Prediction of myelosuppression in cervical cancer after concurrent chemoradiotherapy by CT radiomics-based model.
Authors
Affiliations (3)
Affiliations (3)
- Department of Radiation Oncology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
- School of Medicine, Henan University of Chinese Medicine, Zhengzhou, China. [email protected].
- Department of Radiation Oncology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China. [email protected].
Abstract
Concurrent chemoradiotherapy (CCRT) was highly effective in treating cervical cancer (CC) but raised the risk of bone marrow suppression. However, models to predict the risk of myelosuppression in CC patients after CCRT based on computed tomography (CT) imaging histology are immature. The Region of interest (ROI) of the CT images was segmented, and radiomic features were extracted. Next, important features were further selected. The training and test sets were split 7:3, and 8 machine learning algorithms were applied to classify the features in the training set. The model's performance was assessed in the test set, and the best algorithm was chosen. The selected algorithm predicted the radiomic feature score. The clinical features were compared between mild and severe groups, and a clinical model was constructed using the best algorithm, and predicted clinical feature scores. Finally, logistic regression models were used to identify independent prognostic factors for myelosuppression, and nomograms were drawn. 14 important radiomics features were selected. The random forest (RF) algorithm was considered the best machine learning method in both the classification imaging model and the clinical classification model (0.735 (95% CI = 0.624-0.846), accuracy = 0.719 (95% CI = 0.618-0.802), sensitivity = 0.703 (95% CI = 0.507-0.845), recall = 0.703 (95% CI = 0.507-0.845), and F1 score = 0.782 (95% CI = 0.513-1.000)). A logistic regression model built from the predictions of the RF al-gorithm showed that both the rad_score and Clinical_score could serve as independent factors (p value < 0.05). Furthermore, the nomogram constructed based on these two scores were found to have moderate predictive performance (AUC = 0.724 (95% CI = 0.650-0.798)). A CT-based radiomics model combined with clinical characteristics demonstrated favourable predictive performance in forecasting bone marrow suppression among cervical cancer patients undergoing concurrent chemoradiotherapy. However, further multicentre studies are required to validate its clinical utility.