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Huang J, Saw SN, He T, Yang R, Qin Y, Chen Y, Kiong LC. DCNNLFS: A Dilated Convolutional Neural Network With Late Fusion Strategy for Intelligent Classification of Gastric Histopathology Images. IEEE J Biomed Health Inform 2024; 28:4534-4543. [PMID: 37983160 DOI: 10.1109/jbhi.2023.3334709] [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: 11/22/2023]
Abstract
Gastric cancer has a high incidence rate, significantly threatening patients' health. Gastric histopathology images can reliably diagnose related diseases. Still, the data volume of histopathology images is too large, making misdiagnosis or missed diagnosis easy. The classification model based on deep learning has made some progress on gastric histopathology images. However, traditional convolutional neural networks (CNNs) generally use pooling operations, which will reduce the spatial resolution of the image, resulting in poor prediction results. The image feature in previous CNN has a poor perception of details. Therefore, we design a dilated CNN with a late fusion strategy (DCNNLFS) for gastric histopathology image classification. The DCNNLFS model utilizes dilated convolutions, enabling it to expand the receptive field. The dilated convolutions can learn the different contextual information by adjusting the dilation rate. The DCNNLFS model uses a late fusion strategy to enhance the classification ability of DCNNLFS. We run related experiments on a gastric histopathology image dataset to verify the excellence of the DCNNLFS model, where the three metrics Precision, Accuracy, and F1-Score are 0.938, 0.935, and 0.959.
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Han Z, Zhang Z, Yang X, Li Z, Sang S, Islam MT, Guo AA, Li Z, Wang X, Wang J, Zhang T, Sun Z, Yu L, Wang W, Xiong W, Li G, Jiang Y. Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer. J Immunother Cancer 2024; 12:e008927. [PMID: 38749538 PMCID: PMC11097892 DOI: 10.1136/jitc-2024-008927] [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] [Accepted: 04/24/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Only a subset of patients with gastric cancer experience long-term benefits from immune checkpoint inhibitors (ICIs). Currently, there is a deficiency in precise predictive biomarkers for ICI efficacy. The aim of this study was to develop and validate a pathomics-driven ensemble model for predicting the response to ICIs in gastric cancer, using H&E-stained whole slide images (WSI). METHODS This multicenter study retrospectively collected and analyzed H&E-stained WSIs and clinical data from 584 patients with gastric cancer. An ensemble model, integrating four classifiers: least absolute shrinkage and selection operator, k-nearest neighbors, decision trees, and random forests, was developed and validated using pathomics features, with the objective of predicting the therapeutic efficacy of immune checkpoint inhibition. Model performance was evaluated using metrics including the area under the curve (AUC), sensitivity, and specificity. Additionally, SHAP (SHapley Additive exPlanations) analysis was used to explain the model's predicted values as the sum of the attribution values for each input feature. Pathogenomics analysis was employed to explain the molecular mechanisms underlying the model's predictions. RESULTS Our pathomics-driven ensemble model effectively stratified the response to ICIs in training cohort (AUC 0.985 (95% CI 0.971 to 0.999)), which was further validated in internal validation cohort (AUC 0.921 (95% CI 0.839 to 0.999)), as well as in external validation cohort 1 (AUC 0.914 (95% CI 0.837 to 0.990)), and external validation cohort 2 (0.927 (95% CI 0.802 to 0.999)). The univariate Cox regression analysis revealed that the prediction signature of pathomics-driven ensemble model was a prognostic factor for progression-free survival in patients with gastric cancer who underwent immunotherapy (p<0.001, HR 0.35 (95% CI 0.24 to 0.50)), and remained an independent predictor after multivariable Cox regression adjusted for clinicopathological variables, (including sex, age, carcinoembryonic antigen, carbohydrate antigen 19-9, therapy regime, line of therapy, differentiation, location and programmed death ligand 1 (PD-L1) expression in all patients (p<0.001, HR 0.34 (95% CI 0.24 to 0.50)). Pathogenomics analysis suggested that the ensemble model is driven by molecular-level immune, cancer, metabolism-related pathways, and was correlated with the immune-related characteristics, including immune score, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data score, and tumor purity. CONCLUSIONS Our pathomics-driven ensemble model exhibited high accuracy and robustness in predicting the response to ICIs using WSIs. Therefore, it could serve as a novel and valuable tool to facilitate precision immunotherapy.
