Radiomics preoperative-Fistula Risk Score (RAD-FRS) for pancreatoduodenectomy: development and external validation

Abstract

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/PHAIR-Consortium/POPF-predictor.

Response to: Comment on “Risk Models for Developing Pancreatic Fistula After Pancreatoduodenectomy: Validation in a Nationwide Prospective Cohort”

“…For instance, an international study recently developed a novel fistula risk score based on the radiomic features of preoperative CT scans using machine learning techniques (radiomics-based preoperative-Fistula Risk Score). 4 The model demonstrated good performance both internally (area under the curve 0.90) and externally (area under the curve 0.81) with good calibration. In contrast to the models that we previously described in our study that also used radiomic features to predict the occurrence of pancreatic fistula, the RAD-FRS model was externally validated.…”

Optimizing prediction models for pancreatic fistula after pancreatectomy: Current status and future perspectives

“…However, certain imaging parameters require external software for preoperative evaluation, which poses challenges in terms of accessibility, standardization, and compatibility with different imaging systems, as well as external validation for these models. Additionally, the past three years have witnessed the development of over 10 POPF prediction models based on machine learning algorithms (Table 5 )[ 22 , 48 , 54 - 64 ]. While these models are often considered superior to traditional regression models, it is important to highlight that a recent study revealed machine learning did not outperform logistic regression in predicting POPF after PD[ 22 ].…”

Artificial intelligence in surgery

“…The prediction of complications after surgery has been the goal of many academic studies 139,140 and presents a formidable challenge in a complex postoperative setting, with myriad variables affecting care and outcomes. However, the early detection of complications 138,141 -in particular, devastating outcomes such as anastomotic leaks after rectal cancer surgery and postoperative pancreatic fistulas after pancreatic surgery 142,143 -is likely to have a substantial impact on the ability of healthcare systems to reduce mortality following complications 144,145 . MySurgeryRisk represents one of the few advances in complication prediction, using a machine learning algorithm 50 .…”