Zhaojuan Jiang , Qingwan Li , Jinqiu Ruan , Yanli Li , Dafu Zhang , Yongzhou Xu , Yuting Liao , Xin Zhang , Depei Gao , Zhenhui Li
{"title":"基于机器学习的非小细胞肺癌新辅助化疗后病理反应和预后预测：一项回顾性研究","authors":"Zhaojuan Jiang , Qingwan Li , Jinqiu Ruan , Yanli Li , Dafu Zhang , Yongzhou Xu , Yuting Liao , Xin Zhang , Depei Gao , Zhenhui Li","doi":"10.1016/j.cllc.2024.04.006","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Neoadjuvant chemotherapy has variable efficacy in patients with non–small-cell lung cancer (NSCLC), yet reliable noninvasive predictive markers are lacking. This study aimed to develop a radiomics model predicting pathological complete response and postneoadjuvant chemotherapy survival in NSCLC.</p></div><div><h3>Materials and Methods</h3><p>Retrospective data collection involved 130 patients with NSCLC who underwent neoadjuvant chemotherapy and surgery. Patients were randomly divided into training and independent testing sets. Nine radiomics features from prechemotherapy computed tomography (CT) images were extracted from intratumoral and peritumoral regions. An auto-encoder model was constructed, and its performance was evaluated. X-tile software classified patients into high and low-risk groups based on their predicted probabilities. survival of patients in different risk groups and the role of postoperative adjuvant chemotherapy were examined.</p></div><div><h3>Results</h3><p>The model demonstrated area under the receiver operating characteristic (ROC) curve of 0.874 (training set) and 0.876 (testing set). The larger the area under curve (AUC), the better the model performance. Calibration curve and decision curve analysis indicated excellent model calibration (Hosmer–Lemeshow test, <em>P</em> = .763, the higher the <em>P</em>-value, the better the model fit) and potential clinical applicability. Survival analysis revealed significant differences in overall survival (<em>P</em> = .011) and disease-free survival (<em>P</em> = .017) between different risk groups. Adjuvant chemotherapy significantly improved survival in the low-risk group (<em>P</em> = .041) but not high-risk group (<em>P</em> = 0.56).</p></div><div><h3>Conclusion</h3><p>This study represents the first successful prediction of pathological complete response achievement after neoadjuvant chemotherapy for NSCLC, as well as the patients’ survival, utilizing intratumoral and peritumoral radiomics features.</p></div>","PeriodicalId":10490,"journal":{"name":"Clinical lung cancer","volume":"25 5","pages":"Pages 468-478.e3"},"PeriodicalIF":3.3000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning-Based Prediction of Pathological Responses and Prognosis After Neoadjuvant Chemotherapy for Non–Small-Cell Lung Cancer: A Retrospective Study\",\"authors\":\"Zhaojuan Jiang , Qingwan Li , Jinqiu Ruan , Yanli Li , Dafu Zhang , Yongzhou Xu , Yuting Liao , Xin Zhang , Depei Gao , Zhenhui Li\",\"doi\":\"10.1016/j.cllc.2024.04.006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Neoadjuvant chemotherapy has variable efficacy in patients with non–small-cell lung cancer (NSCLC), yet reliable noninvasive predictive markers are lacking. This study aimed to develop a radiomics model predicting pathological complete response and postneoadjuvant chemotherapy survival in NSCLC.</p></div><div><h3>Materials and Methods</h3><p>Retrospective data collection involved 130 patients with NSCLC who underwent neoadjuvant chemotherapy and surgery. Patients were randomly divided into training and independent testing sets. Nine radiomics features from prechemotherapy computed tomography (CT) images were extracted from intratumoral and peritumoral regions. An auto-encoder model was constructed, and its performance was evaluated. X-tile software classified patients into high and low-risk groups based on their predicted probabilities. survival of patients in different risk groups and the role of postoperative adjuvant chemotherapy were examined.</p></div><div><h3>Results</h3><p>The model demonstrated area under the receiver operating characteristic (ROC) curve of 0.874 (training set) and 0.876 (testing set). The larger the area under curve (AUC), the better the model performance. Calibration curve and decision curve analysis indicated excellent model calibration (Hosmer–Lemeshow test, <em>P</em> = .763, the higher the <em>P</em>-value, the better the model fit) and potential clinical applicability. Survival analysis revealed significant differences in overall survival (<em>P</em> = .011) and disease-free survival (<em>P</em> = .017) between different risk groups. Adjuvant chemotherapy significantly improved survival in the low-risk group (<em>P</em> = .041) but not high-risk group (<em>P</em> = 0.56).</p></div><div><h3>Conclusion</h3><p>This study represents the first successful prediction of pathological complete response achievement after neoadjuvant chemotherapy for NSCLC, as well as the patients’ survival, utilizing intratumoral and peritumoral radiomics features.</p></div>\",\"PeriodicalId\":10490,\"journal\":{\"name\":\"Clinical lung cancer\",\"volume\":\"25 5\",\"pages\":\"Pages 468-478.e3\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical lung cancer\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1525730424000494\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical lung cancer","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1525730424000494","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
Machine Learning-Based Prediction of Pathological Responses and Prognosis After Neoadjuvant Chemotherapy for Non–Small-Cell Lung Cancer: A Retrospective Study
Background
Neoadjuvant chemotherapy has variable efficacy in patients with non–small-cell lung cancer (NSCLC), yet reliable noninvasive predictive markers are lacking. This study aimed to develop a radiomics model predicting pathological complete response and postneoadjuvant chemotherapy survival in NSCLC.
Materials and Methods
Retrospective data collection involved 130 patients with NSCLC who underwent neoadjuvant chemotherapy and surgery. Patients were randomly divided into training and independent testing sets. Nine radiomics features from prechemotherapy computed tomography (CT) images were extracted from intratumoral and peritumoral regions. An auto-encoder model was constructed, and its performance was evaluated. X-tile software classified patients into high and low-risk groups based on their predicted probabilities. survival of patients in different risk groups and the role of postoperative adjuvant chemotherapy were examined.
Results
The model demonstrated area under the receiver operating characteristic (ROC) curve of 0.874 (training set) and 0.876 (testing set). The larger the area under curve (AUC), the better the model performance. Calibration curve and decision curve analysis indicated excellent model calibration (Hosmer–Lemeshow test, P = .763, the higher the P-value, the better the model fit) and potential clinical applicability. Survival analysis revealed significant differences in overall survival (P = .011) and disease-free survival (P = .017) between different risk groups. Adjuvant chemotherapy significantly improved survival in the low-risk group (P = .041) but not high-risk group (P = 0.56).
Conclusion
This study represents the first successful prediction of pathological complete response achievement after neoadjuvant chemotherapy for NSCLC, as well as the patients’ survival, utilizing intratumoral and peritumoral radiomics features.
期刊介绍：
Clinical Lung Cancer is a peer-reviewed bimonthly journal that publishes original articles describing various aspects of clinical and translational research of lung cancer. Clinical Lung Cancer is devoted to articles on detection, diagnosis, prevention, and treatment of lung cancer. The main emphasis is on recent scientific developments in all areas related to lung cancer. Specific areas of interest include clinical research and mechanistic approaches; drug sensitivity and resistance; gene and antisense therapy; pathology, markers, and prognostic indicators; chemoprevention strategies; multimodality therapy; and integration of various approaches.
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