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Chen H, Xu B, Zhang Q, Chen P. Clinical value of measuring plasma D-dimer levels in patients with esophageal cancer. J Cardiothorac Surg 2024; 19:352. [PMID: 38907327 PMCID: PMC11193212 DOI: 10.1186/s13019-024-02895-5] [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: 01/16/2024] [Accepted: 06/15/2024] [Indexed: 06/23/2024] Open
Abstract
BACKGROUND Esophageal cancer represents a significant public health concern; however, reliable diagnostic and prognostic markers have not been established. This study aimed to investigate the clinical value of plasma D-dimer levels in patients with esophageal cancer. METHODS Overall, 120 patients with esophageal cancer who underwent radical surgical resection at our department between January 2019 and 2020 were included (esophageal cancer group). Plasma D-dimer levels were measured preoperatively and on postoperative days 1 and 14. Additionally, 60 healthy participants (control group) with measured plasma D-dimer levels were included. The preoperative D-dimer levels and positive D-dimer test rates were compared between the groups. The 3-year survival rate in patients with esophageal cancer was calculated using the Kaplan-Meier method. RESULTS Preoperative D-dimer concentration in the esophageal cancer group was (0.65 ± 0.859 µg/mL) significantly higher than that in the control group (0.32 ± 0.369 µg/mL). The positivity rate in the esophageal cancer group (35.0%, 42/120) was significantly higher than that in the control group (15%, 9/60). D-dimer concentrations were significantly higher 1 day postoperatively than preoperatively. Conversely, D-dimer concentrations were significantly lower 14 days postoperatively than preoperatively. Patients in the esophageal cancer group with plasma D-dimer concentrations ≤ 0.5 µg/mL had significantly higher 3-year survival rates than those with higher concentrations. In the logistic multivariate analysis, tumor pathological stage and preoperative plasma D-dimer levels were independent prognostic factors of 3-year survival rates in patients with esophageal cancer. CONCLUSION Plasma D-dimer concentrations are clinically valuable in esophageal cancer diagnosis, postoperative recurrence monitoring, and prognosis prediction.
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Affiliation(s)
- Hao Chen
- School of Clinical Medicine, Fujian Medical University, No. 88, Jiaotong Road, Fuzhou, Fujian, 350004, China
- Department of Thoracic and Cardiovascular Surgery, Affiliated Hospital of Putian University, Putian, Fujian, China
| | - Bindong Xu
- School of Clinical Medicine, Fujian Medical University, No. 88, Jiaotong Road, Fuzhou, Fujian, 350004, China.
- Department of Thoracic and Cardiovascular Surgery, Affiliated Hospital of Putian University, Putian, Fujian, China.
| | - Qiang Zhang
- School of Clinical Medicine, Fujian Medical University, No. 88, Jiaotong Road, Fuzhou, Fujian, 350004, China
- Department of Thoracic and Cardiovascular Surgery, Affiliated Hospital of Putian University, Putian, Fujian, China
| | - Pengfei Chen
- School of Clinical Medicine, Fujian Medical University, No. 88, Jiaotong Road, Fuzhou, Fujian, 350004, China
- Department of Thoracic and Cardiovascular Surgery, Affiliated Hospital of Putian University, Putian, Fujian, China
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Lu F, Yang L, Luo Z, He Q, Shangguan L, Cao M, Wu L. Laboratory blood parameters and machine learning for the prognosis of esophageal squamous cell carcinoma. Front Oncol 2024; 14:1367008. [PMID: 38638851 PMCID: PMC11024676 DOI: 10.3389/fonc.2024.1367008] [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: 01/08/2024] [Accepted: 03/18/2024] [Indexed: 04/20/2024] Open
Abstract
Background In contemporary study, the death of esophageal squamous cell carcinoma (ESCC) patients need precise and expedient prognostic methodologies. Objective To develop and validate a prognostic model tailored to ESCC patients, leveraging the power of machine learning (ML) techniques and drawing insights from comprehensive datasets of laboratory-derived blood parameters. Methods Three ML approaches, including Gradient Boosting Machine (GBM), Random Survival Forest (RSF), and the classical Cox method, were employed to develop models on a dataset of 2521 ESCC patients with 27 features. The models were evaluated by concordance index (C-index) and time receiver operating characteristics (Time ROC) curves. We used the optimal model to evaluate the correlation between features and prognosis and divide patients into low- and high-risk groups by risk stratification. Its performance was analyzed by Kaplan-Meier curve and the comparison with AJCC8 stage. We further evaluate the comprehensive effectiveness of the model in ESCC subgroup by risk score and KDE (kernel density