| Zhou Qionghui,Chen Luqiao,Ni Qianxi,et al.Prediction of hematologic toxicity in patients with locally advanced cervical cancer based on radiomics and dosiomics[J].Chinese Journal of Radiological Medicine and Protection,2025,45(3):188-193 |
| Prediction of hematologic toxicity in patients with locally advanced cervical cancer based on radiomics and dosiomics |
| Received:May 16, 2024 |
| DOI：10.3760/cma.j.cn112271-20240516-00179 |
| KeyWords:Machine learning Radiomics Dosiomics Cervical cancer Hematologic toxicity |
| FundProject:湖南省自然科学基金面上项目(2023JJ30373);国家重点研发计划项目(2022YFC2404604);湖南省科技创新计划资助项目(2021SK51116,2023SK4034);湖南省肿瘤医院攀登科研计划重点研发项目(YF2021006) |
| Author Name | Affiliation | E-mail | | Zhou Qionghui | Department of Radiation Oncology, Hunan Cancer Hospital, Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China | | | Chen Luqiao | Department of Radiation Oncology, Hunan Cancer Hospital, Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China | | | Ni Qianxi | Department of Radiation Oncology, Hunan Cancer Hospital, Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China | | | Lan Jing | Department of Gynecologic Oncology, Hunan Cancer Hospital, Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China | | | Zhang Li | Department of Radiation Oncology, Hunan Cancer Hospital, Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China | | | Long Xizi | Hunan Province Key Laboratory of Typical Environmental Pollution and Health Hazards, School of Public Health, University of South China, Hengyang 421001, China | | | Zhu Jun | Teaching Office, Hunan Cancer Hospital, Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China | zhujun@hnca.org.cn |
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| Abstract:: |
| Objective To explore the application of machine learning (ML) models based on radiomics and dosiomics to the assessment of hematologic toxicity (HT) in patients with locally advanced cervical cancer, and to preliminarily explore the comprehensive application of multi-omics features. Methods A retrospective study was conducted on the clinical data, planning computed tomography (CT) images, and dose files of 205 patients with locally advanced cervical cancer who received concurrent chemoradiotherapy at the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, from January 2022 to June 2023. Patients were categorized according to the severity of HT. Radiomics and dosiomics features were extracted from the same regions of interest (ROIs), followed by feature selection utilizing a random forest algorithm. Then, radiomics, dosiomics, and hybrid models were established based on extreme gradient boosting (XGBoost). The classification performance of these models was assessed by calculating their sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results The radiomics model yielded sensitivity, specificity, and AUC of 0.42, 0.86, and 0.78, respectively. The dosiomics model exhibited sensitivity, specificity, and AUC of 0.50, 0.90, and 0.74, respectively. In contrast, the hybrid model achieved sensitivity, specificity, and AUC of 0.50, 0.83, and 0.83, respectively. These findings suggest that the hybrid model possessed an enhanced classification capability compared to the individual radiomics and dosiomics models. Conclusions It is feasible to use ML models based on radiomics and dosiomics to conduct the classification and prediction of HT in patients with locally advanced cervical cancer treated with concurrent chemoradiotherapy. Furthermore, integrating both radiomics features and dosiomics features can improve the classification performance of relevant prediction models, thus holding application potentials to optimize treatment strategies for patients with locally advanced cervical cancer. |
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