AUTHOR=Xu Chao , Liu Wen , Zhao Qi , Zhang Lu , Yin Minyue , Zhou Juying , Zhu Jinzhou , Qin Songbing 

TITLE=CT-based radiomics nomogram for overall survival prediction in patients with cervical cancer treated with concurrent chemoradiotherapy

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1287121

DOI=10.3389/fonc.2023.1287121

ISSN=2234-943X

ABSTRACT=Background and purpose: To establish and validate a hybrid radiomics model to predict overall survival in cervical cancer patients receiving concurrent chemoradiotherapy (CCRT). Methods: We retrospectively collected 367 cervical cancer patients receiving chemoradiotherapy from the First Affiliated Hospital of Soochow University in China and divided them into the training set and test set in a ratio of 7:3. Handcrafted and deep learning(DL)-based radiomics features were extracted from the contrast-enhanced computed tomography (CT), and the two types of radiomics signatures were calculated based on the features selected by the least absolute shrinkage and selection operator (LASSO) Cox regression. A hybrid radiomics nomogram was constructed by integrating independent clinical risk factors, handcrafted radiomics signature, and DLbased radiomics signature in the training set and was validated in the test set. Results: The hybrid radiomics nomogram exhibited favorable performance in predicting overall survival, with AUCs for 1-, 3-, and 5-year in training sets were 0.833, 0.777, and 0.871, in the test set were 0.811,0.713, and 0.730 respectively. Furthermore, the hybrid radiomics nomogram outperformed the single clinical model, handcrafted radiomics signature, and DL-based radiomics signature in both training (C-index: 0.793), and test sets (C-index: 0.721). The calibration curves and decision curve analysis (DCA) indicated our hybrid nomogram had good calibration and clinical benefits. Lastly, our hybrid nomogram demonstrated value in stratifying patients into high and low-risk groups (cut-off value: 5.6). Conclusion: A high-performance hybrid radiomics model based on pre-radiotherapy CT was established, presenting strengths in risk stratification.