The study of CT radionics in assessing the efficacy of pulmonary adenocarcinoma chemotherapy before treatment

Objective To discuss the value of CT soft tissue window radionics in assessing the efficacy of pulmonary adenocarcinoma chemotherapy. Methods Images and case materials of 105 patients pathologically diagnosed with pulmonary adenocarcinoma in our hospital from December 2015 to December 2018 were retrospectively collected. After half-year chemotherapy, the patients were assigned to the response group(35 cases) and non-response group(70 cases) according to RECIST. The non-response group was divided into the stable group(35 cases) and progression group(35 cases). For all patients, their CT soft tissue images before chemotherapy were obtained, and the lesions were manually segmented using ITK-SNAP software. The focal features of those CT soft tissue images analyzed by AK analysis software were extracted using radionics methods, and then subjected to Lasso dimensionality reduction and RTree modeling. The model for comparison between the response group and the non-response group was calculated using the receiver operating characteristic curve(ROC) and image omics model to assess the diagnostic efficiency of chemotherapy efficacy. Results Based on the images of response group and the non-response group, 12 significant texture features were extracted, including 5 co-occurrence matrices(GLCM) and 7 run-length matrices(RLM). The training group's AUC, diagnosis sensitivity and specificity were 0.80, 0.68 and 0.80, respectively. The validation group's AUC, diagnosis sensitivity and specificity were 0.74, 0.70 and 0.81, respectively. The AUC values of both groups were between 0.7-0.9, which also achieved good diagnostic value. The decision curve of image omics model had a good clinical practicability in the range of 0.18-0.76. Conclusion Radionics based on the CT images before chemotherapy may help exactly assess the efficacy of pulmonary adenocarcinoma chemotherapy before treatment.