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Huang K, Chen Z, Yuan XZ, He YS, Lan X, Du CY. Development and validation of machine learning nomograms for predicting survival in stage IV pancreatic cancer: A retrospective study. World J Gastrointest Oncol 2025; 17:102459. [DOI: 10.4251/wjgo.v17.i5.102459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2024] [Revised: 02/17/2025] [Accepted: 03/10/2025] [Indexed: 05/15/2025] Open
Abstract
BACKGROUND Stage IV pancreatic cancer (PC) has a poor prognosis and lacks individualized prognostic tools. Current survival prediction models are limited, and there is a need for more accurate, personalized methods. The Surveillance, Epidemiology, and End Results (SEER) database offers a valuable resource for studying large patient cohorts, yet machine learning-based nomograms for stage IV PC prognosis remain underexplored. This study hypothesizes that a machine learning-based nomogram can predict cancer-specific survival (CSS) and overall survival (OS) with high accuracy in stage IV PC patients.
AIM To construct and validate a machine learning-based nomogram for predicting survival in stage IV PC patients using real-world data.
METHODS Clinical data from stage IV PC patients diagnosed via pathology from 2000 to 2019 were extracted from the SEER database. Patients were randomly divided into a training set and a validation set in a 7:3 ratio. Multivariate Cox proportional hazards, Least Absolute Shrinkage and Selection Operator regression, and Random Survival Forest models were used to identify prognostic variables. A nomogram was constructed to predict CSS and OS at 6, 12, and 18 months. The C-index, receiver operating characteristic curves, and calibration curves were used to evaluate the model’s predictive performance.
RESULTS A total of 1662 patients were included (1163 in the training set, 499 in the validation set). The median follow-up times were 4 months [interquartile range (IQR): 1-10 months] for the training set and 4 months (IQR: 1-11 months) for the validation set. Key independent prognostic factors identified included age, race, marital status, tumor location, N stage, grade, surgery, chemotherapy, and liver metastasis. The nomogram accurately predicted OS and CSS at 6, 12, and 18 months, with a C-index of 0.727 (OS) and 0.727 (CSS) in the training set, and 0.719 (OS) and 0.716 (CSS) in the validation set. Calibration curves demonstrated excellent model accuracy.
CONCLUSION The nomogram developed using age, grade, chemotherapy, surgery, and liver metastasis as predictors can reliably estimate survival outcomes for stage IV PC patients and offers a potential tool for individualized clinical decision-making.
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Affiliation(s)
- Kun Huang
- Department of General Surgery, Mianyang Hospital of Traditional Chinese Medicine, Mianyang 621000, Sichuan Province, China
| | - Zhu Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400000, China
| | - Xin-Zhu Yuan
- Department of Nephrology, The Second Clinical Medical Institution of North Sichuan Medical College (Nanchong Central Hospital) and Nanchong Key Laboratory of Basic Science & Clinical Research on Chronic Kidney Disease, Nanchong 637000, Sichuan Province, China
| | - Yun-Shen He
- Department of General Surgery, Mianyang Hospital of Traditional Chinese Medicine, Mianyang 621000, Sichuan Province, China
| | - Xiang Lan
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400000, China
| | - Chen-You Du
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400000, China
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Liu W, Cai Z, Chen Y, Guan X, Feng J, Chen H, Guo B, OuYang F, Luo C, Zhang R, Chen X, Li X, Zhou C, Yang S, Liu Z, Hu Q. Gadoxetic acid-enhanced MRI for identifying cholangiocyte phenotype hepatocellular carcinoma by interpretable machine learning: individual application of SHAP. BMC Cancer 2025; 25:788. [PMID: 40295993 PMCID: PMC12036154 DOI: 10.1186/s12885-025-14147-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 04/14/2025] [Indexed: 04/30/2025] Open
Abstract
PURPOSE Cholangiocyte phenotype hepatocellular carcinoma (HCC) is highly invasive. This study aims to develop and validate an optimal machine learning model to predict cholangiocyte phenotype HCC based on T1 mapping gadoxetic acid-enhanced MRI and to implement individual applications via the Shapley Additive explanation (SHAP). METHODS We included 180 patients with histologically confirmed HCC from two institutions. Clinical and MRI imaging features were screened for predicting cholangiocyte phenotype hepatocellular carcinoma using Least Absolute Shrinkage and Selection Operator (LASSO) and the logistic regression analysis. Five machine learning models were constructed based on these features. A Kaplan-Meier survival analysis aims to compare prognostic differences between cholangiocyte phenotype-positive HCC groups and classical (cholangiocyte phenotype-negative) HCC groups, and was conducted to explore the prognostic information of the optimal model. RESULTS The most significant clinicoradiological features, including the platelet-to-lymphocyte ratio (PLR), tumor capsule, target sign on hepatobiliary phase (HBP), and T1 relaxation time of 20 min (T1rt-20 min), were selected to construct the prediction model. Finally, we selected the eXtreme Gradient Boosting (XGBoost) model as the optimal predictive model, which achieved AUCs of 0.835, 0.830, 0.816 and 0.776 in training, internal validation, external validation, and prospective validation cohorts, respectively, for visual analysis via SHAP, in which T1rt-20 min made a significant contribution. Survival analysis showed a statistically significant difference in relapse-free survival (RFS) between cholangiocyte phenotype-positive HCC groups and classical HCC groups from institution I (hazard ratio [HR] 1.994; 95% CI, 1.059-3.758; P = 0.027), and the construction XGBoost model can be used to stratify RFS according to prognosis (HR, 1.986; 95% CI, 1.061-3.717; P = 0.029). CONCLUSION The machine learning model utilizing T1 mapping gadoxetic acid-enhanced MRI demonstrates significant potential in identifying cholangiocyte phenotype HCC. Furthermore, personalized prediction is enhanced through the application of SHAP, providing valuable insights to support clinical decision-making processes.
