Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data.

Autor: Wagner M; Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany. martin.wagner@med.uni-heidelberg.de.; National Center for Tumor Diseases (NCT), Heidelberg, Germany. martin.wagner@med.uni-heidelberg.de., Brandenburg JM; Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.; National Center for Tumor Diseases (NCT), Heidelberg, Germany., Bodenstedt S; Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany.; Cluster of Excellence 'Centre for Tactile Internet with Human-in-the-Loop' (CeTI), Technische Universität Dresden, 01062, Dresden, Germany., Schulze A; Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.; National Center for Tumor Diseases (NCT), Heidelberg, Germany., Jenke AC; Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany., Stern A; Corporate Research and Technology, Karl Storz SE & Co KG, Tuttlingen, Germany., Daum MTJ; Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.; National Center for Tumor Diseases (NCT), Heidelberg, Germany., Mündermann L; Corporate Research and Technology, Karl Storz SE & Co KG, Tuttlingen, Germany., Kolbinger FR; Department of Visceral-, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.; Else Kröner Fresenius Center for Digital Health, Technische Universität Dresden, Dresden, Germany., Bhasker N; Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany., Schneider G; Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany., Krause-Jüttler G; Department of Visceral-, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany., Alwanni H; Corporate Research and Technology, Karl Storz SE & Co KG, Tuttlingen, Germany., Fritz-Kebede F; Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany., Burgert O; Research Group Computer Assisted Medicine (CaMed), Reutlingen University, Reutlingen, Germany., Wilhelm D; Department of Surgery, Faculty of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany., Fallert J; Corporate Research and Technology, Karl Storz SE & Co KG, Tuttlingen, Germany., Nickel F; Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany., Maier-Hein L; Department of Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany., Dugas M; Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany., Distler M; Department of Visceral-, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.; National Center for Tumor Diseases (NCT/UCC), Dresden, Germany.; German Cancer Research Center (DKFZ), Heidelberg, Germany.; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.; Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany., Weitz J; Department of Visceral-, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.; National Center for Tumor Diseases (NCT/UCC), Dresden, Germany.; German Cancer Research Center (DKFZ), Heidelberg, Germany.; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.; Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany., Müller-Stich BP; Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.; National Center for Tumor Diseases (NCT), Heidelberg, Germany., Speidel S; Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany.; Cluster of Excellence 'Centre for Tactile Internet with Human-in-the-Loop' (CeTI), Technische Universität Dresden, 01062, Dresden, Germany.
Jazyk: angličtina
Zdroj: Surgical endoscopy [Surg Endosc] 2022 Nov; Vol. 36 (11), pp. 8568-8591. Date of Electronic Publication: 2022 Sep 28.
DOI: 10.1007/s00464-022-09611-1
Abstrakt: Background: Personalized medicine requires the integration and analysis of vast amounts of patient data to realize individualized care. With Surgomics, we aim to facilitate personalized therapy recommendations in surgery by integration of intraoperative surgical data and their analysis with machine learning methods to leverage the potential of this data in analogy to Radiomics and Genomics.
Methods: We defined Surgomics as the entirety of surgomic features that are process characteristics of a surgical procedure automatically derived from multimodal intraoperative data to quantify processes in the operating room. In a multidisciplinary team we discussed potential data sources like endoscopic videos, vital sign monitoring, medical devices and instruments and respective surgomic features. Subsequently, an online questionnaire was sent to experts from surgery and (computer) science at multiple centers for rating the features' clinical relevance and technical feasibility.
Results: In total, 52 surgomic features were identified and assigned to eight feature categories. Based on the expert survey (n = 66 participants) the feature category with the highest clinical relevance as rated by surgeons was "surgical skill and quality of performance" for morbidity and mortality (9.0 ± 1.3 on a numerical rating scale from 1 to 10) as well as for long-term (oncological) outcome (8.2 ± 1.8). The feature category with the highest feasibility to be automatically extracted as rated by (computer) scientists was "Instrument" (8.5 ± 1.7). Among the surgomic features ranked as most relevant in their respective category were "intraoperative adverse events", "action performed with instruments", "vital sign monitoring", and "difficulty of surgery".
Conclusion: Surgomics is a promising concept for the analysis of intraoperative data. Surgomics may be used together with preoperative features from clinical data and Radiomics to predict postoperative morbidity, mortality and long-term outcome, as well as to provide tailored feedback for surgeons.
(© 2022. The Author(s).)
Databáze: MEDLINE
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