Selecting Candidates for Organ‐Preserving Strategies After Neoadjuvant Chemoradiotherapy for Rectal Cancer: Development and Validation of a Model Integrating MRI Radiomics and Pathomics

接收机工作特性 医学 放化疗 逻辑回归 结直肠癌 无线电技术 磁共振成像 阶段(地层学) 放射科 新辅助治疗 人口 核医学 肿瘤科 内科学 癌症 乳腺癌 古生物学 环境卫生 生物
作者
Lijuan Wan,Sun Zhuo,Wenjing Peng,Sicong Wang,Jiangtao Li,Qing Zhao,Shuhao Wang,Han Ouyang,Xinming Zhao,Shuangmei Zou,Hongmei Zhang
出处
期刊:Journal of Magnetic Resonance Imaging [Wiley]
卷期号:56 (4): 1130-1142 被引量:16
标识
DOI:10.1002/jmri.28108
摘要

Histopathologic evaluation after surgery is the gold standard to evaluate treatment response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). However, it cannot be used to guide organ-preserving strategies due to poor timeliness.To develop and validate a multiscale model incorporating radiomics and pathomics features for predicting pathological good response (pGR) of down-staging to stage ypT0-1N0 after nCRT.Retrospective.A total of 153 patients (median age, 55 years; 109 men; 107 training group; 46 validation group) with clinicopathologically confirmed LARC.A 3.0-T; fast spin echo T2 -weighted and single-shot EPI diffusion-weighted images.The differences in clinicoradiological variables between pGR and non-pGR groups were assessed. Pretreatment and posttreatment radiomics signatures, and pathomics signature were constructed. A multiscale pGR prediction model was established. The predictive performance of the model was evaluated and compared to that of the clinicoradiological model.The χ2 test, Fisher's exact test, t-test, the minimum redundancy maximum relevance algorithm, the least absolute shrinkage and selection operator logistic regression algorithm, regression analysis, receiver operating characteristic curve (ROC) analysis, Delong method. P < 0.05 indicated a significant difference.Pretreatment radiomics signature (odds ratio [OR] = 2.53; 95% CI: 1.58-4.66), posttreatment radiomics signature (OR = 9.59; 95% CI: 3.04-41.46), and pathomics signature (OR = 3.14; 95% CI: 1.40-8.31) were independent factors for predicting pGR. The multiscale model presented good predictive performance with areas under the curve (AUC) of 0.93 (95% CI: 0.88-0.98) and 0.90 (95% CI: 0.78-1.00) in the training and validation groups, those were significantly higher than that of the clinicoradiological model with AUCs of 0.69 (95% CI: 0.55-0.82) and 0.68 (95% CI: 0.46-0.91) in both groups.A model incorporating radiomics and pathomics features effectively predicted pGR after nCRT in patients with LARC.3 TECHNICAL EFFICACY: Stage 4.
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