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

• Autor(a) principal: Horvat, Natally
• Data de Publicação: 2023
• Outros Autores: Araujo-Filho, Jose de Arimateia B., Assuncao-Jr, Antonildes N., Machado, Felipe Augusto de M., Sims, John A., Rocha, Camila Carlos Tavares, Oliveira, Brunna Clemente, Horvat, Joao Vicente, Maccali, Claudia, Puga, Anna Luísa Boschiroli Lamanna, Chagas, Aline Lopes, Menezes, Marcos Roberto, Cerri, Giovanni Guido
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Clinics
• Texto Completo: https://www.revistas.usp.br/clinics/article/view/212986
• topic: Carcinoma Hepatocellular; Magnetic Resonance Imaging; Radiomics; Radiofrequency Ablation

OBJECTIVES: 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 >0.7, 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.