplastic
- delta-1-Pyrroline-5-Carboxylate Reductase
- Cell Proliferation
- Lymphocytes, Tumor-Infiltrating/immunology
- Lymphocytes, Tumor-Infiltrating/metabolism
- Signal Transduction
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Affiliation(s)
- Hongquan Chen
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Qing Chen
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Jinyang Chen
- College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350009, China;
| | - Yazhen Mao
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Lidi Duan
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Dongjie Ye
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Wenxiu Cheng
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Jiaxi Chen
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Xinrong Gao
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Renxi Lin
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Weibin Lin
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Mingfang Zhang
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
| | - Yuanlin Qi
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China; (H.C.); (Q.C.); (Y.M.); (L.D.); (D.Y.); (W.C.); (J.C.); (X.G.); (R.L.); (W.L.)
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Gilley P, Zhang K, Abdoli N, Sadri Y, Adhikari L, Fung KM, Qiu Y. Utilizing a Pathomics Biomarker to Predict the Effectiveness of Bevacizumab in Ovarian Cancer Treatment. Bioengineering (Basel) 2024; 11:678. [PMID: 39061760 PMCID: PMC11273783 DOI: 10.3390/bioengineering11070678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2024] [Revised: 06/25/2024] [Accepted: 06/26/2024] [Indexed: 07/28/2024] Open
Abstract
The purpose of this investigation is to develop and initially assess a quantitative image analysis scheme that utilizes histopathological images to predict the treatment effectiveness of bevacizumab therapy in ovarian cancer patients. As a widely accessible diagnostic tool, histopathological slides contain copious information regarding underlying tumor progression that is associated with tumor prognosis. However, this information cannot be readily identified by conventional visual examination. This study utilizes novel pathomics technology to quantify this meaningful information for treatment effectiveness prediction. Accordingly, a total of 9828 features were extracted from segmented tumor tissue, cell nuclei, and cell cytoplasm, which were categorized into geometric, intensity, texture, and subcellular structure features. Next, the best performing features were selected as the input for SVM (support vector machine)-based prediction models. These models were evaluated on an open dataset containing a total of 78 patients and 288 whole slides images. The results indicated that the sufficiently optimized, best-performing model yielded an area under the receiver operating characteristic (ROC) curve of 0.8312. When examining the best model's confusion matrix, 37 and 25 cases were correctly predicted as responders and non-responders, respectively, achieving an overall accuracy of 0.7848. This investigation initially validated the feasibility of utilizing pathomics techniques to predict tumor responses to chemotherapy at an early stage.
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Affiliation(s)
- Patrik Gilley
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA (N.A.)
| | - Ke Zhang
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA (N.A.)
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA
| | - Neman Abdoli
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA (N.A.)
| | - Youkabed Sadri
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA (N.A.)
| | - Laura Adhikari
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA (K.-M.F.)
| | - Kar-Ming Fung
- Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA (K.-M.F.)
| | - Yuchen Qiu
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA (N.A.)
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19
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Bai C, Sun Y, Zhang X, Zuo Z. Assessment of AURKA expression and prognosis prediction in lung adenocarcinoma using machine learning-based pathomics signature. Heliyon 2024; 10:e33107. [PMID: 39022022 PMCID: PMC11253280 DOI: 10.1016/j.heliyon.2024.e33107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 06/07/2024] [Accepted: 06/14/2024] [Indexed: 07/20/2024] Open
Abstract
Objective This study aimed to develop quantitative feature-based models from histopathological images to assess aurora kinase A (AURKA) expression and predict the prognosis of patients with lung adenocarcinoma (LUAD). Methods A dataset of patients with LUAD was derived from the cancer genome atlas (TCGA) with information on clinical characteristics, RNA sequencing and histopathological images. The TCGA-LUAD cohort was randomly divided into training (n = 229) and testing (n = 98) sets. We extracted quantitative image features from histopathological slides of patients with LUAD using computational approaches, constructed a predictive model for AURKA expression in the training set, and estimated their predictive performance in the test set. A Cox proportional hazards model was used to assess whether the pathomic scores (PS) generated by the model independently predicted LUAD survival. Results High AURKA expression was an independent risk factor for overall survival (OS) in patients with LUAD (hazard ratio = 1.816, 95 % confidence intervals = 1.257-2.623, P = 0.001). The model based on histopathological image features had significant predictive value for AURKA expression: the area under the curve of the receiver operating characteristic curve in the training set and validation set was 0.809 and 0.739, respectively. Decision curve analysis showed that the model had clinical utility. Patients with high PS and low PS had different survival rates (P = 0.019). Multivariate analysis suggested that PS was an independent prognostic factor for LUAD (hazard ratio = 1.615, 95 % confidence intervals = 1.071-2.438, P = 0.022). Conclusion Pathomics models based on machine learning can accurately predict AURKA expression and the PS generated by the model can predict LUAD prognosis.
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Affiliation(s)
- Cuiqing Bai
- Department of Respiratory Disease, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Yan Sun
- Department of Respiratory Disease, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Xiuqin Zhang
- Department of Respiratory Disease, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Zhitong Zuo
- Department of Respiratory Disease, Affiliated Hospital of Jiangnan University, Wuxi, China
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20
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Tas MO, Yavuz HS. Enhancing Lung Cancer Survival Prediction: 3D CNN Analysis of CT Images Using Novel GTV1-SliceNum Feature and PEN-BCE Loss Function. Diagnostics (Basel) 2024; 14:1309. [PMID: 38928724 PMCID: PMC11202780 DOI: 10.3390/diagnostics14121309] [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: 05/07/2024] [Revised: 06/16/2024] [Accepted: 06/18/2024] [Indexed: 06/28/2024] Open
Abstract
Lung cancer is a prevalent malignancy associated with a high mortality rate, with a 5-year relative survival rate of 23%. Traditional survival analysis methods, reliant on clinician judgment, may lack accuracy due to their subjective nature. Consequently, there is growing interest in leveraging AI-based systems for survival analysis using clinical data and medical imaging. The purpose of this study is to improve survival classification for lung cancer patients by utilizing a 3D-CNN architecture (ResNet-34) applied to CT images from the NSCLC-Radiomics dataset. Through comprehensive ablation studies, we evaluate the effectiveness of different features and methodologies in classification performance. Key contributions include the introduction of a novel feature (GTV1-SliceNum), the proposal of a novel loss function (PEN-BCE) accounting for false negatives and false positives, and the showcasing of their efficacy in classification. Experimental work demonstrates results surpassing those of the existing literature, achieving a classification accuracy of 0.7434 and an ROC-AUC of 0.7768. The conclusions of this research indicate that the AI-driven approach significantly improves survival prediction for lung cancer patients, highlighting its potential for enhancing personalized treatment strategies and prognostic modeling.
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Affiliation(s)
- Muhammed Oguz Tas
- Electrical and Electronics Engineering Department, Eskisehir Osmangazi University, Eskisehir 26480, Turkey;
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21
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Abel J, Jain S, Rajan D, Padigela H, Leidal K, Prakash A, Conway J, Nercessian M, Kirkup C, Javed SA, Biju R, Harguindeguy N, Shenker D, Indorf N, Sanghavi D, Egger R, Trotter B, Gerardin Y, Brosnan-Cashman JA, Dhoot A, Montalto MC, Parmar C, Wapinski I, Khosla A, Drage MG, Yu L, Taylor-Weiner A. AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis. NPJ Precis Oncol 2024; 8:134. [PMID: 38898127 PMCID: PMC11187064 DOI: 10.1038/s41698-024-00623-9] [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: 08/03/2023] [Accepted: 06/04/2024] [Indexed: 06/21/2024] Open
Abstract
While alterations in nucleus size, shape, and color are ubiquitous in cancer, comprehensive quantification of nuclear morphology across a whole-slide histologic image remains a challenge. Here, we describe the development of a pan-tissue, deep learning-based digital pathology pipeline for exhaustive nucleus detection, segmentation, and classification and the utility of this pipeline for nuclear morphologic biomarker discovery. Manually-collected nucleus annotations were used to train an object detection and segmentation model for identifying nuclei, which was deployed to segment nuclei in H&E-stained slides from the BRCA, LUAD, and PRAD TCGA cohorts. Interpretable features describing the shape, size, color, and texture of each nucleus were extracted from segmented nuclei and compared to measurements of genomic instability, gene expression, and prognosis. The nuclear segmentation and classification model trained herein performed comparably to previously reported models. Features extracted from the model revealed differences sufficient to distinguish between BRCA, LUAD, and PRAD. Furthermore, cancer cell nuclear area was associated with increased aneuploidy score and homologous recombination deficiency. In BRCA, increased fibroblast nuclear area was indicative of poor progression-free and overall survival and was associated with gene expression signatures related to extracellular matrix remodeling and anti-tumor immunity. Thus, we developed a powerful pan-tissue approach for nucleus segmentation and featurization, enabling the construction of predictive models and the identification of features linking nuclear morphology with clinically-relevant prognostic biomarkers across multiple cancer types.
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22
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Claudio Quiros A, Coudray N, Yeaton A, Yang X, Liu B, Le H, Chiriboga L, Karimkhan A, Narula N, Moore DA, Park CY, Pass H, Moreira AL, Le Quesne J, Tsirigos A, Yuan K. Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides. Nat Commun 2024; 15:4596. [PMID: 38862472 DOI: 10.1038/s41467-024-48666-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 05/08/2024] [Indexed: 06/13/2024] Open
Abstract
Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerful, but are inherently limited by the cost and quality of annotations used for training. Therefore, we present Histomorphological Phenotype Learning, a self-supervised methodology requiring no labels and operating via the automatic discovery of discriminatory features in image tiles. Tiles are grouped into morphologically similar clusters which constitute an atlas of histomorphological phenotypes (HP-Atlas), revealing trajectories from benign to malignant tissue via inflammatory and reactive phenotypes. These clusters have distinct features which can be identified using orthogonal methods, linking histologic, molecular and clinical phenotypes. Applied to lung cancer, we show that they align closely with patient survival, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype. These properties are maintained in a multi-cancer study.
