医学
无线电技术
放化疗
队列
单变量
内科学
肿瘤科
食管鳞状细胞癌
多元分析
单变量分析
人工智能
癌症
多元统计
机器学习
放射科
计算机科学
作者
Atsunobu Kasai,Jinsei Miyoshi,Yasushi Sato,Koichi Okamoto,Hiroshi Miyamoto,Takashi Kawanaka,Chisato Tonoiso,Masafumi Harada,Masakazu Goto,Takahiro Yoshida,Akihiro Haga,Tetsuji Takayama
标识
DOI:10.1038/s41598-024-52418-4
摘要
Abstract No clinically relevant biomarker has been identified for predicting the response of esophageal squamous cell carcinoma (ESCC) to chemoradiotherapy (CRT). Herein, we established a CT-based radiomics model with artificial intelligence (AI) to predict the response and prognosis of CRT in ESCC. A total of 44 ESCC patients (stage I-IV) were enrolled in this study; training (n = 27) and validation (n = 17) cohorts. First, we extracted a total of 476 radiomics features from three-dimensional CT images of cancer lesions in training cohort, selected 110 features associated with the CRT response by ROC analysis (AUC ≥ 0.7) and identified 12 independent features, excluding correlated features by Pearson’s correlation analysis (r ≥ 0.7). Based on the 12 features, we constructed 5 prediction models of different machine learning algorithms (Random Forest (RF), Ridge Regression, Naive Bayes, Support Vector Machine, and Artificial Neural Network models). Among those, the RF model showed the highest AUC in the training cohort (0.99 [95%CI 0.86–1.00]) as well as in the validation cohort (0.92 [95%CI 0.71–0.99]) to predict the CRT response. Additionally, Kaplan-Meyer analysis of the validation cohort and all the patient data showed significantly longer progression-free and overall survival in the high-prediction score group compared with the low-prediction score group in the RF model. Univariate and multivariate analyses revealed that the radiomics prediction score and lymph node metastasis were independent prognostic biomarkers for CRT of ESCC. In conclusion, we have developed a CT-based radiomics model using AI, which may have the potential to predict the CRT response as well as the prognosis for ESCC patients with non-invasiveness and cost-effectiveness.
科研通智能强力驱动
Strongly Powered by AbleSci AI