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Wan J, Zeng Y. Prediction of hepatic metastasis in esophageal cancer based on machine learning. Sci Rep 2024; 14:14507. [PMID: 38914571 PMCID: PMC11196737 DOI: 10.1038/s41598-024-63213-6] [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: 03/09/2024] [Accepted: 05/27/2024] [Indexed: 06/26/2024] Open
Abstract
This study aimed to establish a machine learning (ML) model for predicting hepatic metastasis in esophageal cancer. We retrospectively analyzed patients with esophageal cancer recorded in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2020. We identified 11 indicators associated with the risk of liver metastasis through univariate and multivariate logistic regression. Subsequently, these indicators were incorporated into six ML classifiers to build corresponding predictive models. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. A total of 17,800 patients diagnosed with esophageal cancer were included in this study. Age, primary site, histology, tumor grade, T stage, N stage, surgical intervention, radiotherapy, chemotherapy, bone metastasis, and lung metastasis were independent risk factors for hepatic metastasis in esophageal cancer patients. Among the six models developed, the ML model constructed using the GBM algorithm exhibited the highest performance during internal validation of the dataset, with AUC, accuracy, sensitivity, and specificity of 0.885, 0.868, 0.667, and 0.888, respectively. Based on the GBM algorithm, we developed an accessible web-based prediction tool (accessible at https://project2-dngisws9d7xkygjcvnue8u.streamlit.app/ ) for predicting the risk of hepatic metastasis in esophageal cancer.
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Affiliation(s)
- Jun Wan
- Department of Emergency surgery, Yangtze University Jingzhou Hospital, jingzhou, China
| | - Yukai Zeng
- Department of Thoracic Surgery, China-Japan Union Hospital of Jilin University, No. 126 Xiantai street, Changchun, Jilin, China.
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Kato M, Hayashi Y, Uema R, Kanesaka T, Yamaguchi S, Maekawa A, Yamada T, Yamamoto M, Kitamura S, Inoue T, Yamamoto S, Kizu T, Takeda R, Ogiyama H, Yamamoto K, Aoi K, Nagaike K, Sasai Y, Egawa S, Akamatsu H, Ogawa H, Komori M, Akihiro N, Yoshihara T, Tsujii Y, Takehara T. A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria. Gastric Cancer 2024:10.1007/s10120-024-01511-8. [PMID: 38795251 DOI: 10.1007/s10120-024-01511-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 05/15/2024] [Indexed: 05/27/2024]
Abstract
BACKGROUND We developed a machine learning (ML) model to predict the risk of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) who did not meet the existing Japanese endoscopic curability criteria and compared its performance with that of the most common clinical risk scoring system, the eCura system. METHODS We used data from 4,042 consecutive patients with EGC from 21 institutions who underwent endoscopic submucosal dissection (ESD) and/or surgery between 2010 and 2021. All resected EGCs were histologically confirmed not to satisfy the current Japanese endoscopic curability criteria. Of all patients, 3,506 constituted the training cohort to develop the neural network-based ML model, and 536 constituted the validation cohort. The performance of our ML model, as measured by the area under the receiver operating characteristic curve (AUC), was compared with that of the eCura system in the validation cohort. RESULTS LNM rates were 14% (503/3,506) and 7% (39/536) in the training and validation cohorts, respectively. The ML model identified patients with LNM with an AUC of 0.83 (95% confidence interval, 0.76-0.89) in the validation cohort, while the eCura system identified patients with LNM with an AUC of 0.77 (95% confidence interval, 0.70-0.85) (P = 0.006, DeLong's test). CONCLUSIONS Our ML model performed better than the eCura system for predicting LNM risk in patients with EGC who did not meet the existing Japanese endoscopic curability criteria. We developed a neural network-based machine learning model that predicts the risk of lymph node metastasis in patients with early gastric cancer who did not meet the endoscopic curability criteria.