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Affiliation(s)
- Adalberto Claudio Quiros
- School of Computing Science, University of Glasgow, Glasgow, Scotland, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, Scotland, UK
| | - Nicolas Coudray
- Applied Bioinformatics Laboratories, NYU Grossman School of Medicine, New York, NY, USA
- Department of Cell Biology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Medicine, Division of Precision Medicine, NYU Grossman School of Medicine, New York, USA
| | - Anna Yeaton
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
| | - Xinyu Yang
- School of Computing Science, University of Glasgow, Glasgow, Scotland, UK
| | - Bojing Liu
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Soln, Sweden
| | - Hortense Le
- Department of Medicine, Division of Precision Medicine, NYU Grossman School of Medicine, New York, USA
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
| | - Luis Chiriboga
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
| | - Afreen Karimkhan
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
| | - Navneet Narula
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
| | - David A Moore
- Department of Cellular Pathology, University College London Hospital, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Christopher Y Park
- Department of Medicine, Division of Precision Medicine, NYU Grossman School of Medicine, New York, USA
| | - Harvey Pass
- Department of Cardiothoracic Surgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Andre L Moreira
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
| | - John Le Quesne
- School of Cancer Sciences, University of Glasgow, Glasgow, Scotland, UK.
- Cancer Research UK Scotland Institute, Glasgow, Scotland, UK.
- Queen Elizabeth University Hospital, Greater Glasgow and Clyde NHS Trust, Glasgow, Scotland, UK.
| | - Aristotelis Tsirigos
- Applied Bioinformatics Laboratories, NYU Grossman School of Medicine, New York, NY, USA.
- Department of Medicine, Division of Precision Medicine, NYU Grossman School of Medicine, New York, USA.
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA.
| | - Ke Yuan
- School of Computing Science, University of Glasgow, Glasgow, Scotland, UK.
- School of Cancer Sciences, University of Glasgow, Glasgow, Scotland, UK.
- Cancer Research UK Scotland Institute, Glasgow, Scotland, UK.
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Ou DX, Lu CW, Chen LW, Lee WY, Hu HW, Chuang JH, Lin MW, Chen KY, Chiu LY, Chen JS, Chen CM, Hsieh MS. Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images. Cancers (Basel) 2024; 16:2132. [PMID: 38893251 PMCID: PMC11172106 DOI: 10.3390/cancers16112132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 05/25/2024] [Accepted: 06/01/2024] [Indexed: 06/21/2024] Open
Abstract
The presence of spread through air spaces (STASs) in early-stage lung adenocarcinoma is a significant prognostic factor associated with disease recurrence and poor outcomes. Although current STAS detection methods rely on pathological examinations, the advent of artificial intelligence (AI) offers opportunities for automated histopathological image analysis. This study developed a deep learning (DL) model for STAS prediction and investigated the correlation between the prediction results and patient outcomes. To develop the DL-based STAS prediction model, 1053 digital pathology whole-slide images (WSIs) from the competition dataset were enrolled in the training set, and 227 WSIs from the National Taiwan University Hospital were enrolled for external validation. A YOLOv5-based framework comprising preprocessing, candidate detection, false-positive reduction, and patient-based prediction was proposed for STAS prediction. The model achieved an area under the curve (AUC) of 0.83 in predicting STAS presence, with 72% accuracy, 81% sensitivity, and 63% specificity. Additionally, the DL model demonstrated a prognostic value in disease-free survival compared to that of pathological evaluation. These findings suggest that DL-based STAS prediction could serve as an adjunctive screening tool and facilitate clinical decision-making in patients with early-stage lung adenocarcinoma.
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Affiliation(s)
- De-Xiang Ou
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (D.-X.O.); (L.-W.C.); (K.-Y.C.)
| | - Chao-Wen Lu
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan; (C.-W.L.); (J.-H.C.)
- Graduate Institute of Pathology, National Taiwan University College of Medicine, Taipei 100, Taiwan
| | - Li-Wei Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (D.-X.O.); (L.-W.C.); (K.-Y.C.)
| | - Wen-Yao Lee
- Division of Thoracic Surgery, Department of Surgery, Fu Jen Catholic University Hospital, No. 69, Guizi Road, Taishan District, New Taipei City 24352, Taiwan;
| | - Hsiang-Wei Hu
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan;
| | - Jen-Hao Chuang
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan; (C.-W.L.); (J.-H.C.)
| | - Mong-Wei Lin
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan; (C.-W.L.); (J.-H.C.)
| | - Kuan-Yu Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (D.-X.O.); (L.-W.C.); (K.-Y.C.)
| | - Ling-Ying Chiu
- Institute of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan;
| | - Jin-Shing Chen
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan; (C.-W.L.); (J.-H.C.)
| | - Chung-Ming Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 10617, Taiwan; (D.-X.O.); (L.-W.C.); (K.-Y.C.)
| | - Min-Shu Hsieh
- Graduate Institute of Pathology, National Taiwan University College of Medicine, Taipei 100, Taiwan
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan;
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24
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Shao W, Shi H, Liu J, Zuo Y, Sun L, Xia T, Chen W, Wan P, Sheng J, Zhu Q, Zhang D. Multi-Instance Multi-Task Learning for Joint Clinical Outcome and Genomic Profile Predictions From the Histopathological Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2266-2278. [PMID: 38319755 DOI: 10.1109/tmi.2024.3362852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
With the remarkable success of digital histopathology and the deep learning technology, many whole-slide pathological images (WSIs) based deep learning models are designed to help pathologists diagnose human cancers. Recently, rather than predicting categorical variables as in cancer diagnosis, several deep learning studies are also proposed to estimate the continuous variables such as the patients' survival or their transcriptional profile. However, most of the existing studies focus on conducting these predicting tasks separately, which overlooks the useful intrinsic correlation among them that can boost the prediction performance of each individual task. In addition, it is sill challenge to design the WSI-based deep learning models, since a WSI is with huge size but annotated with coarse label. In this study, we propose a general multi-instance multi-task learning framework (HistMIMT) for multi-purpose prediction from WSIs. Specifically, we firstly propose a novel multi-instance learning module (TMICS) considering both common and specific task information across different tasks to generate bag representation for each individual task. Then, a soft-mask based fusion module with channel attention (SFCA) is developed to leverage useful information from the related tasks to help improve the prediction performance on target task. We evaluate our method on three cancer cohorts derived from the Cancer Genome Atlas (TCGA). For each cohort, our multi-purpose prediction tasks range from cancer diagnosis, survival prediction and estimating the transcriptional profile of gene TP53. The experimental results demonstrated that HistMIMT can yield better outcome on all clinical prediction tasks than its competitors.
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Duan L, He Y, Guo W, Du Y, Yin S, Yang S, Dong G, Li W, Chen F. Machine learning-based pathomics signature of histology slides as a novel prognostic indicator in primary central nervous system lymphoma. J Neurooncol 2024; 168:283-298. [PMID: 38557926 PMCID: PMC11147825 DOI: 10.1007/s11060-024-04665-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 03/26/2024] [Indexed: 04/04/2024]
Abstract
PURPOSE To develop and validate a pathomics signature for predicting the outcomes of Primary Central Nervous System Lymphoma (PCNSL). METHODS In this study, 132 whole-slide images (WSIs) of 114 patients with PCNSL were enrolled. Quantitative features of hematoxylin and eosin (H&E) stained slides were extracted using CellProfiler. A pathomics signature was established and validated. Cox regression analysis, receiver operating characteristic (ROC) curves, Calibration, decision curve analysis (DCA), and net reclassification improvement (NRI) were performed to assess the significance and performance. RESULTS In total, 802 features were extracted using a fully automated pipeline. Six machine-learning classifiers demonstrated high accuracy in distinguishing malignant neoplasms. The pathomics signature remained a significant factor of overall survival (OS) and progression-free survival (PFS) in the training cohort (OS: HR 7.423, p < 0.001; PFS: HR 2.143, p = 0.022) and independent validation cohort (OS: HR 4.204, p = 0.017; PFS: HR 3.243, p = 0.005). A significantly lower response rate to initial treatment was found in high Path-score group (19/35, 54.29%) as compared to patients in the low Path-score group (16/70, 22.86%; p < 0.001). The DCA and NRI analyses confirmed that the nomogram showed incremental performance compared with existing models. The ROC curve demonstrated a relatively sensitive and specific profile for the nomogram (1-, 2-, and 3-year AUC = 0.862, 0.932, and 0.927, respectively). CONCLUSION As a novel, non-invasive, and convenient approach, the newly developed pathomics signature is a powerful predictor of OS and PFS in PCNSL and might be a potential predictive indicator for therapeutic response.
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Affiliation(s)
- Ling Duan
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China
| | - Yongqi He
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China
| | - Wenhui Guo
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China
| | - Yanru Du
- Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China
| | - Shuo Yin
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China
| | - Shoubo Yang
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China
| | - Gehong Dong
- Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China.
| | - Wenbin Li
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China.
| | - Feng Chen
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, No.119 West Nansihuan Road, Beijing, 100070, China.
