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Liu W, Wu HY, Lin JX, Qu ST, Gu YJ, Zhu JZ, Xu CF. Combining lymph node ratio to develop prognostic models for postoperative gastric neuroendocrine neoplasm patients. World J Gastrointest Oncol 2024; 16:3507-3520. [DOI: 10.4251/wjgo.v16.i8.3507] [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: 03/28/2024] [Revised: 05/14/2024] [Accepted: 06/12/2024] [Indexed: 08/07/2024] Open
Abstract
BACKGROUND Lymph node ratio (LNR) was demonstrated to play a crucial role in the prognosis of many tumors. However, research concerning the prognostic value of LNR in postoperative gastric neuroendocrine neoplasm (NEN) patients was limited.
AIM To explore the prognostic value of LNR in postoperative gastric NEN patients and to combine LNR to develop prognostic models.
METHODS A total of 286 patients from the Surveillance, Epidemiology, and End Results database were divided into the training set and validation set at a ratio of 8:2. 92 patients from the First Affiliated Hospital of Soochow University in China were designated as a test set. Cox regression analysis was used to explore the relationship between LNR and disease-specific survival (DSS) of gastric NEN patients. Random survival forest (RSF) algorithm and Cox proportional hazards (CoxPH) analysis were applied to develop models to predict DSS respectively, and compared with the 8th edition American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging.
RESULTS Multivariate analyses indicated that LNR was an independent prognostic factor for postoperative gastric NEN patients and a higher LNR was accompanied by a higher risk of death. The RSF model exhibited the best performance in predicting DSS, with the C-index in the test set being 0.769 [95% confidence interval (CI): 0.691-0.846] outperforming the CoxPH model (0.744, 95%CI: 0.665-0.822) and the 8th edition AJCC TNM staging (0.723, 95%CI: 0.613-0.833). The calibration curves and decision curve analysis (DCA) demonstrated the RSF model had good calibration and clinical benefits. Furthermore, the RSF model could perform risk stratification and individual prognosis prediction effectively.
CONCLUSION A higher LNR indicated a lower DSS in postoperative gastric NEN patients. The RSF model outperformed the CoxPH model and the 8th edition AJCC TNM staging in the test set, showing potential in clinical practice.
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Affiliation(s)
- Wen Liu
- Department of Gastroenterology, Changzhou Hospital of Traditional Chinese Medicine, Changzhou 213000, Jiangsu Province, China
| | - Hong-Yu Wu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215006, Jiangsu Province, China
| | - Jia-Xi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215006, Jiangsu Province, China
| | - Shu-Ting Qu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215006, Jiangsu Province, China
| | - Yi-Jie Gu
- Department of Gastroenterology, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou 215200, Jiangsu Province, China
| | - Jin-Zhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215006, Jiangsu Province, China
| | - Chun-Fang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215006, Jiangsu Province, China
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Altaf A, Endo Y, Munir MM, Khan MMM, Rashid Z, Khalil M, Guglielmi A, Ratti F, Marques H, Cauchy F, Lam V, Poultsides G, Kitago M, Popescu I, Martel G, Gleisner A, Hugh T, Shen F, Endo I, Pawlik TM. Impact of an artificial intelligence based model to predict non-transplantable recurrence among patients with hepatocellular carcinoma. HPB (Oxford) 2024; 26:1040-1050. [PMID: 38796346 DOI: 10.1016/j.hpb.2024.05.006] [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: 01/20/2024] [Revised: 05/09/2024] [Accepted: 05/12/2024] [Indexed: 05/28/2024]
Abstract
OBJECTIVE We sought to develop Artificial Intelligence (AI) based models to predict non-transplantable recurrence (NTR) of hepatocellular carcinoma (HCC) following hepatic resection (HR). METHODS HCC patients who underwent HR between 2000-2020 were identified from a multi-institutional database. NTR was defined as recurrence beyond Milan Criteria. Different machine learning (ML) and deep learning (DL) techniques were used to develop and validate two prediction models for NTR, one using only preoperative factors and a second using both preoperative and postoperative factors. RESULTS Overall, 1763 HCC patients were included. Among 877 patients with recurrence, 364 (41.5%) patients developed NTR. An ensemble AI model demonstrated the highest area under ROC curves (AUC) of 0.751 (95% CI: 0.719-0.782) and 0.717 (95% CI:0.653-0.782) in the training and testing cohorts, respectively which improved to 0.858 (95% CI: 0.835-0.884) and 0.764 (95% CI: 0.704-0.826), respectively after incorporation of postoperative pathologic factors. Radiologic tumor burden score and pathological microvascular invasion were the most important preoperative and postoperative factors, respectively to predict NTR. Patients predicted to develop NTR had overall 1- and 5-year survival of 75.6% and 28.2%, versus 93.4% and 55.9%, respectively, among patients predicted to not develop NTR (p < 0.0001). CONCLUSION The AI preoperative model may help inform decision of HR versus LT for HCC, while the combined AI model can frame individualized postoperative care (https://altaf-pawlik-hcc-ntr-calculator.streamlit.app/).
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Affiliation(s)
- Abdullah Altaf
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Yutaka Endo
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Muhammad M Munir
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Muhammad Muntazir M Khan
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Zayed Rashid
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Mujtaba Khalil
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | | | | | - Hugo Marques
- Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal
| | - François Cauchy
- Department of Hepatobiliopancreatic Surgery, APHP, Beaujon Hospital, Clichy, France
| | - Vincent Lam
- Department of Surgery, Westmead Hospital, Sydney, NSW, Australia
| | - George Poultsides
- Department of Surgery, Stanford University, Stanford, CA, United States
| | - Minoru Kitago
- Department of Surgery, Keio University, Tokyo, Japan
| | - Irinel Popescu
- Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania
| | | | - Ana Gleisner
- Department of Surgery, University of Colorado, Aurora, CO, United States
| | - Tom Hugh
- Department of Surgery, School of Medicine, The University of Sydney, Sydney, NSW, Australia
| | - Feng Shen
- Department of Surgery, Eastern Hepatobiliary Surgery Hospital, Shanghai, China
| | - Itaru Endo
- Department of Surgery, Yokohama City University School of Medicine, Yokohama, Japan
| | - Timothy M Pawlik
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA.
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Yang C, Xu J, Wang S, Wang Y, Zhang Y, Piao C. Machine learning to predict the early recurrence of intrahepatic cholangiocarcinoma: A systematic review and meta‑analysis. Oncol Lett 2024; 28:385. [PMID: 38966582 PMCID: PMC11222917 DOI: 10.3892/ol.2024.14518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/12/2024] [Indexed: 07/06/2024] Open
Abstract
The prediction of early recurrent of intrahepatic cholangiocarcinoma (ICC) has been widely investigated; however, the predictive value is currently insufficient. To determine the effectiveness of machine learning (ML) for the diagnosis of early recurrent intrahepatic cholangiocarcinoma (ICC), particularly in comparison with clinical models, the present study aimed to determine which ML model had the best diagnostic performance for inpatients with recurrent ICC. In order to search for studies which could be included, three electronic databases were screened from inception to November 2023. A pairwise meta-analysis was performed to evaluate the diagnostic accuracy of the random effects model. A network meta-analysis was performed to identify the most effective ML-based diagnostic model based on the surface under the cumulative ranking curve score. A total of 5 studies of acceptable quality containing 1,247 patients with ICC were included in the present study. Following pairwise meta-analysis, it was found that the ML-based diagnostic accuracy was greater than that of clinical models (surface under the cumulative ranking curve score closer to 1, with significant differences), which initially proved that the ML-based diagnostic power was more optimal than that of clinical models. According to the network meta-analysis, the nomogram performed the best, indicating that this ML model achieved the best diagnostic accuracy for patients with recurrent ICC. In conclusion, the application of ML-based diagnostic models for patients with recurrent ICC was more optimal than the application of the clinical model. The nomogram model ranked first among the models and is therefore recommended for patients with recurrent ICC.
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Affiliation(s)
- Chao Yang
- Information Construction Department, Department of Ethnic Culture and Vocational Education, Liaoning National Normal College, Shenyang, Liaoning 110032, P.R. China
| | - Jianhui Xu
- Information Construction Department, Department of Ethnic Culture and Vocational Education, Liaoning National Normal College, Shenyang, Liaoning 110032, P.R. China
| | - Shuai Wang
- Department of Clinical Pharmacy, Shenyang Pharmaceutical University, Shenyang, Liaoning 110016, P.R. China
| | - Ying Wang
- Information Construction Department, Department of Ethnic Culture and Vocational Education, Liaoning National Normal College, Shenyang, Liaoning 110032, P.R. China
| | - Yingshi Zhang
- Department of Clinical Pharmacy, Shenyang Pharmaceutical University, Shenyang, Liaoning 110016, P.R. China
| | - Chengzhe Piao
- Information Construction Department, Department of Ethnic Culture and Vocational Education, Liaoning National Normal College, Shenyang, Liaoning 110032, P.R. China
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Chen J, Wang Y, Hong M, Wu J, Zhang Z, Li R, Ding T, Xu H, Zhang X, Chen P. Application of peripheral blood routine parameters in the diagnosis of influenza and Mycoplasma pneumoniae. Virol J 2024; 21:162. [PMID: 39044252 PMCID: PMC11267962 DOI: 10.1186/s12985-024-02429-4] [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: 04/26/2024] [Accepted: 07/05/2024] [Indexed: 07/25/2024] Open
Abstract
OBJECTIVES Influenza and Mycoplasma pneumoniae infections often present concurrent and overlapping symptoms in clinical manifestations, making it crucial to accurately differentiate between the two in clinical practice. Therefore, this study aims to explore the potential of using peripheral blood routine parameters to effectively distinguish between influenza and Mycoplasma pneumoniae infections. METHODS This study selected 209 influenza patients (IV group) and 214 Mycoplasma pneumoniae patients (MP group) from September 2023 to January 2024 at Nansha Division, the First Affiliated Hospital of Sun Yat-sen University. We conducted a routine blood-related index test on all research subjects to develop a diagnostic model. For normally distributed parameters, we used the T-test, and for non-normally distributed parameters, we used the Wilcoxon test. RESULTS Based on an area under the curve (AUC) threshold of ≥ 0.7, we selected indices such as Lym# (lymphocyte count), Eos# (eosinophil percentage), Mon% (monocyte percentage), PLT (platelet count), HFC# (high fluorescent cell count), and PLR (platelet to lymphocyte ratio) to construct the model. Based on these indicators, we constructed a diagnostic algorithm named IV@MP using the random forest method. CONCLUSIONS The diagnostic algorithm demonstrated excellent diagnostic performance and was validated in a new population, with an AUC of 0.845. In addition, we developed a web tool to facilitate the diagnosis of influenza and Mycoplasma pneumoniae infections. The results of this study provide an effective tool for clinical practice, enabling physicians to accurately diagnose and differentiate between influenza and Mycoplasma pneumoniae infection, thereby offering patients more precise treatment plans.