estimation) plotting. Results RSF's C-index (0.746) and AUC (three-year AUC 0.761, five-year AUC 0.771) had slight advantage over GBM and the classical Cox method. Subsequently, 14 features such as N stage, T stage, surgical margin, tumor length, age, Dissected LN number, MCH, Na, FIB, DBIL, CL, treatment, vascular invasion, and tumor grade were selected to build the model. Based on these, we found significant difference for survival rate between low-(3-year OS 81.8%, 5-year OS 69.8%) and high-risk (3-year OS 25.1%, 5-year OS 11.5%) patients in training set, which was also verified in test set (all P < 0.0001). Compared with the AJCC8th stage system, it showed a greater discriminative ability which is also in good agreement with its staging ability. Conclusion We developed an ESCC prognostic model with good performance by clinical features and laboratory blood parameters.
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Affiliation(s)
- Feng Lu
- Department of Experimental Medicine, The People’s Hospital of Jianyang City, Jianyang, Sichuan, China
| | - Linlan Yang
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zhenglian Luo
- Department of Transfusion Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Qiao He
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Lijuan Shangguan
- Outpatient Department, People’s Hospital of Jianyang, Jianyang, Sichuan, China
| | - Mingfei Cao
- Department of Clinical Laboratory, Chuankong Hospital of Jianyang, Jianyang, Sichuan, China
| | - Lichun Wu
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
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Zhang M, Wang Q, Zhang G, Li G, Jin R, Xing H. A nomogram prognostic model for early hepatocellular carcinoma with diabetes mellitus after primary liver resection based on the admission characteristics. Front Pharmacol 2024; 15:1360478. [PMID: 38434702 PMCID: PMC10905961 DOI: 10.3389/fphar.2024.1360478] [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: 12/23/2023] [Accepted: 02/05/2024] [Indexed: 03/05/2024] Open
Abstract
Background: Patients diagnosed with early-stage hepatocellular carcinoma (HCC) and diabetes mellitus (DM) are at a higher risk of experiencing complications and facing increased mortality rates. Hence, it is crucial to develop personalized clinical strategies for this particular subgroup upon their admission. The objective of this study is to determine the key prognostic factors in early HCC patients who received liver resection combined with DM and develop a practical personalized model for precise prediction of overall survival in these individuals. Method: A total of 1496 patients diagnosed hepatitis B virus (HBV) - related liver cancer from Beijing You'an Hospital were retrospectively enrolled, spanning from 1 January 2014, to 31 December 2019, and ultimately, 622 eligible patients of hepatocellular carcinoma (HCC) patients with diabetes were included in this present investigation. A multivariate COX regression analysis was conducted to identify prognostic factors that are independent of each other and develop a nomogram. The performance of the nomogram was evaluated using various statistical measures such as the C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) in both the training and validation groups. Survival rates were estimated using the Kaplan-Meier method. Results: The study included a total of 622 early HCC patients who underwent liver resection combined with DM. Random Forrest model and Multivariate Cox regression analysis revealed that drinking, tumor number, monocyte-to-lymphocyte ratio, white blood cell count and international normalized ratio at admission were identified as independent prognostic factors for early HCC patients who underwent liver resection combined with DM. The nomogram demonstrated good predictive performance in the training and validation cohorts based on the C-index values of 0 .756 and 0 .739 respectively, as well as the area under the curve values for 3-, 5-, and 8-year overall survival (0.797, 0.807, 0.840, and 0.725, 0.791, 0.855). Calibration curves and decision curve analysis indicated high accuracy and net clinical benefit rates. Furthermore, the nomogram successfully stratified enrolled patients into low-risk and high-risk groups based on their risk of overall survival. The difference in overall survival between these two groups was statistically significant in both the training and validation cohorts (p < 0.0001 and p = 0.0064). Conclusion: Our results indicate that the admission characteristics demonstrate a highly effective ability to predict the overall survival of early HCC patients who have undergone liver resection in combination with DM. The developed model has the potential to support healthcare professionals in making more informed initial clinical judgments for this particular subgroup of patients.
