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Bektaş et al. Art Int Surg 2022;2:132-43 https://dx.doi.org/10.20517/ais.2022.20 Page 138

Liver surgery
Twenty-one studies have developed ML algorithms to predict the course of disease in patients that
underwent hepatectomy for malignancies [22-42] . ML models have shown AUCs between 0.63 and 0.99 for
predicting the course of disease, whereas accuracies have been demonstrated to range from 73% to 99%.
Eight studies have applied ML to predict postoperative liver function and complications in patients that
underwent hepatectomy [43-50] . In predicting postoperative liver function and complications, ML models have
demonstrated AUCs ranging from 0.63 to 0.89, and accuracies between 73% and 89% have also been
reported. Four studies have used ML to determine predictors and clusters for HCC and ICC patients [51-54] . By
using ML, the following significant predictors have been found for the survival of HCC and ICC patients:
alpha-fetoprotein, lymphovascular invasion, tumor burden score, tumor number, tumor size, albumin-
bilirubin grade, CA 19-9 levels, and neutrophil levels. For CRLM, lymph node metastasis, metastasis size,
[55]
and carcinoembryonic antigen (CEA) levels appeared to be the key predictors for survival .

Biliary surgery
Three studies have developed ML models to predict intraoperative conversions and complexities [56-58] .
Intraoperative conversions and complexities have been predicted by ML algorithms with accuracies between
83% and 89%. Two studies applied ML algorithms to predict gallstones and related diseases, in which ML
models have shown AUCs from 0.85 to 0.94, along with accuracies up to 97% [59,60] . In addition, Shi et al.
applied ML algorithms to predict the postoperative quality of life in patients with gallstones . A mean
[61]
absolute percentage error of 7.2% and 8.5% was demonstrated, in which a value lower than 10% was
considered accurate.

Pancreatic surgery
Five studies have developed ML models to predict the course of disease in patients with pancreas
carcinomas who received pancreatectomy procedures [62-66] . AUCs of ML models in predicting the course of
disease have ranged from 0.61 to 0.92, and accuracies have been reported to range between 71% and 98%.
Additionally, five studies have developed ML algorithms to predict postoperative complications after
pancreatic surgery [67-71] . For predicting postoperative complications, ML algorithms have demonstrated
AUCs between 0.67 and 0.85, whereas accuracies have varied from 75% to 85%. Two studies have trained
ML models to diagnose IPMN in patients that underwent pancreatectomy, in which IPMN’s were
diagnosed with AUCs of 0.79 and 0.98 [72,73] .

DISCUSSION
This review provides an overview of ML applications within HPB surgery. Several ML models have been
applied within HPB surgery, in which Neural Networks and Radiomics have been used most frequently.
Machine Learning has predominantly been demonstrated for predicting the course of disease, and
postoperative complications. Neural Networks have shown the highest predictive performance based on the
mean accuracy of 88%. The findings of this study suggest that ML algorithms have promising capacities for
patients undergoing HPB surgery.

In predicting the course of disease for patients with HPB malignancies, accuracies of ML models have varied
between 61% and 99%. As a comparison, regression models have predicted similar outcomes with accuracies
up to 82% [74,75] . For years, HPB surgeons have experienced difficulties in treatment strategies for HPB
cancer [76,77] . Multiple clinical trials are conducted to develop optimal treatment strategies to improve patient
[78]
outcomes after surgery . By using ML to predict metastasis and response to chemotherapy, HPB surgeons
could decide to tailor surgery or chemotherapy to patients that could optimally benefit from these
treatments.