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Affiliation(s)
- Zhen Han
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine,Southern Medical University, Guangzhou, Guangdong, China
| | - Zhicheng Zhang
- Department of Gastroenterology, The First Hospital of Jilin University, Changchun, Jilin, China
- JancsiLab, JancsiTech, Hongkong, China
| | - Xianqi Yang
- Department of Gastric Surgery, and State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Zhe Li
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Shengtian Sang
- Department of Radiology, Stanford University School of Medicine, Stanford, California, USA
| | - Md Tauhidul Islam
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA
| | - Alyssa A Guo
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Zihan Li
- Department of Bioengineering, University of Washington, Seattle, Washington, USA
| | - Xiaoyan Wang
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
| | - Jing Wang
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
| | - Taojun Zhang
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine,Southern Medical University, Guangzhou, Guangdong, China
| | - Zepang Sun
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine,Southern Medical University, Guangzhou, Guangdong, China
| | - Lequan Yu
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, Hong Kong
| | - Wei Wang
- Department of Gastric Surgery, and State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Wenjun Xiong
- Department of Gastrointestinal Surgery, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Guoxin Li
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine,Southern Medical University, Guangzhou, Guangdong, China
- School of Clinical Medicine, Tsinghua University, Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Yuming Jiang
- Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
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Farzan R. Artificial intelligence in Immuno-genetics. Bioinformation 2024; 20:29-35. [PMID: 38352901 PMCID: PMC10859949 DOI: 10.6026/973206300200029] [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: 01/01/2024] [Revised: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 02/16/2024] Open
Abstract
Rapid advancements in the field of artificial intelligence (AI) have opened up unprecedented opportunities to revolutionize various scientific domains, including immunology and genetics. Therefore, it is of interest to explore the emerging applications of AI in immunology and genetics, with the objective of enhancing our understanding of the dynamic intricacies of the immune system, disease etiology, and genetic variations. Hence, the use of AI methodologies in immunological and genetic datasets, thereby facilitating the development of innovative approaches in the realms of diagnosis, treatment, and personalized medicine is reviewed.
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Affiliation(s)
- Raed Farzan
- Department of Clinical Laboratory Sciences, College of Applied Medical Scienecs, King Saud University, Riyadh - 11433, Saudi Arabia
- Center of Excellence in Biotechnology Research, King Saud University, Riyadh - 11433, Saudi Arabia
- Medical and Molecular Genetics Research, King Saud University, Riyadh-11433, Saudi Arabia
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Lee J, Cha S, Kim J, Kim JJ, Kim N, Jae Gal SG, Kim JH, Lee JH, Choi YD, Kang SR, Song GY, Yang DH, Lee JH, Lee KH, Ahn S, Moon KM, Noh MG. Ensemble Deep Learning Model to Predict Lymphovascular Invasion in Gastric Cancer. Cancers (Basel) 2024; 16:430. [PMID: 38275871 PMCID: PMC10814827 DOI: 10.3390/cancers16020430] [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: 12/13/2023] [Revised: 01/11/2024] [Accepted: 01/16/2024] [Indexed: 01/27/2024] Open
Abstract
Lymphovascular invasion (LVI) is one of the most important prognostic factors in gastric cancer as it indicates a higher likelihood of lymph node metastasis and poorer overall outcome for the patient. Despite its importance, the detection of LVI(+) in histopathology specimens of gastric cancer can be a challenging task for pathologists as invasion can be subtle and difficult to discern. Herein, we propose a deep learning-based LVI(+) detection method using H&E-stained whole-slide images. The ConViT model showed the best performance in terms of both AUROC and AURPC among the classification models (AUROC: 0.9796; AUPRC: 0.9648). The AUROC and AUPRC of YOLOX computed based on the augmented patch-level confidence score were slightly lower (AUROC: -0.0094; AUPRC: -0.0225) than those of the ConViT classification model. With weighted averaging of the patch-level confidence scores, the ensemble model exhibited the best AUROC, AUPRC, and F1 scores of 0.9880, 0.9769, and 0.9280, respectively. The proposed model is expected to contribute to precision medicine by potentially saving examination-related time and labor and reducing disagreements among pathologists.