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Affiliation(s)
- Jingrou Chen
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
- Department of Laboratory Medicine, Nansha Division, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 511466, China
| | - Yang Wang
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
- Department of Laboratory Medicine, Nansha Division, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 511466, China
| | - Mengzhi Hong
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
- Department of Laboratory Medicine, Nansha Division, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 511466, China
| | - Jiahao Wu
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
- Department of Laboratory Medicine, Nansha Division, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 511466, China
| | - Zongjun Zhang
- Department of Laboratory Medicine, Guangdong Province Prevention and Treatment Center for Occupational Diseases, Guangzhou, 510300, China
| | - Runzhao Li
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
- Department of Laboratory Medicine, Nansha Division, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 511466, China
| | - Tangdan Ding
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
- Department of Laboratory Medicine, Nansha Division, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 511466, China
| | - Hongxu Xu
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
- Department of Laboratory Medicine, Nansha Division, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 511466, China
| | - Xiaoli Zhang
- Department of Pediatrics, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Peisong Chen
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China.
- Department of Laboratory Medicine, Nansha Division, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 511466, China.
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Li C, Lei Q, Ju P, Liu J, Deng W, Chen L. Effect of trans-theoretical model-based nursing intervention on emotion and fear in post-liver cancer surgery patients. Am J Transl Res 2024; 16:2346-2357. [PMID: 39006267 PMCID: PMC11236662 DOI: 10.62347/myea6043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 05/23/2024] [Indexed: 07/16/2024]
Abstract
OBJECTIVE To investigate the efficacy of nursing interventions grounded in the trans-theoretical model on emotion and fear among patients undergoing surgery for hepatocellular carcinoma (HCC). METHODS The study included 188 surgical patients from the Second People's Hospital of Lanzhou City who underwent HCC intervention between March 2020 and May 2022. The control group comprised 81 patients receiving standard postoperative care, while the observation group included 107 patients who received nursing interventions based on the trans-theoretical model. We assessed outcomes using the Fear of Progression Questionnaire-Short Form (FOP-Q-SF), Quality of Life Questionnaire Core 30 (QLQ-C30), Gastrointestinal Comfort Questionnaire (GCQ), Self-Rating Anxiety Scale (SAS), and Self-rating Depression Scale (SDS) before and after the intervention. Logistic regression was used to identify factors influencing post-intervention fear. RESULTS Both groups showed improvement in FOP-Q-SF, QLQ-C30, GCQ, SAS, and SDS scores after the intervention. However, the observation group demonstrated significantly greater improvements (P < 0.05). There was a positive correlation between FOP-Q-SF scores and both SAS and SDS scores (all P < 0.05), and a negative correlation with QLQ-C30 and GCQ scores (both P < 0.05). Multifactorial logistic regression revealed that age (P < 0.001, OR: 8.328), gender (P < 0.001, OR: 0.181), literacy level (P < 0.001, OR: 0.354), and nursing care regimen (P < 0.001, OR: 0.078) were significant independent risk factors for persistence of fearpost-intervention. CONCLUSION The implementation of nursing interventions based on the trans-theoretical model significantly reduces postoperative fear and anxiety, improves pain perception, and enhances overall comfort in patients after liver cancer surgery.
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Affiliation(s)
- Caihong Li
- Department of Hepatology II, The Second People's Hospital of Lanzhou City No. 388 Jingyuan Road, Chengguan District, Lanzhou 730046, Gansu, China
| | - Qingfen Lei
- Department of Hepatology II, The Second People's Hospital of Lanzhou City No. 388 Jingyuan Road, Chengguan District, Lanzhou 730046, Gansu, China
| | - Ping Ju
- Department of Gastroenterology, The Second People's Hospital of Lanzhou City No. 388 Jingyuan Road, Chengguan District, Lanzhou 730046, Gansu, China
| | - Junlan Liu
- Department of Hepatology II, The Second People's Hospital of Lanzhou City No. 388 Jingyuan Road, Chengguan District, Lanzhou 730046, Gansu, China
| | - Wenmin Deng
- Department of Gastroenterology, The Second People's Hospital of Lanzhou City No. 388 Jingyuan Road, Chengguan District, Lanzhou 730046, Gansu, China
| | - Li Chen
- Department of Endoscopy Center, The Second People's Hospital of Lanzhou City No. 388 Jingyuan Road, Chengguan District, Lanzhou 730046, Gansu, China
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Wang S, Shao M, Fu Y, Zhao R, Xing Y, Zhang L, Xu Y. Deep learning models for predicting the survival of patients with hepatocellular carcinoma based on a surveillance, epidemiology, and end results (SEER) database analysis. Sci Rep 2024; 14:13232. [PMID: 38853169 PMCID: PMC11163004 DOI: 10.1038/s41598-024-63531-9] [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: 02/16/2024] [Accepted: 05/29/2024] [Indexed: 06/11/2024] Open
Abstract
Hepatocellular carcinoma (HCC) is a common malignancy with poor survival and requires long-term follow-up. Hence, we collected information on patients with Primary Hepatocellular Carcinoma in the United States from the Surveillance, Epidemiology, and EndResults (SEER) database. We used this information to establish a deep learning with a multilayer neural network (the NMTLR model) for predicting the survival rate of patients with Primary Hepatocellular Carcinoma. HCC patients pathologically diagnosed between January 2011 and December 2015 in the SEER (Surveillance, Epidemiology, and End Results) database of the National Cancer Institute of the United States were selected as study subjects. We utilized two deep learning-based algorithms (DeepSurv and Neural Multi-Task Logistic Regression [NMTLR]) and a machine learning-based algorithm (Random Survival Forest [RSF]) for model training. A multivariable Cox Proportional Hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into a training set and a test set in a 7:3 ratio. The training dataset underwent hyperparameter tuning through 1000 iterations of random search and fivefold cross-validation. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-year, 3-year, and 5-year survival rates was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Area Under the Curve (AUC). The primary outcomes were the 1-year, 3-year, and 5-year overall survival rates. Models were developed using DeepSurv, NMTLR, RSF, and Cox Proportional Hazards regression. Model differentiation was evaluated using the C-index, calibration with concordance plots, and risk stratification capability with the log-rank test. The study included 2197 HCC patients, randomly divided into a training cohort (70%, n = 1537) and a testing cohort (30%, n = 660). Clinical characteristics between the two cohorts showed no significant statistical difference (p > 0.05). The deep learning models outperformed both RSF and CoxPH models, with C-indices of 0.735 (NMTLR) and 0.731 (DeepSurv) in the test dataset. The NMTLR model demonstrated enhanced accuracy and well-calibrated survival estimates, achieving an Area Under the Curve (AUC) of 0.824 for 1-year survival predictions, 0.813 for 3-year, and 0.803 for 5-year survival rates. This model's superior calibration and discriminative ability enhance its utility for clinical prognostication in Primary Hepatocellular Carcinoma. We deployed the NMTLR model as a web application for clinical practice. The NMTLR model have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with primary liver cancer.
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Affiliation(s)
- Shoucheng Wang
- Department of Gastroenterology, The First Affiliated Hospital of Henan University of Chinese Medicine, The First Clinical Medical College of Henan University of Chinese Medicine, Zhengzhou, 450000, China
| | - Mingyi Shao
- Personnel Department, The First Affiliated Hospitalof Henan University of Chinese Medicine, Zhengzhou, 450000, China.
| | - Yu Fu
- Research Department, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, 450000, China
| | - Ruixia Zhao
- Henan Evidence-Based Medicine Center of Traditional Chinese Medicine, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, 450000, China
| | - Yunfei Xing
- Henan Evidence-Based Medicine Center of Traditional Chinese Medicine, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, 450000, China
| | - Liujie Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Henan University of Chinese Medicine, The First Clinical Medical College of Henan University of Chinese Medicine, Zhengzhou, 450000, China
| | - Yang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Henan University of Chinese Medicine, The First Clinical Medical College of Henan University of Chinese Medicine, Zhengzhou, 450000, China
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Li J, Zou L, Ma H, Zhao J, Wang C, Li J, Hu G, Yang H, Wang B, Xu D, Xia Y, Jiang Y, Jiang X, Li N. Interpretable machine learning based on CT-derived extracellular volume fraction to predict pathological grading of hepatocellular carcinoma. Abdom Radiol (NY) 2024:10.1007/s00261-024-04313-9. [PMID: 38703190 DOI: 10.1007/s00261-024-04313-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/23/2024] [Accepted: 03/25/2024] [Indexed: 05/06/2024]
Abstract
PURPOSE To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC). METHODS The study retrospectively analyzed 238 patients who underwent HCC resection surgery between January 2013 and April 2023. Six machine learning algorithms were employed to construct predictive models for HCC PG: logistic regression, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), random forest, adaptive boosting, and Gaussian naive Bayes. Model performance was evaluated using receiver operating characteristic curve analysis, including area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1 score. Calibration plots were used for visual evaluation of model calibration. Clinical decision curve analysis was performed to assess potential clinical utility by calculating net benefit. RESULTS 166 patients from Hospital A were allocated to the training set, while 72 patients from Hospital B (constituting 30.25% of the total sample) were assigned to the test set. The model achieved an AUC of 1.000 (95%CI: 1.000-1.000) in the training set and 0.927 (95%CI: 0.837-0.999) in the validation set, respectively. Ultimately, the model achieved an AUC of 0.909 (95%CI: 0.837-0.980) in the test set, with an accuracy of 0.778, sensitivity of 0.906, specificity of 0.789, negative predictive value of 0.556, and F1 score of 0.908. CONCLUSION This study successfully developed and validated a non-invasive auxiliary assessment method based on CT-derived ECV to predict the HCC PG, providing important supplementary information for clinical decision-making.
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Affiliation(s)
- Jie Li
- Department of Radiology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Bincheng District, Binzhou, 256600, China
| | - Linxuan Zou
- Department of Radiology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Bincheng District, Binzhou, 256600, China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, 264000, China
| | - Jifu Zhao
- Department of Radiology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Bincheng District, Binzhou, 256600, China
| | - Chengyan Wang
- Department of Radiology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Bincheng District, Binzhou, 256600, China
| | - Jun Li
- Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264000, China
| | - Guangchao Hu
- School of Medical Imaging, Binzhou Medical University, No. 346 Guanhai Road, Laishan District, Yantai, 264003, China
| | - Haoran Yang
- School of Medical Imaging, Binzhou Medical University, No. 346 Guanhai Road, Laishan District, Yantai, 264003, China
| | - Beizhong Wang
- Department of Radiology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Bincheng District, Binzhou, 256600, China
| | - Donghao Xu
- School of Medical Imaging, Binzhou Medical University, No. 346 Guanhai Road, Laishan District, Yantai, 264003, China
| | - Yuanhao Xia
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, 264000, China
- School of Medical Imaging, Binzhou Medical University, No. 346 Guanhai Road, Laishan District, Yantai, 264003, China
| | - Yi Jiang
- Department of Vascular Interventional Surgery, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264000, China
| | - Xingyue Jiang
- Department of Radiology, Binzhou Medical University Hospital, No. 661 Huanghe 2nd Road, Bincheng District, Binzhou, 256600, China.
| | - Naixuan Li
- Department of Vascular Interventional Surgery, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264000, China.