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Affiliation(s)
- Menghan Zhang
- Center of Liver Diseases Division 3, Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Qi Wang
- Beijing Ditan Hospital, Capital Medical University, Beijing, China
| | - Gongming Zhang
- Department of General Surgery Center, Beijing YouAn Hospital, Beijing Institute of Hepatology, Capital Medical University, Beijing, China
| | - Guangming Li
- Department of General Surgery Center, Beijing YouAn Hospital, Beijing Institute of Hepatology, Capital Medical University, Beijing, China
| | - Ronghua Jin
- Beijing Ditan Hospital, Capital Medical University, Beijing, China
- Changping Laboratory, Beijing, China
| | - Huichun Xing
- Center of Liver Diseases Division 3, Beijing Ditan Hospital, Capital Medical University, Beijing, China
- Center of Liver Diseases Division 3, Beijing Ditan Hospital, Peking University, Beijing, China
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Jiaao L, Wanli G, Kai Z, Feng G, Yunpeng P. Coagulation parameters for the differential diagnosis of pancreatic cancer in the early stage: a retrospective study. Eur J Med Res 2023; 28:436. [PMID: 37848965 PMCID: PMC10580648 DOI: 10.1186/s40001-023-01379-x] [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: 02/23/2023] [Accepted: 09/18/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND In recent years, conventional coagulation (CC) and thromboelastography (TEG) parameters have been reported to be closely related to the progression of pancreatic cancer (PC). However, the potential utility of these parameters in differentiating benign and malignant pancreatic diseases is still unclear. OBJECTIVES A retrospective study was conducted to evaluate the efficacy of coagulation parameters in differentiating pancreatic cancer/early stage pancreatic cancer (EPC, TNM stages I and II) from benign control conditions, and to further explore whether coagulation parameters could improve the differential value of CA199. METHODS Receiver operating characteristic (ROC) curves and logistic regression analysis were used to identify the diagnostic value of each coagulation parameter or combination of parameters. RESULTS Compared with benign pancreatic disease (BPD), patients with pancreatic malignant tumors had significant coagulation disorders, specifically manifested as abnormal increases or decreases in several CC and TEG parameters (such as activated partial thromboplastin time (APTT), fibrinogen (FIB), D-dimer (DD2), K time, R time, Angle, maximum amplitude (MA), coagulation index (CI), and Ly30). In the training group, ROC curve showed that FIB, DD2, Angle, MA, and CI had favorable efficacy at differentiating PC or EPC from BPD (for PC, AUC = 0.737, 0.654, 0.627, 0.602, 0.648; for EPC, AUC = 0.723, 0.635, 0.630, 0.614, 0.648). However, several combined diagnostic indicators based on FIB, DD2 and CI failed to outperform the individual coagulation indexes in diagnostic efficiency. Combinations of certain coagulation indexes with CA199 outperformed CA199 alone at identifying PC or EPC, especially FIB + CA199 (for PC, AUC = 0.904; for EPC, AUC = 0.905), FIB + DD2 + CA199 (for PC, AUC = 0.902; for EPC, AUC = 0.900), FIB + CI + CA199 (for PC, AUC = 0.906; for EPC, AUC = 0.906), and FIB + DD2 + CI + CA199 (for PC, AUC = 0.905; for EPC, AUC = 0.900). The results from a validation set also confirmed that these combinations have advantageous diagnostic value for PC and EPC. CONCLUSIONS A significant hypercoagulable state was common in PC. Some CC and TEG parameters are valuable in the differential diagnosis of benign and malignant pancreatic diseases. In addition, coagulation indexes combined with CA199 can further enhance the differential diagnosis efficacy of CA199 in PC and EPC.
