Maïté Lewin1,2, Astrid Laurent-Bellue3,4, Christophe Desterke3,5, Adina Radu6, Joëlle Ann Feghali6, Jad Farah6, Hélène Agostini7, Jean-Charles Nault8,9,10, Eric Vibert3,11,12, Catherine Guettier3,4,12. 1. Service de Radiologie, AP-HP-Université Paris Saclay Hôpital Paul Brousse, 12-14 avenue Paul Vaillant Couturier, 94800, Villejuif, France. maite.lewin@aphp.fr. 2. Faculté de Médecine, Université Paris Saclay, 94270, Le Kremlin-Bicêtre, France. maite.lewin@aphp.fr. 3. Faculté de Médecine, Université Paris Saclay, 94270, Le Kremlin-Bicêtre, France. 4. Service d'Anatomopathologie, AP-HP-Université Paris Saclay Hôpital Bicêtre, 94270, Le Kremlin-Bicêtre, France. 5. Service de Bio-informatique, INSERM UA9, Hôpital Paul Brousse, 94800, Villejuif, France. 6. Service de Radiologie, AP-HP-Université Paris Saclay Hôpital Paul Brousse, 12-14 avenue Paul Vaillant Couturier, 94800, Villejuif, France. 7. Service d'Epidémiologie et de Santé Publique, AP-HP-Université Paris Saclay Hôpital Bicêtre, 94270, Le Kremlin-Bicêtre, France. 8. Service d'Hépatologie, AP-HP, Hôpitaux Universitaires Paris-Seine-Saint-Denis, Hôpital Avicenne, 93000, Bobigny, France. 9. Functional Genomics of Solid Tumors Laboratory, Centre de Recherche Des Cordeliers, Sorbonne Université, Inserm, USPC, Université Paris Descartes, Université Paris Diderot, Université Paris 13, 75006, Paris, France. 10. Université Paris 13, Unité de Formation et de Recherche Santé Médecine et Biologie Humaine, 93000, Bobigny, France. 11. AP-HP-Université Paris Saclay, Hôpital Paul Brousse, 94800, Villejuif, France. 12. Centre Hépato-Biliaire, INSERM U1193 Hôpital Paul Brousse, 94800, Villejuif, France.
Abstract
PURPOSE: Evaluation of perfusion CT and dual-energy CT (DECT) quantitative parameters for predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC) prior to surgery. METHODS: This prospective single-center study included fifty-six patients (44 men; median age 67; range 31-84) who provided written informed consent. Inclusion criteria were (1) treatment-naïve patients with a diagnosis of HCC, (2) an indication for hepatic resection, and (3) available arterial DECT phase and perfusion CT (GE revolution HD-GSI). Iodine concentrations (IC), arterial density (AD), and 9 quantitative perfusion parameters for HCC were correlated to pathological results. Radiological parameters based principal component analysis (PCA), corroborated by unsupervised heatmap classification, was meant to deliver a model for predicting MVI in HCC. Survival analysis was performed using univariable log-rank test and multivariable Cox model, both censored at time of relapse. RESULTS: 58 HCC lesions were analyzed (median size 42.3 mm; range of 20-140). PCA showed that the radiological model was predictive of tumor grade (p = 0.01), intratumoral MVI (p = 0.004), peritumoral MVI (p = 0.04), MTM (macrotrabecular-massive) subtype (p = 0.02), and capsular invasion (p = 0.02) in HCC. Heatmap classification of HCC showed tumor heterogeneity, stratified into three main clusters according to the risk of relapse. Survival analysis confirmed that permeability surface-area product (PS) was the only significant independent parameter, among all quantitative tumoral CT parameters, for predicting a risk of relapse (Cox p value = 0.004). CONCLUSION: A perfusion CT and DECT-based quantitative imaging profile can provide a diagnosis of histological MVI in HCC. PS is an independent parameter for relapse. CLINICAL TRIALS: ClinicalTrials.gov: NCT03754192.
PURPOSE: Evaluation of perfusion CT and dual-energy CT (DECT) quantitative parameters for predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC) prior to surgery. METHODS: This prospective single-center study included fifty-six patients (44 men; median age 67; range 31-84) who provided written informed consent. Inclusion criteria were (1) treatment-naïve patients with a diagnosis of HCC, (2) an indication for hepatic resection, and (3) available arterial DECT phase and perfusion CT (GE revolution HD-GSI). Iodine concentrations (IC), arterial density (AD), and 9 quantitative perfusion parameters for HCC were correlated to pathological results. Radiological parameters based principal component analysis (PCA), corroborated by unsupervised heatmap classification, was meant to deliver a model for predicting MVI in HCC. Survival analysis was performed using univariable log-rank test and multivariable Cox model, both censored at time of relapse. RESULTS: 58 HCC lesions were analyzed (median size 42.3 mm; range of 20-140). PCA showed that the radiological model was predictive of tumor grade (p = 0.01), intratumoral MVI (p = 0.004), peritumoral MVI (p = 0.04), MTM (macrotrabecular-massive) subtype (p = 0.02), and capsular invasion (p = 0.02) in HCC. Heatmap classification of HCC showed tumor heterogeneity, stratified into three main clusters according to the risk of relapse. Survival analysis confirmed that permeability surface-area product (PS) was the only significant independent parameter, among all quantitative tumoral CT parameters, for predicting a risk of relapse (Cox p value = 0.004). CONCLUSION: A perfusion CT and DECT-based quantitative imaging profile can provide a diagnosis of histological MVI in HCC. PS is an independent parameter for relapse. CLINICAL TRIALS: ClinicalTrials.gov: NCT03754192.
Authors: Josep M Llovet; Robin Kate Kelley; Augusto Villanueva; Amit G Singal; Eli Pikarsky; Sasan Roayaie; Riccardo Lencioni; Kazuhiko Koike; Jessica Zucman-Rossi; Richard S Finn Journal: Nat Rev Dis Primers Date: 2021-01-21 Impact factor: 52.329