Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study

Wang, Jitao; Zheng, Tao; Liao, Yong; Geng, Shi; Li, Jinlong; Zhang, Zhanguo; Shang, Dong; Liu, Chengyu; Yu, Peng; Huang, Y.; Liu, Chuan; Liu, Yanna; Liu, Shanghao; Wang, Mingguang; Liu, Dengxiang; Miao, Hongrui; Li, Shuang; Zhang, Biao; Huang, Anliang; Zhang, Yewei; Qi, Xiaolong; Chen, Shubo

doi:10.3389/fonc.2022.986867

Cited by 14 publications

(13 citation statements)

References 43 publications

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“…In hepatobiliary surgery, different groups have successfully developed models to improve perioperative management of patients with hepatocellular carcinoma and predict both complications and recurrence patterns [ 17 ]. For example, to predict the risk of posthepatectomy liver failure, several artificial neural network models to help surgeons identify those patients at intermediate and high risk have been described [ 18 ]. Our group defined a risk-scoring model useful to estimate the patients’ level of risk based on the initial presentation and bile duct injury type and detailed how the patient’s risk category may be used to determine the appropriate management [ 19 ].…”

Section: Discussionmentioning

confidence: 99%

Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study

Lopez-Lopez,

Morise,

Albaladejo-González

et al. 2024

Surg Endosc

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Background Artificial intelligence (AI) is becoming more useful as a decision-making and outcomes predictor tool. We have developed AI models to predict surgical complexity and the postoperative course in laparoscopic liver surgery for segments 7 and 8. Methods We included patients with lesions located in segments 7 and 8 operated by minimally invasive liver surgery from an international multi-institutional database. We have employed AI models to predict surgical complexity and postoperative outcomes. Furthermore, we have applied SHapley Additive exPlanations (SHAP) to make the AI models interpretable. Finally, we analyzed the surgeries not converted to open versus those converted to open. Results Overall, 585 patients and 22 variables were included. Multi-layer Perceptron (MLP) showed the highest performance for predicting surgery complexity and Random Forest (RF) for predicting postoperative outcomes. SHAP detected that MLP and RF gave the highest relevance to the variables “resection type” and “largest tumor size” for predicting surgery complexity and postoperative outcomes. In addition, we explored between surgeries converted to open and non-converted, finding statistically significant differences in the variables “tumor location,” “blood loss,” “complications,” and “operation time.” Conclusion We have observed how the application of SHAP allows us to understand the predictions of AI models in surgical complexity and the postoperative outcomes of laparoscopic liver surgery in segments 7 and 8.

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Section: Discussionmentioning

confidence: 99%

Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study

Lopez-Lopez,

Morise,

Albaladejo-González

et al. 2024

Surg Endosc

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“…Wang et al constructed a machine learning clinical model using laboratory values, tumor characteristics, and surgical variables (e.g., surgical approach, extent of resection, intraoperative blood loss) to predict the risk of PHLF. The model outperformed traditional models such as MELD, Child-Turcotte-Pugh, or albumin-bilirubin grade when predicting PHLF [52]. AI-derived algorithms have successfully predicted other surgical complications.…”

Section: Surgical Complicationsmentioning

confidence: 96%

Artificial Intelligence, Machine Learning, and Deep Learning in the Diagnosis and Management of Hepatocellular Carcinoma

Larrain,

Torres-Hernandez,

Hewitt

2024

Livers

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Artificial Intelligence (AI) can be a useful tool in the management of disease processes such as hepatocellular carcinoma (HCC) as treatment decisions are often complex and multifaceted. AI applications in medicine are expanding with the ongoing advances in AI including more sophisticated machine learning and deep learning processes. In preliminary studies, AI algorithms have demonstrated superiority in predicting the development of HCC compared with standard models. Radiomics, a quantitative method used to extract features from medical imaging, has been applied to numerous liver imaging modalities to aid in the diagnosis and prognostication of HCC. Deep learning methodologies can help us to identify patients at higher likelihood of disease progression and improve risk stratification. AI applications have expanded into the field of surgery as models not only help us to predict surgical outcomes but AI methodologies are also used intra-operatively, in real time, to help us to define anatomic structures and aid in the resection of complex lesions. In this review, we discuss promising applications of AI in the management of HCC. While further clinical validation is warranted to improve generalizability through the inclusion of larger and more diverse populations, AI is expected to play a central role in assisting clinicians with the management of complex disease processes such as HCC.