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Affiliation(s)
- Li Jiaao
- Kangda College, Nanjing Medical University, 101 Longmian Road, Nanjing, 210000, Jiangsu, People's Republic of China
| | - Ge Wanli
- Pancreas Center, First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, Jiangsu, People's Republic of China
- Pancreas Institute, Nanjing Medical University, Nanjing, 210029, Jiangsu, People's Republic of China
| | - Zhang Kai
- Pancreas Center, First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, Jiangsu, People's Republic of China
- Pancreas Institute, Nanjing Medical University, Nanjing, 210029, Jiangsu, People's Republic of China
| | - Guo Feng
- Pancreas Center, First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, Jiangsu, People's Republic of China.
- Pancreas Institute, Nanjing Medical University, Nanjing, 210029, Jiangsu, People's Republic of China.
| | - Peng Yunpeng
- Pancreas Center, First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, Jiangsu, People's Republic of China.
- Pancreas Institute, Nanjing Medical University, Nanjing, 210029, Jiangsu, People's Republic of China.
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He Q, Luo Z, Zou H, Ye B, Wu L, Deng Y, Yang M, Wang D, Wang Q, Zhang K. A prognostic nomogram that includes MPV in esophageal squamous cell carcinoma. Cancer Med 2023; 12:20266-20276. [PMID: 37807972 PMCID: PMC10652314 DOI: 10.1002/cam4.6551] [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: 02/15/2023] [Revised: 08/13/2023] [Accepted: 09/08/2023] [Indexed: 10/10/2023] Open
Abstract
BACKGROUND Mean platelet volume (MPV), as a marker of platelet activity, has been shown to be an efficient prognostic biomarker in several types of cancer. Using MPV, this study aimed to create and validate a prognostic nomogram to the overall survival in esophageal squamous cell carcinoma (ESCC) patients. METHODS The nomogram was constructed and tested using data from a retrospective study of 1893 patients who were randomly assigned to the training and testing cohorts with a 7:3 randomization. In order to screen out the optimal predictors for overall survival (OS), we conducted the LASSO-cox regression, univariate, and multivariate cox regression analyses. Subsequently, the predictive accuracy of the nomogram was validated in both the training and the testing cohorts. Finally, decision curve analysis (DCA) was used to confirm clinical validity. RESULTS Age, MPV, nerve invasion, T stage, and N stage were found as independent prognostic variables for OS and were further developed into a nomogram. The nomogram's prediction accuracy for 1-, 3-, and 5-year OS was 0.736, 0.749, 0.774, and 0.724, 0.719, 0.704 in the training and testing cohorts, respectively. Furthermore, DCA results indicated that nomograms outperformed the AJCC 8th and conventional T, N staging systems in both the training and testing cohorts. CONCLUSIONS The nomogram, in conjunction with MPV and standard clinicopathological markers, could improve the accuracy of prediction of OS in ESCC patients.
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Affiliation(s)
- Qiao He
- Department of Clinical LaboratorySichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaChengduChina
| | - Zhenglian Luo
- Department of Transfusion Medicine, West China HospitalSichuan UniversityChengduChina
| | - Haiming Zou
- Department of Clinical LaboratorySichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaChengduChina
| | - Bo Ye
- Department of Clinical LaboratorySichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaChengduChina
| | - Lichun Wu
- Department of Clinical LaboratorySichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaChengduChina
| | - Yao Deng
- Department of Clinical LaboratorySichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaChengduChina
| | - Mu Yang
- Centre for Translational Research in CancerSichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaChengduChina
| | - Dongsheng Wang
- Department of Clinical LaboratorySichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaChengduChina
| | - Qifeng Wang
- Department of Radiation OncologySichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaChengduChina
| | - Kaijiong Zhang
- Department of Clinical LaboratorySichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaChengduChina
