1
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Yang B, Teng M, You H, Dong Y, Chen S. A Nomogram for Predicting Survival in Advanced Non-Small-Cell Lung Carcinoma Patients: A Population-Based Study. Cancer Invest 2023; 41:672-685. [PMID: 37490629 DOI: 10.1080/07357907.2023.2241547] [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: 07/27/2022] [Revised: 12/17/2022] [Accepted: 07/21/2023] [Indexed: 07/27/2023]
Abstract
Non-small-cell lung cancer (NSCLC) remains the most common malignant cancer. We identified 43140 advanced NSCLC patients from the SEER database to develop and validate a new prognostic model. The prognostic performance was evaluated by P value, concordance index, net reclassification index, integrated discrimination improvement, and decision curve analysis. The following variables were contained in the final prognostic model: age, sex, race, TNM stage, and grade and treatment options. Compared to the AJCC staging system, this prognostic model is conducive to the implementation of individualized clinical treatment schemes and can be an important part of the precise medical care of NSCLC tumors.
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Affiliation(s)
- Bo Yang
- Department of Pharmacy, First Affiliated Hospital of Xi'an Jiaotong University, Shannxi, China
| | - Mengmeng Teng
- Department of Pharmacy, First Affiliated Hospital of Xi'an Jiaotong University, Shannxi, China
| | - Haisheng You
- Department of Pharmacy, First Affiliated Hospital of Xi'an Jiaotong University, Shannxi, China
| | - Yalin Dong
- Department of Pharmacy, First Affiliated Hospital of Xi'an Jiaotong University, Shannxi, China
| | - Siying Chen
- Department of Pharmacy, First Affiliated Hospital of Xi'an Jiaotong University, Shannxi, China
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2
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Survival prediction for stage I-IIIA non-small cell lung cancer using deep learning. Radiother Oncol 2023; 180:109483. [PMID: 36690302 DOI: 10.1016/j.radonc.2023.109483] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 01/06/2023] [Accepted: 01/13/2023] [Indexed: 01/21/2023]
Abstract
BACKGROUND AND PURPOSE The aim of this study was to develop and evaluate a prediction model for 2-year overall survival (OS) in stage I-IIIA non-small cell lung cancer (NSCLC) patients who received definitive radiotherapy by considering clinical variables and image features from pre-treatment CT-scans. MATERIALS AND METHODS NSCLC patients who received stereotactic radiotherapy were prospectively collected at the UMCG and split into a training and a hold out test set including 189 and 81 patients, respectively. External validation was performed on 228 NSCLC patients who were treated with radiation or concurrent chemoradiation at the Maastro clinic (Lung1 dataset). A hybrid model that integrated both image and clinical features was implemented using deep learning. Image features were learned from cubic patches containing lung tumours extracted from pre-treatment CT scans. Relevant clinical variables were selected by univariable and multivariable analyses. RESULTS Multivariable analysis showed that age and clinical stage were significant prognostic clinical factors for 2-year OS. Using these two clinical variables in combination with image features from pre-treatment CT scans, the hybrid model achieved a median AUC of 0.76 [95 % CI: 0.65-0.86] and 0.64 [95 % CI: 0.58-0.70] on the complete UMCG and Maastro test sets, respectively. The Kaplan-Meier survival curves showed significant separation between low and high mortality risk groups on these two test sets (log-rank test: p-value < 0.001, p-value = 0.012, respectively) CONCLUSION: We demonstrated that a hybrid model could achieve reasonable performance by utilizing both clinical and image features for 2-year OS prediction. Such a model has the potential to identify patients with high mortality risk and guide clinical decision making.
