Work

Development, validation, and visualization of a novel nomogram for predicting clinical outcomes of radiotherapy combined with chemotherapy in locally advanced cervical cancer

Year: 2025

Type: article

Abstract: Background: Patients with locally advanced cervical cancer (LACC) have been advised to undergo radical chemoradiotherapy. To determine whether local recurrence or distant metastasis (LRDM) will occur in patients with locally advanced cervical cancer (LACC) after chemoradiotherapy, this study aims to develop and validate a model using clinical and radiomic parameters. Methods: A total of 118 patients with LACC who were treated with radiotherapy combined with chemotherapy were included. They were divided into a training set (n=83) and a validation set (n=35) at an 7:3 ratio. All patients' diffusion-weighted imaging (DWI) images were uploaded to the ITK-SNAP software. Regions of interest (ROIs) were manually delineated, and a radiomic model was constructed using radiomic features by the LightGBM algorithm. A comprehensive model was constructed by integrating clinical and radiomic features and was visualized as a nomogram. The area under the curve (AUC) values were used to evaluate their predictive performance, and Decision curve analysis (DCA) was employed to assess the clinical utility of the predictive models. The calibration curves were used to assess the agreement between predicted and observed outcomes for the LRDM in both cohorts. Results: Seven variables were finally chosen for modeling using the least absolute shrinkage and selection operator (LASSO) regression analysis. The AUC values for the training and test sets of the DWI radiomic model were 0.789 and 0.785, respectively. AUC values for the training and test sets were 0.897 and 0.889, respectively, for the combined model LGBM-nomogram that used DWI and clinical characteristics. The nomogram worked remarkably well in both the training and test cohorts, as shown by the calibration curves. Conclusion: The model integrating DWI and clinical features has shown high value in non-invasive prediction of LRDM, which may aid in treatment and prognostication.

Source: Frontiers in Oncology

DOI: 10.3389/fonc.2025.1668971

Authors: Nan Jiang, Xiaoxia Ping, Qian Meng, Yuanqing Liu, Xi Wang, Chunhong Hu

Institution: First Affiliated Hospital of Soochow University

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801