Abstract:

Objective

To investigate the influencing factors for surgery in patients with unresectable hepatocellular carcinoma （uHCC） after interventional therapy combined with targeted immunotherapy， and to construct a nomogram prediction model.

Methods

Clinical data of 190 patients with newly-diagnosed uHCC admitted to the Second Affiliated Hospital of Air Force Medical University from January 2022 to June 2024 were retrospectively analyzed. The informed consents of all patients were obtained and the local ethical committee approval was received. Among them， 163 patients were male and 27 female，aged from 31 to 75 years， with a median age of 55 years. The patients were received interventional therapy combined with targeted immunotherapy. The predictive factors for surgical conversion uHCC were screened by Lasso regression. According to the ratio of 7:3， all patients were divided into the training set （n=133） and test set （n=57）. A nomogram prediction model was constructed in the training set based on the predictive factors by using Logistic regression analysis. ROC curve， calibration curve and clinical decision curve were drawn to assess the degree of discrimination， calibration and clinical applicability of this model.

Results

A total of 51 patients were successfully converted to resection， and the overall conversion rate was 26.8%.Finally， 4 factors were identified by Lasso and Logistic regression analyses， including the Eastern Cooperative Oncology Group-Performance Status （ECOG-PS） score， liver cirrhosis， C-reactive protein-to-albumin （CAR）ratio and neutrophil-to-lymphocyte ratio （NLR）. Based on these 4 factors， a prediction model was constructed and a nomogram was drawn in the training set. The area under the ROC curve （AUC） of this nomogram in the training and testing sets was 0.784 （95%CI: 0.699-0.869） and 0.806 （95%CI: 0.693-0.920）， respectively indicating that a high degree of discrimination. Both the calibration curve and Hosmer-Lemeshow goodnessof-fit test showed that the model had a high degree of calibration （χ²=7.410， P=0.493）. The clinical decision curves were delineated in the training and test sets， revealing that the prediction model yielded high net clinical benefit.

Conclusions

ECOG-PS score， liver cirrhosis， CAR and NLR are the influencing factors of newly-diagnosed uHCC patients receiving interventional therapy combined with targeted immunotherapy before conversion to surgery. The nomogram based on these influencing factors has high predictive capability.

Key words: Carcinoma, hepatocellular, Conversion therapy, Influential factors, Predictive model