Radiomic analysis of MRI to Predict Sustained Complete Response after Radiofrequency Ablation in Patients with Hepatocellular Carcinoma - A Pilot Study

Abstract

To investigate whether quantitative textural features, extracted from pretreatment MRI, can predict sustained complete response to radiofrequency ablation (RFA) in patients with hepatocellular carcinoma (HCC). METHODS: In this IRB-approved study, patients were selected from a maintained six-year database of consecutive patients who underwent both pretreatment MRI imaging with a probable or definitive imaging diagnosis of HCC (LI-RADS 4 or 5) and loco-regional treatment with RFA. An experienced radiologist manually segmented the hepatic nodules in MRI arterial and equilibrium phases to obtain the volume of interest (VOI) for extraction of 107 quantitative textural features, including shape and first-and second-order features. Statistical analysis was performed to evaluate associations between textural features and complete response. RESULTS:The study consisted of 34 patients with 51 treated hepatic nodules. Sustained complete response was achieved by 6 patients (4 with single nodule and 2 with multiple nodules). Of the 107 features from the arterial and equilibrium phases, 20 (18%) and 25 (23%) achieved AUC 40.7, respectively. The three best performing features were found in the equilibrium phase: Dependence Non-Uniformity Normalized and Dependence Variance (both GLDM class, with AUC of 0.78 and 0.76, respectively) and Maximum Probability (GLCM class, AUC of 0.76). CONCLUSIONS: This pilot study demonstrates that a radiomic analysis of pre-treatment MRI might be useful in identifying patients with HCC who are most likely to have a sustained complete response to RFA. Second-order features (GLDM and GLCM) extracted from equilibrium phase obtained highest discriminatory performance.

Risk Factors for Unilateral Trigeminal Neuralgia Based on Machine Learning

“…In this study, there are 55 UC-ITN patients with NVC on the unaffected side, indicating that the presence of NVC did not necessitate the UC-ITN condition. The results showed that SALGLE (19), Coarseness (20), MAL (21), DV (22), Maximum (23), Correlation (17), and the Ofv were the risk factors for UC-ITN with bilateral NVC. UC-ITN patients with bilateral NVC only suffered from unilateral pain, probably due to different degrees of trigeminal degeneration and demyelination between the affected and unaffected side, which might lead to changes in the above-mentioned texture features.…”

Current status and future perspectives of radiomics in hepatocellular carcinoma

“…A large multi-center Chinese study by Chen et al [ 63 ] evaluating treatment response after TACE performed semi-automatic segmentation of the tumor and of the peritumoral region on contrast-enhanced CT in 585 patients, and the validation AUC was 0.90. One small study by Horvat et al [ 74 ] assessed treatment response after RFA using tumor 3D volumes of interest on MRI, yielding an AUC of 0.76 for the radiomics model, although the model lacked validation. Finally, two studies from China evaluated the use of radiomics to predict liver failure after surgical resection[ 75 , 76 ].…”

Machine learning-based radiomic analysis and growth visualization for ablation site recurrence diagnosis in follow-up CT

“…In the current study both HCC and liver metastasis cases were included, reflecting a more diverse patient population and a broader range of liver tumor types. This is in contrast with many previous studies, which have primarily focused on either HCC or liver metastasis alone [ 45 , 46 ]. The promising results obtained in our study, with an AUC of 0.97 and an accuracy of 92.73%, demonstrate the effectiveness of our machine learning-based radiomic analysis and growth visualization approaches in detecting ASR, regardless of the tumor origin.…”