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Huang Z, Huang X, Huang Y, Liang K, Chen L, Zhong C, Chen Y, Chen C, Wang Z, He F, Qin M, Long C, Tang B, Huang Y, Wu Y, Mo X, Weizhong T, Liu J. Identification of KRAS mutation-associated gut microbiota in colorectal cancer and construction of predictive machine learning model. Microbiol Spectr 2024; 12:e0272023. [PMID: 38572984 PMCID: PMC11064510 DOI: 10.1128/spectrum.02720-23] [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: 07/24/2023] [Accepted: 02/27/2024] [Indexed: 04/05/2024] Open
Abstract
Gut microbiota has demonstrated an increasingly important role in the onset and development of colorectal cancer (CRC). Nonetheless, the association between gut microbiota and KRAS mutation in CRC remains enigmatic. We conducted 16S rRNA sequencing on stool samples from 94 CRC patients and employed the linear discriminant analysis effect size algorithm to identify distinct gut microbiota between KRAS mutant and KRAS wild-type CRC patients. Transcriptome sequencing data from nine CRC patients were transformed into a matrix of immune infiltrating cells, which was then utilized to explore KRAS mutation-associated biological functions, including Gene Ontology items and Kyoto Encyclopedia of Genes and Genomes pathways. Subsequently, we analyzed the correlations among these KRAS mutation-associated gut microbiota, host immunity, and KRAS mutation-associated biological functions. At last, we developed a predictive random forest (RF) machine learning model to predict the KRAS mutation status in CRC patients, based on the gut microbiota associated with KRAS mutation. We identified a total of 26 differential gut microbiota between both groups. Intriguingly, a significant positive correlation was observed between Bifidobacterium spp. and mast cells, as well as between Bifidobacterium longum and chemokine receptor CX3CR1. Additionally, we also observed a notable negative correlation between Bifidobacterium and GOMF:proteasome binding. The RF model constructed using the KRAS mutation-associated gut microbiota demonstrated qualified efficacy in predicting the KRAS phenotype in CRC. Our study ascertained the presence of 26 KRAS mutation-associated gut microbiota in CRC and speculated that Bifidobacterium may exert an essential role in preventing CRC progression, which appeared to correlate with the upregulation of mast cells and CX3CR1 expression, as well as the downregulation of GOMF:proteasome binding. Furthermore, the RF model constructed on the basis of KRAS mutation-associated gut microbiota exhibited substantial potential in predicting KRAS mutation status in CRC patients.IMPORTANCEGut microbiota has emerged as an essential player in the onset and development of colorectal cancer (CRC). However, the relationship between gut microbiota and KRAS mutation in CRC remains elusive. Our study not only identified a total of 26 gut microbiota associated with KRAS mutation in CRC but also unveiled their significant correlations with tumor-infiltrating immune cells, immune-related genes, and biological pathways (Gene Ontology items and Kyoto Encyclopedia of Genes and Genomes pathways). We speculated that Bifidobacterium may play a crucial role in impeding CRC progression, potentially linked to the upregulation of mast cells and CX3CR1 expression, as well as the downregulation of GOMF:Proteasome binding. Furthermore, based on the KRAS mutation-associated gut microbiota, the RF model exhibited promising potential in the prediction of KRAS mutation status for CRC patients. Overall, the findings of our study offered fresh insights into microbiological research and clinical prediction of KRAS mutation status for CRC patients.
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Affiliation(s)
- Zigui Huang
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Xiaoliang Huang
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Yili Huang
- College of Oncology, Guangxi Medical University, Nanning, China
| | - Kunmei Liang
- College of Oncology, Guangxi Medical University, Nanning, China
| | - Lei Chen
- College of Oncology, Guangxi Medical University, Nanning, China
| | - Chuzhuo Zhong
- College of Oncology, Guangxi Medical University, Nanning, China
| | - Yingxin Chen
- College of Oncology, Guangxi Medical University, Nanning, China
| | - Chuanbin Chen
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Zhen Wang
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Fuhai He
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Mingjian Qin
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Chenyan Long
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Binzhe Tang
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Yongqi Huang
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Yongzhi Wu
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Xianwei Mo
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Tang Weizhong
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Jungang Liu
- Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
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9
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Zhang W, Song LN, You YF, Qi FN, Cui XH, Yi MX, Zhu G, Chang RA, Zhang HJ. Application of artificial intelligence in the prediction of immunotherapy efficacy in hepatocellular carcinoma: Current status and prospects. Artif Intell Gastroenterol 2024; 5:90096. [DOI: 10.35712/aig.v5.i1.90096] [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: 11/23/2023] [Revised: 01/28/2024] [Accepted: 03/12/2024] [Indexed: 04/29/2024] Open
Abstract
Artificial Intelligence (AI) has increased as a potent tool in medicine, with promising oncology applications. The emergence of immunotherapy has transformed the treatment terrain for hepatocellular carcinoma (HCC), offering new hope to patients with this challenging malignancy. This article examines the role and future of AI in forecasting the effectiveness of immunotherapy in HCC. We highlight the potential of AI to revolutionize the prediction of therapy response, thus improving patient selection and clinical outcomes. The article further outlines the challenges and future research directions in this emerging field.
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Affiliation(s)
- Wei Zhang
- Research Center of Clinical Medicine and Department of General Surgery, The Affiliated Hospital of Nantong University, Nantong 226001, Jiangsu Province, China
| | - Li-Ning Song
- Research Center of Clinical Medicine and Department of General Surgery, The Affiliated Hospital of Nantong University, Nantong 226001, Jiangsu Province, China
| | - Yun-Fei You
- Research Center of Clinical Medicine and Department of General Surgery, The Affiliated Hospital of Nantong University, Nantong 226001, Jiangsu Province, China
| | - Feng-Nan Qi
- Research Center of Clinical Medicine and Department of General Surgery, The Affiliated Hospital of Nantong University, Nantong 226001, Jiangsu Province, China
| | - Xiao-Hong Cui
- Department of General Surgery, Shanghai Electric Power Hospital, Shanghai 200050, China
| | - Ming-Xun Yi
- Research Center of Clinical Medicine and Department of General Surgery, The Affiliated Hospital of Nantong University, Nantong 226001, Jiangsu Province, China
| | - Guang Zhu
- Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Ren-An Chang
- Research Center of Clinical Medicine and Department of General Surgery, The Affiliated Hospital of Nantong University, Nantong 226001, Jiangsu Province, China
| | - Hai-Jian Zhang
- Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong, China
- Research Center of Clinical Medicine, The Affiliated Hospital of Nantong University, Nantong 226001, Jiangsu Province, China
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10
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Shao G, Ma Y, Qu C, Gao R, Zhu C, Qu L, Liu K, Li N, Sun P, Cao J. Machine Learning Model Based on the Neutrophil-to-Eosinophil Ratio Predicts the Recurrence of Hepatocellular Carcinoma After Surgery. J Hepatocell Carcinoma 2024; 11:679-691. [PMID: 38585292 PMCID: PMC10999194 DOI: 10.2147/jhc.s455612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 03/29/2024] [Indexed: 04/09/2024] Open
Abstract
Background Circulating eosinophils are associated with tumor development. An eosinophil-related index, the neutrophil to eosinophil ratio (NER), can be used to predict the prognosis of patients with tumors. However, there is still a lack of efficient prognostic biomarkers for HCC. In this study, we aimed to investigate the predictive value of the NER and develop an optimal machine learning model for the recurrence of HCC patients. Patients and methods: A retrospective collection of 562 patients who underwent hepatectomy with a pathologic diagnosis of HCC was performed. The relationship between NER and progression-free survival (PFS) was investigated. We developed a new machine learning framework with 10 machine learning algorithms and their 101 combinations to select the best model for predicting recurrence after hepatectomy. The performance of the model was assessed by the area under the curve (AUC) of characteristics and calibration curves, and clinical utility was evaluated by decision curve analysis (DCA). Results Kaplan‒Meier curves showed that the PFS in the low NER group was significantly better than that in the high NER group. Multivariate Cox regression analysis showed that NER was an independent risk factor for recurrence after surgery. The random survival forests (RSF) model was selected as the best model that had good predictive efficacy and outperformed the TNM, BCLC, and CNLC staging systems. Conclusion The NER has good predictive value for postoperative recurrence in patients with hepatocellular carcinoma. Machine learning model based on NER can be used for accurate predictions.
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Affiliation(s)
- Guanming Shao
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266000, People’s Republic of China
| | - Yonghui Ma
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266000, People’s Republic of China
| | - Chao Qu
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266000, People’s Republic of China
| | - Ruiqian Gao
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266000, People’s Republic of China
| | - Chengzhan Zhu
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266000, People’s Republic of China
| | - Linlin Qu
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266000, People’s Republic of China
| | - Kui Liu
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266000, People’s Republic of China
| | - Na Li
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266000, People’s Republic of China
| | - Peng Sun
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266000, People’s Republic of China
| | - Jingyu Cao
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266000, People’s Republic of China
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11
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Bahsoun A, Hussain HK. The Missing Link: Sarcopenia's Contribution to Long-term HCC Prognosis. Acad Radiol 2024; 31:1284-1287. [PMID: 38519301 DOI: 10.1016/j.acra.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 03/24/2024]
Affiliation(s)
- Aymen Bahsoun
- Department of Radiology, University of Michigan and Michigan Medicine, Michigan Institute of Imaging Technology and Translation, 1500 E Medical Center Drive, Ann Arbor, Michigan 48109, USA
| | - Hero K Hussain
- Department of Radiology, University of Michigan and Michigan Medicine, Michigan Institute of Imaging Technology and Translation, 1500 E Medical Center Drive, Ann Arbor, Michigan 48109, USA.
