Poster Presentation Clinical Oncology Society of Australia Annual Scientific Meeting 2018

Radiomic analysis using Contrast-Enhanced CT: predict treatment response to pulsed low dose rate radiotherapy in gastric carcinoma with peritoneal cavity metastasis (#309)

Zhen Hou 1 , Yang Yang 2 , Shuangshuang Li 2 , Jing Yan 2 , Wei Ren 2 , Juan Liu 2 , Kangxin Wang 2 , Baorui Liu 2 , Suiren Wan 1
  1. State Key Laboratory of Bioelectronics, Laboratory for Medical Electronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu, China
  2. The Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing, Jiangsu, China

Background: To determine the feasibility of radiomic analysis for predicting the therapeutic response of gastric carcinoma with peritoneal cavity metastasis (GCPCM) to pulsed low dose rate radiotherapy (PLDRT) using contrast-enhanced computed tomography (CECT) images.

Methods: Pretreatment CECT images of 43 GCPCM patients were analyzed. Patients with complete response (CR) and partial response (PR) were considered responders, while stable disease (SD) and progressive disease (PD) as non-responders. Image features were quantified from tumor region. The capability of each feature on treatment response classification was assessed using Kruskal-Wallis test and receiver operating characteristic (ROC) analysis. Moreover, artificial neural network (ANN) and k-nearest neighbor (KNN) predictive models were constructed based on the training set and the testing set validated the reliability of the models. Comparison between the performances of the models was performed by using McNemar’s test.

Results: The analyses showed that six features (1 first order-based, 1 texture-based, 1 LoG-based, and 3 wavelet-based) were significantly different between responders and non-responders (AUCs range from 0.686 to 0.728). Both two prediction models based on features extracted from CECT showed potential in predicting the treatment response with higher accuracies (ANN: 0.714, KNN: 0.749 for the training set; ANN: 0.816, KNN: 0.816 for the testing set). No statistical difference was observed in the performance of the two classifiers (P = 0.999).

Conclusions: Pretreatment radiomic analysis using CECT can potentially provide important information regarding the therapeutic response to PLDRT for GCPCM, improving risk stratification.