Prediction of recurrence risk factors in patients with early-stage cervical cancers by nomogram based on MRI handcrafted radiomics features and deep learning features: a dual-center study

作者全名："Zhang, Yajiao; Wu, Chao; Du, Jinglong; Xiao, Zhibo; Lv, Furong; Liu, Yanbing"

作者地址："[Zhang, Yajiao; Du, Jinglong; Xiao, Zhibo; Liu, Yanbing] Chongqing Med Univ, Coll Med Informat, 1 Med Coll Rd, Chongqing, Peoples R China; [Wu, Chao; Lv, Furong] Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing, Peoples R China"

通信作者："Liu, YB (通讯作者)，Chongqing Med Univ, Coll Med Informat, 1 Med Coll Rd, Chongqing, Peoples R China."

来源：ABDOMINAL RADIOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001107452700002

JCR分区：Q2

影响因子：2.4

年份：2024

卷号：49

期号：1

开始页：258

结束页：270

文献类型：Article

关键词：Cervical cancer; Risk stratification; Deep learning; Radiomics; Nomogram

摘要："PurposeTo establish and validate a deep learning radiomics nomogram (DLRN) based on intratumoral and peritumoral regions of MR images and clinical characteristics to predict recurrence risk factors in early-stage cervical cancer and to clarify whether DLRN could be applied for risk stratification.MethodsTwo hundred and twenty five pathologically confirmed early-stage cervical cancers were enrolled and made up the training cohort and internal validation cohort, and 40 patients from another center were enrolled into the external validation cohort. On the basis of region of interest (ROI) of intratumoral and different peritumoral regions, two sets of features representing deep learning and handcrafted radiomics features were created using combined images of T2-weighted MRI (T2WI) and diffusion-weighted imaging (DWI). The signature subset with the best discriminant features was chosen, and deep learning and handcrafted signatures were created using logistic regression. Integrated with independent clinical factors, a DLRN was built. The discrimination and calibration of DLNR were applied to assess its therapeutic utility.ResultsThe DLRN demonstrated satisfactory performance for predicting recurrence risk factors, with AUCs of 0.944 (95% confidence interval 0.896-0.992) and 0.885 (95% confidence interval 0.834-0.937) in the internal and external validation cohorts. Furthermore, decision curve analysis revealed that the DLRN outperformed the clinical model, deep learning signature, and radiomics signature in terms of net benefit.ConclusionA DLRN based on intratumoral and peritumoral regions had the potential to predict and stratify recurrence risk factors for early-stage cervical cancers and enhance the value of individualized precision treatment."

基金机构：Chongqing Medical University

基金资助正文：The authors would like to thank all staff from the college of Informatics and the department of radiology and gynecology for their hard work and invaluable support for this study. The authors also express gratitude to all the patients for contribution.