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

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∼10−5), 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.

Use of radiomics containing an effective peritumoral area to predict early recurrence of solitary hepatocellular carcinoma ≤5 cm in diameter

“…Of note,this study ( 44 ) have reported on the superior prognostic efficiency of a Vtumor+10 mm area did not study 5-cm tumors individually.In our radiomics model, wavelet features were more heavily weighted, especially in the peritumoral area features are more likely related to effects of recurrence on the roughness of the tumor contour.In addition, the_square_ngtdm_Coarseness and original_shape_SphericalDisproportion features, which reflect tumor growth in more detail, also contributed to our model. In the final model, the wavelet-LHL_glcm_Contrast feature in atrial phase is heavier in the recurrence group, it reveals tumor heterogeneity measured in the dynamic arterial enhanced CT phase, and this is consistent with other study ( 45 ). These radiomic features, related to tumor biology and heterogeneity, complement the visual image content.…”

Beyond Multiparametric MRI and towards Radiomics to Detect Prostate Cancer: A Machine Learning Model to Predict Clinically Significant Lesions

“…Altogether, 132 radiomic features were generated from ADC, exploiting the local approach proposed in [ 12 ], already applied in [ 17 , 18 ], and shown in Figure 2 (using representative metrics).…”

Predictive machine learning model for microvascular invasion identification in hepatocellular carcinoma based on the LI-RADS system

“…Prior studies showed that the reduction in portal flow caused by microscopic tumor thrombin blocking tiny portal vein branches around the tumor caused compensatory hyperperfusion in the AP, which led to corona enhancement ( 40 , 41 ). Some radiomics studies about MVI prediction focus on the value of the peritumoral area ( 21 , 42 ). Corona enhancement was the highest accuracy feature which indicates the importance of the peritumoral area.…”