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3
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Li M, Wang J, Li J, Zhang Y, Zhao X, Lin Y, Deng C, Li F, Peng Q. Develop and validate nomogram to predict cancer-specific survival for patients with testicular yolk sac tumors. Front Public Health 2022; 10:1038502. [PMID: 36324443 PMCID: PMC9619076 DOI: 10.3389/fpubh.2022.1038502] [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: 09/07/2022] [Accepted: 09/29/2022] [Indexed: 01/29/2023] Open
Abstract
Purpose Testicular yolk sac tumor (TYST) is a rare malignant germ cell tumor that mainly occurs in young men. Due to the low incidence of yolk sac tumors, there is a lack of prospective cohort studies with large samples. We aimed to develop a nomogram to predict cancer-specific survival (CSS) in patients with TYST. Materials and methods Patient information was downloaded from the Surveillance, Epidemiology and End Results (SEER) database. We enrolled all patients with TYST from 2000 to 2018, and all patients were randomly divided into a training set and a validation set. Univariate and multivariate Cox proportional hazards regression models were used to identify independent risk factors for patients. We constructed a nomogram based on the multivariate Cox regression model to predict 1-, 3-, and 5-year CSS in patients with TYST. We used a series of validation methods to test the accuracy and reliability of the model, including the concordance index (C-index), calibration curve and the area under the receiver operating characteristic curve (AUC). Results 619 patients with TYST were enrolled in the study. Univariate and multivariate Cox regression analysis showed that age, T stage, M stage and chemotherapy were independent risk factors for CSS. A nomogram was constructed to predict the patient's CSS. The C-index of the training set and the validation set were 0.901 (95%CI: 0.859-0.847) and 0.855 (95%CI: 0.865-0.845), respectively, indicating that the model had excellent discrimination. The AUC showed the same results. The calibration curve also indicated that the model had good accuracy. Conclusions In this study, we constructed the nomogram for the first time to predict the CSS of patients with TYST, which has good accuracy and reliability and can help doctors and patients make clinical decisions.
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Affiliation(s)
- Maoxian Li
- Department of Pediatric Surgery, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China,Department of Urology, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China,*Correspondence: Maoxian Li
| | - Jinkui Wang
- Department of Urology, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Jinfeng Li
- Department of Pediatric Surgery, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yongbo Zhang
- Department of Pediatric Surgery, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xing Zhao
- Department of Pediatric Surgery, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yang Lin
- Department of Pediatric Surgery, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Changkai Deng
- Department of Pediatric Surgery, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Fulin Li
- Department of Pediatric Surgery, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Qiang Peng
- Department of Pediatric Surgery, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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4
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Lian J, Deng J, Hui ES, Koohi-Moghadam M, She Y, Chen C, Vardhanabhuti V. Early stage NSCLS patients' prognostic prediction with multi-information using transformer and graph neural network model. eLife 2022; 11:80547. [PMID: 36194194 PMCID: PMC9531948 DOI: 10.7554/elife.80547] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 09/21/2022] [Indexed: 12/11/2022] Open
Abstract
Background: We proposed a population graph with Transformer-generated and clinical features for the purpose of predicting overall survival (OS) and recurrence-free survival (RFS) for patients with early stage non-small cell lung carcinomas and to compare this model with traditional models. Methods: The study included 1705 patients with lung cancer (stages I and II), and a public data set for external validation (n=127). We proposed a graph with edges representing non-imaging patient characteristics and nodes representing imaging tumour region characteristics generated by a pretrained Vision Transformer. The model was compared with a TNM model and a ResNet-Graph model. To evaluate the models' performance, the area under the receiver operator characteristic curve (ROC-AUC) was calculated for both OS and RFS prediction. The Kaplan–Meier method was used to generate prognostic and survival estimates for low- and high-risk groups, along with net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis. An additional subanalysis was conducted to examine the relationship between clinical data and imaging features associated with risk prediction. Results: Our model achieved AUC values of 0.785 (95% confidence interval [CI]: 0.716–0.855) and 0.695 (95% CI: 0.603–0.787) on the testing and external data sets for OS prediction, and 0.726 (95% CI: 0.653–0.800) and 0.700 (95% CI: 0.615–0.785) for RFS prediction. Additional survival analyses indicated that our model outperformed the present TNM and ResNet-Graph models in terms of net benefit for survival prediction. Conclusions: Our Transformer-Graph model was effective at predicting survival in patients with early stage lung cancer, which was constructed using both imaging and non-imaging clinical features. Some high-risk patients were distinguishable by using a similarity score function defined by non-imaging characteristics such as age, gender, histology type, and tumour location, while Transformer-generated features demonstrated additional benefits for patients whose non-imaging characteristics were non-discriminatory for survival outcomes. Funding: The study was supported by the National Natural Science Foundation of China (91959126, 8210071009), and Science and Technology Commission of Shanghai Municipality (20XD1403000, 21YF1438200).