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Affiliation(s)
- Minoru Kato
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Yoshito Hayashi
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Ryotaro Uema
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Takashi Kanesaka
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | | | - Akira Maekawa
- Department of Internal Medicine, Osaka Police Hospital, Osaka, Japan
| | - Takuya Yamada
- Department of Gastroenterology, Osaka Rosai Hospital, Sakai, Japan
| | - Masashi Yamamoto
- Department of Gastroenterology, Toyonaka Municipal Hospital, Toyonaka, Japan
| | - Shinji Kitamura
- Department of Gastroenterology, Sakai City Medical Center, Sakai, Japan
| | - Takuya Inoue
- Department of Gastroenterology, Osaka General Medical Center, Osaka, Japan
| | - Shunsuke Yamamoto
- Department of Gastroenterology, National Hospital Organization Osaka National Hospital, Osaka, Japan
| | - Takashi Kizu
- Department of Gastroenterology, Yao Municipal Hospital, Yao, Japan
| | - Risato Takeda
- Department of Gastroenterology, Itami City Hospital, Itami, Japan
| | - Hideharu Ogiyama
- Department of Gastroenterology, Ikeda Municipal Hospital, Ikeda, Japan
| | - Katsumi Yamamoto
- Department of Gastroenterology, Japan Community Healthcare Organization Osaka Hospital, Osaka, Japan
| | - Kenji Aoi
- Department of Gastroenterology, Kaizuka City Hospital, Osaka, Japan
| | - Koji Nagaike
- Department of Gastroenterology, Suita Municipal Hospital, Suita, Japan
| | - Yasutaka Sasai
- Department of Gastroenterology, Otemae Hospital, Osaka, Japan
| | - Satoshi Egawa
- Department of Gastroenterology, Kinki Central Hospital, Itami, Japan
| | - Haruki Akamatsu
- Department of Gastroenterology, Higashiosaka City Medical Center, Higashiosaka, Japan
| | - Hiroyuki Ogawa
- Department of Gastroenterology, Nishinomiya Municipal Central Hospital, Nishinomiya, Japan
| | - Masato Komori
- Department of Gastroenterology, Hyogo Prefectural Nishinomiya Hospital, Nishinomiya, Japan
| | | | - Takeo Yoshihara
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yoshiki Tsujii
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Tetsuo Takehara
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, Suita, Japan.
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Huang W, Xu K, Liu Z, Wang Y, Chen Z, Gao Y, Peng R, Zhou Q. Circulating tumor DNA- and cancer tissue-based next-generation sequencing reveals comparable consistency in targeted gene mutations for advanced or metastatic non-small cell lung cancer. Chin Med J (Engl) 2024:00029330-990000000-01055. [PMID: 38711358 DOI: 10.1097/cm9.0000000000003117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND Molecular subtyping is an essential complementarity after pathological analyses for targeted therapy. This study aimed to investigate the consistency of next-generation sequencing (NGS) results between circulating tumor DNA (ctDNA)-based and tissue-based in non-small cell lung cancer (NSCLC) and identify the patient characteristics that favor ctDNA testing. METHODS Patients who diagnosed with NSCLC and received both ctDNA- and cancer tissue-based NGS before surgery or systemic treatment in Lung Cancer Center, Sichuan University West China Hospital between December 2017 and August 2022 were enrolled. A 425-cancer panel with a HiSeq 4000 NGS platform was used for NGS. The unweighted Cohen's kappa coefficient was employed to discriminate the high-concordance group from the low-concordance group with a cutoff value of 0.6. Six machine learning models were used to identify patient characteristics that relate to high concordance between ctDNA-based and tissue-based NGS. RESULTS A total of 85 patients were enrolled, of which 22.4% (19/85) had stage III disease and 56.5% had stage IV disease. Forty-four patients (51.8%) showed consistent gene mutation types between ctDNA-based and tissue-based NGS, while one patient (1.2%) tested negative in both approaches. Advanced diseases and metastases to other organs would be fit for the ctDNA-based NGS, and the generalized linear model showed that T stage, M stage, and tumor mutation burden were the critical discriminators to predict the consistency of results between ctDNA-based and tissue-based NGS. CONCLUSION ctDNA-based NGS showed comparable detection performance in the targeted gene mutations compared with tissue-based NGS, and it could be considered in advanced or metastatic NSCLC.
