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

Martin Wagner; Johanna M Brandenburg; Sebastian Bodenstedt; André Schulze; Alexander C Jenke; Antonia Stern; Marie T J Daum; Lars Mündermann; Fiona R Kolbinger; Nithya Bhasker; Gerd Schneider; Grit Krause-Jüttler; Hisham Alwanni; Fleur Fritz-Kebede; Oliver Burgert; Dirk Wilhelm; Johannes Fallert; Felix Nickel; Lena Maier-Hein; Martin Dugas; Marius Distler; Jürgen Weitz; Beat-Peter Müller-Stich; Stefanie Speidel

doi:10.1007/s00464-022-09611-1

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

Surg Endosc. 2022 Nov;36(11):8568-8591. doi: 10.1007/s00464-022-09611-1. Epub 2022 Sep 28.

Authors

Martin Wagner^{1

2}, Johanna M Brandenburg^{3

4}, Sebastian Bodenstedt^{5

6}, André Schulze^{3

4}, Alexander C Jenke⁵, Antonia Stern⁷, Marie T J Daum^{3

4}, Lars Mündermann⁷, Fiona R Kolbinger^{8

9}, Nithya Bhasker⁵, Gerd Schneider¹⁰, Grit Krause-Jüttler⁸, Hisham Alwanni⁷, Fleur Fritz-Kebede¹⁰, Oliver Burgert¹¹, Dirk Wilhelm¹², Johannes Fallert⁷, Felix Nickel³, Lena Maier-Hein¹³, Martin Dugas¹⁰, Marius Distler^{8

14

15

16

17}, Jürgen Weitz^{8

14

15

16

17}, Beat-Peter Müller-Stich^{3

4}, Stefanie Speidel^{5

6}

Affiliations

¹ Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany. martin.wagner@med.uni-heidelberg.de.
² National Center for Tumor Diseases (NCT), Heidelberg, Germany. martin.wagner@med.uni-heidelberg.de.
³ Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.
⁴ National Center for Tumor Diseases (NCT), Heidelberg, Germany.
⁵ Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany.
⁶ Cluster of Excellence "Centre for Tactile Internet with Human-in-the-Loop" (CeTI), Technische Universität Dresden, 01062, Dresden, Germany.
⁷ Corporate Research and Technology, Karl Storz SE & Co KG, Tuttlingen, Germany.
⁸ Department of Visceral-, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
⁹ Else Kröner Fresenius Center for Digital Health, Technische Universität Dresden, Dresden, Germany.
¹⁰ Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany.
¹¹ Research Group Computer Assisted Medicine (CaMed), Reutlingen University, Reutlingen, Germany.
¹² Department of Surgery, Faculty of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
¹³ Department of Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany.
¹⁴ National Center for Tumor Diseases (NCT/UCC), Dresden, Germany.
¹⁵ German Cancer Research Center (DKFZ), Heidelberg, Germany.
¹⁶ Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
¹⁷ Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany.

Abstract

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.

Keywords: Artificial intelligence; Minimally invasive surgery; Precision medicine; Prediction model; Radiomics; Surgical data science.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Humans
Machine Learning*
Morbidity
Surgeons*