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Affiliation(s)
- Jie Lian
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Jiajun Deng
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Edward S Hui
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China.,Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China
| | - Mohamad Koohi-Moghadam
- Division of Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China
| | - Yunlang She
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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5
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Xiong L, Jiang Y, Hu T. Prognostic nomograms for lung neuroendocrine carcinomas based on lymph node ratio: a SEER database analysis. J Int Med Res 2022; 50:3000605221115160. [PMID: 36076355 PMCID: PMC9465598 DOI: 10.1177/03000605221115160] [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] [Indexed: 11/18/2022] Open
Abstract
Objective The current study aimed to explore the prognostic value of the lymph node
ratio (LNR) in patients with lung neuroendocrine carcinomas (LNECs). Methods Data for 1564 elderly patients with LNECs between 1998 and 2016 were obtained
from the Surveillance, Epidemiology, and End Results database. The cases
were assigned randomly to training (n = 1086) and internal validation
(n = 478) sets. The association between LNR and survival was investigated by
Cox regression. Results Multivariate analyses identified age, tumor grade, summary stage, M stage,
surgery, and LNR as independent prognostic factors for both overall survival
(OS) and lung cancer-specific survival (LCSS). Tumor size was also a
prognostic determinant for LCSS. Prognostic nomograms combining LNR with
other informative variables showed good discrimination and calibration
abilities in both the training and validation sets. In addition, the C-index
of the nomograms was statistically superior to the American Joint Committee
on Cancer (AJCC) staging system in both the training and validation
cohorts. Conclusions These nomograms, based on LNR, showed superior prognostic predictive accuracy
compared with the AJCC staging system for predicting OS and LCSS in patients
with LNECs.
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Affiliation(s)
- Lan Xiong
- Department of Respiration, 585250The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Youfan Jiang
- Department of Respiration, 585250The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Tianyang Hu
- Precision Medicine Center, 585250The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
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6
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Lian J, Long Y, Huang F, Ng K, Lee FMY, Lam DL, Fang BL, Dou Q, Vardhanabhuti V. Imaging-Based Deep Graph Neural Networks for Survival Analysis in Early Stage Lung Cancer Using CT: A Multicenter Study. Front Oncol 2022; 12:868186. [PMID: 35936706 PMCID: PMC9351205 DOI: 10.3389/fonc.2022.868186] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 06/16/2022] [Indexed: 11/25/2022] Open
Abstract
Background Lung cancer is the leading cause of cancer-related mortality, and accurate prediction of patient survival can aid treatment planning and potentially improve outcomes. In this study, we proposed an automated system capable of lung segmentation and survival prediction using graph convolution neural network (GCN) with CT data in non-small cell lung cancer (NSCLC) patients. Methods In this retrospective study, we segmented 10 parts of the lung CT images and built individual lung graphs as inputs to train a GCN model to predict 5-year overall survival. A Cox proportional-hazard model, a set of machine learning (ML) models, a convolutional neural network based on tumor (Tumor-CNN), and the current TNM staging system were used as comparison. Findings A total of 1,705 patients (main cohort) and 125 patients (external validation cohort) with lung cancer (stages I and II) were included. The GCN model was significantly predictive of 5-year overall survival with an AUC of 0.732 (p < 0.0001). The model stratified patients into low- and high-risk groups, which were associated with overall survival (HR = 5.41; 95% CI:, 2.32–10.14; p < 0.0001). On external validation dataset, our GCN model achieved the AUC score of 0.678 (95% CI: 0.564–0.792; p < 0.0001). Interpretation The proposed GCN model outperformed all ML, Tumor-CNN, and TNM staging models. This study demonstrated the value of utilizing medical imaging graph structure data, resulting in a robust and effective model for the prediction of survival in early-stage lung cancer.