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Ge FJ, Dai XY, Qiu Y, Liu XN, Zeng CM, Xu XY, Chen YD, Zhu H, He QJ, Gai RH, Ma SL, Chen XQ, Yang B. Inflammation-related molecular signatures involved in the anticancer activities of brigatinib as well as the prognosis of EML4-ALK lung adenocarcinoma patient. Acta Pharmacol Sin 2024; 45:1252-1263. [PMID: 38360931 PMCID: PMC11130210 DOI: 10.1038/s41401-024-01230-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/18/2024] [Indexed: 02/17/2024] Open
Abstract
Although ALK tyrosine kinase inhibitors (ALK-TKIs) have shown remarkable benefits in EML4-ALK positive NSCLC patients compared to conventional chemotherapy, the optimal sequence of ALK-TKIs treatment remains unclear due to the emergence of primary and acquired resistance and the lack of potential prognostic biomarkers. In this study, we systematically explored the validity of sequential ALK inhibitors (alectinib, lorlatinib, crizotinib, ceritinib and brigatinib) for a heavy-treated patient with EML4-ALK fusion via developing an in vitro and in vivo drug testing system based on patient-derived models. Based on the patient-derived models and clinical responses of the patient, we found that crizotinib might inhibit proliferation of EML4-ALK positive tumors resistant to alectinib and lorlatinib. In addition, NSCLC patients harboring the G1269A mutation, which was identified in alectinib, lorlatinib and crizotinib-resistant NSCLC, showed responsiveness to brigatinib and ceritinib. Transcriptomic analysis revealed that brigatinib suppressed the activation of multiple inflammatory signaling pathways, potentially contributing to its anti-tumor activity. Moreover, we constructed a prognostic model based on the expression of IL6, CXCL1, and CXCL5, providing novel perspectives for predicting prognosis in EML4-ALK positive NSCLC patients. In summary, our results delineate clinical responses of sequential ALK-TKIs treatments and provide insights into the mechanisms underlying the superior effects of brigatinib in patients harboring ALKG1269A mutation and resistant towards alectinib, lorlatinib and crizotinib. The molecular signatures model based on the combination of IL6, CXCL1 and CXCL5 has the potential to predict prognosis of EML4-ALK positive NSCLC patients.
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Affiliation(s)
- Fu-Jing Ge
- Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Xiao-Yang Dai
- Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Yao Qiu
- Department of Thoracic Oncology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Hangzhou Cancer Hospital, Hangzhou, 310002, China
| | - Xiang-Ning Liu
- Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Chen-Ming Zeng
- Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Xiao-Yuan Xu
- China Pharmaceutical University, Nanjing, 210009, China
| | - Yi-Dan Chen
- Department of Thoracic Oncology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Hangzhou Cancer Hospital, Hangzhou, 310002, China
| | - Hong Zhu
- Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Qiao-Jun He
- Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- Cancer Center, Zhejiang University, Hangzhou, 310058, China
| | - Ren-Hua Gai
- Center for Drug Safety Evaluation and Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Sheng-Lin Ma
- Department of Thoracic Oncology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Hangzhou Cancer Hospital, Hangzhou, 310002, China.
- Cancer Center, Zhejiang University, Hangzhou, 310058, China.
| | - Xue-Qin Chen
- Department of Thoracic Oncology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Hangzhou Cancer Hospital, Hangzhou, 310002, China.
- Cancer Center, Zhejiang University, Hangzhou, 310058, China.
| | - Bo Yang
- Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- School of Medicine, Hangzhou City University, Hangzhou, 310015, China.
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27
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Meseguer P, Del Amor R, Naranjo V. MICIL: Multiple-Instance Class-Incremental Learning for skin cancer whole slide images. Artif Intell Med 2024; 152:102870. [PMID: 38663270 DOI: 10.1016/j.artmed.2024.102870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 04/07/2024] [Accepted: 04/08/2024] [Indexed: 05/15/2024]
Abstract
Artificial intelligence (AI) agents encounter the problem of catastrophic forgetting when they are trained in sequentially with new data batches. This issue poses a barrier to the implementation of AI-based models in tasks that involve ongoing evolution, such as cancer prediction. Moreover, whole slide images (WSI) play a crucial role in cancer management, and their automated analysis has become increasingly popular in assisting pathologists during the diagnosis process. Incremental learning (IL) techniques aim to develop algorithms capable of retaining previously acquired information while also acquiring new insights to predict future data. Deep IL techniques need to address the challenges posed by the gigapixel scale of WSIs, which often necessitates the use of multiple instance learning (MIL) frameworks. In this paper, we introduce an IL algorithm tailored for analyzing WSIs within a MIL paradigm. The proposed Multiple Instance Class-Incremental Learning (MICIL) algorithm combines MIL with class-IL for the first time, allowing for the incremental prediction of multiple skin cancer subtypes from WSIs within a class-IL scenario. Our framework incorporates knowledge distillation and data rehearsal, along with a novel embedding-level distillation, aiming to preserve the latent space at the aggregated WSI level. Results demonstrate the algorithm's effectiveness in addressing the challenge of balancing IL-specific metrics, such as intransigence and forgetting, and solving the plasticity-stability dilemma.
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Affiliation(s)
- Pablo Meseguer
- Instituto Universitario de Investigación e Innovación en Tecnología Centarada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València, Valencia, Spain; valgrAI - Valencian Graduate School and Research Network of Artificial Intelligence, Valencia, Spain.
| | - Rocío Del Amor
- Instituto Universitario de Investigación e Innovación en Tecnología Centarada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València, Valencia, Spain
| | - Valery Naranjo
- Instituto Universitario de Investigación e Innovación en Tecnología Centarada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València, Valencia, Spain; valgrAI - Valencian Graduate School and Research Network of Artificial Intelligence, Valencia, Spain
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28
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Yu KH, Healey E, Leong TY, Kohane IS, Manrai AK. Medical Artificial Intelligence and Human Values. N Engl J Med 2024; 390:1895-1904. [PMID: 38810186 DOI: 10.1056/nejmra2214183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Affiliation(s)
- Kun-Hsing Yu
- From the Department of Biomedical Informatics, Harvard Medical School (K.-H.Y., E.H., I.S.K., A.K.M.), the Department of Pathology, Brigham and Women's Hospital (K.-H.Y.), and the Harvard-MIT Division of Health Sciences and Technology (E.H.) - all in Boston; and the School of Computing, National University of Singapore, Singapore (T.-Y.L.)
| | - Elizabeth Healey
- From the Department of Biomedical Informatics, Harvard Medical School (K.-H.Y., E.H., I.S.K., A.K.M.), the Department of Pathology, Brigham and Women's Hospital (K.-H.Y.), and the Harvard-MIT Division of Health Sciences and Technology (E.H.) - all in Boston; and the School of Computing, National University of Singapore, Singapore (T.-Y.L.)
| | - Tze-Yun Leong
- From the Department of Biomedical Informatics, Harvard Medical School (K.-H.Y., E.H., I.S.K., A.K.M.), the Department of Pathology, Brigham and Women's Hospital (K.-H.Y.), and the Harvard-MIT Division of Health Sciences and Technology (E.H.) - all in Boston; and the School of Computing, National University of Singapore, Singapore (T.-Y.L.)
| | - Isaac S Kohane
- From the Department of Biomedical Informatics, Harvard Medical School (K.-H.Y., E.H., I.S.K., A.K.M.), the Department of Pathology, Brigham and Women's Hospital (K.-H.Y.), and the Harvard-MIT Division of Health Sciences and Technology (E.H.) - all in Boston; and the School of Computing, National University of Singapore, Singapore (T.-Y.L.)
| | - Arjun K Manrai
- From the Department of Biomedical Informatics, Harvard Medical School (K.-H.Y., E.H., I.S.K., A.K.M.), the Department of Pathology, Brigham and Women's Hospital (K.-H.Y.), and the Harvard-MIT Division of Health Sciences and Technology (E.H.) - all in Boston; and the School of Computing, National University of Singapore, Singapore (T.-Y.L.)
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29
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Gao R, Yuan X, Ma Y, Wei T, Johnston L, Shao Y, Lv W, Zhu T, Zhang Y, Zheng J, Chen G, Sun J, Wang YG, Yu Z. Harnessing TME depicted by histological images to improve cancer prognosis through a deep learning system. Cell Rep Med 2024; 5:101536. [PMID: 38697103 PMCID: PMC11149411 DOI: 10.1016/j.xcrm.2024.101536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 02/26/2024] [Accepted: 04/08/2024] [Indexed: 05/04/2024]
Abstract
Spatial transcriptomics (ST) provides insights into the tumor microenvironment (TME), which is closely associated with cancer prognosis, but ST has limited clinical availability. In this study, we provide a powerful deep learning system to augment TME information based on histological images for patients without ST data, thereby empowering precise cancer prognosis. The system provides two connections to bridge existing gaps. The first is the integrated graph and image deep learning (IGI-DL) model, which predicts ST expression based on histological images with a 0.171 increase in mean correlation across three cancer types compared with five existing methods. The second connection is the cancer prognosis prediction model, based on TME depicted by spatial gene expression. Our survival model, using graphs with predicted ST features, achieves superior accuracy with a concordance index of 0.747 and 0.725 for The Cancer Genome Atlas breast cancer and colorectal cancer cohorts, outperforming other survival models. For the external Molecular and Cellular Oncology colorectal cancer cohort, our survival model maintains a stable advantage.