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12
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Ramírez-Mejía MM, Méndez-Sánchez N. From prediction to prevention: Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring. World J Gastroenterol 2024; 30:631-635. [PMID: 38515945 PMCID: PMC10950631 DOI: 10.3748/wjg.v30.i7.631] [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: 11/13/2023] [Revised: 12/12/2023] [Accepted: 01/22/2024] [Indexed: 02/21/2024] Open
Abstract
In this editorial, we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma. Hepatocellular carcinoma (HCC), which is characterized by high incidence and mortality rates, remains a major global health challenge primarily due to the critical issue of postoperative recurrence. Early recurrence, defined as recurrence that occurs within 2 years posttreatment, is linked to the hidden spread of the primary tumor and significantly impacts patient survival. Traditional predictive factors, including both patient- and treatment-related factors, have limited predictive ability with respect to HCC recurrence. The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research. The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence. Chall-enges persist, including sample size constraints, issues with handling data, and the need for further validation and interpretability. This study emphasizes the need for collaborative efforts, multicenter studies and comparative analyses to validate and refine the model. Overcoming these challenges and exploring innovative approaches, such as multi-omics integration, will enhance personalized oncology care. This study marks a significant stride toward precise, effi-cient, and personalized oncology practices, thus offering hope for improved patient outcomes in the field of HCC treatment.
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Affiliation(s)
- Mariana Michelle Ramírez-Mejía
- Plan of Combined Studies in Medicine, Faculty of Medicine, National Autonomous University of Mexico, Distrito Federal 04510, Mexico
- Liver Research Unit, Medica Sur Clinic & Foundation, Distrito Federal 14050, Mexico
| | - Nahum Méndez-Sánchez
- Liver Research Unit, Medica Sur Clinic & Foundation, Distrito Federal 14050, Mexico
- Faculty of Medicine, National Autonomous University of Mexico, Distrito Federal 04510, Mexico
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13
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Ravikulan A, Rostami K. Leveraging machine learning for early recurrence prediction in hepatocellular carcinoma: A step towards precision medicine. World J Gastroenterol 2024; 30:424-428. [PMID: 38414588 PMCID: PMC10895597 DOI: 10.3748/wjg.v30.i5.424] [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: 11/21/2023] [Revised: 12/19/2023] [Accepted: 01/12/2024] [Indexed: 01/31/2024] Open
Abstract
The high rate of early recurrence in hepatocellular carcinoma (HCC) post curative surgical intervention poses a substantial clinical hurdle, impacting patient outcomes and complicating postoperative management. The advent of machine learning provides a unique opportunity to harness vast datasets, identifying subtle patterns and factors that elude conventional prognostic methods. Machine learning models, equipped with the ability to analyse intricate relationships within datasets, have shown promise in predicting outcomes in various medical disciplines. In the context of HCC, the application of machine learning to predict early recurrence holds potential for personalized postoperative care strategies. This editorial comments on the study carried out exploring the merits and efficacy of random survival forests (RSF) in identifying significant risk factors for recurrence, stratifying patients at low and high risk of HCC recurrence and comparing this to traditional COX proportional hazard models (CPH). In doing so, the study demonstrated that the RSF models are superior to traditional CPH models in predicting recurrence of HCC and represent a giant leap towards precision medicine.
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Affiliation(s)
- Abhimati Ravikulan
- Department of Gastroenterology, Palmerston North Hospital, Palmerston North 4442, New Zealand
| | - Kamran Rostami
- Department of Gastroenterology, Palmerston North Hospital, Palmerston North 4442, New Zealand
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14
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Amory B, Goumard C, Laurent A, Langella S, Cherqui D, Salame E, Barbier L, Soubrane O, Farges O, Hobeika C, Kawai T, Regimbeau JM, Faitot F, Pessaux P, Truant S, Boleslawski E, Herrero A, Mabrut JY, Chiche L, Di Martino M, Rhaiem R, Schwarz L, Resende V, Calderaro J, Augustin J, Caruso S, Sommacale D, Hofmeyr S, Ferrero A, Fuks D, Vibert E, Torzilli G, Scatton O, Brustia R. Combined hepatocellular-cholangiocarcinoma compared to hepatocellular carcinoma and intrahepatic cholangiocarcinoma: Different survival, similar recurrence: Report of a large study on repurposed databases with propensity score matching. Surgery 2024; 175:413-423. [PMID: 37981553 DOI: 10.1016/j.surg.2023.09.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/06/2023] [Accepted: 09/26/2023] [Indexed: 11/21/2023]
Abstract
BACKGROUND Combined hepatocholangiocarcinoma is a rare cancer with a grim prognosis composed of both hepatocellular carcinoma and intrahepatic cholangiocarcinoma morphologic patterns in the same tumor. The aim of this multicenter, international cohort study was to compare the oncologic outcomes after surgery of combined hepatocholangiocarcinoma to hepatocellular carcinoma and intrahepatic cholangiocarcinoma. METHODS Patients treated by surgery for combined hepatocholangiocarcinoma, hepatocellular carcinoma, and intrahepatic cholangiocarcinoma from 2000 to 2021 from multicenter international databases were analyzed retrospectively. Patients with combined hepatocholangiocarcinoma (cases) were compared with 2 control groups of hepatocellular carcinoma or intrahepatic cholangiocarcinoma, sequentially matched using a propensity score based on 8 preoperative characteristics. Overall and disease-free survival were compared, and predictors of mortality and recurrence were analyzed with Cox regression after propensity score matching. RESULTS During the study period, 3,196 patients were included. Propensity score adjustment and 2 sequential matching processes produced a new cohort (n = 244) comprising 3 balanced groups was obtained (combined hepatocholangiocarcinoma = 56, intrahepatic cholangiocarcinoma = 66, and hepatocellular carcinoma = 122). Kaplan-Meier overall survival estimations at 1, 3, and 5 years were 67%, 45%, and 28% for combined hepatocholangiocarcinoma, 92%, 75%, and 55% for hepatocellular carcinoma, and 86%, 53%, and 42% for the intrahepatic cholangiocarcinoma group, respectively (P = .0014). Estimations of disease-free survival at 1, 3, and 5 years were 51%, 25%, and 17% for combined hepatocholangiocarcinoma, 63%, 35%, and 26% for the hepatocellular carcinoma group, and 51%, 31%, and 28% for the intrahepatic cholangiocarcinoma group, respectively (P = .19). Predictors of mortality were combined hepatocholangiocarcinoma subtype, metabolic syndrome, preoperative tumor markers alpha-fetoprotein and carbohydrate antigen 19-9, and satellite nodules, and recurrence was associated with satellite nodules rather than cancer subtype. CONCLUSION Despite data limitations, overall survival among patients with combined hepatocholangiocarcinoma was worse than both groups and closer intrahepatic cholangiocarcinoma, whereas disease-free survival was similar among the 3 groups. Future research on immunophenotypic profiling may hold more promise than traditional nonmodifiable clinical characteristics (as found in this study) in predicting recurrence or response to salvage treatments.
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Affiliation(s)
- Boris Amory
- Department of Digestive and Hepato-pancreatic-biliary Surgery, AP-HP, Hôpital Henri-Mondor, Paris Est Créteil University, UPEC, France; Assistance Publique-Hôpitaux de Paris, Créteil, France
| | - Claire Goumard
- Department of Hepatobiliary and Liver Transplantation Surgery, AP-HP, Hôpital Pitié Salpêtrière, CRSA, Sorbonne Université, Paris, France
| | - Alexis Laurent
- Department of Digestive and Hepato-pancreatic-biliary Surgery, DMU CARE, Assistance Publique-Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri Mondor, Créteil, France; Paris Est Créteil University, UPEC, France; Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," INSERM U955, Créteil, France; Assistance Publique-Hôpitaux de Paris, Créteil, France
| | - Serena Langella
- Department of General and Oncological Surgery, Ospedale Mauriziano, Torino, Italy
| | - Daniel Cherqui
- Center Hepato-Biliaire, AP-HP Paul Brousse Hospital, Paris-Saclay University, Villejuif, France
| | - Ephrem Salame
- Department of Digestive Surgery and Liver Transplantation, University Hospital of Tours, University of Tours, France; FHU Support, Tours, France
| | - Louise Barbier
- Department of Digestive Surgery and Liver Transplantation, University Hospital of Tours, University of Tours, France; FHU Support, Tours, France
| | - Olivier Soubrane
- Department of Digestive, Oncological, and Metabolic Surgery, Institut Mutualiste Montsouris, Paris, France
| | - Olivier Farges
- Department of HPB Surgery and Liver Transplantation, AP-HP Beaujon Hospital, University of Paris, Clichy, France
| | - Christian Hobeika
- Department of HPB Surgery and Liver Transplantation, AP-HP Beaujon Hospital, University of Paris, Clichy, France
| | - Takayuki Kawai
- Department of Surgery, Medical Research Institute, Kitano Hospital, Osaka and Graduate School of Medicine, Kyoto University, Japan
| | - Jean-Marc Regimbeau
- SSPC (Simplification of Surgical Patients Care) - Clinical Research Unit, University of Picardie Jules Verne, Amiens, France; Department of Digestive Surgery, Amiens University Medical Center, France
| | - François Faitot
- Service de Chirurgie Hépato-Biliaire et Transplantation Hépatique, Hôpital de Hautepierre, Strasbourg, France
| | - Patrick Pessaux
- Unité Chirurgie HBP, Pôle hépato-digestif Nouvel Hôpital Civil, Strasbourg, France; Institut of Viral and Liver Disease, Inserm U1110, Strasbourg, France
| | - Stéphanie Truant
- Department of Digestive Surgery and Transplantation, University Hospitals, Lille, France
| | - Emmanuel Boleslawski
- Department of Digestive Surgery and Transplantation, University Hospitals, Lille, France
| | - Astrid Herrero
- Department of HBP Surgery and Liver Transplantation, Montpellier University Hospital, University of Montpellier, France
| | - Jean-Yves Mabrut
- Croix Rousse University Hospital, Department of General Surgery and Liver Transplantation, Lyon, France; Cancer Research Center of Lyon, INSERM U1052, France
| | - Laurence Chiche
- Department of Hepato-Bilio-Pancreatic Surgery and Liver Transplantation, Haut Lévêque Hospital, Center Hospitalier Universitaire de Bordeaux, France; Inserm UMR 1312-Team 3 "Liver Cancers and Tumoral Invasion," Bordeaux Institute of Oncology, University of Bordeaux, France
| | - Marcello Di Martino
- HPB Unit, Department of General and Digestive Surgery, Hospital Universitario de la Princesa, Instituto de Investigación Sanitaria Princesa (IIS-IP), Universidad Autónoma de Madrid (UAM), Madrid, Spain
| | - Rami Rhaiem
- Department of Hepatobiliary, Pancreatic, and Digestive Surgery, Robert Debré University Hospital, Reims, France; University Reims Champagne-Ardenne, France
| | - Lilian Schwarz
- Department of Genomic and Personalized Medicine in Cancer and Neurological Disorders, Rouen University Hospital, UNIROUEN, UMR 1245 INSERM, Normandie Rouen University, France
| | - Vivian Resende
- Federal University of Minas Gerais School of Medicine, Belo Horizonte, Brazil
| | - Julien Calderaro
- Université Paris Est Créteil, INSERM, IMRB, Créteil, France; Assistance Publique-Hôpitaux de Paris, Henri Mondor-Albert Chenevier University Hospital, Department of Pathology, Créteil, France; Inserm, U955, Team 18, Créteil, France
| | - Jérémy Augustin
- Université Paris Est Créteil, INSERM, IMRB, Créteil, France; Assistance Publique-Hôpitaux de Paris, Henri Mondor-Albert Chenevier University Hospital, Department of Pathology, Créteil, France; Inserm, U955, Team 18, Créteil, France
| | - Stefano Caruso
- Université Paris Est Créteil, INSERM, IMRB, Créteil, France; Assistance Publique-Hôpitaux de Paris, Henri Mondor-Albert Chenevier University Hospital, Department of Pathology, Créteil, France; Inserm, U955, Team 18, Créteil, France
| | - Daniele Sommacale
- Department of Digestive and Hepato-pancreatic-biliary Surgery, DMU CARE, Assistance Publique-Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri Mondor, Créteil, France; Paris Est Créteil University, UPEC, Créteil, France; Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," INSERM U955, Créteil, France; Assistance Publique-Hôpitaux de Paris, Créteil, France
| | - Stefan Hofmeyr
- Division of Surgery, Faculty of Medicine and Health Sciences, Stellenbosch University and Tygerberg Academic Hospital, Cape Town, South Africa
| | - Alessandro Ferrero
- Department of General and Oncological Surgery, Ospedale Mauriziano, Torino, Italy
| | - David Fuks
- Department of Hepato-Pancreatic-Biliary and Endocrine Surgery, Hopital Cochin, Assistance Publique-Hôpitaux de Paris, France; Université Paris Cité, France
| | - Eric Vibert
- Center Hepato-Biliaire, AP-HP Paul Brousse Hospital, Paris-Saclay University, Villejuif, France
| | - Guido Torzilli
- Division of Hepatobiliary and General Surgery, Department of Surgery, Humanitas Research Hospital - IRCCS, Humanitas University, Rozzano, Milan, Italy
| | - Olivier Scatton
- Department of Hepatobiliary and Liver Transplantation Surgery, AP-HP, Hôpital Pitié Salpêtrière, CRSA, Sorbonne Université, Paris, France
| | - Raffaele Brustia
- Department of Digestive and Hepato-pancreatic-biliary Surgery, DMU CARE, Assistance Publique-Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri Mondor, Créteil, France; Paris Est Créteil University, UPEC, France; Team "Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers," INSERM U955, Créteil, France; Assistance Publique-Hôpitaux de Paris, Créteil, France.
