Huzi Li1, Zhi Guo, Tongguo Si, Haitao Wang. 1. Department of Intervention Therapy, Tianjin Medical University Cancer Hospital and Institute, Tianjin, China.
Abstract
OBJECTIVE: The aim of this study was to determine which response evaluation criteria will best help predict the treatment efficacy of cryoablation for hepatocellular carcinoma. MATERIALS AND METHODS: We retrospectively analyzed clinical data of 64 patients with hepatocellular carcinoma treated with cryoablation. Triphasic helical computed tomography scans were analyzed on the basis of WHO, Response Evaluation Criteria In Solid Tumors 1.1 (RECIST 1.1), modified RECIST (mRECIST), and European Association for the Study of the Liver (EASL) guidelines. We assessed the concordance among response guidelines and selected the most reliable model depending upon the correlation with overall survival. RESULTS: Both objective response rates and disease control rates were higher for mRECIST and EASL than for WHO and RECIST 1.1 for both overall responses and target responses. The κ-value of comparisons between WHO and RECIST 1.1 and mRECIST and EASL was not more than 0.20 for both overall responses and target responses. There was consistency between WHO and RECIST 1.1 [κ=0.82, 95% confidence interval (CI): 0.72-0.91 for overall responses and κ=0.87; 95% CI, 0.76-0.94 for target responses], the same as that between mRECIST and EASL (κ=0.91, 95% CI, 0.73-0.98 for overall responses and κ=0.88, 95% CI, 0.72-0.95 for target responses). There was no significant association with survival for WHO and RECIST 1.1 responses or all target responses. The Cox-regression model showed that both mRECIST and EASL were independent predictors of overall survival, with a 51% risk reduction for mRECIST and a 61% risk reduction for EASL. CONCLUSION: The enhancement models including mRECIST and EASL guidelines should be used in preference to WHO, RECIST 1.1, or target responses to assess the efficacy of cryotherapy.
OBJECTIVE: The aim of this study was to determine which response evaluation criteria will best help predict the treatment efficacy of cryoablation for hepatocellular carcinoma. MATERIALS AND METHODS: We retrospectively analyzed clinical data of 64 patients with hepatocellular carcinoma treated with cryoablation. Triphasic helical computed tomography scans were analyzed on the basis of WHO, Response Evaluation Criteria In Solid Tumors 1.1 (RECIST 1.1), modified RECIST (mRECIST), and European Association for the Study of the Liver (EASL) guidelines. We assessed the concordance among response guidelines and selected the most reliable model depending upon the correlation with overall survival. RESULTS: Both objective response rates and disease control rates were higher for mRECIST and EASL than for WHO and RECIST 1.1 for both overall responses and target responses. The κ-value of comparisons between WHO and RECIST 1.1 and mRECIST and EASL was not more than 0.20 for both overall responses and target responses. There was consistency between WHO and RECIST 1.1 [κ=0.82, 95% confidence interval (CI): 0.72-0.91 for overall responses and κ=0.87; 95% CI, 0.76-0.94 for target responses], the same as that between mRECIST and EASL (κ=0.91, 95% CI, 0.73-0.98 for overall responses and κ=0.88, 95% CI, 0.72-0.95 for target responses). There was no significant association with survival for WHO and RECIST 1.1 responses or all target responses. The Cox-regression model showed that both mRECIST and EASL were independent predictors of overall survival, with a 51% risk reduction for mRECIST and a 61% risk reduction for EASL. CONCLUSION: The enhancement models including mRECIST and EASL guidelines should be used in preference to WHO, RECIST 1.1, or target responses to assess the efficacy of cryotherapy.
Authors: Muneeb Ahmed; Luigi Solbiati; Christopher L Brace; David J Breen; Matthew R Callstrom; J William Charboneau; Min-Hua Chen; Byung Ihn Choi; Thierry de Baère; Gerald D Dodd; Damian E Dupuy; Debra A Gervais; David Gianfelice; Alice R Gillams; Fred T Lee; Edward Leen; Riccardo Lencioni; Peter J Littrup; Tito Livraghi; David S Lu; John P McGahan; Maria Franca Meloni; Boris Nikolic; Philippe L Pereira; Ping Liang; Hyunchul Rhim; Steven C Rose; Riad Salem; Constantinos T Sofocleous; Stephen B Solomon; Michael C Soulen; Masatoshi Tanaka; Thomas J Vogl; Bradford J Wood; S Nahum Goldberg Journal: J Vasc Interv Radiol Date: 2014-10-23 Impact factor: 3.464
Authors: Bruno Vincenzi; Massimo Di Maio; Marianna Silletta; Loretta D'Onofrio; Chiara Spoto; Maria Carmela Piccirillo; Gennaro Daniele; Francesca Comito; Eliana Maci; Giuseppe Bronte; Antonio Russo; Daniele Santini; Francesco Perrone; Giuseppe Tonini Journal: PLoS One Date: 2015-07-31 Impact factor: 3.240