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Affiliation(s)
- Wei Liu
- Department of Radiology, The Eighth Affiliated Hospital of Southern Medical University (The First People's Hospital of Shunde Foshan), No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, Guangdong Province, 528308, China
| | - Zhiping Cai
- Department of Radiology, The Eighth Affiliated Hospital of Southern Medical University (The First People's Hospital of Shunde Foshan), No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, Guangdong Province, 528308, China
| | - Yifan Chen
- Department of Radiology, The Eighth Affiliated Hospital of Southern Medical University (The First People's Hospital of Shunde Foshan), No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, Guangdong Province, 528308, China
| | - Xingqun Guan
- Department of Radiology, The Eighth Affiliated Hospital of Southern Medical University (The First People's Hospital of Shunde Foshan), No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, Guangdong Province, 528308, China
| | - Jieying Feng
- Department of Radiology, The Sixth Affiliated Hospital, South China University of Technology, Foshan, Guangdong Province, 528247, China
| | - Haixiong Chen
- Department of Radiology, The Eighth Affiliated Hospital of Southern Medical University (The First People's Hospital of Shunde Foshan), No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, Guangdong Province, 528308, China
| | - Baoliang Guo
- Department of Radiology, The Eighth Affiliated Hospital of Southern Medical University (The First People's Hospital of Shunde Foshan), No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, Guangdong Province, 528308, China
| | - Fusheng OuYang
- Department of Radiology, The Eighth Affiliated Hospital of Southern Medical University (The First People's Hospital of Shunde Foshan), No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, Guangdong Province, 528308, China
| | - Chun Luo
- Department of Radiology, The First Peoples Hospital of Foshan, Foshan, Guangdong Province, 528000, China
| | - Rong Zhang
- Department of Radiology, The Eighth Affiliated Hospital of Southern Medical University (The First People's Hospital of Shunde Foshan), No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, Guangdong Province, 528308, China
| | - Xinjie Chen
- Department of Radiology, The Eighth Affiliated Hospital of Southern Medical University (The First People's Hospital of Shunde Foshan), No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, Guangdong Province, 528308, China
| | - Xiaohong Li
- Department of Radiology, The Eighth Affiliated Hospital of Southern Medical University (The First People's Hospital of Shunde Foshan), No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, Guangdong Province, 528308, China
| | - Cuiru Zhou
- Department of Radiology, The Eighth Affiliated Hospital of Southern Medical University (The First People's Hospital of Shunde Foshan), No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, Guangdong Province, 528308, China
| | - Shaomin Yang
- Xingtan Hospital Affiliated of Southern Medical University Shunde Hospital, No. 222 Xinglong Road, Shunde, China.
| | - Ziwei Liu
- Department of Radiology, The Eighth Affiliated Hospital of Southern Medical University (The First People's Hospital of Shunde Foshan), No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, Guangdong Province, 528308, China.
| | - Qiugen Hu
- Department of Radiology, The Eighth Affiliated Hospital of Southern Medical University (The First People's Hospital of Shunde Foshan), No. 1 Jiazi Road, Lunjiao, Shunde District, Foshan, Guangdong Province, 528308, China.
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Sun J, Xia Y, Shen F, Cheng S. Chinese expert consensus on the diagnosis and treatment of hepatocellular carcinoma with microvascular invasion (2024 edition). Hepatobiliary Surg Nutr 2025; 14:246-266. [PMID: 40342785 PMCID: PMC12057508 DOI: 10.21037/hbsn-24-359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Accepted: 10/10/2024] [Indexed: 05/11/2025]
Abstract
Background Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in China. Surgical resection is the preferred treatment for HCC, but the postoperative recurrence and metastasis rates are high. Current evidence shows that microvascular invasion (MVI) is an independent risk factor for postoperative recurrence and metastasis, but there are still many controversies about the diagnosis, classification, prediction, and treatment of MVI worldwide. Methods Systematic literature reviews to identify knowledge gaps and support consensus statements and a modified Delphi method to develop evidence- and expert-based guidelines and finalization of the clinical consensus statements based on recommendations from a panel of experts. Results After many discussions and revisions, the Chinese Association of Liver Cancer of the Chinese Medical Doctor Association organized domestic experts in related fields to form the "Chinese expert consensus on the diagnosis and treatment of hepatocellular carcinoma with microvascular invasion (2024 edition)" which included eight recommendations to better guide the prediction, diagnosis and treatment of HCC patients with MVI. The MVI pathological grading criteria as outlined in the "Guidelines for Pathological Diagnosis of Primary Liver Cancer" and the Eastern Hepatobiliary Surgery Hospital (EHBH) nomogram for predicting MVI are highly recommended. Conclusions We present an expert consensus on the diagnosis and treatment of MVI and potentially improve recurrence-free survival (RFS) and overall survival (OS) for HCC patients with MVI.