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15
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Qiu Z, Wu Y, Qi W, Li C. PIVKA-II combined with tumor burden score to predict long-term outcomes of AFP-negative hepatocellular carcinoma patients after liver resection. Cancer Med 2024; 13:e6835. [PMID: 38130028 PMCID: PMC10807584 DOI: 10.1002/cam4.6835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 10/13/2023] [Accepted: 12/08/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND This study aimed to establish a simple prognostic scoring model based on tumor burden score (TBS) and PIVKA-II to predict long-term outcomes of α-fetoprotein (AFP)-negative hepatocellular carcinoma (HCC) patients. METHODS 511 patients were divided into the training cohort (n = 305) and the validation cohort (n = 206) at a ratio of 6:4. Receiver operating characteristic curves (ROC) were established to identify cutoff values of TBS and PIVKA-II. Kaplan-Meier curves were used to analyze survival outcomes. The multivariable Cox regression was used to identify variables independently associated with survival outcomes. The predictive performance of the TBS-PIVKA II score (TPS) model was compared with Barcelona clinic liver cancer (BCLC) stage and American Joint Committee on Cancer (AJCC TNM) stage. RESULTS The present study established the TPS model using a simple scoring system (0, 1 for low/high TBS [cutoff value: 4.1]; 0, 1 for low/high PIVKA-II [cutoff value: 239 mAU/mL]). The TPS scoring model was divided into three levels according to the summation of TBS score and PIVKA-II score: TPS 0, TPS 1, and TPS 2. The TPS scoring model was able to stratify OS (training: p < 0.001, validation: p < 0.001) and early recurrence (training: p < 0.001; validation: p = 0.001) in the training cohort and the validation cohort. The TPS score was independently associated with OS (TPS 1 vs. 0, HR: 2.28, 95% CI: 1.01-5.17; TPS 2 vs. 0, HR: 4.21, 95% CI: 2.01-8.84) and early recurrence (TPS 1 vs. 0, HR: 3.50, 95% CI: 1.71-7.16; TPS 2 vs. 0, HR: 3.79, 95% CI: 1.86-7.75) in the training cohort. The TPS scoring model outperformed BCLC stage and AJCC TNM stage in predicting OS and early recurrence in the training cohort and the validation cohort. But the TPS scoring model was unable to stratify the late recurrence in the training cohort (p = 0.872) and the validation cohort (p = 0.458). CONCLUSIONS The TPS model outperformed the BCLC stage and AJCC TNM stage in predicting OS and early recurrence of AFP-negative HCC patients after liver resection, which might better assist surgeons in screening AFP-negative HCC patients who may benefit from liver resection.
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Affiliation(s)
- Zhan‐cheng Qiu
- Division of Liver Surgery, Department of General Surgery, West China HospitalSichuan UniversityChengduSichuan ProvinceChina
| | - You‐wei Wu
- Division of Liver Surgery, Department of General Surgery, West China HospitalSichuan UniversityChengduSichuan ProvinceChina
| | - Wei‐li Qi
- Division of Liver Surgery, Department of General Surgery, West China HospitalSichuan UniversityChengduSichuan ProvinceChina
| | - Chuan Li
- Division of Liver Surgery, Department of General Surgery, West China HospitalSichuan UniversityChengduSichuan ProvinceChina
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Xu C, Liu W, Zhao Q, Zhang L, Yin M, Zhou J, Zhu J, Qin S. CT-based radiomics nomogram for overall survival prediction in patients with cervical cancer treated with concurrent chemoradiotherapy. Front Oncol 2023; 13:1287121. [PMID: 38162501 PMCID: PMC10755472 DOI: 10.3389/fonc.2023.1287121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 11/21/2023] [Indexed: 01/03/2024] Open
Abstract
Background and purpose To establish and validate a hybrid radiomics model to predict overall survival in cervical cancer patients receiving concurrent chemoradiotherapy (CCRT). Methods We retrospectively collected 367 cervical cancer patients receiving chemoradiotherapy from the First Affiliated Hospital of Soochow University in China and divided them into a training set and a test set in a ratio of 7:3. Handcrafted and deep learning (DL)-based radiomics features were extracted from the contrast-enhanced computed tomography (CT), and the two types of radiomics signatures were calculated based on the features selected using the least absolute shrinkage and selection operator (LASSO) Cox regression. A hybrid radiomics nomogram was constructed by integrating independent clinical risk factors, handcrafted radiomics signature, and DL-based radiomics signature in the training set and was validated in the test set. Results The hybrid radiomics nomogram exhibited favorable performance in predicting overall survival, with areas under the receiver operating characteristic curve (AUCs) for 1, 3, and 5 years in the training set of 0.833, 0.777, and 0.871, respectively, and in the test set of 0.811, 0.713, and 0.730, respectively. Furthermore, the hybrid radiomics nomogram outperformed the single clinical model, handcrafted radiomics signature, and DL-based radiomics signature in both the training (C-index: 0.793) and test sets (C-index: 0.721). The calibration curves and decision curve analysis (DCA) indicated that our hybrid nomogram had good calibration and clinical benefits. Finally, our hybrid nomogram demonstrated value in stratifying patients into high- and low-risk groups (cutoff value: 5.6). Conclusion A high-performance hybrid radiomics model based on pre-radiotherapy CT was established, presenting strengths in risk stratification.
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Affiliation(s)
- Chao Xu
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Wen Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Qi Zhao
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Lu Zhang
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Minyue Yin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Juying Zhou
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Songbing Qin
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
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Lin KY, Chen QJ, Tang SC, Lin ZW, Zhang JX, Zheng SM, Li YT, Wang XM, Lu Q, Fu J, Guo LB, Zheng LF, You PH, Wu MM, Lin KC, Zhou WP, Yang T, Zeng YY. Prognostic implications of alpha-fetoprotein and C-reactive protein elevation in hepatocellular carcinoma following resection (PACE): a large cohort study of 2770 patients. BMC Cancer 2023; 23:1190. [PMID: 38053048 PMCID: PMC10696803 DOI: 10.1186/s12885-023-11693-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 11/28/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND Routine clinical staging for hepatocellular carcinoma (HCC) incorporates liver function, general health, and tumor morphology. Further refinement of prognostic assessments and treatment decisions may benefit from the inclusion of tumor biological marker alpha-fetoprotein (AFP) and systemic inflammation indicator C-reactive protein (CRP). METHODS Data from a multicenter cohort of 2770 HCC patients undergoing hepatectomy were analyzed. We developed the PACE risk score (Prognostic implications of AFP and CRP Elevation) after initially assessing preoperative AFP and CRP's prognostic value. Subgroup analyzes were performed in BCLC cohorts A and B using multivariable Cox analysis to evaluate the prognostic stratification ability of the PACE risk score and its complementary utility for BCLC staging. RESULTS Preoperative AFP ≥ 400ng/mL and CRP ≥ 10 mg/L emerged as independent predictors of poorer prognosis in HCC patients who underwent hepatectomy, leading to the creation of the PACE risk score. PACE risk score stratified patients into low, intermediate, and high-risk groups with cumulative 5-year overall (OS) and recurrence-free survival (RFS) rates of 59.6%/44.9%, 43.9%/38.4%, and 20.6%/18.0% respectively (all P < 0.001). Increased PACE risk scores correlated significantly with early recurrence and extrahepatic metastases frequency (all P < 0.001). The multivariable analysis identified intermediate and high-risk PACE scores as independently correlating with poor postoperative OS and RFS. Furthermore, the PACE risk score proficiently stratified the prognosis of BCLC stages A and B patients, with multivariable analyses demonstrating it as an independent prognostic determinant for both stages. CONCLUSION The PACE risk score serves as an effective tool for postoperative risk stratification, potentially supplementing the BCLC staging system.
