Machine Learning Model Based on Prognostic Nutritional Index for Predicting Long‐Term Outcomes in Patients With HCC Undergoing Ablation
ABSTRACT Aims To develop multiple machine learning (ML) models based on the prognostic nutritional index (PNI) and determine the optimal model for predicting long‐term survival outcomes in hepatocellular carcinoma (HCC) patients after local ablation. Methods From January 2009 to December 2019, we an...
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| Format: | Article |
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Wiley
2024-10-01
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| Series: | Cancer Medicine |
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| Online Access: | https://doi.org/10.1002/cam4.70344 |
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| author | Nan Zhang Ke Lin Bin Qiao Liwei Yan Dongdong Jin Daopeng Yang Yue Yang Xiaohua Xie Xiaoyan Xie Bowen Zhuang |
| author_facet | Nan Zhang Ke Lin Bin Qiao Liwei Yan Dongdong Jin Daopeng Yang Yue Yang Xiaohua Xie Xiaoyan Xie Bowen Zhuang |
| author_sort | Nan Zhang |
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| description | ABSTRACT Aims To develop multiple machine learning (ML) models based on the prognostic nutritional index (PNI) and determine the optimal model for predicting long‐term survival outcomes in hepatocellular carcinoma (HCC) patients after local ablation. Methods From January 2009 to December 2019, we analyzed data from 848 primary HCC patients who underwent local ablation. ML models were constructed and evaluated using the concordance index (C‐index), concordance‐discordance area under curve (C/D AUC), and Brier scores. The optimal ML model was interpreted using the partial dependence plot (PDP) and SHapley Additive exPlanations (SHAP) framework. Additionally, the prognostic performance of our model was compared with other models. Results Alkaline phosphatase, preoperation alpha‐fetoprotein level, PNI, tumor number, and tumor size were identified as independent prognostic factors for ML model construction. Among the 19 ML algorithms tested, the Aorsf model showed superior performance in both the training cohort (C/D AUC: 0.733; C‐index: 0.736; Brier score: 0.133) and validation cohort (C/D AUC: 0.713; C‐index: 0.793; Brier score: 0.117). The time‐dependent AUC of the Aorsf model for predicting overall survival was as follows: 1‐, 3‐, 5‐, 7‐, and 9‐year were 0.828, 0.765, 0.781, 0.817, and 0.812 in the training cohort, 0.846, 0.859, 0.824, 0.845, and 0.874 in the validation cohort, respectively. The PDP and SHAP algorithms were employed for visual interpretation. Furthermore, time‐AUC and decision curve analysis demonstrated that the Aorsf model provided superior clinical benefits compared to other models. Conclusion The PNI‐based Aorsf model effectively predicts long‐term survival outcomes after ablation therapy, making a significant contribution to HCC research by improving surveillance, prevention, and treatment strategies. |
| format | Article |
| id | doaj-art-757df4aba2b0442399761f177d5bfe5d |
| institution | OA Journals |
| issn | 2045-7634 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj-art-757df4aba2b0442399761f177d5bfe5d2025-08-20T02:17:59ZengWileyCancer Medicine2045-76342024-10-011320n/an/a10.1002/cam4.70344Machine Learning Model Based on Prognostic Nutritional Index for Predicting Long‐Term Outcomes in Patients With HCC Undergoing AblationNan Zhang0Ke Lin1Bin Qiao2Liwei Yan3Dongdong Jin4Daopeng Yang5Yue Yang6Xiaohua Xie7Xiaoyan Xie8Bowen Zhuang9Division of Interventional Ultrasound, Department of Medical Ultrasonics Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat‐sen University Guangzhou Guangdong ChinaDivision of Interventional Ultrasound, Department of Medical Ultrasonics Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat‐sen University Guangzhou Guangdong ChinaDivision of Interventional Ultrasound, Department of Medical Ultrasonics Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat‐sen University Guangzhou Guangdong ChinaDepartment of Microsurgery and Orthopedic Trauma The First Affiliated Hospital of Sun Yat‐sen University Guangzhou Guangdong ChinaDivision of Interventional Ultrasound, Department of Medical Ultrasonics Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat‐sen University Guangzhou