Predicting Neoadjuvant Treatment Response in Rectal Cancer Using Machine Learning: Evaluation of MRI-Based Radiomic and Clinical Models

医学 淋巴血管侵犯 旁侵犯 癌胚抗原 结直肠癌 阶段(地层学) 磁共振成像 新辅助治疗 接收机工作特性 T级 单变量分析 内科学 放射科 肿瘤科 癌症 多元分析 转移 古生物学 乳腺癌 生物
作者
Kent J. Peterson,Matthew Simpson,Melissa K. Drezdzon,Anikó Szabó,Robin A. Ausman,Andrew S. Nencka,Paul Knechtges,Carrie Y. Peterson,Kirk Ludwig,Timothy J. Ridolfi
出处
期刊:Journal of Gastrointestinal Surgery [Springer Science+Business Media]
卷期号:27 (1): 122-130 被引量:7
标识
DOI:10.1007/s11605-022-05477-9
摘要

BackgroundRadiomics is an approach to medical imaging that quantifies the features normally translated into visual display. While both radiomic and clinical markers have shown promise in predicting response to neoadjuvant chemoradiation therapy (nCRT) for rectal cancer, the interrelationship is not yet clear.MethodsA retrospective, single-institution study of patients treated with nCRT for locally advanced rectal cancer was performed. Clinical and radiomic features were extracted from electronic medical record and pre-treatment magnetic resonance imaging, respectively. Machine learning models were created and assessed for complete response and positive treatment effect using the area under the receiver operating curves.ResultsOf 131 rectal cancer patients evaluated, 68 (51.9%) were identified to have a positive treatment effect and 35 (26.7%) had a complete response. On univariate analysis, clinical T-stage (OR 0.46, p = 0.02), lymphovascular/perineural invasion (OR 0.11, p = 0.03), and statin use (OR 2.45, p = 0.049) were associated with a complete response. Clinical T-stage (OR 0.37, p = 0.01), lymphovascular/perineural invasion (OR 0.16, p = 0.001), and abnormal carcinoembryonic antigen level (OR 0.28, p = 0.002) were significantly associated with a positive treatment effect. The clinical model was the strongest individual predictor of both positive treatment effect (AUC = 0.64) and complete response (AUC = 0.69). The predictive ability of a positive treatment effect increased by adding tumor and mesorectal radiomic features to the clinical model (AUC = 0.73).ConclusionsThe use of a combined model with both clinical and radiomic features resulted in the strongest predictive capability. With the eventual goal of tailoring treatment to the individual, both clinical and radiologic markers offer insight into identifying patients likely to respond favorably to nCRT.
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