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Affiliation(s)
- Jonghyun Lee
- Department of Medical and Digital Engineering, Hanyang University College of Engineering, Seoul 04763, Republic of Korea;
| | - Seunghyun Cha
- Department of Pre-Medicine, Chonnam National University Medical School, 322 Seoyang-ro, Hwasun-eup, Hwasun-gun, Gwangju 58128, Republic of Korea;
| | - Jiwon Kim
- NetTargets, 495 Sinseong-dong, Yuseong, Daejeon 34109, Republic of Korea
| | - Jung Joo Kim
- AMGINE, Inc., Jeongui-ro 8-gil 13, Seoul 05836, Republic of Korea;
| | - Namkug Kim
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 25440, Republic of Korea; (N.K.); (S.G.J.G.)
| | - Seong Gyu Jae Gal
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 25440, Republic of Korea; (N.K.); (S.G.J.G.)
| | - Ju Han Kim
- Division of Biomedical Informatics, Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul 03080, Republic of Korea;
| | - Jeong Hoon Lee
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305-5101, USA;
| | - Yoo-Duk Choi
- Department of Pathology, Chonnam National University Medical School, Gwangju 61469, Republic of Korea;
| | - Sae-Ryung Kang
- Department of Nuclear Medicine, Clinical Medicine Research Center, Chonnam National University Hospital, 671 Jebongno, Gwangju 61469, Republic of Korea;
| | - Ga-Young Song
- Departments of Hematology-Oncology, Chonnam National University Hwasun Hospital, 322 Seoyangro, Hwasun 58128, Republic of Korea; (G.-Y.S.); (D.-H.Y.)
| | - Deok-Hwan Yang
- Departments of Hematology-Oncology, Chonnam National University Hwasun Hospital, 322 Seoyangro, Hwasun 58128, Republic of Korea; (G.-Y.S.); (D.-H.Y.)
| | - Jae-Hyuk Lee
- Department of Pathology, Chonnam National University Hwasun Hospital and Medical School, 322 Seoyang-ro, Hwasun-eup, Hwasun-gun, Hwasun 58128, Republic of Korea (K.-H.L.)
| | - Kyung-Hwa Lee
- Department of Pathology, Chonnam National University Hwasun Hospital and Medical School, 322 Seoyang-ro, Hwasun-eup, Hwasun-gun, Hwasun 58128, Republic of Korea (K.-H.L.)
| | - Sangjeong Ahn
- Department of Pathology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea;
| | - Kyoung Min Moon
- Division of Pulmonary and Allergy Medicine, Department of Internal Medicine, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea
- Artificial Intelligence, ZIOVISION Co., Ltd., Chuncheon 24341, Republic of Korea
| | - Myung-Giun Noh
- Department of Pathology, Chonnam National University Hwasun Hospital and Medical School, 322 Seoyang-ro, Hwasun-eup, Hwasun-gun, Hwasun 58128, Republic of Korea (K.-H.L.)
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Yang Y, Sun K, Gao Y, Wang K, Yu G. Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance. Diagnostics (Basel) 2023; 13:3115. [PMID: 37835858 PMCID: PMC10572440 DOI: 10.3390/diagnostics13193115] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
The pathology is decisive for disease diagnosis but relies heavily on experienced pathologists. In recent years, there has been growing interest in the use of artificial intelligence in pathology (AIP) to enhance diagnostic accuracy and efficiency. However, the impressive performance of deep learning-based AIP in laboratory settings often proves challenging to replicate in clinical practice. As the data preparation is important for AIP, the paper has reviewed AIP-related studies in the PubMed database published from January 2017 to February 2022, and 118 studies were included. An in-depth analysis of data preparation methods is conducted, encompassing the acquisition of pathological tissue slides, data cleaning, screening, and subsequent digitization. Expert review, image annotation, dataset division for model training and validation are also discussed. Furthermore, we delve into the reasons behind the challenges in reproducing the high performance of AIP in clinical settings and present effective strategies to enhance AIP's clinical performance. The robustness of AIP depends on a randomized collection of representative disease slides, incorporating rigorous quality control and screening, correction of digital discrepancies, reasonable annotation, and sufficient data volume. Digital pathology is fundamental in clinical-grade AIP, and the techniques of data standardization and weakly supervised learning methods based on whole slide image (WSI) are effective ways to overcome obstacles of performance reproduction. The key to performance reproducibility lies in having representative data, an adequate amount of labeling, and ensuring consistency across multiple centers. Digital pathology for clinical diagnosis, data standardization and the technique of WSI-based weakly supervised learning will hopefully build clinical-grade AIP.
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Affiliation(s)
- Yuanqing Yang
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha 410013, China; (Y.Y.); (K.S.)
