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Kato M, Hayashi Y, Uema R, Kanesaka T, Yamaguchi S, Maekawa A, Yamada T, Yamamoto M, Kitamura S, Inoue T, Yamamoto S, Kizu T, Takeda R, Ogiyama H, Yamamoto K, Aoi K, Nagaike K, Sasai Y, Egawa S, Akamatsu H, Ogawa H, Komori M, Akihiro N, Yoshihara T, Tsujii Y, Takehara T. A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria. Gastric Cancer 2024; 27:1069-1077. [PMID: 38795251 DOI: 10.1007/s10120-024-01511-8] [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: 12/06/2023] [Accepted: 05/15/2024] [Indexed: 05/27/2024]
Abstract
BACKGROUND We developed a machine learning (ML) model to predict the risk of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) who did not meet the existing Japanese endoscopic curability criteria and compared its performance with that of the most common clinical risk scoring system, the eCura system. METHODS We used data from 4,042 consecutive patients with EGC from 21 institutions who underwent endoscopic submucosal dissection (ESD) and/or surgery between 2010 and 2021. All resected EGCs were histologically confirmed not to satisfy the current Japanese endoscopic curability criteria. Of all patients, 3,506 constituted the training cohort to develop the neural network-based ML model, and 536 constituted the validation cohort. The performance of our ML model, as measured by the area under the receiver operating characteristic curve (AUC), was compared with that of the eCura system in the validation cohort. RESULTS LNM rates were 14% (503/3,506) and 7% (39/536) in the training and validation cohorts, respectively. The ML model identified patients with LNM with an AUC of 0.83 (95% confidence interval, 0.76-0.89) in the validation cohort, while the eCura system identified patients with LNM with an AUC of 0.77 (95% confidence interval, 0.70-0.85) (P = 0.006, DeLong's test). CONCLUSIONS Our ML model performed better than the eCura system for predicting LNM risk in patients with EGC who did not meet the existing Japanese endoscopic curability criteria. We developed a neural network-based machine learning model that predicts the risk of lymph node metastasis in patients with early gastric cancer who did not meet the endoscopic curability criteria.
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Affiliation(s)
- Minoru Kato
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Yoshito Hayashi
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Ryotaro Uema
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Takashi Kanesaka
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | | | - Akira Maekawa
- Department of Internal Medicine, Osaka Police Hospital, Osaka, Japan
| | - Takuya Yamada
- Department of Gastroenterology, Osaka Rosai Hospital, Sakai, Japan
| | - Masashi Yamamoto
- Department of Gastroenterology, Toyonaka Municipal Hospital, Toyonaka, Japan
| | - Shinji Kitamura
- Department of Gastroenterology, Sakai City Medical Center, Sakai, Japan
| | - Takuya Inoue
- Department of Gastroenterology, Osaka General Medical Center, Osaka, Japan
| | - Shunsuke Yamamoto
- Department of Gastroenterology, National Hospital Organization Osaka National Hospital, Osaka, Japan
| | - Takashi Kizu
- Department of Gastroenterology, Yao Municipal Hospital, Yao, Japan
| | - Risato Takeda
- Department of Gastroenterology, Itami City Hospital, Itami, Japan
| | - Hideharu Ogiyama
- Department of Gastroenterology, Ikeda Municipal Hospital, Ikeda, Japan
| | - Katsumi Yamamoto
- Department of Gastroenterology, Japan Community Healthcare Organization Osaka Hospital, Osaka, Japan
| | - Kenji Aoi
- Department of Gastroenterology, Kaizuka City Hospital, Osaka, Japan
| | - Koji Nagaike
- Department of Gastroenterology, Suita Municipal Hospital, Suita, Japan
| | - Yasutaka Sasai
- Department of Gastroenterology, Otemae Hospital, Osaka, Japan
| | - Satoshi Egawa
- Department of Gastroenterology, Kinki Central Hospital, Itami, Japan
| | - Haruki Akamatsu
- Department of Gastroenterology, Higashiosaka City Medical Center, Higashiosaka, Japan
| | - Hiroyuki Ogawa
- Department of Gastroenterology, Nishinomiya Municipal Central Hospital, Nishinomiya, Japan
| | - Masato Komori
- Department of Gastroenterology, Hyogo Prefectural Nishinomiya Hospital, Nishinomiya, Japan
| | | | - Takeo Yoshihara
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yoshiki Tsujii
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Tetsuo Takehara
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, Suita, Japan.
