TY - JOUR
T1 - Radiomics preoperative-Fistula Risk Score (RAD-FRS) for pancreatoduodenectomy
T2 - development and external validation
AU - Ingwersen, Erik W.
AU - Bereska, Jacqueline I.
AU - Balduzzi, Alberto
AU - Janssen, Boris V.
AU - Besselink, Marc G.
AU - Kazemier, Geert
AU - Marchegiani, Giovanni
AU - Malleo, Giuseppe
AU - Marquering, Henk A.
AU - Nio, C. Yung
AU - de Robertis, Riccardo
AU - Salvia, Roberto
AU - Pancreatobiliary and Hepatic Artificial Intelligence Research (PHAIR) consortium
AU - Steyerberg, Ewout W.
AU - Stoker, Jaap
AU - Struik, Femke
AU - Verpalen, Inez M.
AU - Daams, Freek
N1 - Funding Information: E.W.I. and J.I.B. contributed equally to this work. I.M.V. and F.D. share last authorship. Publisher Copyright: © 2023 John Wiley and Sons Inc.. All rights reserved.
PY - 2023/10/1
Y1 - 2023/10/1
N2 - Background: Accurately predicting the risk of clinically relevant postoperative pancreatic fistula after pancreatoduodenectomy before surgery may assist surgeons in making more informed treatment decisions and improved patient counselling. The aim was to evaluate the predictive accuracy of a radiomics-based preoperative-Fistula Risk Score (RAD-FRS) for clinically relevant postoperative pancreatic fistula. Methods: Radiomic features were derived from preoperative CT scans from adult patients after pancreatoduodenectomy at a single centre in the Netherlands (Amsterdam, 2013–2018) to develop the radiomics-based preoperative-Fistula Risk Score. Extracted radiomic features were analysed with four machine learning classifiers. The model was externally validated in a single centre in Italy (Verona, 2020–2021). The radiomics-based preoperative-Fistula Risk Score was compared with the Fistula Risk Score and the updated alternative Fistula Risk Score. Results: Overall, 359 patients underwent a pancreatoduodenectomy, of whom 89 (25 per cent) developed a clinically relevant postoperative pancreatic fistula. The radiomics-based preoperative-Fistula Risk Score model was developed using CT scans of 118 patients, of which three radiomic features were included in the random forest model, and externally validated in 57 patients. The model performed well with an area under the curve of 0.90 (95 per cent c.i. 0.71 to 0.99) and 0.81 (95 per cent c.i. 0.69 to 0.92) in the Amsterdam test set and Verona data set respectively. The radiomics-based preoperative-Fistula Risk Score performed similarly to the Fistula Risk Score (area under the curve 0.79) and updated alternative Fistula Risk Score (area under the curve 0.79). Conclusion: The radiomics-based preoperative-Fistula Risk Score, which uses only preoperative CT features, is a new and promising radiomics-based score that has the potential to be integrated with hospital CT report systems and improve patient counselling before surgery. The model with underlying code is readily available via www.pancreascalculator.com and www.github.com/PHAIRConsortium/POPF-predictor.
AB - Background: Accurately predicting the risk of clinically relevant postoperative pancreatic fistula after pancreatoduodenectomy before surgery may assist surgeons in making more informed treatment decisions and improved patient counselling. The aim was to evaluate the predictive accuracy of a radiomics-based preoperative-Fistula Risk Score (RAD-FRS) for clinically relevant postoperative pancreatic fistula. Methods: Radiomic features were derived from preoperative CT scans from adult patients after pancreatoduodenectomy at a single centre in the Netherlands (Amsterdam, 2013–2018) to develop the radiomics-based preoperative-Fistula Risk Score. Extracted radiomic features were analysed with four machine learning classifiers. The model was externally validated in a single centre in Italy (Verona, 2020–2021). The radiomics-based preoperative-Fistula Risk Score was compared with the Fistula Risk Score and the updated alternative Fistula Risk Score. Results: Overall, 359 patients underwent a pancreatoduodenectomy, of whom 89 (25 per cent) developed a clinically relevant postoperative pancreatic fistula. The radiomics-based preoperative-Fistula Risk Score model was developed using CT scans of 118 patients, of which three radiomic features were included in the random forest model, and externally validated in 57 patients. The model performed well with an area under the curve of 0.90 (95 per cent c.i. 0.71 to 0.99) and 0.81 (95 per cent c.i. 0.69 to 0.92) in the Amsterdam test set and Verona data set respectively. The radiomics-based preoperative-Fistula Risk Score performed similarly to the Fistula Risk Score (area under the curve 0.79) and updated alternative Fistula Risk Score (area under the curve 0.79). Conclusion: The radiomics-based preoperative-Fistula Risk Score, which uses only preoperative CT features, is a new and promising radiomics-based score that has the potential to be integrated with hospital CT report systems and improve patient counselling before surgery. The model with underlying code is readily available via www.pancreascalculator.com and www.github.com/PHAIRConsortium/POPF-predictor.
UR - http://www.scopus.com/inward/record.url?scp=85175146510&partnerID=8YFLogxK
U2 - https://doi.org/10.1093/bjsopen/zrad100
DO - https://doi.org/10.1093/bjsopen/zrad100
M3 - Article
C2 - 37811791
SN - 2474-9842
VL - 7
JO - BJS Open
JF - BJS Open
IS - 5
M1 - zrad100
ER -