Automatically Extracted Machine Learning Features from Preoperative CT to Early Predict Microvascular Invasion in HCC: The Role of the Zone of Transition (ZOT)

SIMPLE SUMMARY: Microvascular invasion (MVI) is a universally recognised predictor of hepatocellular carcinoma (HCC) recurrence after curative treatments, whose diagnosis, nowadays, is still postponed to surgery, through histopathological specimen investigation. This retrospective study aims to exploit a radiomic approach to pre-procedural diagnosis MVI in early-stage HCC, with a diameter ≤ 3 cm. The main novelty of the study is the use of the zone of transition (ZOT), crossing tumour and peritumour, detected adaptively through a standardized procedure based on the analysis of image gradients. After generating radiomics features from ZOT and tumour core in arterial and venous computed tomography phases, a classifier is trained and validated using a signature of four features only. The achieved radiomic model enables the early diagnosis of small HCC (≤3 cm) showing MVI with high specificity (82%) and sensitivity (79%), perspectively providing an effective tool to detect the best candidates for surgical treatment and liver transplantation (negative predictive value = 87%). ABSTRACT: Background: Microvascular invasion (MVI) is a consolidated predictor of hepatocellular carcinoma (HCC) recurrence after treatments. No reliable radiological imaging findings are available for preoperatively diagnosing MVI, despite some progresses of radiomic analysis. Furthermore, current MVI radiomic studies have not been designed for small HCC nodules, for which a plethora of treatments exists. This study aimed to identify radiomic MVI predictors in nodules ≤3.0 cm by analysing the zone of transition (ZOT), crossing tumour and peritumour, automatically detected to face the uncertainties of radiologist’s tumour segmentation. Methods: The study considered 117 patients imaged by contrast-enhanced computed tomography; 78 patients were finally enrolled in the radiomic analysis. Radiomic features were extracted from the tumour and the ZOT, detected using an adaptive procedure based on local image contrast variations. After data oversampling, a support vector machine classifier was developed and validated. Classifier performance was assessed using receiver operating characteristic (ROC) curve analysis and related metrics. Results: The original 89 HCC nodules (32 MVI+ and 57 MVI−) became 169 (62 MVI+ and 107 MVI−) after oversampling. Of the four features within the signature, three are ZOT heterogeneity measures regarding both arterial and venous phases. On the test set (19MVI+ and 33MVI−), the classifier predicts MVI+ with area under the curve of 0.86 (95%CI (0.70–0.93), p∼10 [Formula: see text]), sensitivity = 79% and specificity = 82%. The classifier showed negative and positive predictive values of 87% and 71%, respectively. Conclusions: The classifier showed the highest diagnostic performance in the literature, disclosing the role of ZOT heterogeneity in predicting the MVI+ status.