A Deep Learning Radiomics Nomogram to Predict Response to Neoadjuvant Chemotherapy for Locally Advanced Cervical Cancer: A Two-Center Study

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

作者地址："[Zhang, Yajiao; Liu, Yanbing] Chongqing Med Univ, Coll Med Informat, Chongqing 400016, Peoples R China; [Wu, Chao; Xiao, Zhibo; Lv, Furong] Chongqing Med Univ, Dept Radiol, Affiliated Hosp 1, Chongqing 400016, Peoples R China"

通信作者："Liu, YB (通讯作者)，Chongqing Med Univ, Coll Med Informat, Chongqing 400016, Peoples R China."

来源：DIAGNOSTICS

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000958839900001

JCR分区：Q1

影响因子：3.6

年份：2023

卷号：13

期号：6

开始页：　

结束页：　

文献类型：Article

关键词：deep learning; radiomics nomogram; locally advanced cervical cancer; neoadjuvant chemotherapy

摘要："Purpose: This study aimed to establish a deep learning radiomics nomogram (DLRN) based on multiparametric MR images for predicting the response to neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC). Methods: Patients with LACC (FIGO stage IB-IIIB) who underwent preoperative NACT were enrolled from center 1 (220 cases) and center 2 (independent external validation dataset, 65 cases). Handcrafted and deep learning-based radiomics features were extracted from T2WI, DWI and contrast-enhanced (CE)-T1WI, and radiomics signatures were built based on the optimal features. Two types of radiomics signatures and clinical features were integrated into the DLRN for prediction. The AUC, calibration curve and decision curve analysis (DCA) were employed to illustrate the performance of these models and their clinical utility. In addition, disease-free survival (DFS) was assessed by Kaplan-Meier survival curves based on the DLRN. Results: The DLRN showed favorable predictive values in differentiating responders from nonresponders to NACT with AUCs of 0.963, 0.940 and 0.910 in the three datasets, with good calibration (all p > 0.05). Furthermore, the DLRN performed better than the clinical model and handcrafted radiomics signature in all datasets (all p < 0.05) and slightly higher than the DL-based radiomics signature in the internal validation dataset (p = 0.251). DCA indicated that the DLRN has potential in clinical applications. Furthermore, the DLRN was strongly correlated with the DFS of LACC patients (HR = 0.223; p = 0.004). Conclusion: The DLRN performed well in preoperatively predicting the therapeutic response in LACC and could provide valuable information for individualized treatment."

基金机构：Cultivation plan for the 'top-notch' postgraduate of Chongqing Medical University [BJRC202111]; program of Intelligence Medicine Special Research and Development of Chongqing Medical University [YJSZHYX202101]

基金资助正文："This work was funded by the project supported by the Cultivation plan for the 'top-notch' postgraduate of Chongqing Medical University in 2021(BJRC202111), and the program of Intelligence Medicine Special Research and Development of Chongqing Medical University in 2021(YJSZHYX202101)."