Predicting pathological complete response by comparing MRI‐based radiomics pre‐ and postneoadjuvant radiotherapy for locally advanced rectal cancer

Cancer Medicine

Pei Yang¹  Zhongyi Zhou¹  Qian Pei¹  Haiping Pei¹  Chenglong Li¹  Yuqiang Li¹  Xiangping Song¹  Fengbo Tan¹  Dan Wang¹  Cenap Güngör²  Yang Xu²  Hong Zhu³  Wenxue Liu⁴  Lilan Zhao⁵ 

[1] Department of Gastrointestinal Surgery Xiangya Hospital Central South University Changsha China;Department of General Visceral and Thoracic Surgery University Medical Center Hamburg‐Eppendorf Hamburg Germany;Department of Oncology Xiangya Hospital Central South University Changsha China;Department of Rheumatology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China;Department of Thoracic surgery Fujian Provincial Hospital Fuzhou China;

关键词: locally advanced rectal cancer;    MRI‐based radiomics;    neoadjuvant chemoradiotherapy;    pathologic complete response;    predictive model;   

DOI  :  10.1002/cam4.2636

来源: DOAJ

【 摘 要 】

Abstract Background Total mesorectal excision following neoadjuvant chemoradiotherapy (nCRT) is recommended in the latest treatment of locally advanced rectal cancer (LARC). Objective To predict whether patients with LARC can achieve pathologic complete response (pCR), comparing MRI‐based radiomics between before and after neoadjuvant radiotherapy (nRT) was performed. Methods One hundred and sixty‐five MRI‐based radiomics features in axial T2‐weighted images were obtained quantitatively from Imaging Biomarker Explorer Software. The specific features of conventional and developing radiomics were selected with the analysis of least absolute shrinkage and selection operator logistic regression, of which the predictive performance was analyzed with receiver operating curve and calibration curve, and applied to an independent cohort. Results One hundred and thirty‐one target patients were enrolled in the present study. A radiomics signature founded on seven radiomics features was generated in the primary cohort. A remarkable difference about Rad‐score between pCR and non‐pCR group occurred in both of primary (P < .001) or validation cohorts (P < .001). The value of area under the curves was 0.92 (95% CI, 0.86‐0.99) and 0.87 (95% CI, 0.74‐1.00) in the primary and validation cohorts, respectively. The Rad‐score (OR = 23.581; P < .001) from multivariate logistic regression analysis was significant as an independent factor of pCR. Conclusion Our predictive model based on radiomics features was an independent predictor for pCR in LARC and could be a candidate in clinical practice.

【 授权许可】

Unknown