Tzu-Ming Harry Hsu1, Khoschy Schawkat2, Seth J Berkowitz3, Jesse L Wei3, Alina Makoyeva3, Kaila Legare3, Corinne DeCicco4, S Nicolas Paez3, Jim S H Wu3, Peter Szolovits1, Ron Kikinis5, Arthur J Moser4, Alexander Goehler6. 1. MIT Computer Science & Artificial Intelligence Laboratory, 32 Vassar St, Cambridge, MA 02139, United States. 2. Department of Radiology, Beth-Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States; Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland. 3. Department of Radiology, Beth-Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States. 4. The Pancreas and Liver Institute, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States. 5. Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, 75 Francis St. Boston, MA 02215, United States. 6. MIT Computer Science & Artificial Intelligence Laboratory, 32 Vassar St, Cambridge, MA 02139, United States; Center for Evidence Based Imaging, Department of Radiology, Brigham and Women's Hospital, 20 Kent Street, Brookline, MA 02445, United States. Electronic address: agoehler@post.harvard.edu.
Abstract
BACKGROUND: Body composition is associated with mortality; however its routine assessment is too time-consuming. PURPOSE: To demonstrate the value of artificial intelligence (AI) to extract body composition measures from routine studies, we aimed to develop a fully automated AI approach to measure fat and muscles masses, to validate its clinical discriminatory value, and to provide the code, training data and workflow solutions to facilitate its integration into local practice. METHODS: We developed a neural network that quantified the tissue components at the L3 vertebral body level using data from the Liver Tumor Challenge (LiTS) and a pancreatic cancer cohort. We classified sarcopenia using accepted skeletal muscle index cut-offs and visceral fat based its median value. We used Kaplan Meier curves and Cox regression analysis to assess the association between these measures and mortality. RESULTS: Applying the algorithm trained on LiTS data to the local cohort yielded good agreement [>0.8 intraclass correlation (ICC)]; when trained on both datasets, it had excellent agreement (>0.9 ICC). The pancreatic cancer cohort had 136 patients (mean age: 67 ± 11 years; 54% women); 15% had sarcopenia; mean visceral fat was 142 cm2. Concurrent with prior research, we found a significant association between sarcopenia and mortality [mean survival of 15 ± 12 vs. 22 ± 12 (p < 0.05), adjusted HR of 1.58 (95% CI: 1.03-3.33)] but no association between visceral fat and mortality. The detector analysis took 1 ± 0.5 s. CONCLUSIONS: AI body composition analysis can provide meaningful imaging biomarkers from routine exams demonstrating AI's ability to further enhance the clinical value of radiology reports.
BACKGROUND: Body composition is associated with mortality; however its routine assessment is too time-consuming. PURPOSE: To demonstrate the value of artificial intelligence (AI) to extract body composition measures from routine studies, we aimed to develop a fully automated AI approach to measure fat and muscles masses, to validate its clinical discriminatory value, and to provide the code, training data and workflow solutions to facilitate its integration into local practice. METHODS: We developed a neural network that quantified the tissue components at the L3 vertebral body level using data from the Liver Tumor Challenge (LiTS) and a pancreatic cancer cohort. We classified sarcopenia using accepted skeletal muscle index cut-offs and visceral fat based its median value. We used Kaplan Meier curves and Cox regression analysis to assess the association between these measures and mortality. RESULTS: Applying the algorithm trained on LiTS data to the local cohort yielded good agreement [>0.8 intraclass correlation (ICC)]; when trained on both datasets, it had excellent agreement (>0.9 ICC). The pancreatic cancer cohort had 136 patients (mean age: 67 ± 11 years; 54% women); 15% had sarcopenia; mean visceral fat was 142 cm2. Concurrent with prior research, we found a significant association between sarcopenia and mortality [mean survival of 15 ± 12 vs. 22 ± 12 (p < 0.05), adjusted HR of 1.58 (95% CI: 1.03-3.33)] but no association between visceral fat and mortality. The detector analysis took 1 ± 0.5 s. CONCLUSIONS: AI body composition analysis can provide meaningful imaging biomarkers from routine exams demonstrating AI's ability to further enhance the clinical value of radiology reports.
Authors: Nick Lasse Beetz; Dominik Geisel; Christoph Maier; Timo Alexander Auer; Seyd Shnayien; Thomas Malinka; Christopher Claudius Maximilian Neumann; Uwe Pelzer; Uli Fehrenbach Journal: J Clin Med Date: 2022-04-22 Impact factor: 4.964
Authors: Alexander Goehler; Tzu-Ming Harry Hsu; Jacqueline A Seiglie; Mark J Siedner; Janet Lo; Virginia Triant; John Hsu; Andrea Foulkes; Ingrid Bassett; Ramin Khorasani; Deborah J Wexler; Peter Szolovits; James B Meigs; Jennifer Manne-Goehler Journal: Open Forum Infect Dis Date: 2021-05-28 Impact factor: 3.835