- Department of Biomedical Engineering, School of Medical, Tsinghua University, Beijing 100084, China
| | - Kai Sun
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha 410013, China; (Y.Y.); (K.S.)
- Furong Laboratory, Changsha 410013, China
| | - Yanhua Gao
- Department of Ultrasound, Shaanxi Provincial People’s Hospital, Xi’an 710068, China;
| | - Kuansong Wang
- Department of Pathology, School of Basic Medical Sciences, Central South University, Changsha 410013, China;
- Department of Pathology, Xiangya Hospital, Central South University, Changsha 410013, China
| | - Gang Yu
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha 410013, China; (Y.Y.); (K.S.)
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Yavuz A, Alpsoy A, Gedik EO, Celik MY, Bassorgun CI, Unal B, Elpek GO. Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology. Artif Intell Gastroenterol 2022; 3:142-162. [DOI: 10.35712/aig.v3.i5.142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/25/2022] [Accepted: 12/14/2022] [Indexed: 12/28/2022] Open
Abstract
Recent research has provided a wealth of data supporting the application of artificial intelligence (AI)-based applications in routine pathology practice. Indeed, it is clear that these methods can significantly support an accurate and rapid diagnosis by eliminating errors, increasing reliability, and improving workflow. In addition, the effectiveness of AI in the pathological evaluation of prognostic parameters associated with behavior, course, and treatment in many types of tumors has also been noted. Regarding gastrointestinal system (GIS) cancers, the contribution of AI methods to pathological diagnosis has been investigated in many studies. On the other hand, studies focusing on AI applications in evaluating parameters to determine tumor behavior are relatively few. For this purpose, the potential of AI models has been studied over a broad spectrum, from tumor subtyping to the identification of new digital biomarkers. The capacity of AI to infer genetic alterations of cancer tissues from digital slides has been demonstrated. Although current data suggest the merit of AI-based approaches in assessing tumor behavior in GIS cancers, a wide range of challenges still need to be solved, from laboratory infrastructure to improving the robustness of algorithms, before incorporating AI applications into real-life GIS pathology practice. This review aims to present data from AI applications in evaluating pathological parameters related to the behavior of GIS cancer with an overview of the opportunities and challenges encountered in implementing AI in pathology.
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Affiliation(s)
- Aysen Yavuz
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Anil Alpsoy
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Elif Ocak Gedik
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | | | | | - Betul Unal
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Gulsum Ozlem Elpek
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
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7
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Deep learning model to predict Epstein-Barr virus associated gastric cancer in histology. Sci Rep 2022; 12:18466. [PMID: 36323712 PMCID: PMC9630260 DOI: 10.1038/s41598-022-22731-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 10/18/2022] [Indexed: 11/20/2022] Open
Abstract
The detection of Epstein-Barr virus (EBV) in gastric cancer patients is crucial for clinical decision making, as it is related with specific treatment responses and prognoses. Despite its importance, the limited medical resources preclude universal EBV testing. Herein, we propose a deep learning-based EBV prediction method from H&E-stained whole-slide images (WSI). Our model was developed using 319 H&E stained WSI (26 EBV positive; TCGA dataset) from the Cancer Genome Atlas, and 108 WSI (8 EBV positive; ISH dataset) from an independent institution. Our deep learning model, EBVNet consists of two sequential components: a tumor classifier and an EBV classifier. We visualized the learned representation by the classifiers using UMAP. We externally validated the model using 60 additional WSI (7 being EBV positive; HGH dataset). We compared the model's performance with those of four pathologists. EBVNet achieved an AUPRC of 0.65, whereas the four pathologists yielded a mean AUPRC of 0.41. Moreover, EBVNet achieved an negative predictive value, sensitivity, specificity, precision, and F1-score of 0.98, 0.86, 0.92, 0.60, and 0.71, respectively. Our proposed model is expected to contribute to prescreen patients for confirmatory testing, potentially to save test-related cost and labor.