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Affiliation(s)
- Juxian Sun
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China
| | - Yong Xia
- Department of Hepatic Surgery IV, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China
| | - Feng Shen
- Department of Hepatic Surgery IV, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China
| | - Shuqun Cheng
- Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China
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Xu ZL, Qian GX, Li YH, Lu JL, Wei MT, Bu XY, Ge YS, Cheng Y, Jia WD. Evaluating microvascular invasion in hepatitis B virus-related hepatocellular carcinoma based on contrast-enhanced computed tomography radiomics and clinicoradiological factors. World J Gastroenterol 2024; 30:4801-4816. [PMID: 39649551 PMCID: PMC11606376 DOI: 10.3748/wjg.v30.i45.4801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 08/28/2024] [Accepted: 09/23/2024] [Indexed: 11/13/2024] Open
Abstract
BACKGROUND Microvascular invasion (MVI) is a significant indicator of the aggressive behavior of hepatocellular carcinoma (HCC). Expanding the surgical resection margin and performing anatomical liver resection may improve outcomes in patients with MVI. However, no reliable preoperative method currently exists to predict MVI status or to identify patients at high-risk group (M2). AIM To develop and validate models based on contrast-enhanced computed tomography (CECT) radiomics and clinicoradiological factors to predict MVI and identify M2 among patients with hepatitis B virus-related HCC (HBV-HCC). The ultimate goal of the study was to guide surgical decision-making. METHODS A total of 270 patients who underwent surgical resection were retrospectively analyzed. The cohort was divided into a training dataset (189 patients) and a validation dataset (81) with a 7:3 ratio. Radiomics features were selected using intra-class correlation coefficient analysis, Pearson or Spearman's correlation analysis, and the least absolute shrinkage and selection operator algorithm, leading to the construction of radscores from CECT images. Univariate and multivariate analyses identified significant clinicoradiological factors and radscores associated with MVI and M2, which were subsequently incorporated into predictive models. The models' performance was evaluated using calibration, discrimination, and clinical utility analysis. RESULTS Independent risk factors for MVI included non-smooth tumor margins, absence of a peritumoral hypointensity ring, and a high radscore based on delayed-phase CECT images. The MVI prediction model incorporating these factors achieved an area under the curve (AUC) of 0.841 in the training dataset and 0.768 in the validation dataset. The M2 prediction model, which integrated the radscore from the 5 mm peritumoral area in the CECT arterial phase, α-fetoprotein level, enhancing capsule, and aspartate aminotransferase level achieved an AUC of 0.865 in the training dataset and 0.798 in the validation dataset. Calibration and decision curve analyses confirmed the models' good fit and clinical utility. CONCLUSION Multivariable models were constructed by combining clinicoradiological risk factors and radscores to preoperatively predict MVI and identify M2 among patients with HBV-HCC. Further studies are needed to evaluate the practical application of these models in clinical settings.
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Affiliation(s)
- Zi-Ling Xu
- Department of General Surgery, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei 230001, Anhui Province, China
| | - Gui-Xiang Qian
- Department of General Surgery, Anhui Provincial Hospital, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Science and Medicine, University of Science and Technology of China, Hefei 230001, Anhui Province, China
| | - Yong-Hai Li
- Department of Anorectal Surgery, The First People's Hospital of Hefei, Hefei 230001, Anhui Province, China
| | - Jian-Lin Lu
- Department of General Surgery, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei 230001, Anhui Province, China
| | - Ming-Tong Wei
- Department of General Surgery, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei 230001, Anhui Province, China
| | - Xiang-Yi Bu
- Department of General Surgery, Anhui Provincial Hospital, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Science and Medicine, University of Science and Technology of China, Hefei 230001, Anhui Province, China
| | - Yong-Sheng Ge
- Department of General Surgery, Anhui Provincial Hospital, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Science and Medicine, University of Science and Technology of China, Hefei 230001, Anhui Province, China
| | - Yuan Cheng
- Department of General Surgery, Anhui Provincial Hospital, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Science and Medicine, University of Science and Technology of China, Hefei 230001, Anhui Province, China
| | - Wei-Dong Jia
- Department of General Surgery, Anhui Provincial Hospital, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Science and Medicine, University of Science and Technology of China, Hefei 230001, Anhui Province, China
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Xu X, Li L, Chen D, Chen S, Chen L, Feng X. Establishment and validation of apnea risk prediction models in preterm infants: a retrospective case control study. BMC Pediatr 2024; 24:654. [PMID: 39394551 PMCID: PMC11468346 DOI: 10.1186/s12887-024-05125-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 09/30/2024] [Indexed: 10/13/2024] Open
Abstract