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Affiliation(s)
- Weijia Huang
- Lung Cancer Center/Lung Cancer Institute, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Kai Xu
- Lung Cancer Center/Lung Cancer Institute, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Zhenkun Liu
- Lung Cancer Center/Lung Cancer Institute, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yifeng Wang
- Lung Cancer Center/Lung Cancer Institute, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Zijia Chen
- Lung Cancer Center/Lung Cancer Institute, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yanyun Gao
- Department of General Thoracic Surgery, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3010, Switzerland
| | - Renwang Peng
- Department of General Thoracic Surgery, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3010, Switzerland
| | - Qinghua Zhou
- Lung Cancer Center/Lung Cancer Institute, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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Sun B, Li H, Gu X, Cai H. Prognostic Implication of Lymphovascular Invasion in Early Gastric Cancer Meeting Endoscopic Submucosal Dissection Criteria: Insights from Radical Surgery Outcomes. Cancers (Basel) 2024; 16:979. [PMID: 38473340 DOI: 10.3390/cancers16050979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 02/12/2024] [Accepted: 02/23/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND The management of early gastric cancer (EGC) has witnessed a rise in the utilization of endoscopic submucosal dissection (ESD) as a treatment modality, although prognostic markers are needed to guide management strategies. This study investigates the prognostic implications of lymphovascular invasion (LVI) in ESD-eligible EGC patients, specifically its implications for subsequent radical surgery. MATERIAL AND METHODS A retrospective, multicenter study from two primary hospitals analyzed clinicopathological data from 1369 EGC patients eligible for ESD, who underwent gastrectomy at Shanghai Cancer Center and Huashan Hospital between 2009 and 2018. We evaluated the relationship between LVI and lymph node metastasis (LNM), as well as the influence of LVI on recurrence-free survival (RFS) and overall survival (OS). RESULTS We found a strong association between LVI and LNM (p < 0.001). Advanced machine learning approaches, including Random Forest, Gradient Boosting Machine, and eXtreme Gradient Boosting, confirmed the pivotal role of LVI in forecasting LNM from both centers. Multivariate analysis identified LVI as an independent negative prognostic factor for both RFS and OS, with hazard ratios of 4.5 (95% CI: 2.4-8.5, p < 0.001) and 4.4 (95% CI: 2.1-8.9, p < 0.001), respectively. CONCLUSIONS LVI is crucial for risk stratification in ESD-eligible EGC patients, underscoring the necessity for radical gastrectomy. Future research should explore the potential incorporation of LVI status into existing TNM staging systems and novel therapeutic strategies.
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Affiliation(s)
- Bo Sun
- Department of Gastric Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China
- Department of Oncology, Shanghai Medical College of Fudan University, Shanghai 200032, China
| | - Huanhuan Li
- Department of Oncology, Shanghai Medical College of Fudan University, Shanghai 200032, China
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Xiaodong Gu
- Department of General Surgery, Huashan Hospital, Fudan University, Shanghai 200031, China
| | - Hong Cai
- Department of Gastric Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China
- Department of Oncology, Shanghai Medical College of Fudan University, Shanghai 200032, China
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Hayashi T, Takasawa K, Yoshikawa T, Hashimoto T, Sekine S, Wada T, Yamagata Y, Suzuki H, Abe S, Yoshinaga S, Saito Y, Kouno N, Hamamoto R. A discrimination model by machine learning to avoid gastrectomy for early gastric cancer. Ann Gastroenterol Surg 2023; 7:913-921. [PMID: 37927931 PMCID: PMC10623978 DOI: 10.1002/ags3.12714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 06/21/2023] [Accepted: 06/27/2023] [Indexed: 11/07/2023] Open
Abstract
Aim Gastrectomy is recommended for patients with early gastric cancer (EGC) because the possibility of lymph node metastasis (LNM) cannot be completely denied. The aim of this study was to develop a discrimination model to select patients who do not require surgery using machine learning. Methods Data from 382 patients who received gastrectomy for gastric cancer and who were diagnosed with pT1b were extracted for developing a discrimination model. For the validation of this discrimination model, data from 140 consecutive patients who underwent endoscopic resection followed by gastrectomy, with a diagnosis of pT1b EGC, were extracted. We applied XGBoost to develop a discrimination model for clinical and pathological variables. The performance of the discrimination model was evaluated based on the number of cases classified as true negatives for LNM, with no false negatives for LNM allowed. Results Lymph node metastasis was observed in 95 patients (25%) in the development cohort and 11 patients (8%) in the validation cohort. The discrimination model was developed to identify 27 (7%) patients with no indications for additional surgery due to the prediction of an LNM-negative status with no false negatives. In the validation cohort, 13 (9%) patients were identified as having no indications for additional surgery and no patients with LNM were classified into this group. Conclusion The discrimination model using XGBoost algorithms could select patients with no risk of LNM from patients with pT1b EGC. This discrimination model was considered promising for clinical decision-making in relation to patients with EGC.
