To construct a prediction model for pathologic complete response (pCR) in liver cancer patients after neoadjuvant/conversion therapy followed by sequential surgical resection based on CT radiomics and clinical features, and to evaluate its predictive value.

Clinical data of 74 liver cancer patients undergoing neoadjuvant/conversion therapy, followed by sequential surgical resection in Nanfang Hospital of Southern Medical University from December 2019 to December 2022 were retrospectively analyzed. The informed consents of all patients were obtained and the local ethical committee approval was received. Among them, 65 patients were male and 9 female, aged from 22 to 74 years, with a median age of 52 years. According to postoperative tumor specimens (n=80), they were randomly divided into the training (n=40) and validation sets (n=40). Feature indexes were extracted and screened based on CT radiomics of the patients, and the radiomics score (Rad-score) was obtained. A nomogram prediction model for pCR was constructed based on multivariate Logistic analysis of clinical factors and CT radiomics in the training set. The consistency of nomogram prediction model was evaluated by C-index and Hosmer-Lemeshow goodness-of-fit test. Bootstrap method was used for internal verification to evaluate the accuracy of calibration curve. The area under the ROC curve (AUC) was utilized to evaluate the degree of discrimination of nomogram prediction model. Decision curve analysis (DCA) was drawn to evaluate clinical utility of the prediction model. Clinical impact curve (CIC) was delineated to evaluate clinical effectiveness of the prediction model. The diagnostic efficiency of the prediction model was assessed by Delong test. The visual graph of Rad-score was drawn to display the diagnostic performance of the radiomics model.

Multivariate Logistic analysis showed that positive AFP after treatment was an independent influencing factor of pCR (OR=5.250, 95%CI: 1.069-25.789; P<0.05). A total of 4 148 radiomic features were extracted by Radiomics, and 11 radiomic features were screened by Lasso analysis and 10-fold cross-validation. AFP combined with Rad-score were used to construct a nomogram prediction model for pCR (combined model). The calibration curve of the combined model yielded high consistency between the predicted and actual pCR probability. The C-index in the training and validation sets was 0.887 and 0.895. Hosmer-Lemeshow test showed a high goodness of fit (χ²=5.96, 4.78; both P>0.05). Internal verification using Bootstrap method in the training and validation sets demonstrated that the average absolute error of the calibration curve of the combined model was 0.063 and 0.040, with high accuracy. DCA and CIC analyses showed that the combined model possessed high clinical utility and clinical effectiveness rate. In the training and validation sets, the AUC of the combined model was 0.887 and 0.895. The AUC of clinical and radiomics models in the training set was 0.887, 0.667 and 0.857, respectively. The diagnostic efficiency of combined model was significantly better than that of clinical model (Z=2.797, P=0.005 2). The diagnostic efficiency of combined model in the validation set was also significantly better than that of clinical model (Z=2.027, P=0.042 7). Rad-score waterfall plot revealed the proportion of incorrect prediction was not high, and the visualization effect was good.

The combined model based on CT radiomics and AFP possesses high prediction value for pCR after neoadjuvant/conversion therapy followed by sequential surgical resection for liver cancer and yields the optimal diagnostic efficiency.