BACKGROUND Apnea is common in preterm infants and can be accompanied with severe hypoxic damage. Early assessment of apnea risk can impact the prognosis of preterm infants. We constructed a prediction model to assess apnea risk in premature infants for identifying high-risk groups. METHODS A total of 162 and 324 preterm infants with and without apnea who were admitted to the neonatal intensive care unit of Xiamen University between January 2018 and December 2021 were selected as the case and control groups, respectively. Demographic characteristics, laboratory indicators, complications of the patients, pregnancy-related factors, and perinatal risk factors of the mother were collected retrospectively. The participants were randomly divided into modeling (n = 388) and validation (n = 98) sets in an 8:2 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression analyses were used to independently filter variables from the modeling set and build a model. A nomogram was used to visualize models. The calibration and clinical utility of the model was evaluated using consistency index, receiver operating characteristic (ROC) curve, calibration curve, and decision curve, and the model was verified using the validation set. RESULTS Results of LASSO combined with multivariate logistic regression analysis showed that gestational age at birth, birth length, Apgar score, and neonatal respiratory distress syndrome were predictors of apnea development in preterm infants. The model was presented as a nomogram and the Hosmer-Lemeshow goodness of fit test showed a good model fit (χ2=5.192, df=8, P=0.737), with Nagelkerke R2 of 0.410 and C-index of 0.831. The area under the ROC curve and 95% CI were 0.831 (0.787-0.874) and 0.829 (0.722-0.935), respectively. Delong's test comparing the AUC of the two data sets showed no significant difference (P=0.976). The calibration curve showed good agreement between the predicted and actual observations. The decision curve results showed that the threshold probability range of the model was 0.07-1.00, the net benefit was high, and the constructed clinical prediction model had clinical utility. CONCLUSIONS Our risk prediction model based on gestational age, birth length, Apgar score 10 min post-birth, and neonatal respiratory distress syndrome was validated in many aspects and had good predictive efficacy and clinical utility.
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MESH Headings
- Humans
- Infant, Newborn
- Retrospective Studies
- Female
- Infant, Premature
- Case-Control Studies
- Apnea/etiology
- Apnea/diagnosis
- Risk Assessment/methods
- Male
- Nomograms
- Logistic Models
- ROC Curve
- Gestational Age
- Risk Factors
- Respiratory Distress Syndrome, Newborn/etiology
- Respiratory Distress Syndrome, Newborn/epidemiology
- Infant, Premature, Diseases/diagnosis
- Infant, Premature, Diseases/etiology
- Infant, Premature, Diseases/epidemiology
- Intensive Care Units, Neonatal
- Apgar Score
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Affiliation(s)
- Xiaodan Xu
- Zhongshan Hospital Affiliated to Xiamen University, Xiamen, Fujian Province, 361000, China
| | - Lin Li
- Fujian Medical University Union Hospital, Fuzhou, Fujian, 350001, China.
| | - Daiquan Chen
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou, Fujian Province, 350001, China
| | - Shunmei Chen
- Zhongshan Hospital Affiliated to Xiamen University, Xiamen, Fujian Province, 361000, China
| | - Ling Chen
- Zhongshan Hospital Affiliated to Xiamen University, Xiamen, Fujian Province, 361000, China
| | - Xiao Feng
- Zhongshan Hospital Affiliated to Xiamen University, Xiamen, Fujian Province, 361000, China
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Chen JL, Chen YS, Hsieh KC, Lee HM, Chen CY, Chen JH, Hung CM, Hsu CT, Huang YL, Ker CG. Clinical Nomogram Model for Pre-Operative Prediction of Microvascular Invasion of Hepatocellular Carcinoma before Hepatectomy. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:1410. [PMID: 39336451 PMCID: PMC11433876 DOI: 10.3390/medicina60091410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Revised: 08/09/2024] [Accepted: 08/21/2024] [Indexed: 09/30/2024]
Abstract
Background and Objectives: Microvascular invasion (MVI) significantly impacts recurrence and survival rates after liver resection in hepatocellular carcinoma (HCC). Pre-operative prediction of MVI is crucial in determining the treatment strategy. This study aims to develop a nomogram model to predict the probability of MVI based on clinical features in HCC patients. Materials and Methods: A total of 489 patients with a pathological diagnosis of HCC were enrolled from our hospital. Those registered from 2012-2015 formed the derivation cohort, and those from 2016-2019 formed the validation cohort for pre-operative prediction of MVI. A nomogram model for prediction was created using a regression model, with risk factors derived from clinical and tumor-related features before surgery. Results: Using the nomogram model to predict the odds ratio of MVI before hepatectomy, the AFP, platelet count, GOT/GPT ratio, albumin-alkaline phosphatase ratio, ALBI score, and GNRI were identified as significant variables for predicting MVI. The Youden index scores for each risk variable were 0.287, 0.276, 0.196, 0.185, 0.115, and 0.112, respectively, for the AFP, platelet count, GOT/GPT ratio, AAR, ALBI, and GNRI. The maximum value of the total nomogram scores was 220. An increase in the number of nomogram points indicated a higher probability of MVI occurrence. The accuracy rates ranged from 55.9% to 64.4%, and precision rates ranged from 54.3% to 68.2%. Overall survival rates were 97.6%, 83.4%, and 73.9% for MVI(-) and 80.0%, 71.8%, and 41.2% for MVI(+) (p < 0.001). The prognostic effects of MVI(+) on tumor-free survival and overall survival were poor in both the derivation and validation cohorts. Conclusions: Our nomogram model, which integrates clinical factors, showed reliable calibration for predicting MVI and provides a useful tool enabling surgeons to estimate the probability of MVI before resection. Consequently, surgical strategies and post-operative care programs can be adapted to improve the prognosis of HCC patients where possible.