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Affiliation(s)
- Tsutomu Hayashi
- Department of Gastric SurgeryNational Cancer Center HospitalTokyoJapan
| | - Ken Takasawa
- Division of Medical AI Research and DevelopmentNational Cancer Center Research InstituteTokyoJapan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence ProjectTokyoJapan
| | - Takaki Yoshikawa
- Department of Gastric SurgeryNational Cancer Center HospitalTokyoJapan
| | - Taiki Hashimoto
- Department of Diagnostic PathologyNational Cancer Center HospitalTokyoJapan
| | - Shigeki Sekine
- Department of Diagnostic PathologyNational Cancer Center HospitalTokyoJapan
| | - Takeyuki Wada
- Department of Gastric SurgeryNational Cancer Center HospitalTokyoJapan
| | - Yukinori Yamagata
- Department of Gastric SurgeryNational Cancer Center HospitalTokyoJapan
| | | | - Seiichirou Abe
- Endoscopy DivisionNational Cancer Center HospitalTokyoJapan
| | | | - Yutaka Saito
- Endoscopy DivisionNational Cancer Center HospitalTokyoJapan
| | - Nobuji Kouno
- Division of Medical AI Research and DevelopmentNational Cancer Center Research InstituteTokyoJapan
| | - Ryuji Hamamoto
- Division of Medical AI Research and DevelopmentNational Cancer Center Research InstituteTokyoJapan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence ProjectTokyoJapan
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Li MP, Liu WC, Wu JB, Luo K, Liu Y, Zhang Y, Xiao SN, Liu ZL, Huang SH, Liu JM. Machine learning for the prediction of postoperative nosocomial pulmonary infection in patients with spinal cord injury. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2023; 32:3825-3835. [PMID: 37195363 DOI: 10.1007/s00586-023-07772-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 04/20/2023] [Accepted: 05/05/2023] [Indexed: 05/18/2023]
Abstract
PURPOSE The purpose of this study was to establish the best prediction model for postoperative nosocomial pulmonary infection through machine learning (ML) and assist physicians to make accurate diagnosis and treatment decisions. METHODS Patients with spinal cord injury (SCI) who admitted to a general hospital between July 2014 and April 2022 were included in this study. The data were segmented according to the ratio of seven to three, 70% were randomly selected to train the model, and the other 30% were used for testing. We used LASSO regression to screen the variables, and the selected variables were used in the construction of six different ML models. Shapley additive explanations and permutation importance were used to explain the output of the ML models. Finally, sensitivity, specificity, accuracy and area under receiver operating characteristic curve (AUC) were used as the evaluation index of the model. RESULTS A total of 870 patients were enrolled in this study, of whom 98 (11.26%) developed pulmonary infection. Seven variables were used for ML model construction and multivariate logistic regression analysis. Among these variables, age, ASIA scale and tracheotomy were found to be the independent risk factors for postoperative nosocomial pulmonary infection in SCI patients. Meanwhile, the prediction model based on RF algorithm performed best in the training and test sets. (AUC = 0.721, accuracy = 0.664, sensitivity = 0.694, specificity = 0.656). CONCLUSION Age, ASIA scale and tracheotomy were the independent risk factors of postoperative nosocomial pulmonary infection in SCI. The prediction model based on RF algorithm had the best performance.