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Zou Y, Mao Q, Zhao Z, Zhou X, Pan Y, Zuo Z, Zhang W. Intratumoural and peritumoural CT-based radiomics for diagnosing lepidic-predominant adenocarcinoma in patients with pure ground-glass nodules: a machine learning approach. Clin Radiol 2024; 79:e211-e218. [PMID: 38044199 DOI: 10.1016/j.crad.2023.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 10/10/2023] [Accepted: 11/06/2023] [Indexed: 12/05/2023]
Abstract
AIM To develop and validate a diagnostic model utilising machine-learning algorithms that differentiates lepidic predominant adenocarcinoma (LPA) from other pathological subtypes in patients with pure ground-glass nodules (pGGNs). MATERIALS AND METHODS This bicentric study was conducted across two medical centres and included 151 patients diagnosed with lung adenocarcinoma based on histopathological confirmation of pGGNs. The training cohort consisted of 99 patients from Institution 1, while the test cohort included 52 patients from Institution 2. Radiomics features were extracted from both tumours and the 2 mm peritumoural parenchyma. The tumoural and peritumoural radiomics were designated as Modeltumoural and Modelperitumoural, respectively. The diagnostic efficacy of various models was evaluated through the receiver operating characteristic (ROC) curve analysis. Subsequently, a machine-learning-based prediction model that combined Modeltumoural, Modelperitumoural, and Modelclinical-radiological was developed to differentiate LPA from other pathological subtypes in patients with pGGNs. RESULTS Modeltumoural achieved area under the curve (AUC) values of 0.762 and 0.783 in the training and validation sets, respectively. Modelperitumoural attained AUCs of 0.742 and 0.667, and Modelclinical-radiological generated an AUC of 0.727 and 0.739 in the training and validation sets, respectively. Among the machine-learning models evaluated, gradient boosting machines demonstrated the best diagnostic efficacy, with accuracy, AUC, F1 score, and log loss values of 0.885, 0.956, 0.943, and 0.260, respectively. CONCLUSION The combined model based on machine learning that incorporated tumour and peritumoural parenchyma, as well as clinical and imaging characteristics, may offer benefits in assessing the pathological subtype of pGGNs.
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Affiliation(s)
- Y Zou
- Department of Radiology, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, 545006, China; Guangxi Key Clinical Specialties of Medical Imaging, Liuzhou, 545006, China; Liuzhou Key Laboratory of Molecular Imaging, Liuzhou, 545006, China
| | - Q Mao
- Department of Radiology, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, 545006, China; Guangxi Key Clinical Specialties of Medical Imaging, Liuzhou, 545006, China; Liuzhou Key Laboratory of Molecular Imaging, Liuzhou, 545006, China
| | - Z Zhao
- Department of Radiology, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, 545006, China; Guangxi Key Clinical Specialties of Medical Imaging, Liuzhou, 545006, China; Liuzhou Key Laboratory of Molecular Imaging, Liuzhou, 545006, China
| | - X Zhou
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, 411000, China
| | - Y Pan
- Department of Radiology, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, 545006, China; Guangxi Key Clinical Specialties of Medical Imaging, Liuzhou, 545006, China; Liuzhou Key Laboratory of Molecular Imaging, Liuzhou, 545006, China
| | - Z Zuo
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, 411000, China
| | - W Zhang
- Department of Radiology, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, 545006, China; Guangxi Key Clinical Specialties of Medical Imaging, Liuzhou, 545006, China; Liuzhou Key Laboratory of Molecular Imaging, Liuzhou, 545006, China.
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Morais R, Libanio D, Santos-Antunes J. eCura and W-eCura: different scores, different populations, same goal. Gut 2024:gutjnl-2024-331924. [PMID: 38286588 DOI: 10.1136/gutjnl-2024-331924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 01/17/2024] [Indexed: 01/31/2024]
Affiliation(s)
- Rui Morais
- Gastroenterology, Centro Hospitalar de Sao Joao, EPE, Porto, Portugal
| | | | - João Santos-Antunes
- Gastroenterology Department, Centro Hospitalar Universitário São João, Porto, Portugal
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Zadeh Shirazi A, Tofighi M, Gharavi A, Gomez GA. The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide. Technol Cancer Res Treat 2024; 23:15330338241250324. [PMID: 38775067 PMCID: PMC11113055 DOI: 10.1177/15330338241250324] [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: 10/29/2023] [Revised: 03/28/2024] [Accepted: 04/08/2024] [Indexed: 05/25/2024] Open
Abstract
Advancements in AI have notably changed cancer research, improving patient care by enhancing detection, survival prediction, and treatment efficacy. This review covers the role of Machine Learning, Soft Computing, and Deep Learning in oncology, explaining key concepts and algorithms (like SVM, Naïve Bayes, and CNN) in a clear, accessible manner. It aims to make AI advancements understandable to a broad audience, focusing on their application in diagnosing, classifying, and predicting various cancer types, thereby underlining AI's potential to better patient outcomes. Moreover, we present a tabular summary of the most significant advances from the literature, offering a time-saving resource for readers to grasp each study's main contributions. The remarkable benefits of AI-powered algorithms in cancer care underscore their potential for advancing cancer research and clinical practice. This review is a valuable resource for researchers and clinicians interested in the transformative implications of AI in cancer care.
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Affiliation(s)
- Amin Zadeh Shirazi
- Centre for Cancer Biology, SA Pathology and the University of South Australia, Adelaide, SA, Australia
| | - Morteza Tofighi
- Department of Electrical Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
| | - Alireza Gharavi
- Department of Computer Science, Azad University, Mashhad Branch, Mashhad, Iran
| | - Guillermo A. Gomez
- Centre for Cancer Biology, SA Pathology and the University of South Australia, Adelaide, SA, Australia
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