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Affiliation(s)
- Jen-Lung Chen
- Department of General Surgery, E-Da Hospital, I-Shou University, Kaohsiung 824, Taiwan; (J.-L.C.); (Y.-S.C.); (K.-C.H.); (H.-M.L.); (C.-Y.C.); (J.-H.C.)
| | - Yaw-Sen Chen
- Department of General Surgery, E-Da Hospital, I-Shou University, Kaohsiung 824, Taiwan; (J.-L.C.); (Y.-S.C.); (K.-C.H.); (H.-M.L.); (C.-Y.C.); (J.-H.C.)
| | - Kun-Chou Hsieh
- Department of General Surgery, E-Da Hospital, I-Shou University, Kaohsiung 824, Taiwan; (J.-L.C.); (Y.-S.C.); (K.-C.H.); (H.-M.L.); (C.-Y.C.); (J.-H.C.)
| | - Hui-Ming Lee
- Department of General Surgery, E-Da Hospital, I-Shou University, Kaohsiung 824, Taiwan; (J.-L.C.); (Y.-S.C.); (K.-C.H.); (H.-M.L.); (C.-Y.C.); (J.-H.C.)
| | - Chung-Yen Chen
- Department of General Surgery, E-Da Hospital, I-Shou University, Kaohsiung 824, Taiwan; (J.-L.C.); (Y.-S.C.); (K.-C.H.); (H.-M.L.); (C.-Y.C.); (J.-H.C.)
| | - Jian-Han Chen
- Department of General Surgery, E-Da Hospital, I-Shou University, Kaohsiung 824, Taiwan; (J.-L.C.); (Y.-S.C.); (K.-C.H.); (H.-M.L.); (C.-Y.C.); (J.-H.C.)
| | - Chao-Ming Hung
- Department of General Surgery, E-Da Cancer Hospital, I-Shou University, Kaohsiung 824, Taiwan;
| | - Chao-Tien Hsu
- Department of Pathology, E-Da Hospital, I-Shou University, Kaohsiung 824, Taiwan;
| | - Ya-Ling Huang
- Cancer Registration Center, E-Da Cancer Hospital, I-Shou University, Kaohsiung 824, Taiwan;
| | - Chen-Guo Ker
- Department of General Surgery, E-Da Hospital, I-Shou University, Kaohsiung 824, Taiwan; (J.-L.C.); (Y.-S.C.); (K.-C.H.); (H.-M.L.); (C.-Y.C.); (J.-H.C.)
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Sun JX, Yang Z, Wu JY, Shi J, Yu HM, Yan ML, Zheng SS, Cheng SQ. A new scoring system for predicting the outcome of hepatocellular carcinoma patients without microvascular invasion-a large-scale multicentre study. HPB (Oxford) 2024; 26:741-752. [PMID: 38472016 DOI: 10.1016/j.hpb.2024.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 02/03/2024] [Accepted: 02/11/2024] [Indexed: 03/14/2024]
Abstract
BACKGROUND The prognosis of HCC patients without MVI (so called M0) is highly heterogeneous and the need for adjuvant therapy is still controversial. METHODS Patients with HCC with M0 who underwent liver resection (LR) or liver transplantation (LT) as an initial therapy were included. The Eastern Hepatobiliary Surgery Hospital (EHBH)-M0 score was developed from a retrospective cohort to form the training cohort. The classification which was developed using multivariate cox regression analysis was externally validated. RESULTS The score was developed using the following factors: α-fetoprotein level, tumour diameter, liver cirrhosis, total bilirubin, albumin and aspartate aminotransferase. The score differentiated two groups of M0 patients (≤3, >3 points) with distinct long-term prognoses outcomes (median overall survival (OS), 98.0 vs. 46.0 months; p < 0.001). The predictive accuracy of the score was greater than the other commonly used staging systems for HCC. And for M0 patients with a higher score underwent LR. Adjuvant transcatheter arterial chemoembolization (TACE) was effective to prolong OS. CONCLUSIONS The EHBH M0 scoring system was more accurate in predicting the prognosis of HCC patients with M0 after LR or LT. Adjuvant therapy is recommended for HCC patients who have a higher score.
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Affiliation(s)
- Ju-Xian Sun
- Department of Hepatic Surgery VI, Third Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Zhe Yang
- Shulan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University Shulan International Medical College, Hangzhou, China
| | - Jia-Yi Wu
- Department of Hepatobiliary Surgery, Fujian Provincial Hospital, the Shengli Clinical Medical College of Fujian Medical University, Fujian, China
| | - Jie Shi
- Department of Hepatic Surgery VI, Third Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Hong-Ming Yu
- Department of Hepatic Surgery VI, Third Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Mao-Lin Yan
- Department of Hepatobiliary Surgery, Fujian Provincial Hospital, the Shengli Clinical Medical College of Fujian Medical University, Fujian, China
| | - Shu-Sen Zheng
- Shulan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University Shulan International Medical College, Hangzhou, China.
| | - Shu-Qun Cheng
- Department of Hepatic Surgery VI, Third Affiliated Hospital of Naval Medical University, Shanghai, China.