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“…recently reported a multicenter study showing that the AUC of LightGBM was 0.944, 0.870, and 0.822 for predicting PHLF on training set, validation set, and test set, respectively, and the AUCs were higher than those of traditional clinical prediction models [15] . However, they included only one ML model and did not uniformly compare other models, and the best model for patients with PHLF has not been reported.…”

Section: Introductionmentioning

confidence: 94%

Comparison of traditional machine learning algorithms for predicting post-hepatectomy liver failure in hepatocellular carcinoma

Chen

Zhou

Zhang

2023

Preprint

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Objective: Post-hepatectomy liver failure (PHLF) is a terrible and serious complication after liver resection. Machine learning algorithms are emerging for data mining in recent years and have been shown to have greater advantages over traditional statistics. This study includes a comparison of different traditional machine learning algorithms and selects the best model for predicting PHLF. Materials and Methods:Review the data of patients who had undergone resection of hepatocellular carcinoma from January 2013 to October 2022 in our hospital and randomly divide the data into a training set and a validation set at a 7:3 ratio. Using mutual information to screen 10 clinical characteristics with a higher correlation to PHLF. The data was trained and validated using Logistic Regression, Decision Tree, Gradient Boosting Decision Tree(GBDT), Random Forest, Extreme Gradient Boosting(XGBoost), LightGBM, multi-model fusion(hard voting), and multi-model fusion(soft voting). The hyperparameter of different machine learning was searched to achieve the best-fitting performance. Different traditional machine learning algorithms are evaluated comprehensively through accuracy rate, precision rate, recall rate, F1 score, and Receiver Operating Characteristic (ROC) and its area under the curve(AUC). Based on the feature importance ranking of the best model, clinical characteristics related to PHLF were ranked. Results: A total of 319 patients’ data were included in this study, with 9.4% of the patients in the liver failure group(n=30). 10 clinical characteristics with higher correlation to PHLF are preoperative platelet count, preoperative prothrombin time, perioperative blood loss, perioperative transfusion(Yes/No), duration of surgery, clinically significant portal hypertension(Yes/No), preoperative aspartate aminotransferase, preoperative albumin, preoperative total bilirubin, and type of resection(minor/major). XGBoost and LightGBM showed the best performance on training set with an accuracy rate of 1. However, their performance decreased on validation set with an accuracy rate of 0.9375 and 0.9167, respectively. GBDT had the best anti-fitting performance in the training and validation sets, with an accuracy rate of 0.9462 and 0.9479, respectively. Preoperative albumin, perioperative blood loss, preoperative platelet count, duration of surgery, and preoperative alanine transaminase had higher weights in GBDT. The accuracy rate of the multi-model fusion(hard voting) was 0.9955 and 0.9583 in the training and validation cohort, respectively, while the accuracy rate of the multi-model fusion(soft voting) was 0.9731 and 0.9479 on training set and validation set, respectively. Conclusion: GBDT performed the best among different traditional machine learning algorithms, and XGBoost and LightGBM still have great potential. Both multi-model fusion(hard voting) and multi-model fusion(soft voting) have improved the anti-fitting performance to some extent. Preoperative albumin, perioperative blood loss, preoperative platelet count, duration of surgery, and preoperative aspartate aminotransferase are the five most important clinical characteristics. Retrospectively registered:Ethics Y(2022)130; 2022/09/17

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Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study

Cited by 14 publications

References 43 publications

Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study

Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study

Artificial Intelligence, Machine Learning, and Deep Learning in the Diagnosis and Management of Hepatocellular Carcinoma

Comparison of traditional machine learning algorithms for predicting post-hepatectomy liver failure in hepatocellular carcinoma

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