TY - JOUR
T1 - Body composition features predict overall survival in patients with hepatocellular carcinoma
AU - Singal, Amit G.
AU - Zhang, Peng
AU - Waljee, Akbar K.
AU - Ananthakrishnan, Lakshmi
AU - Parikh, Neehar D.
AU - Sharma, Pratima
AU - Barman, Pranab
AU - Krishnamurthy, Venkataramu
AU - Wang, Lu
AU - Wang, Stewart C.
AU - Su, Grace L.
N1 - Funding Information:
Guarantor of the article: Grace L. Su, MD. Specific author contributions: Singal AG: data collection, interpretation of data, drafting of manuscript, and critical revision for intellectual concept; Zhang P: data analysis, interpretation of data, drafting of manuscript, and critical revision for intellectual concept; Waljee AK: data analysis, interpretation of data, and critical revision for intellectual concept; Ananthakrishnan L: data collection and critical revision for intellectual concept; Parikh ND: interpretation of data and critical revision for intellectual concept; Sharma P: interpretation of data and critical revision for intellectual concept; Barman P: data collection and critical revision for intellectual concept; Krishnamurthy V: data collection and critical revision for intellectual concept; Wang L: data analysis and critical revision for intellectual concept; Wang SC: study concept and critical revision for intellectual concept; Su GL: study concept, data collection, data analysis, interpretation of data, drafting of manuscript, and critical revision for intellectual concept. Financial support: Singal was supported in part by the AHRQ Center for Patient-Centered Outcomes Research (R24 HS022418). Waljee’s research is funded by a VA HSR&D CDA-2 Career Development Award 1IK2HX000775. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality or the VA. Potential competing interests: None.
Publisher Copyright:
© 2016 Lippincott Williams and Wilkins. All rights reserved.
PY - 2016/5/1
Y1 - 2016/5/1
N2 - OBJECTIVES: Existing prognostic models for patients with hepatocellular carcinoma (HCC) have limitations. Analytic morphomics, a novel process to measure body composition using computational image-processing algorithms, may offer further prognostic information. The aim of this study was to develop and validate a prognostic model for HCC patients using body composition features and objective clinical information. METHODS: Using computed tomography scans from a cohort of HCC patients at the VA Ann Arbor Healthcare System between January 2006 and December 2013, we developed a prognostic model using analytic morphomics and routine clinical data based on multivariate Cox regression and regularization methods. We assessed model performance using C-statistics and validated predicted survival probabilities. We validated model performance in an external cohort of HCC patients from Parkland Hospital, a safety-net health system in Dallas County. RESULTS: The derivation cohort consisted of 204 HCC patients (20.1% Barcelona Clinic Liver Cancer classification (BCLC) 0/A), and the validation cohort had 225 patients (22.2% BCLC 0/A). The analytic morphomics model had good prognostic accuracy in the derivation cohort (C-statistic 0.80, 95% confidence interval (CI) 0.71 0.89) and external validation cohort (C-statistic 0.75, 95% CI 0.68 0.82). The accuracy of the analytic morphomics model was significantly higher than that of TNM and BCLC staging systems in derivation (Po0.001 for both) and validation (Po0.001 for both) cohorts. For calibration, mean absolute errors in predicted 1-year survival probabilities were 5.3% (90% quantile of 7.5%) and 7.6% (90% quantile of 12.5%) in the derivation and validation cohorts, respectively. CONCLUSION: Body composition features, combined with readily available clinical data, can provide valuable prognostic information for patients with newly diagnosed HCC.
AB - OBJECTIVES: Existing prognostic models for patients with hepatocellular carcinoma (HCC) have limitations. Analytic morphomics, a novel process to measure body composition using computational image-processing algorithms, may offer further prognostic information. The aim of this study was to develop and validate a prognostic model for HCC patients using body composition features and objective clinical information. METHODS: Using computed tomography scans from a cohort of HCC patients at the VA Ann Arbor Healthcare System between January 2006 and December 2013, we developed a prognostic model using analytic morphomics and routine clinical data based on multivariate Cox regression and regularization methods. We assessed model performance using C-statistics and validated predicted survival probabilities. We validated model performance in an external cohort of HCC patients from Parkland Hospital, a safety-net health system in Dallas County. RESULTS: The derivation cohort consisted of 204 HCC patients (20.1% Barcelona Clinic Liver Cancer classification (BCLC) 0/A), and the validation cohort had 225 patients (22.2% BCLC 0/A). The analytic morphomics model had good prognostic accuracy in the derivation cohort (C-statistic 0.80, 95% confidence interval (CI) 0.71 0.89) and external validation cohort (C-statistic 0.75, 95% CI 0.68 0.82). The accuracy of the analytic morphomics model was significantly higher than that of TNM and BCLC staging systems in derivation (Po0.001 for both) and validation (Po0.001 for both) cohorts. For calibration, mean absolute errors in predicted 1-year survival probabilities were 5.3% (90% quantile of 7.5%) and 7.6% (90% quantile of 12.5%) in the derivation and validation cohorts, respectively. CONCLUSION: Body composition features, combined with readily available clinical data, can provide valuable prognostic information for patients with newly diagnosed HCC.
UR - http://www.scopus.com/inward/record.url?scp=85015927111&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015927111&partnerID=8YFLogxK
U2 - 10.1038/ctg.2016.31
DO - 10.1038/ctg.2016.31
M3 - Article
C2 - 27228403
AN - SCOPUS:85015927111
SN - 2155-384X
VL - 7
JO - Clinical and translational gastroenterology
JF - Clinical and translational gastroenterology
IS - 5
M1 - e172
ER -