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Affiliation(s)
- Ruitian Gao
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xin Yuan
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yanran Ma
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ting Wei
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Luke Johnston
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yanfei Shao
- Department of General Surgery, Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Wenwen Lv
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Tengteng Zhu
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yue Zhang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Junke Zheng
- Key Laboratory of Cell Differentiation and Apoptosis of the Chinese Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Guoqiang Chen
- State Key Laboratory of Systems Medicine for Cancer, Ren-Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jing Sun
- Department of General Surgery, Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
| | - Yu Guang Wang
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China; Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China; Zhangjiang Institute of Advanced Research, Shanghai Jiao Tong University, Shanghai 201203, China.
| | - Zhangsheng Yu
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai 200240, China; School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China; Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
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30
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Ahn B, Moon D, Kim HS, Lee C, Cho NH, Choi HK, Kim D, Lee JY, Nam EJ, Won D, An HJ, Kwon SY, Shin SJ, Jung HR, Kwon D, Park H, Kim M, Cha YJ, Park H, Lee Y, Noh S, Lee YM, Choi SE, Kim JM, Sung SH, Park E. Histopathologic image-based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer. Nat Commun 2024; 15:4253. [PMID: 38762636 PMCID: PMC11102549 DOI: 10.1038/s41467-024-48667-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 05/09/2024] [Indexed: 05/20/2024] Open
Abstract
Platinum-based chemotherapy is the cornerstone treatment for female high-grade serous ovarian carcinoma (HGSOC), but choosing an appropriate treatment for patients hinges on their responsiveness to it. Currently, no available biomarkers can promptly predict responses to platinum-based treatment. Therefore, we developed the Pathologic Risk Classifier for HGSOC (PathoRiCH), a histopathologic image-based classifier. PathoRiCH was trained on an in-house cohort (n = 394) and validated on two independent external cohorts (n = 284 and n = 136). The PathoRiCH-predicted favorable and poor response groups show significantly different platinum-free intervals in all three cohorts. Combining PathoRiCH with molecular biomarkers provides an even more powerful tool for the risk stratification of patients. The decisions of PathoRiCH are explained through visualization and a transcriptomic analysis, which bolster the reliability of our model's decisions. PathoRiCH exhibits better predictive performance than current molecular biomarkers. PathoRiCH will provide a solid foundation for developing an innovative tool to transform the current diagnostic pipeline for HGSOC.
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Affiliation(s)
- Byungsoo Ahn
- Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Damin Moon
- Artificial Intelligence Research Center, JLK Inc., Seoul, South Korea
| | - Hyun-Soo Kim
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Chung Lee
- Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Nam Hoon Cho
- Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Heung-Kook Choi
- Artificial Intelligence Research Center, JLK Inc., Seoul, South Korea
| | - Dongmin Kim
- Artificial Intelligence Research Center, JLK Inc., Seoul, South Korea
| | - Jung-Yun Lee
- Department of Obstetrics and Gynecology, Institute of Women's Life Medical Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Eun Ji Nam
- Department of Obstetrics and Gynecology, Institute of Women's Life Medical Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Dongju Won
- Department of Laboratory Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Hee Jung An
- Department of Pathology, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, South Korea
| | - Sun Young Kwon
- Department of Pathology, Keimyung University School of Medicine, Daegu, South Korea
| | - Su-Jin Shin
- Department of Pathology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Hye Ra Jung
- Department of Pathology, Keimyung University School of Medicine, Daegu, South Korea
| | - Dohee Kwon
- Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Heejung Park
- Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Milim Kim
- Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Yoon Jin Cha
- Department of Pathology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
- Institute of Breast Cancer Precision Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Hyunjin Park
- Department of Pathology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Yangkyu Lee
- Department of Pathology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Songmi Noh
- Department of Diagnostic Pathology, Gangnam CHA Medical Center, CHA University College of Medicine, Seoul, South Korea
| | - Yong-Moon Lee
- Department of Pathology, Dankook University School of Medicine, Cheonan, South Korea
| | - Sung-Eun Choi
- Department of Pathology, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, South Korea
| | - Ji Min Kim
- Department of Pathology, Ewha Womans University, Seoul, South Korea
| | - Sun Hee Sung
- Department of Pathology, Ewha Womans University, Seoul, South Korea
| | - Eunhyang Park
- Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
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31
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Lotter W, Hassett MJ, Schultz N, Kehl KL, Van Allen EM, Cerami E. Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions. Cancer Discov 2024; 14:711-726. [PMID: 38597966 PMCID: PMC11131133 DOI: 10.1158/2159-8290.cd-23-1199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/29/2024] [Accepted: 02/28/2024] [Indexed: 04/11/2024]
Abstract
Artificial intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of the field, with a specific focus on clinical integration. AI applications are structured according to cancer type and clinical domain, focusing on the four most common cancers and tasks of detection, diagnosis, and treatment. These applications encompass various data modalities, including imaging, genomics, and medical records. We conclude with a summary of existing challenges, evolving solutions, and potential future directions for the field. SIGNIFICANCE AI is increasingly being applied to all aspects of oncology, where several applications are maturing beyond research and development to direct clinical integration. This review summarizes the current state of the field through the lens of clinical translation along the clinical care continuum. Emerging areas are also highlighted, along with common challenges, evolving solutions, and potential future directions for the field.
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Affiliation(s)
- William Lotter
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael J. Hassett
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nikolaus Schultz
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center; New York, NY, USA
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kenneth L. Kehl
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Eliezer M. Van Allen
- Harvard Medical School, Boston, MA, USA
- Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ethan Cerami
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Zhou H, Watson M, Bernadt CT, Lin S(S, Lin CY, Ritter JH, Wein A, Mahler S, Rawal S, Govindan R, Yang C, Cote RJ. AI-guided histopathology predicts brain metastasis in lung cancer patients. J Pathol 2024; 263:89-98. [PMID: 38433721 PMCID: PMC11210939 DOI: 10.1002/path.6263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 11/30/2023] [Accepted: 01/16/2024] [Indexed: 03/05/2024]
Abstract
Brain metastases can occur in nearly half of patients with early and locally advanced (stage I-III) non-small cell lung cancer (NSCLC). There are no reliable histopathologic or molecular means to identify those who are likely to develop brain metastases. We sought to determine if deep learning (DL) could be applied to routine H&E-stained primary tumor tissue sections from stage I-III NSCLC patients to predict the development of brain metastasis. Diagnostic slides from 158 patients with stage I-III NSCLC followed for at least 5 years for the development of brain metastases (Met+, 65 patients) versus no progression (Met-, 93 patients) were subjected to whole-slide imaging. Three separate iterations were performed by first selecting 118 cases (45 Met+, 73 Met-) to train and validate the DL algorithm, while 40 separate cases (20 Met+, 20 Met-) were used as the test set. The DL algorithm results were compared to a blinded review by four expert pathologists. The DL-based algorithm was able to distinguish the eventual development of brain metastases with an accuracy of 87% (p < 0.0001) compared with an average of 57.3% by the four pathologists and appears to be particularly useful in predicting brain metastases in stage I patients. The DL algorithm appears to focus on a complex set of histologic features. DL-based algorithms using routine H&E-stained slides may identify patients who are likely to develop brain metastases from those who will remain disease free over extended (>5 year) follow-up and may thus be spared systemic therapy. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Haowen Zhou
- Department of Electrical Engineering, California Institute of Technology, Pasadena CA, USA
| | - Mark Watson
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Cory T. Bernadt
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Steven (Siyu) Lin
- Department of Electrical Engineering, California Institute of Technology, Pasadena CA, USA
| | - Chieh-yu Lin
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jon. H. Ritter
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Alexander Wein
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Simon Mahler
- Department of Electrical Engineering, California Institute of Technology, Pasadena CA, USA
| | - Sid Rawal
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Ramaswamy Govindan
- Department of Medicine, Washington University School of Medicine, Saint Louis, MO, USA
| | - Changhuei Yang
- Department of Electrical Engineering, California Institute of Technology, Pasadena CA, USA
| | - Richard J. Cote
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, USA
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Hijazi A, Bifulco C, Baldin P, Galon J. Digital Pathology for Better Clinical Practice. Cancers (Basel) 2024; 16:1686. [PMID: 38730638 PMCID: PMC11083211 DOI: 10.3390/cancers16091686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/13/2024] Open
Abstract
(1) Background: Digital pathology (DP) is transforming the landscape of clinical practice, offering a revolutionary approach to traditional pathology analysis and diagnosis. (2) Methods: This innovative technology involves the digitization of traditional glass slides which enables pathologists to access, analyze, and share high-resolution whole-slide images (WSI) of tissue specimens in a digital format. By integrating cutting-edge imaging technology with advanced software, DP promises to enhance clinical practice in numerous ways. DP not only improves quality assurance and standardization but also allows remote collaboration among experts for a more accurate diagnosis. Artificial intelligence (AI) in pathology significantly improves cancer diagnosis, classification, and prognosis by automating various tasks. It also enhances the spatial analysis of tumor microenvironment (TME) and enables the discovery of new biomarkers, advancing their translation for therapeutic applications. (3) Results: The AI-driven immune assays, Immunoscore (IS) and Immunoscore-Immune Checkpoint (IS-IC), have emerged as powerful tools for improving cancer diagnosis, prognosis, and treatment selection by assessing the tumor immune contexture in cancer patients. Digital IS quantitative assessment performed on hematoxylin-eosin (H&E) and CD3+/CD8+ stained slides from colon cancer patients has proven to be more reproducible, concordant, and reliable than expert pathologists' evaluation of immune response. Outperforming traditional staging systems, IS demonstrated robust potential to enhance treatment efficiency in clinical practice, ultimately advancing cancer patient care. Certainly, addressing the challenges DP has encountered is essential to ensure its successful integration into clinical guidelines and its implementation into clinical use. (4) Conclusion: The ongoing progress in DP holds the potential to revolutionize pathology practices, emphasizing the need to incorporate powerful AI technologies, including IS, into clinical settings to enhance personalized cancer therapy.
