A Radiomics Approach to Predict the Emergence of New Hepatocellular Carcinoma in Computed Tomography for High-Risk Patients with Liver Cirrhosis

Abstract

Liver cirrhosis poses a major risk for the development of hepatocellular carcinoma (HCC). This retrospective study investigated to what extent radiomic features allow the prediction of emerging HCC in patients with cirrhosis in contrast-enhanced computed tomography (CECT). A total of 51 patients with liver cirrhosis and newly detected HCC lesions (n = 82) during follow-up (FU-CT) after local tumor therapy were included. These lesions were not to have been detected by the radiologist in the chronologically prior CECT (PRE-CT). For training purposes, segmentations of 22 patients with liver cirrhosis but without HCC-recurrence were added. A total of 186 areas (82 HCCs and 104 cirrhotic liver areas without HCC) were analyzed. Using univariate analysis, four independent features were identified, and a multivariate logistic regression model was trained to classify the outlined regions as “HCC probable” or “HCC improbable”. In total, 60/82 (73%) of segmentations with later detected HCC and 84/104 (81%) segmentations without HCC were classified correctly (AUC of 81%, 95% CI 74–87%), yielding a sensitivity of 72% (95% CI 57–83%) and a specificity of 86% (95% CI 76–96%). In conclusion, the model predicted the occurrence of new HCCs within segmented areas with an acceptable sensitivity and specificity in cirrhotic liver tissue in CECT.

Tumor cellularity beyond the visible in soft tissue sarcomas: Results of an ADC-based, single center, and preliminary radiomics study

“…This last evidence, together with other previous promising results provided by PET/MR-based histogram analyses and texture features, suggests that the role of hybrid imaging should be further assessed, especially about the possibility to simultaneously collect data regarding metabolic activity and functional information (12,20). Regarding, in particular, the Imc1 feature, it quantifies the complexity of the texture and its importance has been previously demonstrated in several studies on different types of cancer (21)(22)(23)(24). For instance, our results are in line with those of Liao and colleagues who identified more negative values in patients with brain metastasis due to non-small lung cancer with poor local tumor control, thus suggesting that tumors with high intralesion heterogeneity might be associated with a worse clinical course, in our study represented by the occurrence of metastasis.…”

“…Regarding, in particular, the Imc1 feature, it quantifies the complexity of the texture and its importance has been previously demonstrated in several studies on different types of cancer ( 21 – 24 ). For instance, our results are in line with those of Liao and colleagues who identified more negative values in patients with brain metastasis due to non-small lung cancer with poor local tumor control, thus suggesting that tumors with high intralesion heterogeneity might be associated with a worse clinical course, in our study represented by the occurrence of metastasis.…”