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Wang Q, Sheng S, Xiong Y, Han M, Jin R, Hu C. Machine learning-based model for predicting tumor recurrence after interventional therapy in HBV-related hepatocellular carcinoma patients with low preoperative platelet-albumin-bilirubin score. Front Immunol 2024; 15:1409443. [PMID: 38863693 PMCID: PMC11165108 DOI: 10.3389/fimmu.2024.1409443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 05/14/2024] [Indexed: 06/13/2024] Open
Abstract
Introduction This study aimed to develop a prognostic nomogram for predicting the recurrence-free survival (RFS) of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients with low preoperative platelet-albumin-bilirubin (PALBI) scores after transarterial chemoembolization (TACE) combined with local ablation treatment. Methods We gathered clinical data from 632 HBV-related HCC patients who received the combination treatment at Beijing You'an Hospital, affiliated with Capital Medical University, from January 2014 to January 2020. The patients were divided into two groups based on their PALBI scores: low PALBI group (n=247) and high PALBI group (n=385). The low PALBI group was then divided into two cohorts: training cohort (n=172) and validation cohort (n=75). We utilized eXtreme Gradient Boosting (XGBoost), random survival forest (RSF), and multivariate Cox analysis to pinpoint the risk factors for RFS. Then, we developed a nomogram based on the screened factors and assessed its risk stratification capabilities and predictive performance. Results The study finally identified age, aspartate aminotransferase (AST), and prothrombin time activity (PTA) as key predictors. The three variables were included to develop the nomogram for predicting the 1-, 3-, and 5-year RFS of HCC patients. We confirmed the nomogram's ability to effectively discern high and low risk patients, as evidenced by Kaplan-Meier curves. We further corroborated the excellent discrimination, consistency, and clinical utility of the nomogram through assessments using the C-index, area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Conclusion Our study successfully constructed a robust nomogram, effectively predicting 1-, 3-, and 5-year RFS for HBV-related HCC patients with low preoperative PALBI scores after TACE combined with local ablation therapy.
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Affiliation(s)
- Qi Wang
- Interventional Therapy Center for Oncology, Beijing You’an Hospital, Capital Medical University, Beijing, China
- National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Shugui Sheng
- National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Yiqi Xiong
- Interventional Therapy Center for Oncology, Beijing You’an Hospital, Capital Medical University, Beijing, China
| | - Ming Han
- National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Ronghua Jin
- National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, China
- Changping Laboratory, Beijing, China
| | - Caixia Hu
- Interventional Therapy Center for Oncology, Beijing You’an Hospital, Capital Medical University, Beijing, China
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Tian J, Cui R, Song H, Zhao Y, Zhou T. Prediction of acute kidney injury in patients with liver cirrhosis using machine learning models: evidence from the MIMIC-III and MIMIC-IV. Int Urol Nephrol 2024; 56:237-247. [PMID: 37256426 DOI: 10.1007/s11255-023-03646-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 05/23/2023] [Indexed: 06/01/2023]
Abstract
PURPOSE To develop and validate a machine learning (ML)-based prediction model for acute kidney injury (AKI) in patients with liver cirrhosis. METHODS Data on liver cirrhosis patients were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) and MIMIC-IV databases in this retrospective cohort study. ML algorithms, including random forest (RF), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and gradient boosting decision tree (GBDT) were applied to construct prediction models. Predictors were screened via univariate logistic regression, and then the models were developed with all data of the included patients. A bootstrap resampling method was adopted to validate the models. The predictive abilities of our final model were compared with those of the sequential organ failure assessment score (SOFA), simplified acute physiology score II (SAPS II), Model for End-stage Liver Disease (MELD), and MELD Na. RESULTS This study included 950 patients, of which 429 (45.16%) had AKI. Mechanical ventilation, vasopressor, international normalized ratio (INR), bilirubin, Charlson comorbidity index (CCI), prothrombin time (PT), estimated glomerular filtration rate (EGFR), partial thromboplastin time (PTT), and heart rate served as predictors. In the derivation set, the developed RF [area under curve (AUC) = 0.747], XGB (AUC = 0.832), LGBM (AUC = 0.785), and GBDT (AUC = 0.811) models exhibited significantly greater predictive performance than the logistic regression model (AUC = 0.699) (all P < 0.05). Among the ML-based models, the XGB model had the greatest AUC. In internal validation, the predictive capacity of the XGB model (AUC = 0.833) was significantly superior to that of the logistic regression model (AUC = 0.701) (P = 0.045). Hence, the XGB model was selected as the final model for AKI prediction. In contrast to the XGB model (AUC = 0.832), the SOFA (AUC = 0.609), MELD (AUC = 0.690), MELD Na (AUC = 0.690), and SAPS II (AUC = 0.641) had significantly lower predictive abilities in the derivation set (all P < 0.001). The XGB model was internally validated to have an AUC of 0.833, which was significantly higher than the SOFA (AUC = 0.609), MELD (AUC = 0.690), MELD Na (AUC = 0.688), and SAPS II (AUC = 0.641) (all P < 0.05). CONCLUSION The XGB model had a better performance than the logistic regression model, SOFA, MELD, MELD Na, and SAPS II in AKI prediction for cirrhosis patients, which may help identify patients at a risk of AKI, and then provide timely interventions.