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Affiliation(s)
- Assia Hijazi
- The French National Institute of Health & Medical Research (INSERM), Laboratory of Integrative Cancer Immunology, F-75006 Paris, France;
- Equipe Labellisée Ligue Contre le Cancer, F-75006 Paris, France
- Centre de Recherche des Cordeliers, Sorbonne Université, Université Paris Cité, F-75006 Paris, France
| | - Carlo Bifulco
- Providence Genomics, Portland, OR 02912, USA;
- Earle A Chiles Research Institute, Portland, OR 97213, USA
| | - Pamela Baldin
- Department of Pathology, Cliniques Universitaires Saint Luc, UCLouvain, 1200 Brussels, Belgium;
| | - Jérôme Galon
- The French National Institute of Health & Medical Research (INSERM), Laboratory of Integrative Cancer Immunology, F-75006 Paris, France;
- Equipe Labellisée Ligue Contre le Cancer, F-75006 Paris, France
- Centre de Recherche des Cordeliers, Sorbonne Université, Université Paris Cité, F-75006 Paris, France
- Veracyte, 13009 Marseille, France
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34
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Sali R, Jiang Y, Attaranzadeh A, Holmes B, Li R. Morphological diversity of cancer cells predicts prognosis across tumor types. J Natl Cancer Inst 2024; 116:555-564. [PMID: 37982756 PMCID: PMC10995848 DOI: 10.1093/jnci/djad243] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/23/2023] [Accepted: 11/13/2023] [Indexed: 11/21/2023] Open
Abstract
BACKGROUND Intratumor heterogeneity drives disease progression and treatment resistance, which can lead to poor patient outcomes. Here, we present a computational approach for quantification of cancer cell diversity in routine hematoxylin-eosin-stained histopathology images. METHODS We analyzed publicly available digitized whole-slide hematoxylin-eosin images for 2000 patients. Four tumor types were included: lung, head and neck, colon, and rectal cancers, representing major histology subtypes (adenocarcinomas and squamous cell carcinomas). We performed single-cell analysis on hematoxylin-eosin images and trained a deep convolutional autoencoder to automatically learn feature representations of individual cancer nuclei. We then computed features of intranuclear variability and internuclear diversity to quantify tumor heterogeneity. Finally, we used these features to build a machine-learning model to predict patient prognosis. RESULTS A total of 68 million cancer cells were segmented and analyzed for nuclear image features. We discovered multiple morphological subtypes of cancer cells (range = 15-20) that co-exist within the same tumor, each with distinct phenotypic characteristics. Moreover, we showed that a higher morphological diversity is associated with chromosome instability and genomic aneuploidy. A machine-learning model based on morphological diversity demonstrated independent prognostic values across tumor types (hazard ratio range = 1.62-3.23, P < .035) in validation cohorts and further improved prognostication when combined with clinical risk factors. CONCLUSIONS Our study provides a practical approach for quantifying intratumor heterogeneity based on routine histopathology images. The cancer cell diversity score can be used to refine risk stratification and inform personalized treatment strategies.
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Affiliation(s)
- Rasoul Sali
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Yuming Jiang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Armin Attaranzadeh
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Brittany Holmes
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
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35
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Zhang P, Gao C, Huang Y, Chen X, Pan Z, Wang L, Dong D, Li S, Qi X. Artificial intelligence in liver imaging: methods and applications. Hepatol Int 2024; 18:422-434. [PMID: 38376649 DOI: 10.1007/s12072-023-10630-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 12/18/2023] [Indexed: 02/21/2024]
Abstract
Liver disease is regarded as one of the major health threats to humans. Radiographic assessments hold promise in terms of addressing the current demands for precisely diagnosing and treating liver diseases, and artificial intelligence (AI), which excels at automatically making quantitative assessments of complex medical image characteristics, has made great strides regarding the qualitative interpretation of medical imaging by clinicians. Here, we review the current state of medical-imaging-based AI methodologies and their applications concerning the management of liver diseases. We summarize the representative AI methodologies in liver imaging with focusing on deep learning, and illustrate their promising clinical applications across the spectrum of precise liver disease detection, diagnosis and treatment. We also address the current challenges and future perspectives of AI in liver imaging, with an emphasis on feature interpretability, multimodal data integration and multicenter study. Taken together, it is revealed that AI methodologies, together with the large volume of available medical image data, might impact the future of liver disease care.
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Affiliation(s)
- Peng Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Chaofei Gao
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Yifei Huang
- Department of Gastroenterology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiangyi Chen
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Zhuoshi Pan
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Lan Wang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Shao Li
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China.
| | - Xiaolong Qi
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Southeast University, Nanjing, China.
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Kim PJ, Hwang HS, Choi G, Sung HJ, Ahn B, Uh JS, Yoon S, Kim D, Chun SM, Jang SJ, Go H. A new model using deep learning to predict recurrence after surgical resection of lung adenocarcinoma. Sci Rep 2024; 14:6366. [PMID: 38493247 PMCID: PMC10944489 DOI: 10.1038/s41598-024-56867-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 03/12/2024] [Indexed: 03/18/2024] Open
Abstract
This study aimed to develop a deep learning (DL) model for predicting the recurrence risk of lung adenocarcinoma (LUAD) based on its histopathological features. Clinicopathological data and whole slide images from 164 LUAD cases were collected and used to train DL models with an ImageNet pre-trained efficientnet-b2 architecture, densenet201, and resnet152. The models were trained to classify each image patch into high-risk or low-risk groups, and the case-level result was determined by multiple instance learning with final FC layer's features from a model from all patches. Analysis of the clinicopathological and genetic characteristics of the model-based risk group was performed. For predicting recurrence, the model had an area under the curve score of 0.763 with 0.750, 0.633 and 0.680 of sensitivity, specificity, and accuracy in the test set, respectively. High-risk cases for recurrence predicted by the model (HR group) were significantly associated with shorter recurrence-free survival and a higher stage (both, p < 0.001). The HR group was associated with specific histopathological features such as poorly differentiated components, complex glandular pattern components, tumor spread through air spaces, and a higher grade. In the HR group, pleural invasion, necrosis, and lymphatic invasion were more frequent, and the size of the invasion was larger (all, p < 0.001). Several genetic mutations, including TP53 (p = 0.007) mutations, were more frequently found in the HR group. The results of stages I-II were similar to those of the general cohort. DL-based model can predict the recurrence risk of LUAD and identify the presence of the TP53 gene mutation by analyzing histopathologic features.
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Affiliation(s)
- Pil-Jong Kim
- School of Dentistry and Dental Research Institute, Seoul National University, Seoul, Republic of Korea
| | - Hee Sang Hwang
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Gyuheon Choi
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hyun-Jung Sung
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Bokyung Ahn
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ji-Su Uh
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Shinkyo Yoon
- Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Deokhoon Kim
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sung-Min Chun
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Se Jin Jang
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Heounjeong Go
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Cherie N, Deress T, Berta DM, Chane E, Teketelew BB, Adane K, Nigus M. Navigating Quality Assessment Hurdles in Clinical Laboratory Services: A Comprehensive Review in Resource-Limited Settings. Risk Manag Healthc Policy 2024; 17:497-504. [PMID: 38476199 PMCID: PMC10929212 DOI: 10.2147/rmhp.s453020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 02/28/2024] [Indexed: 03/14/2024] Open
Abstract
External quality assessment is the process of evaluating the performance of a laboratory and the competence of professionals. Participation in EQA and standardizing the quality of laboratory services are a mandatory requirements for accreditation. This review is aimed at identifying and discussing challenges that hinder the effective implementation of an EQA program in countries with resource limited setting. To obtain abundant information, articles were identified by searching the literature publishedin English using the National Library of Medicine, PubMed, Science Direct and AMC digital library databases. The articles identified in the references were manually searched and included. After the article was identified, it was imported to Endnote version 8.1 and exported to Microsoft Word for citation. Based on this review, the major identified challenges that hinder the implementation of an EQA program include the high cost of control materials, malfunction and lack of maintenance for equipment failure and breakdown, a knowledge gap among laboratory professionals, noncommutability of control samples, and difficulty in assigning target values. In addition, failing to participate in EQA and failing to take corrective action are the major challenges identified. As a result, applying to an EQA program in resource-limited counties was highly challenging. To attain high performance in the laboratory and to provide quality laboratory service for patient care, the EQA supplier and the user laboratory must pay attention to these issues and take appropriate corrective actions for ongoing quality improvement and accreditation.
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Affiliation(s)
- Negesse Cherie
- Department of Quality Assurance and Laboratory Management, School of Biomedical and Laboratory Sciences, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Teshiwal Deress
- Department of Quality Assurance and Laboratory Management, School of Biomedical and Laboratory Sciences, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Dereje Mengesha Berta
- Department of Hematology and Immunohematology, School of Biomedical and Laboratory Sciences, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Elias Chane
- Department of Clinical Chemistry, School of Biomedical and Laboratory Sciences, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Bisrat Birke Teketelew
- Department of Hematology and Immunohematology, School of Biomedical and Laboratory Sciences, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Kasaw Adane
- Department of Quality Assurance and Laboratory Management, School of Biomedical and Laboratory Sciences, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Mesele Nigus
- Department of Quality Assurance and Laboratory Management, School of Biomedical and Laboratory Sciences, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
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Huffman N, Pasqualini I, Khan ST, Klika AK, Deren ME, Jin Y, Kunze KN, Piuzzi NS. Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review. JBJS Rev 2024; 12:01874474-202403000-00006. [PMID: 38466797 DOI: 10.2106/jbjs.rvw.23.00232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.
