Intelligent prognosis evaluation system for stage I-III resected non-small-cell lung cancer patients on CT images: a multi-center studyResearch in context
Summary: Background: Prognosis is crucial for personalized treatment and surveillance suggestion of the resected non-small-cell lung cancer (NSCLC) patients in stage I-III. Although the tumor-node-metastasis (TNM) staging system is a powerful predictor, it is not perfect enough to accurately distin...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-11-01
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| Series: | EClinicalMedicine |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589537023004479 |
| _version_ | 1797449431540301824 |
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| author | Siqi Zhang Xiaohong Liu Lixin Zhou Kai Wang Jun Shao Jianyu Shi Xuan Wang Jiaxing Mu Tianrun Gao Zeyu Jiang Kezhong Chen Chengdi Wang Guangyu Wang |
| author_facet | Siqi Zhang Xiaohong Liu Lixin Zhou Kai Wang Jun Shao Jianyu Shi Xuan Wang Jiaxing Mu Tianrun Gao Zeyu Jiang Kezhong Chen Chengdi Wang Guangyu Wang |
| author_sort | Siqi Zhang |
| collection | DOAJ |
| description | Summary: Background: Prognosis is crucial for personalized treatment and surveillance suggestion of the resected non-small-cell lung cancer (NSCLC) patients in stage I-III. Although the tumor-node-metastasis (TNM) staging system is a powerful predictor, it is not perfect enough to accurately distinguish all the patients, especially within the same TNM stage. In this study, we developed an intelligent prognosis evaluation system (IPES) using pre-therapy CT images to assist the traditional TNM staging system for more accurate prognosis prediction of resected NSCLC patients. Methods: 20,333 CT images of 6371 patients from June 12, 2009 to March 24, 2022 in West China Hospital of Sichuan University, Mianzhu People's Hospital, Peking University People's Hospital, Chengdu Shangjin Nanfu Hospital and Guangan Peoples' Hospital were included in this retrospective study. We developed the IPES based on self-supervised pre-training and multi-task learning, which aimed to predict an overall survival (OS) risk for each patient. We further evaluated the prognostic accuracy of the IPES and its ability to stratify NSCLC patients with the same TNM stage and with the same EGFR genotype. Findings: The IPES was able to predict OS risk for stage I-III resected NSCLC patients in the training set (C-index 0.806; 95% CI: 0.744–0.846), internal validation set (0.783; 95% CI: 0.744–0.825) and external validation set (0.817; 95% CI: 0.786–0.849). In addition, IPES performed well in early-stage (stage I) and EGFR genotype prediction. Furthermore, by adopting IPES-based survival score (IPES-score), resected NSCLC patients in the same stage or with the same EGFR genotype could be divided into low- and high-risk subgroups with good and poor prognosis, respectively (p < 0.05 for all). Interpretation: The IPES provided a non-invasive way to obtain prognosis-related information from patients. The identification of IPES for resected NSCLC patients with low and high prognostic risk in the same TNM stage or with the same EGFR genotype suggests that IPES have potential to offer more personalized treatment and surveillance suggestion for NSCLC patients. Funding: This study was funded by the National Natural Science Foundation of China (grant 62272055, 92259303, 92059203), New Cornerstone Science Foundation through the XPLORER PRIZE, Young Elite Scientists Sponsorship Program by CAST (2021QNRC001), Clinical Medicine Plus X - Young Scholars Project, Peking University, the Fundamental Research Funds for the Central Universities (K.C.), Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences (2021RU002), BUPT Excellent Ph.D. Students Foundation (CX2022104). |
| first_indexed | 2024-03-09T14:25:56Z |
| format | Article |
| id | doaj.art-8f04c780217d47beb1b67fcc657ab42f |
| institution | Directory Open Access Journal |
| issn | 2589-5370 |
| language | English |
| last_indexed | 2024-03-09T14:25:56Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EClinicalMedicine |
| spelling | doaj.art-8f04c780217d47beb1b67fcc657ab42f2023-11-28T07:26:46ZengElsevierEClinicalMedicine2589-53702023-11-0165102270Intelligent prognosis evaluation system for stage I-III resected non-small-cell lung cancer patients on CT images: a multi-center studyResearch in contextSiqi Zhang0Xiaohong Liu1Lixin Zhou2Kai Wang3Jun Shao4Jianyu Shi5Xuan Wang6Jiaxing Mu7Tianrun Gao8Zeyu Jiang9Kezhong Chen10Chengdi Wang11Guangyu Wang12State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, ChinaUCL Cancer Institute, University College London, London, WC1E 6DD, UKThoracic Oncology Institute and Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, ChinaCollege of Future Technology, Peking University and Peking-Tsinghua Center for Life Sciences, Beijing, 100871, ChinaState Key Laboratory of Respiratory Health and Multimorbidity, Department of Pulmonary