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Affiliation(s)
- Jia Tian
- Department of Nephrology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, People's Republic of China
| | - Rui Cui
- Department of Nephrology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, People's Republic of China
| | - Huinan Song
- Department of Nephrology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, People's Republic of China
| | - Yingzi Zhao
- Department of Nephrology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, People's Republic of China
| | - Ting Zhou
- The Ward No. 2, Department of Gastroenterology, The Fourth Affiliated Hospital of Harbin Medical University, No. 37 Yiyuan Street, Nangang District, Harbin, 150001, Heilongjiang, People's Republic of China.
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Li X, Bao H, Shi Y, Zhu W, Peng Z, Yan L, Chen J, Shu X. Machine learning methods for accurately predicting survival and guiding treatment in stage I and II hepatocellular carcinoma. Medicine (Baltimore) 2023; 102:e35892. [PMID: 37960763 PMCID: PMC10637529 DOI: 10.1097/md.0000000000035892] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 10/11/2023] [Indexed: 11/15/2023] Open
Abstract
Accurately predicting survival in patients with early hepatocellular carcinoma (HCC) is essential for making informed decisions about treatment and prognosis. Herein, we have developed a machine learning (ML) model that can predict patient survival and guide treatment decisions. We obtained patient demographic information, tumor characteristics, and treatment details from the SEER database. To analyze the data, we employed a Cox proportional hazards (CoxPH) model as well as 3 ML algorithms: neural network multitask logistic regression (N-MLTR), DeepSurv, and random survival forest (RSF). Our evaluation relied on the concordance index (C-index) and Integrated Brier Score (IBS). Additionally, we provided personalized treatment recommendations regarding surgery and chemotherapy choices and validated models' efficacy. A total of 1136 patients with early-stage (I, II) hepatocellular carcinoma (HCC) who underwent liver resection or transplantation were randomly divided into training and validation cohorts at a ratio of 3:7. Feature selection was conducted using Cox regression analyses. The ML models (NMLTR: C-index = 0.6793; DeepSurv: C-index = 0.7028; RSF: C-index = 0.6890) showed better discrimination in predicting survival than the standard CoxPH model (C-index = 0.6696). Patients who received recommended treatments had higher survival rates than those who received unrecommended treatments. ML-based surgery treatment recommendations yielded higher hazard ratios (HRs): NMTLR HR = 0.36 (95% CI: 0.25-0.51, P < .001), DeepSurv HR = 0.34 (95% CI: 0.24-0.49, P < .001), and RSF HR = 0.37 (95% CI: 0.26-0.52, P = <.001). Chemotherapy treatment recommendations were associated with significantly improved survival for DeepSurv (HR: 0.57; 95% CI: 0.4-0.82, P = .002) and RSF (HR: 0.66; 95% CI: 0.46-0.94, P = .020). The ML survival model has the potential to benefit prognostic evaluation and treatment of HCC. This novel analytical approach could provide reliable information on individual survival and treatment recommendations.
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Affiliation(s)
- Xianguo Li
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haijun Bao
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yongping Shi
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenzhong Zhu
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zuojie Peng
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lizhao Yan
- Department of Hand Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jinhuang Chen
- Department of Emergency Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaogang Shu
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Endo Y, Alaimo L, Lima HA, Moazzam Z, Ratti F, Marques HP, Soubrane O, Lam V, Kitago M, Poultsides GA, Popescu I, Alexandrescu S, Martel G, Workneh A, Guglielmi A, Hugh T, Aldrighetti L, Endo I, Pawlik TM. A Novel Online Calculator to Predict Risk of Microvascular Invasion in the Preoperative Setting for Hepatocellular Carcinoma Patients Undergoing Curative-Intent Surgery. Ann Surg Oncol 2023; 30:725-733. [PMID: 36103014 DOI: 10.1245/s10434-022-12494-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 07/25/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND The presence of microvascular invasion (MVI) has been highlighted as an important determinant of hepatocellular carcinoma (HCC) prognosis. We sought to build and validate a novel model to predict MVI in the preoperative setting. METHODS Patients who underwent curative-intent surgery for HCC between 2000 and 2020 were identified using a multi-institutional database. Preoperative predictive models for MVI were built, validated, and used to develop a web-based calculator. RESULTS Among 689 patients, MVI was observed in 323 patients (46.9%). On multivariate analysis in the test cohort, preoperative parameters associated with MVI included α-fetoprotein (AFP; odds ratio [OR] 1.50, 95% confidence interval [CI] 1.23-1.83), imaging tumor burden score (TBS; hazard ratio [HR] 1.11, 95% CI 1.04-1.18), and neutrophil-to-lymphocyte ratio (NLR; OR 1.18, 95% CI 1.03-1.35). An online calculator to predict MVI was developed based on the weighted β-coefficients of these three variables ( https://yutaka-endo.shinyapps.io/MVIrisk/ ). The c-index of the test and validation cohorts was 0.71 and 0.72, respectively. Patients with a high risk of MVI had worse disease-free survival (DFS) and overall survival (OS) compared with low-risk MVI patients (3-year DFS: 33.0% vs. 51.9%, p < 0.001; 5-year OS: 44.2% vs. 64.8%, p < 0.001). DFS was worse among patients who underwent an R1 versus R0 resection among those patients at high risk of MVI (R0 vs. R1 resection: 3-year DFS, 36.3% vs. 16.1%, p = 0.002). In contrast, DFS was comparable among patients at low risk of MVI regardless of margin status (R0 vs. R1 resection: 3-year DFS, 52.9% vs. 47.3%, p = 0.16). CONCLUSION Preoperative assessment of MVI using the online tool demonstrated very good accuracy to predict MVI.
