Alexandra Petukhova-Greenstein1, Tal Zeevi2, Junlin Yang3, Nathan Chai2, Paul DiDomenico4, Yanhong Deng5, Maria Ciarleglio5, Stefan P Haider2, Ifeyinwa Onyiuke6, Rohil Malpani2, MingDe Lin7, Ahmet S Kucukkaya1, Luzie A Gottwald1, Bernhard Gebauer8, Margarita Revzin2, John Onofrey9, Lawrence Staib10, Gowthaman Gunabushanam4, Tamar Taddei11, Julius Chapiro12. 1. Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Institute of Radiology, Berlin, Germany. 2. Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut. 3. Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, Connecticut. 4. Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; VA Connecticut Healthcare System, West Haven, Connecticut. 5. Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut. 6. VA Connecticut Healthcare System, West Haven, Connecticut. 7. Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Visage Imaging, Inc., San Diego, California. 8. Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Institute of Radiology, Berlin, Germany. 9. Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Department of Urology, Yale School of Medicine, New Haven, Connecticut. 10. Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, Connecticut. 11. Department of Internal Medicine, Section of Digestive Diseases, Yale School of Medicine, New Haven, Connecticut; VA Connecticut Healthcare System, West Haven, Connecticut. 12. Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut. Electronic address: julius.chapiro@yale.edu.
Abstract
PURPOSE: To assess the Liver Imaging Reporting and Data System (LI-RADS) and radiomic features in pretreatment magnetic resonance (MR) imaging for predicting progression-free survival (PFS) in patients with nodular hepatocellular carcinoma (HCC) treated with radiofrequency (RF) ablation. MATERIAL AND METHODS: Sixty-five therapy-naïve patients with 85 nodular HCC tumors <5 cm in size were included in this Health Insurance Portability and Accountability Act-compliant, institutional review board-approved, retrospective study. All patients underwent RF ablation as first-line treatment and demonstrated complete response on the first follow-up imaging. Gadolinium-enhanced MR imaging biomarkers were analyzed for LI-RADS features by 2 board-certified radiologists or by analysis of nodular and perinodular radiomic features from 3-dimensional segmentations. A radiomic signature was calculated with the most informative features of a least absolute shrinkage and selection operator Cox regression model using leave-one-out cross-validation. The association between both LI-RADS features and radiomic signatures with PFS was assessed via the Kaplan-Meier analysis and a weighted log-rank test. RESULTS: The median PFS was 19 months (95% confidence interval, 16.1-19.4) for a follow-up period of 24 months. Multifocality (P = .033); the appearance of capsular continuity, compared with an absent or discontinuous capsule (P = .012); and a higher radiomic signature based on nodular and perinodular features (P = .030) were associated with poorer PFS in early-stage HCC. The observation size, presence of arterial hyperenhancement, nonperipheral washout, and appearance of an enhancing "capsule" were not associated with PFS (P > .05). CONCLUSIONS: Although multifocal HCC clearly indicates a more aggressive phenotype even in early-stage disease, the continuity of an enhancing capsule and a higher radiomic signature may add value as MR imaging biomarkers for poor PFS in HCC treated with RF ablation.
PURPOSE: To assess the Liver Imaging Reporting and Data System (LI-RADS) and radiomic features in pretreatment magnetic resonance (MR) imaging for predicting progression-free survival (PFS) in patients with nodular hepatocellular carcinoma (HCC) treated with radiofrequency (RF) ablation. MATERIAL AND METHODS: Sixty-five therapy-naïve patients with 85 nodular HCC tumors <5 cm in size were included in this Health Insurance Portability and Accountability Act-compliant, institutional review board-approved, retrospective study. All patients underwent RF ablation as first-line treatment and demonstrated complete response on the first follow-up imaging. Gadolinium-enhanced MR imaging biomarkers were analyzed for LI-RADS features by 2 board-certified radiologists or by analysis of nodular and perinodular radiomic features from 3-dimensional segmentations. A radiomic signature was calculated with the most informative features of a least absolute shrinkage and selection operator Cox regression model using leave-one-out cross-validation. The association between both LI-RADS features and radiomic signatures with PFS was assessed via the Kaplan-Meier analysis and a weighted log-rank test. RESULTS: The median PFS was 19 months (95% confidence interval, 16.1-19.4) for a follow-up period of 24 months. Multifocality (P = .033); the appearance of capsular continuity, compared with an absent or discontinuous capsule (P = .012); and a higher radiomic signature based on nodular and perinodular features (P = .030) were associated with poorer PFS in early-stage HCC. The observation size, presence of arterial hyperenhancement, nonperipheral washout, and appearance of an enhancing "capsule" were not associated with PFS (P > .05). CONCLUSIONS: Although multifocal HCC clearly indicates a more aggressive phenotype even in early-stage disease, the continuity of an enhancing capsule and a higher radiomic signature may add value as MR imaging biomarkers for poor PFS in HCC treated with RF ablation.
Authors: F Edward Boas; Bao Do; John D Louie; Nishita Kothary; Gloria L Hwang; William T Kuo; David M Hovsepian; Mark Kantrowitz; Daniel Y Sze Journal: J Vasc Interv Radiol Date: 2014-11-04 Impact factor: 3.464
Authors: Matthew S Davenport; Shokoufeh Khalatbari; Peter S C Liu; Katherine E Maturen; Ravi K Kaza; Ashish P Wasnik; Mahmoud M Al-Hawary; Daniel I Glazer; Erica B Stein; Jeet Patel; Deepak K Somashekar; Benjamin L Viglianti; Hero K Hussain Journal: Radiology Date: 2014-02-18 Impact factor: 11.105
Authors: Roger Sun; Elaine Johanna Limkin; Maria Vakalopoulou; Laurent Dercle; Stéphane Champiat; Shan Rong Han; Loïc Verlingue; David Brandao; Andrea Lancia; Samy Ammari; Antoine Hollebecque; Jean-Yves Scoazec; Aurélien Marabelle; Christophe Massard; Jean-Charles Soria; Charlotte Robert; Nikos Paragios; Eric Deutsch; Charles Ferté Journal: Lancet Oncol Date: 2018-08-14 Impact factor: 41.316
Authors: Freddie Bray; Jacques Ferlay; Isabelle Soerjomataram; Rebecca L Siegel; Lindsey A Torre; Ahmedin Jemal Journal: CA Cancer J Clin Date: 2018-09-12 Impact factor: 508.702
Authors: Joost J M van Griethuysen; Andriy Fedorov; Chintan Parmar; Ahmed Hosny; Nicole Aucoin; Vivek Narayan; Regina G H Beets-Tan; Jean-Christophe Fillion-Robin; Steve Pieper; Hugo J W L Aerts Journal: Cancer Res Date: 2017-11-01 Impact factor: 12.701