Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study.

Autor:	Jiang Y; Department of General Surgery and Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA., Zhang Z; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA; Xiaohe Healthcare, ByteDance, Guangzhou, China., Yuan Q; Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China., Wang W; Department of Gastric Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China., Wang H; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA; School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China., Li T; Department of General Surgery and Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China., Huang W; Department of General Surgery and Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China., Xie J; Graduate Group of Epidemiology, University of California Davis, Davis, CA, USA., Chen C; Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China., Sun Z; Department of General Surgery and Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China., Yu J; Department of General Surgery and Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China., Xu Y; Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China., Poultsides GA; Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA., Xing L; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA., Zhou Z; Department of Gastric Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China. Electronic address: zhouzhw@sysucc.org.cn., Li G; Department of General Surgery and Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China. Electronic address: gzliguoxin@163.com., Li R; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: rli2@stanford.edu.
Jazyk:	angličtina
Zdroj:	The Lancet. Digital health [Lancet Digit Health] 2022 May; Vol. 4 (5), pp. e340-e350.
DOI:	10.1016/S2589-7500(22)00040-1
Abstrakt:	Background: Peritoneal recurrence is the predominant pattern of relapse after curative-intent surgery for gastric cancer and portends a dismal prognosis. Accurate individualised prediction of peritoneal recurrence is crucial to identify patients who might benefit from intensive treatment. We aimed to develop predictive models for peritoneal recurrence and prognosis in gastric cancer. Methods: In this retrospective multi-institution study of 2320 patients, we developed a multitask deep learning model for the simultaneous prediction of peritoneal recurrence and disease-free survival using preoperative CT images. Patients in the training cohort (n=510) and the internal validation cohort (n=767) were recruited from Southern Medical University, Guangzhou, China. Patients in the external validation cohort (n=1043) were recruited from Sun Yat-sen University Cancer Center, Guangzhou, China. We evaluated the prognostic accuracy of the model as well as its association with chemotherapy response. Furthermore, we assessed whether the model could improve the ability of clinicians to predict peritoneal recurrence. Findings: The deep learning model had a consistently high accuracy in predicting peritoneal recurrence in the training cohort (area under the receiver operating characteristic curve [AUC] 0·857; 95% CI 0·826-0·889), internal validation cohort (0·856; 0·829-0·882), and external validation cohort (0·843; 0·819-0·866). When informed by the artificial intelligence (AI) model, the sensitivity and inter-rater agreement of oncologists for predicting peritoneal recurrence was improved. The model was able to predict disease-free survival in the training cohort (C-index 0·654; 95% CI 0·616-0·691), internal validation cohort (0·668; 0·643-0·693), and external validation cohort (0·610; 0·583-0·636). In multivariable analysis, the model predicted peritoneal recurrence and disease-free survival independently of clinicopathological variables (p<0·0001 for all). For patients with a predicted high risk of peritoneal recurrence and low survival, adjuvant chemotherapy was associated with improved disease-free survival in both stage II disease (hazard ratio [HR] 0·543 [95% CI 0·362-0·815]; p=0·003) and stage III disease (0·531 [0·432-0·652]; p<0·0001). By contrast, chemotherapy had no impact on disease-free survival for patients with a predicted low risk of peritoneal recurrence and high survival. For the remaining patients, the benefit of chemotherapy depended on stage: only those with stage III disease derived benefit from chemotherapy (HR 0·637 [95% CI 0·484-0·838]; p=0·001). Interpretation: The deep learning model could allow accurate prediction of peritoneal recurrence and survival in patients with gastric cancer. Prospective studies are required to test the clinical utility of this model in guiding personalised treatment in combination with clinicopathological criteria. Funding: None. Competing Interests: Declaration of interests RL has received research grants from the US National Institutes of Health (R01 CA222512 and R01 CA233578) that are unrelated to the present study. GL is supported by research grants from the Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Cancer (2020B121201004), and the Guangdong Provincial Major Talents Project (2019JC05Y361). All other authors declare no competing interests. (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)
Databáze:	MEDLINE
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