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Affiliation(s)
- Kong-Ying Lin
- Department of Hepatopancreatobiliary Surgery, First Affiliated Hospital of Fujian Medical University, Fuzhou, 350000, China
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350000, China
| | - Qing-Jing Chen
- Department of Hepatopancreatobiliary Surgery, First Affiliated Hospital of Fujian Medical University, Fuzhou, 350000, China
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350000, China
| | - Shi-Chuan Tang
- Department of Hepatopancreatobiliary Surgery, First Affiliated Hospital of Fujian Medical University, Fuzhou, 350000, China
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350000, China
| | - Zhi-Wen Lin
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350000, China
| | - Jian-Xi Zhang
- Department of Hepatobiliary Surgery, Xiamen Hospital, Beijing University of Chinese Medicine, Xiamen, 361000, China
| | - Si-Ming Zheng
- Department of Hepatopancreatobiliary Surgery, First Affiliated Hospital of Ningbo University, Ningbo, 315000, China
| | - Yun-Tong Li
- Department of Hepatobiliary Surgery, Zhongshan Hospital of Xiamen University, Xiamen, 361000, China
| | - Xian-Ming Wang
- Department of General Surgery, First Affiliated Hospital of Shandong First Medical University, Shandong, 250014, China
| | - Qiang Lu
- Department of Hepatopancreatobiliary Surgery, Third Hospital of Zhangzhou, Zhangzhou, 363000, China
| | - Jun Fu
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350000, China
| | - Luo-Bin Guo
- Department of Hepatopancreatobiliary Surgery, First Affiliated Hospital of Fujian Medical University, Fuzhou, 350000, China
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350000, China
| | - Li-Fang Zheng
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350000, China
| | - Peng-Hui You
- Biobank in Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350000, China
| | - Meng-Meng Wu
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350000, China
| | - Ke-Can Lin
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350000, China
| | - Wei-Ping Zhou
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Navy Medical University (Second Military Medical University), Shanghai, 200000, China
| | - Tian Yang
- Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Navy Medical University (Second Military Medical University), Shanghai, 200000, China.
| | - Yong-Yi Zeng
- Department of Hepatopancreatobiliary Surgery, First Affiliated Hospital of Fujian Medical University, Fuzhou, 350000, China.
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350000, China.
- Liver Disease Research Center of Fujian Province, Fuzhou, 350000, China.
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, 350025, China.
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Shi S, Zhao YX, Fan JL, Chang LY, Yu DX. Development and External Validation of a Nomogram Including Body Composition Parameters for Predicting Early Recurrence of Hepatocellular Carcinoma After Hepatectomy. Acad Radiol 2023; 30:2940-2953. [PMID: 37798207 DOI: 10.1016/j.acra.2023.05.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/20/2023] [Accepted: 05/21/2023] [Indexed: 10/07/2023]
Abstract
RATIONALE AND OBJECTIVES Body composition, including adipose and muscle tissues, evaluated by computer tomography is correlated with the prognosis of hepatocellular carcinoma (HCC). However, its relationship with early recurrence (ER) remains unclear. This study aimed at establishing and validating a nomogram based on body composition and clinicopathological indices to predict ER of HCC. MATERIALS AND METHODS One hundred ninety-five patients from institution A formed the training cohort and internal validation cohort, and 50 patients from institution B formed the external validation cohort. Independent predictors of ER were identified using LASSO and Cox regression analyses. The performance of nomogram was evaluated using the calibration curve, concordance index (C-index), area under the curve (AUC), and decision curve analysis (DCA). RESULTS After data screening, the nomogram was constructed using eight independent predictors of ER, including the tumor size, alpha fetoprotein, body mass index, Edmondson Steiner grade, visceral adipose tissue radiodensity, intermuscular adipose tissue index, intramuscular adipose tissue content, and skeletal muscle area. The calibration curve exhibited excellent concordances, with C-indices of 0.808 (95%CI: 0.771-0.860), 0.802 (95%CI: 0.747-0.942), and 0.804 (95%CI: 0.701-0.861) in training, internal validation, and external validation cohorts, respectively. In addition, compared to conventional staging systems and pure clinical model, the nomogram exhibited a higher AUC and wider range of threshold probabilities in DCA, which indicated better discriminative ability and greater clinical benefit. Finally, patients with nomogram scores of <183.07, 183.07-243.09, and >243.09 were considered to have low, moderate, and high risks of ER, respectively. CONCLUSION The nomogram exhibits excellent ER predictive ability for patients with HCC who underwent hepatectomy.
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Affiliation(s)
- Shuo Shi
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong 250012, China
| | - Yu-Xuan Zhao
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong 250012, China
| | - Jin-Lei Fan
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong 250012, China
| | - Ling-Yu Chang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong 250012, China
| | - De-Xin Yu
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong 250012, China.
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Zhang YB, Yang G, Bu Y, Lei P, Zhang W, Zhang DY. Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma. World J Gastroenterol 2023; 29:5804-5817. [PMID: 38074914 PMCID: PMC10701309 DOI: 10.3748/wjg.v29.i43.5804] [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: 08/26/2023] [Revised: 10/07/2023] [Accepted: 11/03/2023] [Indexed: 11/20/2023] Open
Abstract
BACKGROUND Surgical resection is the primary treatment for hepatocellular carcinoma (HCC). However, studies indicate that nearly 70% of patients experience HCC recurrence within five years following hepatectomy. The earlier the recurrence, the worse the prognosis. Current studies on postoperative recurrence primarily rely on postoperative pathology and patient clinical data, which are lagging. Hence, developing a new pre-operative prediction model for postoperative recurrence is crucial for guiding individualized treatment of HCC patients and enhancing their prognosis. AIM To identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC. METHODS The demographic and clinical data of 371 HCC patients were collected for this retrospective study. These data were randomly divided into training and test sets at a ratio of 8:2. The training set was analyzed, and key feature variables with predictive value for early HCC recurrence were selected to construct six different machine learning prediction models. Each model was evaluated, and the best-performing model was selected for interpreting the importance of each variable. Finally, an online calculator based on the model was generated for daily clinical practice. RESULTS Following machine learning analysis, eight key feature variables (age, intratumoral arteries, alpha-fetoprotein, pre-operative blood glucose, number of tumors, glucose-to-lymphocyte ratio, liver cirrhosis, and pre-operative platelets) were selected to construct six different prediction models. The XGBoost model outperformed other models, with the area under the receiver operating characteristic curve in the training, validation, and test datasets being 0.993 (95% confidence interval: 0.982-1.000), 0.734 (0.601-0.867), and 0.706 (0.585-0.827), respectively. Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value. CONCLUSION The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence. This model may guide surgical strategies and postoperative individualized medicine.
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Affiliation(s)
- Yu-Bo Zhang
- Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan 750003, Ningxia Hui Autonomous Region, China
| | - Gang Yang
- Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan 750003, Ningxia Hui Autonomous Region, China
| | - Yang Bu
- Department of Hepatobiliary Surgery, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan 750003, Ningxia Hui Autonomous Region, China
| | - Peng Lei
- Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan 750003, Ningxia Hui Autonomous Region, China
| | - Wei Zhang
- Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan 750003, Ningxia Hui Autonomous Region, China
| | - Dan-Yang Zhang
- Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan 750003, Ningxia Hui Autonomous Region, China
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Liu R, Wu S, Yu HY, Zeng K, Liang Z, Li S, Hu Y, Yang Y, Ye L. Prediction model for hepatocellular carcinoma recurrence after hepatectomy: Machine learning-based development and interpretation study. Heliyon 2023; 9:e22458. [PMID: 38034691 PMCID: PMC10687050 DOI: 10.1016/j.heliyon.2023.e22458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 09/10/2023] [Accepted: 11/13/2023] [Indexed: 12/02/2023] Open
Abstract
Background Identifying patients with hepatocellular carcinoma (HCC) at high risk of recurrence after hepatectomy can help to implement timely interventional treatment. This study aimed to develop a machine learning (ML) model to predict the recurrence risk of HCC patients after hepatectomy. Methods We retrospectively collected 315 HCC patients who underwent radical hepatectomy at the Third Affiliated Hospital of Sun Yat-sen University from April 2013 to October 2017, and randomly divided them into the training and validation sets at a ratio of 7:3. According to the postoperative recurrence of HCC patients, the patients were divided into recurrence group and non-recurrence group, and univariate and multivariate logistic regression were performed for the two groups. We applied six machine learning algorithms to construct the prediction models and performed internal validation by 10-fold cross-validation. Shapley additive explanations (SHAP) method was applied to interpret the machine learning model. We also built a web calculator based on the best machine learning model to personalize the assessment of the recurrence risk of HCC patients after hepatectomy. Results A total of 13 variables were included in the machine learning models. The multilayer perceptron (MLP) machine learning model was proved to achieve optimal predictive value in test set (AUC = 0.680). The SHAP method displayed that γ-glutamyl transpeptidase (GGT), fibrinogen, neutrophil, aspartate aminotransferase (AST) and total bilirubin (TB) were the top 5 important factors for recurrence risk of HCC patients after hepatectomy. In addition, we further demonstrated the reliability of the model by analyzing two patients. Finally, we successfully constructed an online web prediction calculator based on the MLP machine learning model. Conclusion MLP was an optimal machine learning model for predicting the recurrence risk of HCC patients after hepatectomy. This predictive model can help identify HCC patients at high recurrence risk after hepatectomy to provide early and personalized treatment.
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Affiliation(s)
- Rongqiang Liu
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Shinan Wu
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Hao yuan Yu
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Kaining Zeng
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhixing Liang
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Siqi Li
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yongwei Hu
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yang Yang
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Linsen Ye
- Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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Milana F, Famularo S, Torzilli G. Advancing prognostic precision in hepatocellular carcinoma: a paradigm shift towards machine learning. Hepatobiliary Surg Nutr 2023; 12:810-812. [PMID: 37886194 PMCID: PMC10598293 DOI: 10.21037/hbsn-23-360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 08/22/2023] [Indexed: 10/28/2023]
Affiliation(s)
- Flavio Milana
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Simone Famularo
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Guido Torzilli
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Milan, Italy
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Zhou Z, Cao S, Chen C, Chen J, Xu X, Liu Y, Liu Q, Wang K, Han B, Yin Y. A Novel Nomogram for the Preoperative Prediction of Edmondson-Steiner Grade III-IV in Hepatocellular Carcinoma Patients. J Hepatocell Carcinoma 2023; 10:1399-1409. [PMID: 37641593 PMCID: PMC10460586 DOI: 10.2147/jhc.s417878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/17/2023] [Indexed: 08/31/2023] Open
Abstract
Background Edmondson-Steiner (E-S) grade is a pathological indicator of the degree of hepatocellular carcinoma (HCC) differentiation, and E-S grade III-IV is a poor prognostic factor for HCC patients. Predicting poorly differentiated HCC has essential significance for clinical decision-making. Although some studies have developed predictive models based on magnetic resonance imaging (MRI) and radiomics, radiomic features that require specific software for analysis are impractical for clinical work. This study aims to develop a novel and user-friendly nomogram model to predict E-S grade III-IV. Patients and Methods Medical data on patients meeting the inclusion criteria were obtained from the Nanjing Drum Tower Hospital HCC database (January 2020 to December 2022). Univariate analysis was used to screen for risk factors associated with E-S grade III-IV. A novel nomogram was established based on the subsequent multivariate logistic regression analysis. The performance of the established model was evaluated through diagnostic ability, calibration, and clinical benefits. Results Overall, 240 HCC patients were included in this study. Among them, 103 were highly differentiated (E-S grade I-II) HCC and 137 were poorly differentiated (E-S grade III-IV) HCC. A nomogram model that integrated alpha-fetoprotein (AFP), des-γ-carboxy prothrombin (DCP), hepatitis B virus surface antigen (HBsAg), hepatitis C virus antibodies (HCVAb), aspartate aminotransferase to lymphocyte ratio index (ALRI), and macrovascular invasion was established. The novel model had a good diagnostic performance with an area under the curve (AUC) value of 0.763. Meanwhile, the model had a diagnostic accuracy of 72.5%, a sensitivity of 78.1%, and a specificity of 65.1%. The calibration curve showed good calibration of the nomogram model (mean absolute error = 0.043), and the decision curve analysis (DCA) demonstrated that the clinical benefit was provided. Conclusion Our developed nomogram model could successfully predict E-S grade III-IV in HCC patients, which may be helpful in clinical decision-making.