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Huang Q, Chen J, Huang Y, Xiong Y, Zhou J, Zhang Y, Lu M, Hu W, Zheng F, Zheng C. The prognostic role of coagulation markers in the progression and metastasis of laryngeal squamous cell carcinoma. BMC Cancer 2023; 23:901. [PMID: 37749514 PMCID: PMC10519099 DOI: 10.1186/s12885-023-11381-5] [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: 01/20/2023] [Accepted: 09/05/2023] [Indexed: 09/27/2023] Open
Abstract
BACKGROUND The application of coagulation-related markers in laryngeal squamous cell carcinoma(LSCC) remains unclear. This study explored the prognostic role of coagulation markers in the progression and metastasis of LSCC. METHODS Coagulation markers of patients with LSCC receiving surgery in the Second Affiliated Hospital of Fujian Medical University in China, from January 2013 to May 2022 were retrospectively analyzed and compared with those of contemporary patients with benign laryngeal diseases. The relationship between clinicopathological features of LSCC and coagulation markers was analyzed with the chi-square and rank sum tests. The ROC curve analysis was utilized to evaluate the diagnostic efficacy of seven coagulation markers for LSCC and its different clinicopathological features, and to find the optimal cutoff value of each coagulation marker. RESULTS 303 patients with LSCC and 533 patients with benign laryngeal diseases were included in the present analysis. Compared to the control group, prothrombin time (PT) (p < 0.001), activated partial thromboplastin time (APTT) (p = 0.001), and Fib (p < 0.001) in patients with LSCC were significantly higher, while mean platelet volume (MPV) (p < 0.001) was significantly shorter. Significant increases were detected in PT (Z = 14.342, p = 0.002), Fib (Z = 25.985, p < 0.001), platelet count (PC) (Z = 12.768, p = 0.005), PCT (Z = 9.178, p = 0.027), MPV (F = 2.948, p = 0.033) in T4 stage. Fib had the highest prognostic value among the seven coagulation markers in different T stages (AUC = 0.676, p < 0.001), N stages (AUC = 0.717, p < 0.001), tumor stage (AUC = 0.665, p < 0.001), differentiation degree (AUC = 0.579, p = 0.022), and neurovascular invasion (AUC = 0.651, p = 0.007). Fib (Z = 25.832, p < 0.001), PC (Z = 23.842, p < 0.001), and PCT (Z = 20.15, p < 0.001) in N1 and N3 stages were significantly higher than in N0 stage. PT (Z = 12.174, p = 0.007), Fib (Z = 23.873, p < 0.001), PC (Z = 17.785, p < 0.001), and PCT (Z = 14.693, p = 0.002) were significantly higher in stage IV than in stage I and II. APTT (Z=-1.983, p = 0.047), Fib (Z=-2.68, p = 0.007), PC (Z=-2.723, p = 0.006), and PCT (Z=-2.592, p = 0.01) increased significantly when the tumor invaded neurovascular tissue. CONCLUSIONS Coagulation markers have the potential to act as biomarkers for predicting pathological features of LSCC. The high level of Fib was helpful for the diagnosis of LSCC and the detection of advanced LSCC. TRIAL REGISTRATION Not applicable.
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Affiliation(s)
- Qiongling Huang
- Department of Otolaryngology, the Second Affiliated Hospital, Fujian Medical University, Quanzhou, 362000, Fujian Province, China
| | - Jing Chen
- Quanzhou Medical College, Quanzhou, 362000, Fujian Province, China
| | - Yanjun Huang
- Department of Otolaryngology, the Second Affiliated Hospital, Fujian Medical University, Quanzhou, 362000, Fujian Province, China
| | - Yu Xiong
- Department of Neurosurgery, the Second Affiliated Hospital, Fujian Medical University, Quanzhou, 362000, Fujian Province, China
| | - Jiao Zhou
- Department of Otolaryngology, the Second Affiliated Hospital, Fujian Medical University, Quanzhou, 362000, Fujian Province, China
| | - Yizheng Zhang
- Department of Otolaryngology, the Second Affiliated Hospital, Fujian Medical University, Quanzhou, 362000, Fujian Province, China
| | - Ming Lu
- Department of Otolaryngology, the Second Affiliated Hospital, Fujian Medical University, Quanzhou, 362000, Fujian Province, China
| | - Weipeng Hu
- Department of Neurosurgery, the Second Affiliated Hospital, Fujian Medical University, Quanzhou, 362000, Fujian Province, China.
| | - Feng Zheng
- Department of Neurosurgery, the Second Affiliated Hospital, Fujian Medical University, Quanzhou, 362000, Fujian Province, China.
| | - Chaohui Zheng
- Department of Otolaryngology, the Second Affiliated Hospital, Fujian Medical University, Quanzhou, 362000, Fujian Province, China.