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Affiliation(s)
- Yutaka Endo
- Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, Health Services Management and Policy, James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Laura Alaimo
- Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, Health Services Management and Policy, James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, OH, USA.,Department of Surgery, University of Verona, Verona, Italy
| | - Henrique A Lima
- Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, Health Services Management and Policy, James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Zorays Moazzam
- Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, Health Services Management and Policy, James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | | | - Hugo P Marques
- Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal
| | - Olivier Soubrane
- Department of Hepatibiliopancreatic Surgery, APHP, Beaujon Hospital, Clichy, France
| | - Vincent Lam
- Department of Surgery, Westmead Hospital, Sydney, NSW, Australia
| | - Minoru Kitago
- Department of Surgery, Keio University, Tokyo, Japan
| | | | - Irinel Popescu
- Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania
| | | | | | - Aklile Workneh
- Department of Surgery, University of Ottawa, Ottawa, ON, Canada
| | | | - Tom Hugh
- Department of Surgery, School of Medicine, The University of Sydney, Sydney, NSW, Australia
| | | | - Itaru Endo
- Yokohama City University School of Medicine, Yokohama, Japan
| | - Timothy M Pawlik
- Department of Surgery, The Urban Meyer III and Shelley Meyer Chair for Cancer Research, Health Services Management and Policy, James Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
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Wang J, Zheng T, Liao Y, Geng S, Li J, Zhang Z, Shang D, Liu C, Yu P, Huang Y, Liu C, Liu Y, Liu S, Wang M, Liu D, Miao H, Li S, Zhang B, Huang A, Zhang Y, Qi X, Chen S. Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study. Front Oncol 2022; 12:986867. [PMID: 36408144 PMCID: PMC9667038 DOI: 10.3389/fonc.2022.986867] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 10/14/2022] [Indexed: 09/16/2023] Open
Abstract
Introduction Post-hepatectomy liver failure (PHLF) is one of the most serious complications and causes of death in patients with hepatocellular carcinoma (HCC) after hepatectomy. This study aimed to develop a novel machine learning (ML) model based on the light gradient boosting machines (LightGBM) algorithm for predicting PHLF. Methods A total of 875 patients with HCC who underwent hepatectomy were randomized into a training cohort (n=612), a validation cohort (n=88), and a testing cohort (n=175). Shapley additive explanation (SHAP) was performed to determine the importance of individual variables. By combining these independent risk factors, an ML model for predicting PHLF was established. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and decision curve analyses (DCA) were used to evaluate the accuracy of the ML model and compare it to that of other noninvasive models. Results The AUCs of the ML model for predicting PHLF in the training cohort, validation cohort, and testing cohort were 0.944, 0.870, and 0.822, respectively. The ML model had a higher AUC for predicting PHLF than did other non-invasive models. The ML model for predicting PHLF was found to be more valuable than other noninvasive models. Conclusion A novel ML model for the prediction of PHLF using common clinical parameters was constructed and validated. The novel ML model performed better than did existing noninvasive models for the prediction of PHLF.
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Affiliation(s)
- Jitao Wang
- Xingtai Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People’s Hospital, Xingtai, Hebei, China
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, Jiangsu, China
| | - Tianlei Zheng
- Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, China
| | - Yong Liao
- Xingtai Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People’s Hospital, Xingtai, Hebei, China
| | - Shi Geng
- Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Jinlong Li
- Xingtai Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People’s Hospital, Xingtai, Hebei, China
| | - Zhanguo Zhang
- Department of Hepatobiliary Surgery, Tongji Hospital Affiliated to Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Dong Shang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Chengyu Liu
- Xingtai Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People’s Hospital, Xingtai, Hebei, China
| | - Peng Yu
- Department of Hepatobiliary Surgery, Fifth Medical Center of People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Yifei Huang
- Institute of Portal Hypertension, The First Hospital of Lanzhou University, Lanzhou, China
| | - Chuan Liu
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, Jiangsu, China
| | - Yanna Liu
- Department of Microbiology and Infectious Disease Center, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Shanghao Liu
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, Jiangsu, China
| | - Mingguang Wang
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, Jiangsu, China
| | - Dengxiang Liu
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, Jiangsu, China
| | - Hongrui Miao
- Department of Hepatobiliary Surgery, Tongji Hospital Affiliated to Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shuang Li
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Biao Zhang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Anliang Huang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Yewei Zhang
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xiaolong Qi
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, Jiangsu, China
| | - Shubo Chen
- Xingtai Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People’s Hospital, Xingtai, Hebei, China
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