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Vuong TTL, Song B, Kwak JT, Kim K. Prediction of Epstein-Barr Virus Status in Gastric Cancer Biopsy Specimens Using a Deep Learning Algorithm. JAMA Netw Open 2022; 5:e2236408. [PMID: 36205993 PMCID: PMC9547324 DOI: 10.1001/jamanetworkopen.2022.36408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
IMPORTANCE Epstein-Barr virus (EBV)-associated gastric cancer (EBV-GC) is 1 of 4 molecular subtypes of GC and is confirmed by an expensive molecular test, EBV-encoded small RNA in situ hybridization. EBV-GC has 2 histologic characteristics, lymphoid stroma and lace-like tumor pattern, but projecting EBV-GC at biopsy is difficult even for experienced pathologists. OBJECTIVE To develop and validate a deep learning algorithm to predict EBV status from pathology images of GC biopsy. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study developed a deep learning classifier to predict EBV-GC using image patches of tissue microarray (TMA) and whole slide images (WSIs) of GC and applied it to GC biopsy specimens from GCs diagnosed at Kangbuk Samsung Hospital between 2011 and 2020. For a quantitative evaluation and EBV-GC prediction on biopsy specimens, the area of each class and the fraction in total tissue or tumor area were calculated. Data were analyzed from March 5, 2021, to February 10, 2022. MAIN OUTCOMES AND MEASURES Evaluation metrics of predictive model performance were assessed on accuracy, recall, precision, F1 score, area under the receiver operating characteristic curve (AUC), and κ coefficient. RESULTS This study included 137 184 image patches from 16 TMAs (708 tissue cores), 24 WSIs, and 286 biopsy images of GC. The classifier was able to classify EBV-GC image patches from TMAs and WSIs with 94.70% accuracy, 0.936 recall, 0.938 precision, 0.937 F1 score, and 0.909 κ coefficient. The classifier was used for predicting and measuring the area and fraction of EBV-GC on biopsy tissue specimens. A 10% cutoff value for the predicted fraction of EBV-GC to tissue (EBV-GC/tissue area) produced the best prediction results in EBV-GC biopsy specimens and showed the highest AUC value (0.8723; 95% CI, 0.7560-0.9501). That cutoff also obtained high sensitivity (0.895) and moderate specificity (0.745) compared with experienced pathologist sensitivity (0.842) and specificity (0.854) when using the presence of lymphoid stroma and a lace-like pattern as diagnostic criteria. On prediction maps, EBV-GCs with lace-like pattern and lymphoid stroma showed the same prediction results as EBV-GC, but cases lacking these histologic features revealed heterogeneous prediction results of EBV-GC and non-EBV-GC areas. CONCLUSIONS AND RELEVANCE This study showed the feasibility of EBV-GC prediction using a deep learning algorithm, even in biopsy samples. Use of such an image-based classifier before a confirmatory molecular test will reduce costs and tissue waste.
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Affiliation(s)
- Trinh Thi Le Vuong
- School of Electrical Engineering, Korea University, Seoul, Republic of Korea
| | - Boram Song
- Department of Pathology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jin T. Kwak
- School of Electrical Engineering, Korea University, Seoul, Republic of Korea
| | - Kyungeun Kim
- Department of Pathology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
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Kim M, Seo AN. Molecular Pathology of Gastric Cancer. J Gastric Cancer 2022; 22:273-305. [PMID: 36316106 PMCID: PMC9633931 DOI: 10.5230/jgc.2022.22.e35] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 10/10/2022] [Indexed: 08/29/2023] Open
Abstract
Gastric cancer (GC) is one of the most common lethal malignant neoplasms worldwide, with limited treatment options for both locally advanced and/or metastatic conditions, resulting in a dismal prognosis. Although the widely used morphological classifications may be helpful for endoscopic or surgical treatment choices, they are still insufficient to guide precise and/or personalized therapy for individual patients. Recent advances in genomic technology and high-throughput analysis may improve the understanding of molecular pathways associated with GC pathogenesis and aid in the classification of GC at the molecular level. Advances in next-generation sequencing have enabled the identification of several genetic alterations through single experiments. Thus, understanding the driver alterations involved in gastric carcinogenesis has become increasingly important because it can aid in the discovery of potential biomarkers and therapeutic targets. In this article, we review the molecular classifications of GC, focusing on The Cancer Genome Atlas (TCGA) classification. We further describe the currently available biomarker-targeted therapies and potential biomarker-guided therapies. This review will help clinicians by providing an inclusive understanding of the molecular pathology of GC and may assist in selecting the best treatment approaches for patients with GC.
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Affiliation(s)
- Moonsik Kim
- Department of Pathology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Korea
| | - An Na Seo
- Department of Pathology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Korea.
