Artificial intelligence to assess body composition on routine abdominal CT scans and predict mortality in pancreatic cancer- A recipe for your local application
| Autor: | Jim S. Wu, Khoschy Schawkat, Alexander Goehler, Kaila Legare, A.J. Moser, S. Nicolas Paez, Tzu-Ming Harry Hsu, Seth A. Berkowitz, Peter Szolovits, Jesse Wei, Ron Kikinis, Alina Makoyeva, Corinne Decicco |
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| Rok vydání: | 2020 |
| Předmět: |
Male
Sarcopenia Liver tumor Intraclass correlation Abdominal ct 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Artificial Intelligence Pancreatic cancer medicine Humans Radiology Nuclear Medicine and imaging Muscle Skeletal Aged Training set business.industry Proportional hazards model General Medicine Middle Aged medicine.disease Pancreatic Neoplasms 030220 oncology & carcinogenesis Cohort Body Composition Female Artificial intelligence business Tomography X-Ray Computed |
| Zdroj: | European journal of radiology. 142 |
| ISSN: | 1872-7727 |
| Popis: | 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 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. |
| Databáze: | OpenAIRE |
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