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Affiliation(s)
- Meng-Pan Li
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- The First Clinical Medical College of Nanchang University, Nanchang, 330006, People's Republic of China
| | - Wen-Cai Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- The First Clinical Medical College of Nanchang University, Nanchang, 330006, People's Republic of China
- Department of Orthopaedics, Shanghai Jiao Tong University Affifiliated Sixth People's Hospital, Shanghai, China
| | - Jia-Bao Wu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
| | - Kun Luo
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
| | - Yu Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
| | - Yu Zhang
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
| | - Shi-Ning Xiao
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
| | - Zhi-Li Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
| | - Shan-Hu Huang
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China.
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China.
| | - Jia-Ming Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China.
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China.
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Wang X, Zhang X, Li H, Zhang M, Liu Y, Li X. Application of machine learning algorithm in prediction of lymph node metastasis in patients with intermediate and high-risk prostate cancer. J Cancer Res Clin Oncol 2023:10.1007/s00432-023-04816-w. [PMID: 37127828 PMCID: PMC10374763 DOI: 10.1007/s00432-023-04816-w] [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: 03/16/2023] [Accepted: 04/23/2023] [Indexed: 05/03/2023]
Abstract
PURPOSE This study aims to establish the best prediction model of lymph node metastasis (LNM) in patients with intermediate- and high-risk prostate cancer (PCa) through machine learning (ML), and provide the guideline of accurate clinical diagnosis and precise treatment for clinicals. METHODS A total of 24,470 patients with intermediate- and high-risk PCa were included in this study. Multivariate logistic regression model was used to screen the independent risk factors of LNM. At the same time, six algorithms, namely random forest (RF), naive Bayesian classifier (NBC), xgboost (XGB), gradient boosting machine (GBM), logistic regression (LR) and decision tree (DT) are used to establish risk prediction models. Based on the best prediction performance of ML algorithm, a prediction model is established, and the performance of the model is evaluated from three aspects: area under curve (AUC), sensitivity and specificity. RESULTS In multivariate logistic regression analysis, T stage, PSA, Gleason score and bone metastasis were independent predictors of LNM in patients with intermediate- and high-risk PCa. By comprehensively comparing the prediction model performance of training set and test set, GBM model has the best prediction performance (F1 score = 0.838, AUROC = 0.804). Finally, we developed a preliminary calculator model that can quickly and accurately calculate the regional LNM in patients with intermediate- and high-risk PCa. CONCLUSION T stage, PSA, Gleason and bone metastasis were independent risk factors for predicting LNM in patients with intermediate- and high-risk PCa. The prediction model established in this study performs well; however, the GBM model is the best one.
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Affiliation(s)
- Xiangrong Wang
- Department of Urology, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Xiangxiang Zhang
- Department of Urology, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Hengping Li
- Department of Urology, Gansu Provincial Hospital, Lanzhou, Gansu, China.