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10
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Wong ANN, He Z, Leung KL, To CCK, Wong CY, Wong SCC, Yoo JS, Chan CKR, Chan AZ, Lacambra MD, Yeung MHY. Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers. Cancers (Basel) 2022; 14:3780. [PMID: 35954443 PMCID: PMC9367360 DOI: 10.3390/cancers14153780] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/27/2022] [Accepted: 08/01/2022] [Indexed: 02/05/2023] Open
Abstract
The implementation of DP will revolutionize current practice by providing pathologists with additional tools and algorithms to improve workflow. Furthermore, DP will open up opportunities for development of AI-based tools for more precise and reproducible diagnosis through computational pathology. One of the key features of AI is its capability to generate perceptions and recognize patterns beyond the human senses. Thus, the incorporation of AI into DP can reveal additional morphological features and information. At the current rate of AI development and adoption of DP, the interest in computational pathology is expected to rise in tandem. There have already been promising developments related to AI-based solutions in prostate cancer detection; however, in the GI tract, development of more sophisticated algorithms is required to facilitate histological assessment of GI specimens for early and accurate diagnosis. In this review, we aim to provide an overview of the current histological practices in AP laboratories with respect to challenges faced in image preprocessing, present the existing AI-based algorithms, discuss their limitations and present clinical insight with respect to the application of AI in early detection and diagnosis of GI cancer.
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Affiliation(s)
- Alex Ngai Nick Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Zebang He
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Ka Long Leung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Curtis Chun Kit To
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, China; (C.C.K.T.); (C.K.R.C.); (M.D.L.)
| | - Chun Yin Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Sze Chuen Cesar Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Jung Sun Yoo
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
| | - Cheong Kin Ronald Chan
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, China; (C.C.K.T.); (C.K.R.C.); (M.D.L.)
| | - Angela Zaneta Chan
- Department of Anatomical and Cellular Pathology, Prince of Wales Hospital, Shatin, Hong Kong SAR, China;
| | - Maribel D. Lacambra
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, China; (C.C.K.T.); (C.K.R.C.); (M.D.L.)
| | - Martin Ho Yin Yeung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China; (A.N.N.W.); (Z.H.); (K.L.L.); (C.Y.W.); (S.C.C.W.); (J.S.Y.)
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Interpretable tumor differentiation grade and microsatellite instability recognition in gastric cancer using deep learning. J Transl Med 2022; 102:641-649. [PMID: 35177797 DOI: 10.1038/s41374-022-00742-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 01/18/2022] [Accepted: 01/22/2022] [Indexed: 12/13/2022] Open
Abstract
Gastric cancer possesses great histological and molecular diversity, which creates obstacles for rapid and efficient diagnoses. Classic diagnoses either depend on the pathologist's judgment, which relies heavily on subjective experience, or time-consuming molecular assays for subtype diagnosis. Here, we present a deep learning (DL) system to achieve interpretable tumor differentiation grade and microsatellite instability (MSI) recognition in gastric cancer directly using hematoxylin-eosin (HE) staining whole-slide images (WSIs). WSIs from 467 patients were divided into three cohorts: the training cohort with 348 annotated WSIs, the testing cohort with 88 annotated WSIs, and the integration testing cohort with 31 original WSIs without tumor contour annotation. First, the DL models comprehensibly achieved tumor differentiation recognition with an F1 values of 0.8615 and 0.8977 for poorly differentiated adenocarcinoma (PDA) and well-differentiated adenocarcinoma (WDA) classes. Its ability to extract pathological features about the glandular structure formation, which is the key to distinguishing between PDA and WDA, increased the interpretability of the DL models. Second, the DL models achieved MSI status recognition with a patient-level accuracy of 86.36% directly from HE-stained WSIs in the testing cohort. Finally, the integrated end-to-end system achieved patient-level MSI recognition from original HE staining WSIs with an accuracy of 83.87% in the integration testing cohort with no tumor contour annotation. The proposed system, therefore, demonstrated high accuracy and interpretability, which can potentially promote the implementation of artificial intelligence healthcare.
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Laleh NG, Muti HS, Loeffler CML, Echle A, Saldanha OL, Mahmood F, Lu MY, Trautwein C, Langer R, Dislich B, Buelow RD, Grabsch HI, Brenner H, Chang-Claude J, Alwers E, Brinker TJ, Khader F, Truhn D, Gaisa NT, Boor P, Hoffmeister M, Schulz V, Kather JN. Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Med Image Anal 2022; 79:102474. [DOI: 10.1016/j.media.2022.102474] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 04/07/2022] [Accepted: 05/03/2022] [Indexed: 02/07/2023]
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