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Affiliation(s)
- Nickelas Huffman
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | | | - Shujaa T Khan
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Alison K Klika
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Matthew E Deren
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Yuxuan Jin
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
| | - Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York
| | - Nicolas S Piuzzi
- Cleveland Clinic, Department of Orthopaedic Surgery, Cleveland, Ohio
- Department of Biomedical Engineering, Cleveland Clinic Foundation, Cleveland, Ohio
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Åkerla J, Nevalainen J, Pesonen JS, Pöyhönen A, Koskimäki J, Häkkinen J, Tammela TLJ, Auvinen A. Do LUTS Predict Mortality? An Analysis Using Random Forest Algorithms. Clin Interv Aging 2024; 19:237-245. [PMID: 38371602 PMCID: PMC10873145 DOI: 10.2147/cia.s432368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/17/2024] [Indexed: 02/20/2024] Open
Abstract
Purpose To evaluate a random forest (RF) algorithm of lower urinary tract symptoms (LUTS) as a predictor of all-cause mortality in a population-based cohort. Materials and Methods A population-based cohort of 3143 men born in 1924, 1934, and 1944 was evaluated using a mailed questionnaire including the Danish Prostatic Symptom Score (DAN-PSS-1) to assess LUTS as well as questions on medical conditions and behavioral and sociodemographic factors. Surveys were repeated in 1994, 1999, 2004, 2009 and 2015. The cohort was followed-up for vital status until the end of 2018. RF uses an ensemble of classification trees for prediction with a good flexibility and without overfitting. RF algorithms were developed to predict the five-year mortality using LUTS, demographic, medical, and behavioral factors alone and in combinations. Results A total of 2663 men were included in the study, of whom 917 (34%) died during follow-up (median follow-up time 15.0 years). The LUTS-based RF algorithm showed an area under the curve (AUC) 0.60 (95% CI 0.52-0.69) for five-year mortality. An expanded RF algorithm, including LUTS, medical history, and behavioral and sociodemographic factors, yielded an AUC 0.73 (0.65-0.81), while an algorithm excluding LUTS yielded an AUC 0.71 (0.62-0.78). Conclusion An exploratory RF algorithm using LUTS can predict all-cause mortality with acceptable discrimination at the group level. In clinical practice, it is unlikely that LUTS will improve the accuracy to predict death if the patient's background is well known.
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Affiliation(s)
- Jonne Åkerla
- Department of Urology, Tampere University Hospital, Tampere, Finland
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | - Jori S Pesonen
- Department of Surgery, Päijät-Häme Central Hospital, Lahti, Finland
| | - Antti Pöyhönen
- Centre for Military Medicine, The Finnish Defence Forces, Riihimäki, Finland
| | - Juha Koskimäki
- Department of Urology, Tampere University Hospital, Tampere, Finland
| | - Jukka Häkkinen
- Department of Urology, Länsi-Pohja healthcare District, Kemi, Finland
| | - Teuvo L J Tammela
- Department of Urology, Tampere University Hospital, Tampere, Finland
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Anssi Auvinen
- Faculty of Social Sciences, Tampere University, Tampere, Finland
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Chen Z, Liu Y, Lin Z, Huang W. Understand how machine learning impact lung cancer research from 2010 to 2021: A bibliometric analysis. Open Med (Wars) 2024; 19:20230874. [PMID: 38463530 PMCID: PMC10921441 DOI: 10.1515/med-2023-0874] [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: 06/03/2023] [Revised: 11/18/2023] [Accepted: 11/20/2023] [Indexed: 03/12/2024] Open
Abstract
Advances in lung cancer research applying machine learning (ML) technology have generated many relevant literature. However, there is absence of bibliometric analysis review that aids a comprehensive understanding of this field and its progress. Present article for the first time performed a bibliometric analysis to clarify research status and focus from 2010 to 2021. In the analysis, a total of 2,312 relevant literature were searched and retrieved from the Web of Science Core Collection database. We conducted a bibliometric analysis and further visualization. During that time, exponentially growing annual publication and our model have shown a flourishing research prospect. Annual citation reached the peak in 2017. Researchers from United States and China have produced most of the relevant literature and strongest partnership between them. Medical image analysis and Nature appeared to bring more attention to the public. The computer-aided diagnosis, precision medicine, and survival prediction were the focus of research, reflecting the development trend at that period. ML did make a big difference in lung cancer research in the past decade.
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Affiliation(s)
- Zijian Chen
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Yangqi Liu
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Zeying Lin
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Weizhe Huang
- Department of Cardiothoracic Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, China
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Zhu Q, Dai H, Qiu F, Lou W, Wang X, Deng L, Shi C. Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer. Transl Oncol 2024; 40:101855. [PMID: 38185058 PMCID: PMC10808968 DOI: 10.1016/j.tranon.2023.101855] [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: 09/06/2023] [Revised: 11/27/2023] [Accepted: 11/30/2023] [Indexed: 01/09/2024] Open
Abstract
BACKGROUND Chemotherapy resistance is the main cause of ovarian cancer progression and even death. However, there are no clear indicators for predicting the risk of drug resistance in patients. Intra-tumor heterogeneity (ITH) is one of the characteristics of malignant tumors, which is associated with the treatment and prognosis of tumors. Accordingly, our study aims to investigate the correlation between the image features of intra-tumor heterogeneity and drug resistance of ovarian cancer based on artificial intelligence. METHODS We obtained hematoxylin and eosin staining frozen histopathological images of ovarian cancer and paracarcinoma tissues from the Cancer Genome Atlas. We extracted quantitative image features of whole-slide images based on the automatic image nuclear segmentation processing technology. After that, we used bioinformatics analysis to find the relationship between image features of intra-tumor heterogeneity and drug resistance. RESULTS Our results show that our automatic image processing process based on computer artificial intelligence can extract image features effectively, and the key image features extracted are closely related to ITH. Among them, the Perimeter.sd image feature with the most prominent ITH feature can accurately predict the risk of platinum-based chemotherapy drug resistance in ovarian cancer patients. CONCLUSION Automatic image processing and feature extraction based on artificial intelligence have excellent results. Perimeter.sd can be used as a useful image feature indicator for evaluating ITH. ITH is associated with drug resistance of ovarian cancer, so ITH characteristics can be used as an effective indicator to evaluate drug resistance in patients with ovarian cancer.
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Affiliation(s)
- Qiuli Zhu
- Department of Genetics, Gaoxin Branch of The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Hua Dai
- Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Feng Qiu
- Department of Oncology, Gaoxin Branch of The First Affiliated Hospital of Nanchang University, No.7889 of Changdong avenue, Gaoxin District, Nanchang, Jiangxi, China
| | - Weiming Lou
- The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Xin Wang
- Queen Mary School of Nanchang University, Nanchang University, Nanchang, China
| | - Libin Deng
- Jiangxi Provincial Key Laboratory of Preventive Medicine, School of Public Health, Nanchang University, Nanchang, China.
| | - Chao Shi
- Department of Oncology, Gaoxin Branch of The First Affiliated Hospital of Nanchang University, No.7889 of Changdong avenue, Gaoxin District, Nanchang, Jiangxi, China.
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Zhang C, Bedi T, Moon C, Xie Y, Chen M, Li Q. Bayesian Landmark-based Shape Analysis of Tumor Pathology Images. J Am Stat Assoc 2024; 119:798-810. [PMID: 39280355 PMCID: PMC11395925 DOI: 10.1080/01621459.2023.2298031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 12/16/2023] [Indexed: 09/18/2024]
Abstract
Medical imaging is a form of technology that has revolutionized the medical field over the past decades. Digital pathology imaging, which captures histological details at the cellular level, is rapidly becoming a routine clinical procedure for cancer diagnosis support and treatment planning. Recent developments in deep-learning methods have facilitated tumor region segmentation from pathology images. The traditional shape descriptors that characterize tumor boundary roughness at the anatomical level are no longer suitable. New statistical approaches to model tumor shapes are in urgent need. In this paper, we consider the problem of modeling a tumor boundary as a closed polygonal chain. A Bayesian landmark-based shape analysis model is proposed. The model partitions the polygonal chain into mutually exclusive segments, accounting for boundary roughness. Our Bayesian inference framework provides uncertainty estimations on both the number and locations of landmarks, while outputting metrics that can be used to quantify boundary roughness. The performance of our model is comparable with that of a recently developed landmark detection model for planar elastic curves. In a case study of 143 consecutive patients with stage I to IV lung cancer, we demonstrated the heterogeneity of tumor boundary roughness derived from our model effectively predicted patient prognosis (p-value < 0.001).