| | - Mao Zhang
- Department of Urology, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Yang Liu
- Department of Urology, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Xuanpeng Li
- Department of Urology, Gansu Provincial Hospital, Lanzhou, Gansu, China
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Li MP, Liu WC, Sun BL, Zhong NS, Liu ZL, Huang SH, Zhang ZH, Liu JM. Prediction of bone metastasis in non-small cell lung cancer based on machine learning. Front Oncol 2023; 12:1054300. [PMID: 36698411 PMCID: PMC9869148 DOI: 10.3389/fonc.2022.1054300] [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: 09/26/2022] [Accepted: 11/21/2022] [Indexed: 01/12/2023] Open
Abstract
Objective The purpose of this paper was to develop a machine learning algorithm with good performance in predicting bone metastasis (BM) in non-small cell lung cancer (NSCLC) and establish a simple web predictor based on the algorithm. Methods Patients who diagnosed with NSCLC between 2010 and 2018 in the Surveillance, Epidemiology and End Results (SEER) database were involved. To increase the extensibility of the research, data of patients who first diagnosed with NSCLC at the First Affiliated Hospital of Nanchang University between January 2007 and December 2016 were also included in this study. Independent risk factors for BM in NSCLC were screened by univariate and multivariate logistic regression. At this basis, we chose six commonly machine learning algorithms to build predictive models, including Logistic Regression (LR), Decision tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Naive Bayes classifiers (NBC) and eXtreme gradient boosting (XGB). Then, the best model was identified to build the web-predictor for predicting BM of NSCLC patients. Finally, area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity were used to evaluate the performance of these models. Results A total of 50581 NSCLC patients were included in this study, and 5087(10.06%) of them developed BM. The sex, grade, laterality, histology, T stage, N stage, and chemotherapy were independent risk factors for NSCLC. Of these six models, the machine learning model built by the XGB algorithm performed best in both internal and external data setting validation, with AUC scores of 0.808 and 0.841, respectively. Then, the XGB algorithm was used to build a web predictor of BM from NSCLC. Conclusion This study developed a web predictor based XGB algorithm for predicting the risk of BM in NSCLC patients, which may assist doctors for clinical decision making.
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Affiliation(s)
- Meng-Pan Li
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,The First Clinical Medical College of Nanchang University, Nanchang, China
| | - Wen-Cai Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,The First Clinical Medical College of Nanchang University, Nanchang, China,Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Bo-Lin Sun
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China
| | - Nan-Shan Zhong
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China
| | - Zhi-Li Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China
| | - Shan-Hu Huang
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China
| | - Zhi-Hong Zhang
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China,*Correspondence: Jia-Ming Liu, ; Zhi-Hong Zhang,
| | - Jia-Ming Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Institute of Spine and Spinal Cord, Nanchang University, Nanchang, China,*Correspondence: Jia-Ming Liu, ; Zhi-Hong Zhang,
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Ma J, Wen X, Xu Z, Xia P, Jin Y, Lin J, Qian J. Predicting the influence of Circ_0059706 expression on prognosis in patients with acute myeloid leukemia using classical statistics and machine learning. Front Genet 2022; 13:961142. [PMID: 36338954 PMCID: PMC9633654 DOI: 10.3389/fgene.2022.961142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 10/10/2022] [Indexed: 11/25/2022] Open
Abstract
Background: Various circular RNA (circRNA) molecules are abnormally expressed in acute myeloid leukemia (AML), and associated with disease occurrence and development, as well as patient prognosis. The roles of circ_0059706, a circRNA derived from ID1, in AML remain largely unclear. Results: Here, we reported circ_0059706 expression in de novo AML and its association with prognosis. We found that circ_0059706 expression was significantly lower in AML patients than in controls (p < 0.001). Survival analysis of patients with AML divided into two groups according to high and low circ_0059706 expression showed that overall survival (OS) of patients with high circ_0059706 expression was significantly longer than that of those with low expression (p < 0.05). Further, female patients with AML and those aged >60 years old in the high circ_0059706 expression group had longer OS than male patients and those younger than 60 years. Multiple regression analysis showed that circ_0059706 was an independent factor-affecting prognosis of all patients with AML. To evaluate the prospects for application of circ_0059706 in machine learning predictions, we developed seven types of algorithm. The gradient boosting (GB) model exhibited higher performance in prediction of 1-year prognosis and 3-year prognosis, with AUROC 0.796 and 0.847. We analyzed the importance of variables and found that circ_0059706 expression level was the first important variables among all 26 factors included in the GB algorithm, suggesting the importance of circ_0059706 in prediction model. Further, overexpression of circ_0059706 inhibited cell growth and increased apoptosis of leukemia cells in vitro. Conclusion: These results provide evidence that high expression of circ_0059706 is propitious for patient prognosis and suggest circ_0059706 as a potential new biomarker for diagnosis and prognosis evaluation in AML, with high predictive value and good prospects for application in machine learning algorithms.
