Preoperative prediction of lymphovascular invasion of colorectal cancer by radiomics based on 18F-FDG PET-CT and clinical factors

Frontiers in Radiology

Radiology

Qing Li¹  Yan Yang¹  Huanhuan Wei¹  Yaping Wu²  Fangfang Fu²  Meiyun Wang²  Wei Wei²  Yan Bai² 

[1] Department of Medical Imaging, People’s Hospital of Zhengzhou University, Henan Provincial People’s Hospital, Zhengzhou, China;Henan Key Laboratory of Neurological Imaging, Henan Provincial People’s Hospital, Zhengzhou, China;

关键词: colorectal cancer;    lymphovascular invasion;    PET-CT;    radiomics;    preoperative prediction;   

DOI  :  10.3389/fradi.2023.1212382

 received in 2023-04-27, accepted in 2023-07-28,  发布年份 2023

来源: Frontiers

【 摘 要 】

PurposeThe purpose of this study was to investigate the value of a clinical radiomics model based on Positron emission tomography-computed tomography (PET-CT) radiomics features combined with clinical predictors of Lymphovascular invasion (LVI) in predicting preoperative LVI in patients with colorectal cancer (CRC).MethodsA total of 95 CRC patients who underwent preoperative 18F-fluorodeoxyglucose (FDG) PET-CT examination were retrospectively enrolled. Univariate and multivariate logistic regression analyses were used to analyse clinical factors and PET metabolic data in the LVI-positive and LVI-negative groups to identify independent predictors of LVI. We constructed four prediction models based on radiomics features and clinical data to predict LVI status. The predictive efficacy of different models was evaluated according to the receiver operating characteristic curve. Then, the nomogram of the best model was constructed, and its performance was evaluated using calibration and clinical decision curves.ResultsMean standardized uptake value (SUVmean), maximum tumour diameter and lymph node metastasis were independent predictors of LVI in CRC patients (P < 0.05). The clinical radiomics model obtained the best prediction performance, with an Area Under Curve (AUC) of 0.922 (95%CI 0.820–0.977) and 0.918 (95%CI 0.782–0.982) in the training and validation cohorts, respectively. A nomogram based on the clinical radiomics model was constructed, and the calibration curve fitted well (P > 0.05).ConclusionThe clinical radiomics prediction model constructed in this study has high value in the preoperative individualized prediction of LVI in CRC patients.

【 授权许可】

Unknown   
© 2023 Yang, Wei, Fu, Wei, Wu, Bai, Li and Wang.