Guangdong ChinaDivision of Interventional Ultrasound, Department of Medical Ultrasonics Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat‐sen University Guangzhou Guangdong ChinaDivision of Interventional Ultrasound, Department of Medical Ultrasonics Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat‐sen University Guangzhou Guangdong ChinaDivision of Interventional Ultrasound, Department of Medical Ultrasonics Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat‐sen University Guangzhou Guangdong ChinaDivision of Interventional Ultrasound, Department of Medical Ultrasonics Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat‐sen University Guangzhou Guangdong ChinaDivision of Interventional Ultrasound, Department of Medical Ultrasonics Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat‐sen University Guangzhou Guangdong ChinaABSTRACT Aims To develop multiple machine learning (ML) models based on the prognostic nutritional index (PNI) and determine the optimal model for predicting long‐term survival outcomes in hepatocellular carcinoma (HCC) patients after local ablation. Methods From January 2009 to December 2019, we analyzed data from 848 primary HCC patients who underwent local ablation. ML models were constructed and evaluated using the concordance index (C‐index), concordance‐discordance area under curve (C/D AUC), and Brier scores. The optimal ML model was interpreted using the partial dependence plot (PDP) and SHapley Additive exPlanations (SHAP) framework. Additionally, the prognostic performance of our model was compared with other models. Results Alkaline phosphatase, preoperation alpha‐fetoprotein level, PNI, tumor number, and tumor size were identified as independent prognostic factors for ML model construction. Among the 19 ML algorithms tested, the Aorsf model showed superior performance in both the training cohort (C/D AUC: 0.733; C‐index: 0.736; Brier score: 0.133) and validation cohort (C/D AUC: 0.713; C‐index: 0.793; Brier score: 0.117). The time‐dependent AUC of the Aorsf model for predicting overall survival was as follows: 1‐, 3‐, 5‐, 7‐, and 9‐year were 0.828, 0.765, 0.781, 0.817, and 0.812 in the training cohort, 0.846, 0.859, 0.824, 0.845, and 0.874 in the validation cohort, respectively. The PDP and SHAP algorithms were employed for visual interpretation. Furthermore, time‐AUC and decision curve analysis demonstrated that the Aorsf model provided superior clinical benefits compared to other models. Conclusion The PNI‐based Aorsf model effectively predicts long‐term survival outcomes after ablation therapy, making a significant contribution to HCC research by improving surveillance, prevention, and treatment strategies.https://doi.org/10.1002/cam4.70344hepatocellular carcinomalocal ablationmachine learning modelprognosisprognostic nutritional index |
| spellingShingle | Nan Zhang Ke Lin Bin Qiao Liwei Yan Dongdong Jin Daopeng Yang Yue Yang Xiaohua Xie Xiaoyan Xie Bowen Zhuang Machine Learning Model Based on Prognostic Nutritional Index for Predicting Long‐Term Outcomes in Patients With HCC Undergoing Ablation Cancer Medicine hepatocellular carcinoma local ablation machine learning model prognosis prognostic nutritional index |
| title | Machine Learning Model Based on Prognostic Nutritional Index for Predicting Long‐Term Outcomes in Patients With HCC Undergoing Ablation |
| title_full | Machine Learning Model Based on Prognostic Nutritional Index for Predicting Long‐Term Outcomes in Patients With HCC Undergoing Ablation |
| title_fullStr | Machine Learning Model Based on Prognostic Nutritional Index for Predicting Long‐Term Outcomes in Patients With HCC Undergoing Ablation |
| title_full_unstemmed | Machine Learning Model Based on Prognostic Nutritional Index for Predicting Long‐Term Outcomes in Patients With HCC Undergoing Ablation |
| title_short | Machine Learning Model Based on Prognostic Nutritional Index for Predicting Long‐Term Outcomes in Patients With HCC Undergoing Ablation |
| title_sort | machine learning model based on prognostic nutritional index for predicting long term outcomes in patients with hcc undergoing ablation |
| topic | hepatocellular carcinoma local ablation machine learning model prognosis prognostic nutritional index |
| url | https://doi.org/10.1002/cam4.70344 |
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