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Affiliation(s)
- Zheyu Zhou
- Department of General Surgery, Nanjing Drum Tower Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Graduate School of Peking Union Medical College, Nanjing, 210008, People’s Republic of China
| | - Shuya Cao
- Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University; Key Laboratory of Liver Transplantation, Chinese Academy of Medical Science; NHC Key Laboratory of Living Donor Liver Transplantation (Nanjing Medical University), Nanjing, 210029, People’s Republic of China
| | - Chaobo Chen
- Department of General Surgery, Xishan People’s Hospital of Wuxi City, Wuxi, 214105, People’s Republic of China
- Department of Hepatobiliary and Transplantation Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008, People’s Republic of China
| | - Jun Chen
- Department of Pathology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008, People’s Republic of China
| | - Xiaoliang Xu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, People’s Republic of China
| | - Yang Liu
- Department of Hepatobiliary and Transplantation Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008, People’s Republic of China
| | - Qiaoyu Liu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, People’s Republic of China
| | - Ke Wang
- Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University; Key Laboratory of Liver Transplantation, Chinese Academy of Medical Science; NHC Key Laboratory of Living Donor Liver Transplantation (Nanjing Medical University), Nanjing, 210029, People’s Republic of China
| | - Bing Han
- Department of Hepatobiliary and Transplantation Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008, People’s Republic of China
| | - Yin Yin
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, People’s Republic of China
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Tu H, Feng S, Chen L, Huang Y, Zhang J, Wu X. Contrast enhanced ultrasound combined with serology predicts hepatocellular carcinoma recurrence: a retrospective observation cohort study. Front Oncol 2023; 13:1154064. [PMID: 37519810 PMCID: PMC10380982 DOI: 10.3389/fonc.2023.1154064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023] Open
Abstract
Objectives To construct a novel model based on contrast-enhanced ultrasound (CEUS) and serological biomarkers to predict the early recurrence (ER) of primary hepatocellular carcinoma within 2 years after hepatectomy. Methods A total of 466 patients who underwent CEUS and curative resection between 2016.1.1 and 2019.1.1 were retrospectively recruited from one institution. The training and testing cohorts comprised 326 and 140 patients, respectively. Data on general characteristics, CEUS Liver Imaging Reporting and Data System (LI-RADS) parameters, and serological were collected. Univariate analysis and multivariate Cox proportional hazards regression model were used to evaluate the independent prognostic factors for tumor recurrence, and the Contrast-enhanced Ultrasound Serological (CEUSS) model was constructed. Different models were compared using prediction error and time-dependent area under the receiver operating characteristic curve (AUC). The CEUSS model's performances in ER prediction were assessed. Results The baseline data of the training and testing cohorts were equal. LI-RADS category, α-fetoprotein level, tumor maximum diameter, total bilirubin level, starting time, iso-time, and enhancement pattern were independent hazards, and their hazards ratios were 1.417, 1.309, 1.133, 1.036, 0.883, 0.985, and 0.70, respectively. The AUCs of CEUSS, BCLC,TNM, and CNLC were 0.706, 0.641, 0.647, and 0.636, respectively, in the training cohort and 0.680, 0.583, 0.607, and 0.597, respectively, in the testing cohort. The prediction errors of CEUSS, BCLC, TNM, and CNLC were 0.202, 0.205, 0.205, and 0.200, respectively, in the training cohort and 0.204, 0.221, 0.219, and 0.211, respectively, in the testing cohort. Conclusions The CEUSS model can accurately and individually predict ER before surgery and may represent a new tool for individualized treatment.
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Affiliation(s)
- Haibin Tu
- Department of Ultrasound, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Siyi Feng
- Department of Ultrasound, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Lihong Chen
- Department of Ultrasound, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Yujie Huang
- Department of Ultrasound, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Juzhen Zhang
- Department of Ultrasound, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Xiaoxiong Wu
- Department of Oncology, Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Wenpei G, Yuan L, Liangbo L, Jingjun M, Bo W, Zhiqiang N, Yijie N, Lixin L. Predictive value of preoperative inflammatory indexes for postoperative early recurrence of hepatitis B-related hepatocellular carcinoma. Front Oncol 2023; 13:1142168. [PMID: 37519805 PMCID: PMC10373589 DOI: 10.3389/fonc.2023.1142168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 06/26/2023] [Indexed: 08/01/2023] Open
Abstract
Objective To investigate the predictive value of preoperative neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic inflammation response index (SIRI), and systemic immune inflammation index (SII) for early recurrence after liver resection in patients with hepatitis B-related hepatocellular carcinoma. Methods A retrospective study was conducted on 162 patients who underwent hepatitis B-related hepatocellular carcinoma (HCC) resection between January 2013 and April 2016. The Youden index was utilized to calculate the optimal cut-off value. The Pearson Chi-square test was applied to analyze the relationship between inflammatory indexes and common clinical and pathological features. The Kaplan-Meier method and Log-Rank test were implemented to compare the recurrence-free survival rate within 2 years of the population. The Cox regression analysis was used to identify the risk factors for early postoperative recurrence. Results The best cut-off values of SIRI, PLR, NLR and SII were 0.785, 86.421, 2.231 and 353.64, respectively. Tumor diameter, degree of tumor differentiation, vascular invasion, SIRI>0.785, PLR>86.421, NLR>2.231 and SII>353.64 were risk factors for early recurrence. Combining the above seven risk factors to construct a joint index, the AUC of the joint prediction model was 0.804. The areas under the ROC curves of SIRI, PLR, NLR, and SII were 0.659, 0.725, 0.680, and 0.723, respectively. There was no significant difference in the predictive ability between the single inflammatory index models, but the predictive performance of the joint prediction model was significantly higher than that of the single inflammatory index models. The patients with lower SIRI, PLR, NLR, SII and joint index value had longer recurrence-free survival within 2 years. Conclusion The joint index CIP, constructed by combining preoperative SIRI, PLR, NLP and SII with pathological features, can better predict the early recurrence of HBV-related HCC patients after surgery, which is beneficial in identifying high-risk patients and assisting clinicians to make better clinical choices.
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Affiliation(s)
- Guo Wenpei
- Department of Gastroenterology and Hepatology, The First Hospital of Shanxi Medical University, Taiyuan, China
| | - Li Yuan
- Department of Respiratory Medicine, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Li Liangbo
- Department of Stomatology, Chinese PLA General Hospital, Beijing, China
| | - Mu Jingjun
- Department of Urinary Surgery, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Wang Bo
- Department of Pathology, Shanxi Province Cancer Hospital, The First Hospital of Shanxi Medical University, Taiyuan, China
| | - Niu Zhiqiang
- Department of Hepatobiliary Surgery, The First Hospital of Shanxi Medical University, Taiyuan, China
| | - Ning Yijie
- Department of Neurosurgery, The First Hospital of Shanxi Medical University, Taiyuan, China
| | - Liu Lixin
- Department of Gastroenterology and Hepatology, The First Hospital of Shanxi Medical University, Taiyuan, China
- Experimental Center of Science and Research, The First Hospital of Shanxi Medical University, Taiyuan, China
- Institute of Liver Diseases and Organ Transplantation, The First Hospital of Shanxi Medical University, Taiyuan, China
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Deng Y, Chen Q, Li C, Chen J, Cai J, Li Y, Zhao H. Nomogram predicting early recurrence defined by the minimum P value approach for colorectal liver metastasis patients receiving colorectal cancer resection with simultaneous liver metastasis resection: development and validation. J Gastrointest Oncol 2023; 14:1279-1292. [PMID: 37435225 PMCID: PMC10331761 DOI: 10.21037/jgo-22-934] [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: 09/24/2022] [Accepted: 04/30/2023] [Indexed: 07/13/2023] Open
Abstract
Background Simultaneous resections have been increasingly performed for colorectal liver metastasis patients. However, studies explored risk stratification for these patients are scarce. Among which, a clear definition of early recurrence remains controversial and models for predicting early recurrence in these patients are lacking. Methods Colorectal liver metastasis patients who developed recurrence followed by simultaneous resection were enrolled. Early recurrence was determined by the minimum P value method, and patients were divided into an early recurrence group and late recurrence group. Standard clinical data were collected from each patient including demographics features, preoperative laboratory tests and postoperative regular follow-up results. All the data were accessed by clinicians and recorded accordingly. The nomogram for early recurrence was constructed in the training cohort and validated externally in the test cohort. Results The optimal value of early recurrence by the minimum P value method was 13 months. A total of 323 patients were included in the training cohort, of which 241 (74.6%) experienced early recurrence. Seventy-one patients were included in the test cohort, of which 49 (69.0%) experienced early recurrence. Significantly worse post-recurrence survival (median 27.0 vs. 52.8 months, P=0.00083) and overall survival (median 33.8 vs. 70.9 months, P<0.0001) were observed in patients with early recurrence in the training cohort. Positive lymph node metastases (P=0.003), tumour burden scores ≥4.09 (P=0.001), preoperative neutrophil-to-lymphocyte ratios ≥1.44 (P=0.006), preoperative blood urea nitrogen levels ≥3.55 µmol/L (P=0.017) and postoperative complications (P=0.042) were independently associated with early recurrence, and all these predictors were included in the nomogram. The nomogram for predicting early recurrence had a receiver operating characteristic curve of 0.720 in the training cohort and a receiver operating characteristic curve of 0.740 in the test cohort. The Hosmer-Lemeshow test and calibration curves showed acceptable model calibration in the training set (P=0.7612) and in the test set (P=0.8671). The decision curve analysis results for the training cohort and test cohort also indicated that the nomogram showed good clinical applicability. Conclusions Our findings provide clinicians with new insights into accurate risk stratification for colorectal liver metastasis patients receiving simultaneous resection and contributing to the management of patients.
