1
|
Perri G, Marchegiani G, Partelli S, Andreasi V, Luchini C, Bariani E, Bannone E, Fermi F, Mattiolo P, Falconi M, Salvia R, Bassi C. Either High or Low Risk: The Acinar Score at the Resection Margin Dichotomizes the Risk Spectrum of Pancreas-specific Complications After Pancreatoduodenectomy. Ann Surg 2023; 278:e1242-e1249. [PMID: 37325905 DOI: 10.1097/sla.0000000000005943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
BACKGROUND Pancreatic acinar content (Ac) has been associated with pancreas-specific complications after pancreatoduodenectomy. The aim of this study was to improve the prediction ability of intraoperative risk stratification by integrating the pancreatic acinar score. METHODS A training and validation cohort underwent pancreatoduodenectomy with a subsequent histologic assessment of pancreatic section margins for Ac, fibrosis (Fc), and fat. Intraoperative risk stratification (pancreatic texture, duct diameter) and pancreas-specific complications (postoperative hyperamylasemia [POH], postpancreatectomy acute pancreatitis [PPAP], pancreatic fistula [POPF]) were classified according to ISGPS definitions. RESULTS In the validation cohort (n= 373), the association of pancreas-specific complications with higher Ac and lower Fc was replicated (all P <0.001). In the entire cohort (n= 761), the ISGPS classification allocated 275 (36%) patients into intermediate-risk classes B (POH 32%/PPAP 3%/POPF 17%) and C (POH 36%/PPAP 9%/POPF 33%). Using the acinar score (Ac ≥60% and/or Fc ≤10%), intermediate-risk patients could be dichotomized into a low-risk (POH 5%/PPAP 1%/POPF 6%) and a high-risk (POH 51%/PPAP 9%/POPF 38%) group (all P <0.001). The acinar score AUC for POPF prediction was 0.70 in the ISGPS intermediate-risk classes. Overall, 239 (31%) patients were relocated into the high-risk group from lower ISGPS risk classes using the acinar score. CONCLUSIONS The risk of pancreas-specific complications appears to be dichotomous-either high or low-according to the acinar score, a tool to better target the application of mitigation strategies in cases of intermediate macroscopic features.
Collapse
Affiliation(s)
- Giampaolo Perri
- Department of General and Pancreatic Surgery, Verona University Hospital, Verona, Italy
| | - Giovanni Marchegiani
- Department of General and Pancreatic Surgery, Verona University Hospital, Verona, Italy
| | - Stefano Partelli
- Division of Pancreatic Surgery, Vita-Salute San Raffaele University, Milan, Italy
| | - Valentina Andreasi
- Division of Pancreatic Surgery, Vita-Salute San Raffaele University, Milan, Italy
| | - Claudio Luchini
- Division of Pathology, Verona University Hospital, Verona, Italy
| | - Elena Bariani
- Division of Pathology, Verona University Hospital, Verona, Italy
| | - Elisa Bannone
- Department of General and Pancreatic Surgery, Verona University Hospital, Verona, Italy
| | - Francesca Fermi
- Division of Pancreatic Surgery, Vita-Salute San Raffaele University, Milan, Italy
| | - Paola Mattiolo
- Division of Pathology, Verona University Hospital, Verona, Italy
| | - Massimo Falconi
- Division of Pancreatic Surgery, Vita-Salute San Raffaele University, Milan, Italy
| | - Roberto Salvia
- Department of General and Pancreatic Surgery, Verona University Hospital, Verona, Italy
| | - Claudio Bassi
- Department of General and Pancreatic Surgery, Verona University Hospital, Verona, Italy
| |
Collapse
|
2
|
Wagner M, Brandenburg JM, Bodenstedt S, Schulze A, Jenke AC, Stern A, Daum MTJ, Mündermann L, Kolbinger FR, Bhasker N, Schneider G, Krause-Jüttler G, Alwanni H, Fritz-Kebede F, Burgert O, Wilhelm D, Fallert J, Nickel F, Maier-Hein L, Dugas M, Distler M, Weitz J, Müller-Stich BP, Speidel S. Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data. Surg Endosc 2022; 36:8568-8591. [PMID: 36171451 PMCID: PMC9613751 DOI: 10.1007/s00464-022-09611-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/03/2022] [Indexed: 01/06/2023]
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.
Collapse
Affiliation(s)
- Martin Wagner
- 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.
| | - Johanna M Brandenburg
- 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
| | - Sebastian Bodenstedt
- 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
| | - André Schulze
- 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
| | - Alexander C Jenke
- Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
| | - Antonia Stern
- Corporate Research and Technology, Karl Storz SE & Co KG, Tuttlingen, Germany
| | - Marie T J Daum
- 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
| | - Lars Mündermann
- Corporate Research and Technology, Karl Storz SE & Co KG, Tuttlingen, Germany
| | - Fiona R Kolbinger
- 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
| | - Nithya Bhasker
- Department of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
| | - Gerd Schneider
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Grit Krause-Jüttler
- Department of Visceral-, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Hisham Alwanni
- Corporate Research and Technology, Karl Storz SE & Co KG, Tuttlingen, Germany
| | - Fleur Fritz-Kebede
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Oliver Burgert
- Research Group Computer Assisted Medicine (CaMed), Reutlingen University, Reutlingen, Germany
| | - Dirk Wilhelm
- Department of Surgery, Faculty of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Johannes Fallert
- Corporate Research and Technology, Karl Storz SE & Co KG, Tuttlingen, Germany
| | - Felix Nickel
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
| | - Lena Maier-Hein
- Department of Intelligent Medical Systems (IMSY), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin Dugas
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Marius Distler
- 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
| | - Jürgen Weitz
- 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
| | - Beat-Peter Müller-Stich
- 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
| | - Stefanie Speidel
- 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
| |
Collapse
|