and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, ChinaThoracic Oncology Institute and Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, ChinaThoracic Oncology Institute and Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, ChinaThoracic Oncology Institute and Department of Thoracic Surgery, Peking University People's Hospital, Beijing, 100044, ChinaState Key Laboratory of Respiratory Health and Multimorbidity, Department of Pulmonary and Critical Care Medicine, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, ChinaSummary: Background: Prognosis is crucial for personalized treatment and surveillance suggestion of the resected non-small-cell lung cancer (NSCLC) patients in stage I-III. Although the tumor-node-metastasis (TNM) staging system is a powerful predictor, it is not perfect enough to accurately distinguish all the patients, especially within the same TNM stage. In this study, we developed an intelligent prognosis evaluation system (IPES) using pre-therapy CT images to assist the traditional TNM staging system for more accurate prognosis prediction of resected NSCLC patients. Methods: 20,333 CT images of 6371 patients from June 12, 2009 to March 24, 2022 in West China Hospital of Sichuan University, Mianzhu People's Hospital, Peking University People's Hospital, Chengdu Shangjin Nanfu Hospital and Guangan Peoples' Hospital were included in this retrospective study. We developed the IPES based on self-supervised pre-training and multi-task learning, which aimed to predict an overall survival (OS) risk for each patient. We further evaluated the prognostic accuracy of the IPES and its ability to stratify NSCLC patients with the same TNM stage and with the same EGFR genotype. Findings: The IPES was able to predict OS risk for stage I-III resected NSCLC patients in the training set (C-index 0.806; 95% CI: 0.744–0.846), internal validation set (0.783; 95% CI: 0.744–0.825) and external validation set (0.817; 95% CI: 0.786–0.849). In addition, IPES performed well in early-stage (stage I) and EGFR genotype prediction. Furthermore, by adopting IPES-based survival score (IPES-score), resected NSCLC patients in the same stage or with the same EGFR genotype could be divided into low- and high-risk subgroups with good and poor prognosis, respectively (p < 0.05 for all). Interpretation: The IPES provided a non-invasive way to obtain prognosis-related information from patients. The identification of IPES for resected NSCLC patients with low and high prognostic risk in the same TNM stage or with the same EGFR genotype suggests that IPES have potential to offer more personalized treatment and surveillance suggestion for NSCLC patients. Funding: This study was funded by the National Natural Science Foundation of China (grant 62272055, 92259303, 92059203), New Cornerstone Science Foundation through the XPLORER PRIZE, Young Elite Scientists Sponsorship Program by CAST (2021QNRC001), Clinical Medicine Plus X - Young Scholars Project, Peking University, the Fundamental Research Funds for the Central Universities (K.C.), Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences (2021RU002), BUPT Excellent Ph.D. Students Foundation (CX2022104).http://www.sciencedirect.com/science/article/pii/S2589537023004479Self-supervised pre-trainingMulti-task learningPrognosisCT imageResected NSCLC |
| spellingShingle | Siqi Zhang Xiaohong Liu Lixin Zhou Kai Wang Jun Shao Jianyu Shi Xuan Wang Jiaxing Mu Tianrun Gao Zeyu Jiang Kezhong Chen Chengdi Wang Guangyu Wang Intelligent prognosis evaluation system for stage I-III resected non-small-cell lung cancer patients on CT images: a multi-center studyResearch in context EClinicalMedicine Self-supervised pre-training Multi-task learning Prognosis CT image Resected NSCLC |
| title | Intelligent prognosis evaluation system for stage I-III resected non-small-cell lung cancer patients on CT images: a multi-center studyResearch in context |
| title_full | Intelligent prognosis evaluation system for stage I-III resected non-small-cell lung cancer patients on CT images: a multi-center studyResearch in context |
| title_fullStr | Intelligent prognosis evaluation system for stage I-III resected non-small-cell lung cancer patients on CT images: a multi-center studyResearch in context |
| title_full_unstemmed | Intelligent prognosis evaluation system for stage I-III resected non-small-cell lung cancer patients on CT images: a multi-center studyResearch in context |
| title_short | Intelligent prognosis evaluation system for stage I-III resected non-small-cell lung cancer patients on CT images: a multi-center studyResearch in context |
| title_sort | intelligent prognosis evaluation system for stage i iii resected non small cell lung cancer patients on ct images a multi center studyresearch in context |
| topic | Self-supervised pre-training Multi-task learning Prognosis CT image Resected NSCLC |
| url | http://www.sciencedirect.com/science/article/pii/S2589537023004479 |
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