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Affiliation(s)
- Yiqiao Deng
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qichen Chen
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Cong Li
- Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Jinghua Chen
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianqiang Cai
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuan Li
- Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Hong Zhao
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Jiang Y, Cai J, Zeng Y, Ye H, Yang T, Liu Z, Liu Q. Development and validation of a machine learning model to predict imminent new vertebral fractures after vertebral augmentation. BMC Musculoskelet Disord 2023; 24:472. [PMID: 37296426 PMCID: PMC10251538 DOI: 10.1186/s12891-023-06557-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND Accurately predicting the occurrence of imminent new vertebral fractures (NVFs) in patients with osteoporotic vertebral compression fractures (OVCFs) undergoing vertebral augmentation (VA) is challenging with yet no effective approach. This study aim to examine a machine learning model based on radiomics signature and clinical factors in predicting imminent new vertebral fractures after vertebral augmentation. METHODS A total of 235 eligible patients with OVCFs who underwent VA procedures were recruited from two independent institutions and categorized into three groups, including training set (n = 138), internal validation set (n = 59), and external validation set (n = 38). In the training set, radiomics features were computationally retrieved from L1 or adjacent vertebral body (T12 or L2) on T1-w MRI images, and a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm (LASSO). Predictive radiomics signature and clinical factors were fitted into two final prediction models using the random survival forest (RSF) algorithm or COX proportional hazard (CPH) analysis. Independent internal and external validation sets were used to validate the prediction models. RESULTS The two prediction models were integrated with radiomics signature and intravertebral cleft (IVC). The RSF model with C-indices of 0.763, 0.773, and 0.731 and time-dependent AUC (2 years) of 0.855, 0.907, and 0.839 (p < 0.001 for all) was found to be better predictive than the CPH model in training, internal and external validation sets. The RSF model provided better calibration, larger net benefits (determined by decision curve analysis), and lower prediction error (time-dependent brier score of 0.156, 0.151, and 0.146, respectively) than the CPH model. CONCLUSIONS The integrated RSF model showed the potential to predict imminent NVFs following vertebral augmentation, which will aid in postoperative follow-up and treatment.
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Affiliation(s)
- Yang Jiang
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Jinhui Cai
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Yurong Zeng
- Department of Radiology, Huizhou Central People's Hospital, Huizhou, China
| | - Haoyi Ye
- Department of Radiology, The Fourth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Tingqian Yang
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Zhifeng Liu
- Department of Radiology, The Fourth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Qingyu Liu
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.
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Wu JL, Luo JY, Jiang ZB, Huang SB, Chen GR, Ran HY, Liang QY, Huang MS, Lai LS, Chen JW. Inflammation-related nomogram for predicting survival of patients with unresectable hepatocellular carcinoma received conversion therapy. World J Gastroenterol 2023; 29:3168-3184. [PMID: 37346152 PMCID: PMC10280795 DOI: 10.3748/wjg.v29.i20.3168] [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: 03/03/2023] [Revised: 04/02/2023] [Accepted: 04/24/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND The efficacy of conversion therapy for patients with unresectable hepatocellular carcinoma (HCC) is a common clinical concern.
AIM To analyse the prognostic factors of overall survival (OS) in patients with unresectable HCC who received conversion therapy.
METHODS One hundred and fifty patients who met the inclusion criteria were enrolled and divided into a training cohort (n = 120) and a validation cohort (n = 30). Using the independent risk factors in the training cohort, a nomogram model was constructed to predict OS for patients treated with transarterial chemoembolization following hepatic resection. The nomogram was internally validated with the bootstrapping method. The predictive performance of nomogram was assessed by Harrell’s concordance index (C-index), calibration plot and time-dependent receiver operating characteristic curves and compared with six other conventional HCC staging systems.
RESULTS Multivariate Cox analysis identified that albumin, blood urea nitrogen, gamma-glutamyl transpeptidase to platelet ratio, platelet to lymphocyte ratio, macrovascular invasion and tumour number were the six independent prognostic factors correlated with OS in nomogram model. The C-index in the training cohort and validation cohort were 0.752 and 0.807 for predicting OS, which were higher than those of the six conventional HCC staging systems (0.563 to 0.715 for the training cohort and 0.458 to 0.571 for the validation cohort). The calibration plots showed good consistency between the nomogram prediction of OS and the actual observations of OS. Decision curve analyses indicated satisfactory clinical utility. With a total nomogram score of 196, patients were accurately classified into low-risk and high-risk groups. Furthermore, we have deployed the model into online calculators that can be accessed for free at https://ctmodelforunresectablehcc.shinyapps.io/DynNomapp/.
CONCLUSION The nomogram achieved optimal individualized prognostication of OS in HCC patients who received conversion therapy, which could be a useful clinical tool to help guide postoperative personalized interventions and prognosis judgement.
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Affiliation(s)
- Jia-Lin Wu
- Department of Interventional Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, Guangdong Province, China
| | - Jun-Yang Luo
- Department of Interventional Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, Guangdong Province, China
| | - Zai-Bo Jiang
- Department of Interventional Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, Guangdong Province, China
| | - Si-Bo Huang
- The First Clinical Medical College, Guangdong Medical University, Zhanjiang 524000, Guangdong Province, China
| | - Ge-Run Chen
- The First Clinical Medical College, Guangdong Medical University, Zhanjiang 524000, Guangdong Province, China
| | - Hui-Ying Ran
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Qi-Yue Liang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Ming-Sheng Huang
- Department of Interventional Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, Guangdong Province, China
| | - Li-Sha Lai
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510010, Guangdong Province, China
| | - Jun-Wei Chen
- Department of Interventional Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, Guangdong Province, China
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Wang L, Song D, Wang W, Li C, Zhou Y, Zheng J, Rao S, Wang X, Shao G, Cai J, Yang S, Dong J. Data-Driven Assisted Decision Making for Surgical Procedure of Hepatocellular Carcinoma Resection and Prognostic Prediction: Development and Validation of Machine Learning Models. Cancers (Basel) 2023; 15:cancers15061784. [PMID: 36980670 PMCID: PMC10046511 DOI: 10.3390/cancers15061784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/02/2023] [Accepted: 03/09/2023] [Indexed: 03/18/2023] Open
Abstract
Background: Currently, surgical decisions for hepatocellular carcinoma (HCC) resection are difficult and not sufficiently personalized. We aimed to develop and validate data driven prediction models to assist surgeons in selecting the optimal surgical procedure for patients. Methods: Retrospective data from 361 HCC patients who underwent radical resection in two institutions were included. End-to-end deep learning models were built to automatically segment lesions from the arterial phase (AP) of preoperative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Clinical baseline characteristics and radiomic features were rigorously screened. The effectiveness of radiomic features and radiomic-clinical features was also compared. Three ensemble learning models were proposed to perform the surgical procedure decision and the overall survival (OS) and recurrence-free survival (RFS) predictions after taking different solutions, respectively. Results: SegFormer performed best in terms of automatic segmentation, achieving a Mean Intersection over Union (mIoU) of 0.8860. The five-fold cross-validation results showed that inputting radiomic-clinical features outperformed using only radiomic features. The proposed models all outperformed the other mainstream ensemble models. On the external test set, the area under the receiver operating characteristic curve (AUC) of the proposed decision model was 0.7731, and the performance of the prognostic prediction models was also relatively excellent. The application web server based on automatic lesion segmentation was deployed and is available online. Conclusions: In this study, we developed and externally validated the surgical decision-making procedures and prognostic prediction models for HCC for the first time, and the results demonstrated relatively accurate predictions and strong generalizations, which are expected to help clinicians optimize surgical procedures.
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Affiliation(s)
- Liyang Wang
- School of Clinical Medicine, Tsinghua University, Beijing 100084, China
- Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
| | - Danjun Song
- Department of Interventional Therapy, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, China
- Department of Liver Surgery, Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Wentao Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Chengquan Li
- School of Clinical Medicine, Tsinghua University, Beijing 100084, China
| | - Yiming Zhou
- Department of Hepatobiliary and Pancreatic Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Jiaping Zheng
- Department of Interventional Therapy, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Shengxiang Rao
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Xiaoying Wang
- Department of Liver Surgery, Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Guoliang Shao
- Department of Interventional Therapy, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, China
- Department of Radiology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Jiabin Cai
- Department of Liver Surgery, Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
- Correspondence: (J.C.); (S.Y.)
| | - Shizhong Yang
- Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
- Correspondence: (J.C.); (S.Y.)
| | - Jiahong Dong
- School of Clinical Medicine, Tsinghua University, Beijing 100084, China
- Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
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Wang L, Wu M, Zhu C, Li R, Bao S, Yang S, Dong J. Ensemble learning based on efficient features combination can predict the outcome of recurrence-free survival in patients with hepatocellular carcinoma within three years after surgery. Front Oncol 2022; 12:1019009. [PMID: 36439437 PMCID: PMC9686395 DOI: 10.3389/fonc.2022.1019009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 10/25/2022] [Indexed: 04/11/2024] Open
Abstract
Preoperative prediction of recurrence outcome in hepatocellular carcinoma (HCC) facilitates physicians' clinical decision-making. Preoperative imaging and related clinical baseline data of patients are valuable for evaluating prognosis. With the widespread application of machine learning techniques, the present study proposed the ensemble learning method based on efficient feature representations to predict recurrence outcomes within three years after surgery. Radiomics features during arterial phase (AP) and clinical data were selected for training the ensemble models. In order to improve the efficiency of the process, the lesion area was automatically segmented by 3D U-Net. It was found that the mIoU of the segmentation model was 0.8874, and the Light Gradient Boosting Machine (LightGBM) was the most superior, with an average accuracy of 0.7600, a recall of 0.7673, a F1 score of 0.7553, and an AUC of 0.8338 when inputting radiomics features during AP and clinical baseline indicators. Studies have shown that the proposed strategy can relatively accurately predict the recurrence outcome within three years, which is helpful for physicians to evaluate individual patients before surgery.
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Affiliation(s)
- Liyang Wang
- School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Meilong Wu
- Division of Hepatobiliary and Pancreas Surgery, Department of General Surgery, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China
| | - Chengzhan Zhu
- Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Rui Li
- Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Shiyun Bao
- Division of Hepatobiliary and Pancreas Surgery, Department of General Surgery, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China
| | - Shizhong Yang
- Hepato-pancreato-biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsing-hua University, Beijing, China
| | - Jiahong Dong
- School of Clinical Medicine, Tsinghua University, Beijing, China
- Hepato-pancreato-biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsing-hua University, Beijing, China
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