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Affiliation(s)
- Cong Zhang
- Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, Texas
| | - Tejasv Bedi
- Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, Texas
| | - Chul Moon
- Department of Statistics and Data Science, Southern Methodist University, Dallas, Texas
| | - Yang Xie
- Quantitative Biomedical Research Center, School of Public Health, The University of Texas Southwestern Medical Center, Dallas, Texas
| | - Min Chen
- Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, Texas
| | - Qiwei Li
- Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, Texas
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Acharya V, Choi D, Yener B, Beamer G. Prediction of Tuberculosis From Lung Tissue Images of Diversity Outbred Mice Using Jump Knowledge Based Cell Graph Neural Network. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2024; 12:17164-17194. [PMID: 38515959 PMCID: PMC10956573 DOI: 10.1109/access.2024.3359989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
Tuberculosis (TB), primarily affecting the lungs, is caused by the bacterium Mycobacterium tuberculosis and poses a significant health risk. Detecting acid-fast bacilli (AFB) in stained samples is critical for TB diagnosis. Whole Slide (WS) Imaging allows for digitally examining these stained samples. However, current deep-learning approaches to analyzing large-sized whole slide images (WSIs) often employ patch-wise analysis, potentially missing the complex spatial patterns observed in the granuloma essential for accurate TB classification. To address this limitation, we propose an approach that models cell characteristics and interactions as a graph, capturing both cell-level information and the overall tissue micro-architecture. This method differs from the strategies in related cell graph-based works that rely on edge thresholds based on sparsity/density in cell graph construction, emphasizing a biologically informed threshold determination instead. We introduce a cell graph-based jumping knowledge neural network (CG-JKNN) that operates on the cell graphs where the edge thresholds are selected based on the length of the mycobacteria's cords and the activated macrophage nucleus's size to reflect the actual biological interactions observed in the tissue. The primary process involves training a Convolutional Neural Network (CNN) to segment AFBs and macrophage nuclei, followed by converting large (42831*41159 pixels) lung histology images into cell graphs where an activated macrophage nucleus/AFB represents each node within the graph and their interactions are denoted as edges. To enhance the interpretability of our model, we employ Integrated Gradients and Shapely Additive Explanations (SHAP). Our analysis incorporated a combination of 33 graph metrics and 20 cell morphology features. In terms of traditional machine learning models, Extreme Gradient Boosting (XGBoost) was the best performer, achieving an F1 score of 0.9813 and an Area under the Precision-Recall Curve (AUPRC) of 0.9848 on the test set. Among graph-based models, our CG-JKNN was the top performer, attaining an F1 score of 0.9549 and an AUPRC of 0.9846 on the held-out test set. The integration of graph-based and morphological features proved highly effective, with CG-JKNN and XGBoost showing promising results in classifying instances into AFB and activated macrophage nucleus. The features identified as significant by our models closely align with the criteria used by pathologists in practice, highlighting the clinical applicability of our approach. Future work will explore knowledge distillation techniques and graph-level classification into distinct TB progression categories.
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Affiliation(s)
| | - Diana Choi
- Cummings School of Veterinary Medicine, Tufts University, North Grafton, MA 02155, USA
| | - BüLENT Yener
- Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Gillian Beamer
- Research Pathology, Aiforia Technologies, Cambridge, MA 02142, USA
- Texas Biomedical Research Institute, San Antonio, TX 78227, USA
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Yang L, Lei Y, Huang Z, Geng M, Liu Z, Wang B, Luo D, Huang W, Liang D, Pang Z, Hu Z. An interactive nuclei segmentation framework with Voronoi diagrams and weighted convex difference for cervical cancer pathology images. Phys Med Biol 2024; 69:025021. [PMID: 37972412 DOI: 10.1088/1361-6560/ad0d44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 11/16/2023] [Indexed: 11/19/2023]
Abstract
Objective.Nuclei segmentation is crucial for pathologists to accurately classify and grade cancer. However, this process faces significant challenges, such as the complex background structures in pathological images, the high-density distribution of nuclei, and cell adhesion.Approach.In this paper, we present an interactive nuclei segmentation framework that increases the precision of nuclei segmentation. Our framework incorporates expert monitoring to gather as much prior information as possible and accurately segment complex nucleus images through limited pathologist interaction, where only a small portion of the nucleus locations in each image are labeled. The initial contour is determined by the Voronoi diagram generated from the labeled points, which is then input into an optimized weighted convex difference model to regularize partition boundaries in an image. Specifically, we provide theoretical proof of the mathematical model, stating that the objective function monotonically decreases. Furthermore, we explore a postprocessing stage that incorporates histograms, which are simple and easy to handle and prevent arbitrariness and subjectivity in individual choices.Main results.To evaluate our approach, we conduct experiments on both a cervical cancer dataset and a nasopharyngeal cancer dataset. The experimental results demonstrate that our approach achieves competitive performance compared to other methods.Significance.The Voronoi diagram in the paper serves as prior information for the active contour, providing positional information for individual cells. Moreover, the active contour model achieves precise segmentation results while offering mathematical interpretability.
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Affiliation(s)
- Lin Yang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
- College of Mathematics and Statistics, Henan University, Kaifeng 475004, People's Republic of China
| | - Yuanyuan Lei
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, People's Republic of China
| | - Zhenxing Huang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
| | - Mengxiao Geng
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
- College of Mathematics and Statistics, Henan University, Kaifeng 475004, People's Republic of China
| | - Zhou Liu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, People's Republic of China
| | - Baijie Wang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, People's Republic of China
| | - Dehong Luo
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, People's Republic of China
| | - Wenting Huang
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, People's Republic of China
| | - Dong Liang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
| | - Zhifeng Pang
- College of Mathematics and Statistics, Henan University, Kaifeng 475004, People's Republic of China
- Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan 430072, People's Republic of China
| | - Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
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Li J, Wang D, Zhang C. Establishment of a pathomic-based machine learning model to predict CD276 (B7-H3) expression in colon cancer. Front Oncol 2024; 13:1232192. [PMID: 38260829 PMCID: PMC10802857 DOI: 10.3389/fonc.2023.1232192] [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: 05/31/2023] [Accepted: 11/29/2023] [Indexed: 01/24/2024] Open
Abstract
CD276 is a promising prognostic indicator and an attractive therapeutic target in various malignancies. However, current methods for CD276 detection are time-consuming and expensive, limiting extensive studies and applications of CD276. We aimed to develop a pathomic model for CD276 prediction from H&E-stained pathological images, and explore the underlying mechanism of the pathomic features by associating the pathomic model with transcription profiles. A dataset of colon adenocarcinoma (COAD) patients was retrieved from the Cancer Genome Atlas (TCGA) database. The dataset was divided into the training and validation sets according to the ratio of 8:2 by a stratified sampling method. Using the gradient boosting machine (GBM) algorithm, we established a pathomic model to predict CD276 expression in COAD. Univariate and multivariate Cox regression analyses were conducted to assess the predictive performance of the pathomic model for overall survival in COAD. Gene Set Enrichment Analysis (GESA) was performed to explore the underlying biological mechanisms of the pathomic model. The pathomic model formed by three pathomic features for CD276 prediction showed an area under the curve (AUC) of 0.833 (95%CI: 0.784-0.882) in the training set and 0.758 (95%CI: 0.637-0.878) in the validation set, respectively. The calibration curves and Hosmer-Lemeshow goodness of fit test showed that the prediction probability of high/low expression of CD276 was in favorable agreement with the real situation in both the training and validation sets (P=0.176 and 0.255, respectively). The DCA curves suggested that the pathomic model acquired high clinical benefit. All the subjects were categorized into high pathomic score (PS) (PS-H) and low PS (PS-L) groups according to the cutoff value of PS. Univariate and multivariate Cox regression analysis indicated that PS was a risk factor for overall survival in COAD. Furthermore, through GESA analysis, we found several immune and inflammatory-related pathways and genes were associated with the pathomic model. We constructed a pathomics-based machine learning model for CD276 prediction directly from H&E-stained images in COAD. Through integrated analysis of the pathomic model and transcriptomics, the interpretability of the pathomic model provide a theoretical basis for further hypothesis and experimental research.
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Affiliation(s)
- Jia Li
- Department of Gastroenterology, The 983rd Hospital of Joint Logistic Support Force of PLA, Tianjin, China
| | - Dongxu Wang
- Department of Gastroenterology, The 983rd Hospital of Joint Logistic Support Force of PLA, Tianjin, China
| | - Chenxin Zhang
- Department of General Surgery, The 983rd Hospital of Joint Logistic Support Force of PLA, Tianjin, China
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Jahangiri L. Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods: A Review. Med Sci (Basel) 2024; 12:5. [PMID: 38249081 PMCID: PMC10801560 DOI: 10.3390/medsci12010005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 12/28/2023] [Accepted: 01/03/2024] [Indexed: 01/23/2024] Open
Abstract
Neuroblastoma, a paediatric malignancy with high rates of cancer-related morbidity and mortality, is of significant interest to the field of paediatric cancers. High-risk NB tumours are usually metastatic and result in survival rates of less than 50%. Machine learning approaches have been applied to various neuroblastoma patient data to retrieve relevant clinical and biological information and develop predictive models. Given this background, this study will catalogue and summarise the literature that has used machine learning and statistical methods to analyse data such as multi-omics, histological sections, and medical images to make clinical predictions. Furthermore, the question will be turned on its head, and the use of machine learning to accurately stratify NB patients by risk groups and to predict outcomes, including survival and treatment response, will be summarised. Overall, this study aims to catalogue and summarise the important work conducted to date on the subject of expression-based predictor models and machine learning in neuroblastoma for risk stratification and patient outcomes including survival, and treatment response which may assist and direct future diagnostic and therapeutic efforts.