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Zhang K, Ye B, Wu L, Ni S, Li Y, Wang Q, Zhang P, Wang D. Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma. Sci Rep 2023; 13:13532. [PMID: 37598277 PMCID: PMC10439907 DOI: 10.1038/s41598-023-40780-8] [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: 09/19/2022] [Accepted: 08/16/2023] [Indexed: 08/21/2023] Open
Abstract
The current prognostic tools for esophageal squamous cell carcinoma (ESCC) lack the necessary accuracy to facilitate individualized patient management strategies. To address this issue, this study was conducted to develop a machine learning (ML) prediction model for ESCC patients' survival management. Six ML approaches, including Rpart, Elastic Net, GBM, Random Forest, GLMboost, and the machine learning-extended CoxPH method, were employed to develop risk prediction models. The model was trained on a dataset of 1954 ESCC patients with 27 clinical features and validated on a dataset of 487 ESCC patients. The discriminative performance of the models was assessed using the concordance index (C-index). The best performing model was used for risk stratification and clinical evaluation. The study found that N stage, T stage, surgical margin, tumor grade, tumor length, sex, MPV, AST, FIB, and Mg are the important feature for ESCC patients' survival. The machine learning-extended CoxPH model, Elastic Net, and Random Forest had similar performance in predicting the mortality risk of ESCC patients, and outperformed GBM, GLMboost, and Rpart. The risk scores derived from the CoxPH model effectively stratified ESCC patients into low-, intermediate-, and high-risk groups with distinctly different 3-year overall survival (OS) probabilities of 80.8%, 58.2%, and 29.5%, respectively. This risk stratification was also observed in the validation cohort. Furthermore, the risk model demonstrated greater discriminative ability and net benefit than the AJCC8th stage, suggesting its potential as a prognostic tool for predicting survival events and guiding clinical decision-making. The classical algorithm of the CoxPH method was also found to be sufficiently good for interpretive studies.
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Affiliation(s)
- Kaijiong Zhang
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Bo Ye
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Lichun Wu
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Sujiao Ni
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Yang Li
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qifeng Wang
- Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
| | - Peng Zhang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Dongsheng Wang
- Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
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Luo L, Tan Y, Zhao S, Yang M, Che Y, Li K, Liu J, Luo H, Jiang W, Li Y, Wang W. The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer. BMC Cancer 2023; 23:496. [PMID: 37264319 DOI: 10.1186/s12885-023-10990-4] [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: 02/16/2023] [Accepted: 05/21/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis. METHODS Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve. RESULTS From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients. CONCLUSIONS This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.
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Affiliation(s)
- Liping Luo
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Yubo Tan
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shixuan Zhao
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Man Yang
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yurou Che
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Kezhen Li
- School of Medicine, Southwest Medical University, Luzhou, China
| | - Jieke Liu
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Huaichao Luo
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Wenjun Jiang
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Yongjie Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Weidong Wang
- Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
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9
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Wang Y. Does Pretreatment Platelet Distribution Width Predict Prognosis in Esophageal Cancer? Ann Surg Oncol 2022; 29:8207-8208. [PMID: 36008743 DOI: 10.1245/s10434-022-12478-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 08/05/2022] [Indexed: 11/18/2022]
Affiliation(s)
- Yan Wang
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Guoxuexiang No. 37, Chengdu, 610041, China.
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10
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Wang Q, Cao B, Mi K. ASO Author Reflections: Prognostic Role of a Coagulation Index for Patients with Esophageal Squamous Cell Cancer. Ann Surg Oncol 2021; 28:8462-8463. [PMID: 34106387 DOI: 10.1245/s10434-021-10267-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 05/24/2021] [Indexed: 11/18/2022]
Affiliation(s)
- Qifeng Wang
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.,Department of Radiation Oncology, Sichuan Cancer Hospital and Institution, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Bangrong Cao
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Kun Mi
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
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