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Lee JH, Song G, Lee J, Kang S, Moon KM, Choi Y, Shen J, Noh M, Yang D. Prediction of immunochemotherapy response for diffuse large B-cell lymphoma using artificial intelligence digital pathology. J Pathol Clin Res 2024; 10:e12370. [PMID: 38584594 PMCID: PMC10999948 DOI: 10.1002/2056-4538.12370] [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: 10/27/2023] [Revised: 02/13/2024] [Accepted: 03/04/2024] [Indexed: 04/09/2024]
Abstract
Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous and prevalent subtype of aggressive non-Hodgkin lymphoma that poses diagnostic and prognostic challenges, particularly in predicting drug responsiveness. In this study, we used digital pathology and deep learning to predict responses to immunochemotherapy in patients with DLBCL. We retrospectively collected 251 slide images from 216 DLBCL patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP), with their immunochemotherapy response labels. The digital pathology images were processed using contrastive learning for feature extraction. A multi-modal prediction model was developed by integrating clinical data and pathology image features. Knowledge distillation was employed to mitigate overfitting on gigapixel histopathology images to create a model that predicts responses based solely on pathology images. Based on the importance derived from the attention mechanism of the model, we extracted histological features that were considered key textures associated with drug responsiveness. The multi-modal prediction model achieved an impressive area under the ROC curve of 0.856, demonstrating significant associations with clinical variables such as Ann Arbor stage, International Prognostic Index, and bulky disease. Survival analyses indicated their effectiveness in predicting relapse-free survival. External validation using TCGA datasets supported the model's ability to predict survival differences. Additionally, pathology-based predictions show promise as independent prognostic indicators. Histopathological analysis identified centroblastic and immunoblastic features to be associated with treatment response, aligning with previous morphological classifications and highlighting the objectivity and reproducibility of artificial intelligence-based diagnosis. This study introduces a novel approach that combines digital pathology and clinical data to predict the response to immunochemotherapy in patients with DLBCL. This model shows great promise as a diagnostic and prognostic tool for clinical management of DLBCL. Further research and genomic data integration hold the potential to enhance its impact on clinical practice, ultimately improving patient outcomes.
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Affiliation(s)
- Jeong Hoon Lee
- Department of RadiologyStanford University School of MedicineStanfordCAUSA
| | - Ga‐Young Song
- Department of Hematology‐OncologyChonnam National University Hwasun HospitalHwasunRepublic of Korea
| | - Jonghyun Lee
- Department of Medical and Digital EngineeringHanyang University College of EngineeringSeoulRepublic of Korea
| | - Sae‐Ryung Kang
- Department of Nuclear MedicineChonnam National University Hwasun Hospital and Medical SchoolHwasun‐gunRepublic of Korea
| | - Kyoung Min Moon
- Division of Pulmonary and Allergy Medicine, Department of Internal MedicineChung‐Ang University Hospital, Chung‐Ang University College of MedicineSeoulRepublic of Korea
- Artificial Intelligence, Ziovision Co., Ltd.ChuncheonRepublic of Korea
| | - Yoo‐Duk Choi
- Department of PathologyChonnam National University Medical SchoolGwangjuRepublic of Korea
| | - Jeanne Shen
- Department of Pathology and Center for Artificial Intelligence in Medicine & ImagingStanford University School of MedicineStanfordCAUSA
| | - Myung‐Giun Noh
- Department of PathologyChonnam National University Medical SchoolGwangjuRepublic of Korea
- Department of PathologySchool of Medicine, Ajou UniversitySuwonRepublic of Korea
| | - Deok‐Hwan Yang
- Department of Hematology‐OncologyChonnam National University Hwasun HospitalHwasunRepublic of Korea
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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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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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Li Y, Zhou D, Liu TT, Shen XZ. Application of deep learning in image recognition and diagnosis of gastric cancer. Artif Intell Gastrointest Endosc 2021; 2:12-24. [DOI: 10.37126/aige.v2.i2.12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/30/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
In recent years, artificial intelligence has been extensively applied in the diagnosis of gastric cancer based on medical imaging. In particular, using deep learning as one of the mainstream approaches in image processing has made remarkable progress. In this paper, we also provide a comprehensive literature survey using four electronic databases, PubMed, EMBASE, Web of Science, and Cochrane. The literature search is performed until November 2020. This article provides a summary of the existing algorithm of image recognition, reviews the available datasets used in gastric cancer diagnosis and the current trends in applications of deep learning theory in image recognition of gastric cancer. covers the theory of deep learning on endoscopic image recognition. We further evaluate the advantages and disadvantages of the current algorithms and summarize the characteristics of the existing image datasets, then combined with the latest progress in deep learning theory, and propose suggestions on the applications of optimization algorithms. Based on the existing research and application, the label, quantity, size, resolutions, and other aspects of the image dataset are also discussed. The future developments of this field are analyzed from two perspectives including algorithm optimization and data support, aiming to improve the diagnosis accuracy and reduce the risk of misdiagnosis.