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Affiliation(s)
- Leila Jahangiri
- School of Science and Technology, Nottingham Trent University, Clifton Site, Nottingham NG11 8NS, UK;
- Division of Cellular and Molecular Pathology, Addenbrookes Hospital, University of Cambridge, Cambridge CB2 0QQ, UK
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Yang Z, Zhang Y, Tang L, Yang X, Song L, Shen C, Zvyagin AV, Li Y, Yang B, Lin Q. "All in one" nanoprobe Au-TTF-1 for target FL/CT bioimaging, machine learning technology and imaging-guided photothermal therapy against lung adenocarcinoma. J Nanobiotechnology 2024; 22:22. [PMID: 38184620 PMCID: PMC10770976 DOI: 10.1186/s12951-023-02280-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 12/19/2023] [Indexed: 01/08/2024] Open
Abstract
The accurate preoperative diagnosis and tracking of lung adenocarcinoma is hindered by non-targeting and diffusion of dyes used for marking tumors. Hence, there is an urgent need to develop a practical nanoprobe for tracing lung adenocarcinoma precisely even treating them noninvasively. Herein, Gold nanoclusters (AuNCs) conjugate with thyroid transcription factor-1 (TTF-1) antibody, then multifunctional nanoprobe Au-TTF-1 is designed and synthesized, which underscores the paramount importance of advancing the machine learning diagnosis and bioimaging-guided treatment of lung adenocarcinoma. Bright fluorescence (FL) and strong CT signal of Au-TTF-1 set the stage for tracking. Furthermore, the high specificity of TTF-1 antibody facilitates selective targeting of lung adenocarcinoma cells as compared to common lung epithelial cells, so machine learning software Lung adenocarcinoma auxiliary detection system was designed, which combined with Au-TTF-1 to assist the intelligent recognition of lung adenocarcinoma jointly. Besides, Au-TTF-1 not only contributes to intuitive and targeted visualization, but also guides the following noninvasive photothermal treatment. The boundaries of tumor are light up by Au-TTF-1 for navigation, it penetrates into tumor and implements noninvasive photothermal treatment, resulting in ablating tumors in vivo locally. Above all, Au-TTF-1 serves as a key platform for target bio-imaging navigation, machine learning diagnosis and synergistic PTT as a single nanoprobe, which demonstrates attractive performance on lung adenocarcinoma.
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Affiliation(s)
- Zhe Yang
- State Key Laboratory of Supramolecular Structure and Material, College of Chemistry, Jilin University, Changchun, 130012, China
| | - Yujia Zhang
- Department of Respiratory Medicine, The First Hospital of Jilin University, Changchun, 130021, China
| | - Lu Tang
- Department of Breast, China-Japan Union Hospital of Jilin University, Changchun, 130031, China
| | - Xiao Yang
- College of Computer Science and Technology Jilin University, Changchun, 130012, China
| | - Lei Song
- Department of Breast, China-Japan Union Hospital of Jilin University, Changchun, 130031, China
| | - Chun Shen
- College of Computer Science and Technology Jilin University, Changchun, 130012, China
| | - Andrei V Zvyagin
- Australian Research Council Centre of Excellence for Nanoscale Biophotonics, Macquarie University, Sydney, NSW, 2109, Australia
| | - Yang Li
- Department of Respiratory Medicine, The First Hospital of Jilin University, Changchun, 130021, China.
| | - Bai Yang
- State Key Laboratory of Supramolecular Structure and Material, College of Chemistry, Jilin University, Changchun, 130012, China
| | - Quan Lin
- State Key Laboratory of Supramolecular Structure and Material, College of Chemistry, Jilin University, Changchun, 130012, China.
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Amin A, Cardoso SA, Suyambu J, Abdus Saboor H, Cardoso RP, Husnain A, Isaac NV, Backing H, Mehmood D, Mehmood M, Maslamani ANJ. Future of Artificial Intelligence in Surgery: A Narrative Review. Cureus 2024; 16:e51631. [PMID: 38318552 PMCID: PMC10839429 DOI: 10.7759/cureus.51631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/03/2024] [Indexed: 02/07/2024] Open
Abstract
Artificial intelligence (AI) is the capability of a machine to execute cognitive processes that are typically considered to be functions of the human brain. It is the study of algorithms that enable machines to reason and perform mental tasks, including problem-solving, object and word recognition, and decision-making. Once considered science fiction, AI today is a fact and an increasingly prevalent subject in both academic and popular literature. It is expected to reshape medicine, benefiting both healthcare professionals and patients. Machine learning (ML) is a subset of AI that allows machines to learn and make predictions by recognizing patterns, thus empowering the medical team to deliver better care to patients through accurate diagnosis and treatment. ML is expanding its footprint in a variety of surgical specialties, including general surgery, ophthalmology, cardiothoracic surgery, and vascular surgery, to name a few. In recent years, we have seen AI make its way into the operating theatres. Though it has not yet been able to replace the surgeon, it has the potential to become a highly valuable surgical tool. Rest assured that the day is not far off when AI shall play a significant intraoperative role, a projection that is currently marred by safety concerns. This review aims to explore the present application of AI in various surgical disciplines and how it benefits both patients and physicians, as well as the current obstacles and limitations facing its seemingly unstoppable rise.
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Affiliation(s)
- Aamir Amin
- Cardiothoracic Surgery, Harefield Hospital, Guy's and St Thomas' NHS Foundation Trust, London, GBR
| | - Swizel Ann Cardoso
- Major Trauma Services, University Hospital Birmingham NHS Foundation Trust DC, Birmingham, GBR
| | - Jenisha Suyambu
- Medicine, University of Perpetual Help System Data - Jonelta Foundation School of Medicine, Las Piñas, PHL
| | | | - Rayner P Cardoso
- Medicine and Surgery, All India Institute of Medical Sciences, Jodhpur, Jodhpur, IND
| | - Ali Husnain
- Radiology, Northwestern University, Lahore, PAK
| | - Natasha Varghese Isaac
- Medicine and Surgery, St John's Medical College Hospital, Rajiv Gandhi University of Health Sciences, Bengaluru, IND
| | - Haydee Backing
- Medicine, Universidad de San Martin de Porres, Lima, PER
| | - Dalia Mehmood
- Community Medicine, Fatima Jinnah Medical University, Lahore, PAK
| | - Maria Mehmood
- Internal Medicine, Shalamar Medical and Dental College, Lahore, PAK
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Liu P, Ji L, Ye F, Fu B. AdvMIL: Adversarial multiple instance learning for the survival analysis on whole-slide images. Med Image Anal 2024; 91:103020. [PMID: 37926034 DOI: 10.1016/j.media.2023.103020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 04/05/2023] [Accepted: 10/31/2023] [Indexed: 11/07/2023]
Abstract
The survival analysis on histological whole-slide images (WSIs) is one of the most important means to estimate patient prognosis. Although many weakly-supervised deep learning models have been developed for gigapixel WSIs, their potential is generally restricted by classical survival analysis rules and fully-supervised learning requirements. As a result, these models provide patients only with a completely-certain point estimation of time-to-event, and they could only learn from the labeled WSI data currently at a small scale. To tackle these problems, we propose a novel adversarial multiple instance learning (AdvMIL) framework. This framework is based on adversarial time-to-event modeling, and integrates the multiple instance learning (MIL) that is much necessary for WSI representation learning. It is a plug-and-play one, so that most existing MIL-based end-to-end methods can be easily upgraded by applying this framework, gaining the improved abilities of survival distribution estimation and semi-supervised learning. Our extensive experiments show that AdvMIL not only could often bring performance improvement to mainstream WSI survival analysis methods at a relatively low computational cost, but also enables these methods to effectively utilize unlabeled data via semi-supervised learning. Moreover, it is observed that AdvMIL could help improving the robustness of models against patch occlusion and two representative image noises. The proposed AdvMIL framework could promote the research of survival analysis in computational pathology with its novel adversarial MIL paradigm.
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Affiliation(s)
- Pei Liu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Xiyuan Ave, Chengdu, 611731, Sichuan, China.
| | - Luping Ji
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Xiyuan Ave, Chengdu, 611731, Sichuan, China.
| | - Feng Ye
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
| | - Bo Fu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Xiyuan Ave, Chengdu, 611731, Sichuan, China.
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50
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Xiong DD, He RQ, Huang ZG, Wu KJ, Mo YY, Liang Y, Yang DP, Wu YH, Tang ZQ, Liao ZT, Chen G. Global bibliometric mapping of the research trends in artificial intelligence-based digital pathology for lung cancer over the past two decades. Digit Health 2024; 10:20552076241277735. [PMID: 39233894 PMCID: PMC11372859 DOI: 10.1177/20552076241277735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 08/08/2024] [Indexed: 09/06/2024] Open
Abstract
Background and Objective The rapid development of computer technology has led to a revolutionary transformation in artificial intelligence (AI)-assisted healthcare. The integration of whole-slide imaging technology with AI algorithms has facilitated the development of digital pathology for lung cancer (LC). However, there is a lack of comprehensive scientometric analysis in this field. Methods A bibliometric analysis was conducted on 197 publications related to digital pathology in LC from 502 institutions across 39 countries, published in 97 academic journals in the Web of Science Core Collection between 2004 and 2023. Results Our analysis has identified the United States and China as the primary research nations in the field of digital pathology in LC. However, it is important to note that the current research primarily consists of independent studies among countries, emphasizing the necessity of strengthening academic collaboration and data sharing between nations. The current focus and challenge of research related to digital pathology in LC lie in enhancing the accuracy of classification and prediction through improved deep learning algorithms. The integration of multi-omics studies presents a promising future research direction. Additionally, researchers are increasingly exploring the application of digital pathology in immunotherapy for LC patients. Conclusions In conclusion, this study provides a comprehensive knowledge framework for digital pathology in LC, highlighting research trends, hotspots, and gaps in this field. It also provides a theoretical basis for the application of AI in clinical decision-making for LC patients.
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Affiliation(s)
- Dan-Dan Xiong
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Rong-Quan He
- Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Zhi-Guang Huang
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Kun-Jun Wu
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Ying-Yu Mo
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yue Liang
- Department of Pathology, Liuzhou People's Hospital, Liuzhou, Guangxi, China
| | - Da-Ping Yang
- Department of Pathology, Guigang City People's Hospital, Guigang, Guangxi, China
| | - Ying-Hui Wu
- Department of Pathology, The First People's Hospital of Yulin, Yulin, Guangxi, China
| | - Zhong-Qing Tang
- Department of Pathology, Gongren Hospital of Wuzhou, Wuzhou, Guangxi, China
| | - Zu-Tuan Liao
- Department of Pathology, The First People's Hospital of Hechi, Hechi, Guangxi, China
| | - Gang Chen
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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