A radiomics approach for predicting acute hematologic toxicity in patients with cervical or endometrial cancer undergoing external-beam radiotherapy. (May 2023)

• Record Type: Journal Article
• Authors: Le, Ziyu; Wu, Dongmei; Chen, Xuming; Wang, Lei; Xu, Yi; Zhao, Guoqi; Zhang, Chengxiu; Chen, Ying; Hu, Ye; Yao, Shengyu; Chen, Tingfeng; Ren, Jiangping; Yang, Guang; Liu, Yong
• Is Part Of: Radiotherapy and oncology. Volume 182(2023)
• Publication Date: 2023-05
• Subjects: Radiomics -- Pelvic bone marrow -- Computed tomography -- Hematologic toxicity -- Cervical and endometrial cancer -- Machine learning; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2023.109489
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: We developed prediction models for severe HT during radiation in patients with cervical or endometrial cancer via machine learning. The model which incorporates pelvic radiomics signature based on planning CT and clinical risk factors outperforms clinical model and radiomics model in terms of AUC. This is the first study that shows a predictive value of CT-based radiomic features in acute HT. Abstract: Purpose: This study is purposed to establish a predictive model for acute severe hematologic toxicity (HT) during radiotherapy in patients with cervical or endometrial cancer and investigate whether the integration of clinical features and computed tomography (CT) radiomics features of the pelvic bone marrow (BM) could define a more precise model. Methods: A total of 207 patients with cervical or endometrial cancer from three cohorts were retrospectively included in this study. Forty-one clinical variables and 2226 pelvic BM radiomic features that were extracted from planning CT scans were included in the model construction. Following feature selection, model training was performed on the clinical and radiomics features via machine learning, respectively. The radiomics score, which was the output of the final radiomics model, was integrated with the variables that were selected by the clinical model to construct a combined model. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The best-performing prediction model comprised two clinical features (FIGO stage and cycles of postoperative chemotherapy) and radiomics score and achieved an AUC of 0.88 (95% CI, 0.81–0.93) in the training set, 0.80 (95% CI, 0.62–0.92) in the internal-test set and 0.85 (95% CI, 0.71–0.94) in the external-test dataset. Conclusion: The proposed model which incorporates radiomics signature and clinical factors outperforms the models based on clinical or radiomics features alone in terms of the AUC. The value of the pelvic BM radiomics in chemoradiotherapy-induced HT is worthy of further investigation.