Your browser doesn't support javascript.
loading
Artificial intelligence to assess body composition on routine abdominal CT scans and predict mortality in pancreatic cancer- A recipe for your local application.
Hsu, Tzu-Ming Harry; Schawkat, Khoschy; Berkowitz, Seth J; Wei, Jesse L; Makoyeva, Alina; Legare, Kaila; DeCicco, Corinne; Paez, S Nicolas; Wu, Jim S H; Szolovits, Peter; Kikinis, Ron; Moser, Arthur J; Goehler, Alexander.
Afiliación
  • Hsu TH; MIT Computer Science & Artificial Intelligence Laboratory, 32 Vassar St, Cambridge, MA 02139, United States.
  • Schawkat K; 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.
  • Berkowitz SJ; Department of Radiology, Beth-Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States.
  • Wei JL; Department of Radiology, Beth-Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States.
  • Makoyeva A; Department of Radiology, Beth-Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States.
  • Legare K; Department of Radiology, Beth-Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States.
  • DeCicco C; The Pancreas and Liver Institute, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States.
  • Paez SN; Department of Radiology, Beth-Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States.
  • Wu JSH; Department of Radiology, Beth-Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States.
  • Szolovits P; MIT Computer Science & Artificial Intelligence Laboratory, 32 Vassar St, Cambridge, MA 02139, United States.
  • Kikinis R; Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, 75 Francis St. Boston, MA 02215, United States.
  • Moser AJ; The Pancreas and Liver Institute, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, United States.
  • Goehler A; 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
Eur J Radiol ; 142: 109834, 2021 Sep.
Article en En | MEDLINE | ID: mdl-34252866
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.
Asunto(s)
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Pancreáticas / Sarcopenia Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Eur J Radiol Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Pancreáticas / Sarcopenia Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Eur J Radiol Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos