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155 Delta radiomics analysis for predicting risk factors for patients with locally advanced cervical cancer receiving neoadjuvant therapy
  1. Wenjuan Chen,
  2. Rongrong Wu,
  3. Xingyun Xie and
  4. Chengyi Liu
  1. Department of Radiation Oncology, Gynecology, Clinical Oncology School of Fujian Medical University.Fujian Cancer Hospital., Fuzhou, China


Introduction/Background Cervical cancer is the fourth most commonly diagnosed cancer in women globally.Neoajuvant chemotherapy( NACT ) followed by surgery has been rendered an alternative radical treatment modality for patients with locally advanced cervical cancer (LACC), in addition to concurrent chemoradiotherapy. Radiomics may aid in extracting many radiomics features and help in personalized and precise treatment .

In this study, we aimed to assess the feasibility of using magnetic resonance imaging (MRI)-based Delta radiomics characteristics to identify intermediary and high risk factors in patients with cervical cancer undergoing surgery following neoadjuvant therapy.

Methodology A total of 157 patients were divided into two groups: only one intermediate risk factor (negative group) and more than one intermediate risk factor (positive group). Radiomics features were extracted using the Ax-LAVA+ C MRI sequences and the data were divided into training and test sets in a ratio of 8:2.

The training set data features were selected using the Mann-Whitney U test and the Least Absolute Shrinkage and Selection Operator (LASSO) test. The clinical model, the radiomics model, and the combined clinic and radiomics model were developed utilizing the random forest Algorithm. The receiver operating characteristic (ROC) curve, decision curve analysis (DCA), accuracy, sensitivity, and specificity were used to assess the predictive efficacy and clinical benefits of each model.

Results The training and test sets of the three models, the AUC values assessed using the clinical model, radiomics model, and combined clinical and radiomics models were 0.76 and 0.70, 0.88 and 0.86, and 0.91 and 0.89, respectively. This combined model showed excellent diagnostic performance and can be potentially used for preoperative prediction of postoperative risk factors.

Conclusion The use of machine learning algorithms to analyze Delta Ax LAVA+C MRI radiomics features can aid in the prediction of intermediary- and highrisk factors in patients with cervical cancer receiving neoadjuvant therapy.

Disclosures Ok.

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