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Affiliation(s)
- Jichun Ma
- Deparrtment of Central Lab, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
- Zhenjiang Clinical Research Center of Hematology, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
- The Key Lab of Precision Diagnosis and Treatment in Hematologic Malignancies of Zhenjiang City, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
| | - Xiangmei Wen
- Deparrtment of Central Lab, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
- Zhenjiang Clinical Research Center of Hematology, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
- The Key Lab of Precision Diagnosis and Treatment in Hematologic Malignancies of Zhenjiang City, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
| | - Zijun Xu
- Deparrtment of Central Lab, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
- Zhenjiang Clinical Research Center of Hematology, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
- The Key Lab of Precision Diagnosis and Treatment in Hematologic Malignancies of Zhenjiang City, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
| | - Peihui Xia
- Deparrtment of Central Lab, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
- Zhenjiang Clinical Research Center of Hematology, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
- The Key Lab of Precision Diagnosis and Treatment in Hematologic Malignancies of Zhenjiang City, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
| | - Ye Jin
- Zhenjiang Clinical Research Center of Hematology, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
- The Key Lab of Precision Diagnosis and Treatment in Hematologic Malignancies of Zhenjiang City, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
- Deparrtment of Hematology, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
| | - Jiang Lin
- Deparrtment of Central Lab, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
- Zhenjiang Clinical Research Center of Hematology, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
- The Key Lab of Precision Diagnosis and Treatment in Hematologic Malignancies of Zhenjiang City, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
- *Correspondence: Jiang Lin, ; Jun Qian,
| | - Jun Qian
- Zhenjiang Clinical Research Center of Hematology, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
- The Key Lab of Precision Diagnosis and Treatment in Hematologic Malignancies of Zhenjiang City, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
- Deparrtment of Hematology, Affiliated People’s Hospital of Jiangsu University, Zhenjiang, China
- *Correspondence: Jiang Lin, ; Jun Qian,
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Li Y, Xie F, Xiong Q, Lei H, Feng P. Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis. Front Oncol 2022; 12:946038. [PMID: 36059703 PMCID: PMC9433672 DOI: 10.3389/fonc.2022.946038] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/01/2022] [Indexed: 01/19/2023] Open
Abstract
Objective To evaluate the diagnostic performance of machine learning (ML) in predicting lymph node metastasis (LNM) in patients with gastric cancer (GC) and to identify predictors applicable to the models. Methods PubMed, EMBASE, Web of Science, and Cochrane Library were searched from inception to March 16, 2022. The pooled c-index and accuracy were used to assess the diagnostic accuracy. Subgroup analysis was performed based on ML types. Meta-analyses were performed using random-effect models. Risk of bias assessment was conducted using PROBAST tool. Results A total of 41 studies (56182 patients) were included, and 33 of the studies divided the participants into a training set and a test set, while the rest of the studies only had a training set. The c-index of ML for LNM prediction in training set and test set was 0.837 [95%CI (0.814, 0.859)] and 0.811 [95%CI (0.785-0.838)], respectively. The pooled accuracy was 0.781 [(95%CI (0.756-0.805)] in training set and 0.753 [95%CI (0.721-0.783)] in test set. Subgroup analysis for different ML algorithms and staging of GC showed no significant difference. In contrast, in the subgroup analysis for predictors, in the training set, the model that included radiomics had better accuracy than the model with only clinical predictors (F = 3.546, p = 0.037). Additionally, cancer size, depth of cancer invasion and histological differentiation were the three most commonly used features in models built for prediction. Conclusion ML has shown to be of excellent diagnostic performance in predicting the LNM of GC. One of the models covering radiomics and its ML algorithms showed good accuracy for the risk of LNM in GC. However, the results revealed some methodological limitations in the development process. Future studies should focus on refining and improving existing models to improve the accuracy of LNM prediction. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022320752
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