Use of a Pathomics Signature to Predict the Prognosis of Hepatocellular Carcinoma with Cirrhosis: A Multicentre...

Hepatocellular carcinoma (HCC) is a highly aggressive and heterogeneous malignancy which predominantly arises in the setting of cirrhosis, and there is lack of models to predict prognosis in cirrhotic HCC. This study aims to develop and validate a prediction model based on the pathomics signature and clinicopathological characteristics to predict the prognosis of HCC with cirrhosis.

In this multicenter, retrospective study, 389 patients were enrolled (training cohort: 268; independent validation cohort: 121). A total of 351 pathomics features were extracted from digital H-E-stained images, and a pathomics signature (PS_HCC) was constructed using a least absolute shrinkage and selection operator Cox regression model. Then two nomograms were established by combining the PS_HCC and clinicopathological characteristics. Further validation was performed in the validation cohort.

This study included 389 patients. A 24 feature-based PS_HCC was constructed. A higher PS_HCC was significantly associated with worse OS and DFS in both the training (OS: hazard ratio [HR], 4.341 [95% CI, 3.109-6.062]; DFS: HR, 3.058 [95% CI, 2.223-4.207]) and validation (OS: HR, 4.145 [95% CI, 2.357-7.291]; DFS: HR, 3.395 [95% CI, 2.104-5.479]) cohorts (p < 0.001 for all comparisons). Multivariable analysis revealed that the PS_HCC was an independent factor associated with OS and DFS. Integrating the PS_HCC into pathomics nomograms resulted in better performance for prognosis prediction than the traditional model in both cohorts.

The PS_HCC may serve as a reliable surrogate for prognosis, and the nomograms offer promising tools to predict individual outcomes, facilitating personalized management of HCC with cirrhosis.