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Affiliation(s)
- Yu Li
- Department of Gastroenterology and Hepatology, Zhongshan Hospital Affiliated to Fudan University, Shanghai 200032, China
| | - Da Zhou
- Department of Gastroenterology and Hepatology, Zhongshan Hospital Affiliated to Fudan University, Shanghai 200032, China
| | - Tao-Tao Liu
- Department of Gastroenterology and Hepatology, Zhongshan Hospital Affiliated to Fudan University, Shanghai 200032, China
| | - Xi-Zhong Shen
- Department of Gastroenterology and Hepatology, Zhongshan Hospital Affiliated to Fudan University, Shanghai 200032, China
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Molecular Chaperones and Thyroid Cancer. Int J Mol Sci 2021; 22:ijms22084196. [PMID: 33919591 PMCID: PMC8073690 DOI: 10.3390/ijms22084196] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/15/2021] [Accepted: 04/16/2021] [Indexed: 12/17/2022] Open
Abstract
Thyroid cancers are the most common of the endocrine system malignancies and progress must be made in the areas of differential diagnosis and treatment to improve patient management. Advances in the understanding of carcinogenic mechanisms have occurred in various fronts, including studies of the chaperone system (CS). Components of the CS are found to be quantitatively increased or decreased, and some correlations have been established between the quantitative changes and tumor type, prognosis, and response to treatment. These correlations provide the basis for identifying distinctive patterns useful in differential diagnosis and for planning experiments aiming at elucidating the role of the CS in tumorigenesis. Here, we discuss studies of the CS components in various thyroid cancers (TC). The chaperones belonging to the families of the small heat-shock proteins Hsp70 and Hsp90 and the chaperonin of Group I, Hsp60, have been quantified mostly by immunohistochemistry and Western blot in tumor and normal control tissues and in extracellular vesicles. Distinctive differences were revealed between the various thyroid tumor types. The most frequent finding was an increase in the chaperones, which can be attributed to the augmented need for chaperones the tumor cells have because of their accelerated metabolism, growth, and division rate. Thus, chaperones help the tumor cell rather than protect the patient, exemplifying chaperonopathies by mistake or collaborationism. This highlights the need for research on chaperonotherapy, namely the development of means to eliminate/inhibit pathogenic chaperones.
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Abstract
Diagnostic processes typically rely on traditional and laborious methods, that are prone to human error, resulting in frequent misdiagnosis of diseases. Computational approaches are being increasingly used for more precise diagnosis of the clinical pathology, diagnosis of genetic and microbial diseases, and analysis of clinical chemistry data. These approaches are progressively used for improving the reliability of testing, resulting in reduced diagnostic errors. Artificial intelligence (AI)-based computational approaches mostly rely on training sets obtained from patient data stored in clinical databases. However, the use of AI is associated with several ethical issues, including patient privacy and data ownership. The capacity of AI-based mathematical models to interpret complex clinical data frequently leads to data bias and reporting of erroneous results based on patient data. In order to improve the reliability of computational approaches in clinical diagnostics, strategies to reduce data bias and analyzing real-life patient data need to be further refined.
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Affiliation(s)
- Mohammed A Alaidarous
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Majmaah, Kingdom of Saudi Arabia. E-mail.
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Yasuda Y, Tokunaga K, Koga T, Sakamoto C, Goldberg IG, Saitoh N, Nakao M. Computational analysis of morphological and molecular features in gastric cancer tissues. Cancer Med 2020; 9:2223-2234. [PMID: 32012497 PMCID: PMC7064096 DOI: 10.1002/cam4.2885] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 11/13/2019] [Accepted: 01/14/2020] [Indexed: 02/06/2023] Open
Abstract
Biological morphologies of cells and tissues represent their physiological and pathological conditions. The importance of quantitative assessment of morphological information has been highly recognized in clinical diagnosis and therapeutic strategies. In this study, we used a supervised machine learning algorithm wndchrm to classify hematoxylin and eosin (H&E)‐stained images of human gastric cancer tissues. This analysis distinguished between noncancer and cancer tissues with different histological grades. We then classified the H&E‐stained images by expression levels of cancer‐associated nuclear ATF7IP/MCAF1 and membranous PD‐L1 proteins using immunohistochemistry of serial sections. Interestingly, classes with low and high expressions of each protein exhibited significant morphological dissimilarity in H&E images. These results indicated that morphological features in cancer tissues are correlated with expression of specific cancer‐associated proteins, suggesting the usefulness of biomolecular‐based morphological classification.
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Affiliation(s)
- Yoko Yasuda
- Department of Medical Cell Biology, Institute of Molecular Embryology and Genetics, Kumamoto University, Kumamoto, Japan.,Department of Health Science, Faculty of Medical Science, Kyushu University, Fukuoka, Japan
| | - Kazuaki Tokunaga
- Department of Medical Cell Biology, Institute of Molecular Embryology and Genetics, Kumamoto University, Kumamoto, Japan
| | - Tomoaki Koga
- Department of Medical Cell Biology, Institute of Molecular Embryology and Genetics, Kumamoto University, Kumamoto, Japan
| | - Chiyomi Sakamoto
- Department of Medical Cell Biology, Institute of Molecular Embryology and Genetics, Kumamoto University, Kumamoto, Japan
| | - Ilya G Goldberg
- Image Informatics and Computational Biology Unit, Laboratory of Genetics, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | | | - Mitsuyoshi Nakao
- Department of Medical Cell Biology, Institute of Molecular Embryology and Genetics, Kumamoto University, Kumamoto, Japan
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