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Affiliation(s)
- Jie Lian
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Yonghao Long
- Department of Computer Science, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Fan Huang
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Kei Shing Ng
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Faith M. Y. Lee
- Faculty of Medicine, University College London, London, United Kingdom
| | - David C. L. Lam
- Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Benjamin X. L. Fang
- Department of Radiology, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
| | - Qi Dou
- Department of Computer Science, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
- *Correspondence: Varut Vardhanabhuti, ; Qi Dou,
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- *Correspondence: Varut Vardhanabhuti, ; Qi Dou,
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7
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Potential Impact of Cancer Susceptibility Genes on Lung Cancer Metastasis. JOURNAL OF ONCOLOGY 2022; 2022:1516946. [PMID: 35479964 PMCID: PMC9038395 DOI: 10.1155/2022/1516946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 03/23/2022] [Indexed: 11/17/2022]
Abstract
Background. Studies of prognosis-related molecular markers are an important tool to uncover the mechanism of tumour metastasis. Cancer susceptibility gene testing is an important tool for genetic counselling of cancer risk. However, the impact of lung cancer susceptibility genes (LCSGs) on lung cancer metastasis and prognosis has not been well studied. Methods. The list of lung cancer susceptibility genes was retrospectively analysed and updated. After expression profiling and functional analysis, LCSG-based signatures for prognosis were identified by Cox regression and LASSO regression analyses. For translational purposes, nomograms integrating LCSGs and clinical characteristics were constructed. Results. A total of 301 LCSGs were employed for modelling. For lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), 10-gene and 7-gene signatures were created and independently validated. The LCSG-based risk score could stratify LUAD survival (univariate: hazard ratio
, 95% confidence interval
–1.103,
; multivariate:
, 95%
–1.095,
) and LUSC survival (univariate:
, 95%
−1.239,
; multivariate:
, 95%
−1.228,
). One of the processes affected by differentially expressed genes in both LUAD and LUSC was the negative regulation of epithelial cell differentiation. Conclusions. Overall, novel LCSG-based gene signatures for LUAD and LUSC were constructed. These findings could expand the understanding of the impact of LCSG expression on cancer metastasis and prognosis.
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8
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Wang Z, Hu F, Chang R, Yu X, Xu C, Liu Y, Wang R, Chen H, Liu S, Xia D, Chen Y, Ge X, Zhou T, Zhang S, Pang H, Fang X, Zhang Y, Li J, Hu K, Cai Y. Development and Validation of a Prognostic Model to Predict Overall Survival for Lung Adenocarcinoma: A Population-Based Study From the SEER Database and the Chinese Multicenter Lung Cancer Database. Technol Cancer Res Treat 2022; 21:15330338221133222. [PMID: 36412085 PMCID: PMC9706045 DOI: 10.1177/15330338221133222] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/15/2022] [Accepted: 09/29/2022] [Indexed: 10/31/2023] Open
Abstract
Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small-cell lung cancer (NSCLC). The aim of our study was to determine prognostic risk factors and establish a novel nomogram for lung adenocarcinoma patients. Methods: This retrospective cohort study is based on the Surveillance, Epidemiology, and End Results (SEER) database and the Chinese multicenter lung cancer database. We selected 22,368 eligible LUAD patients diagnosed between 2010 and 2015 from the SEER database and screened them based on the inclusion and exclusion criteria. Subsequently, the patients were randomly divided into the training cohort (n = 15,657) and the testing cohort (n = 6711), with a ratio of 7:3. Meanwhile, 736 eligible LUAD patients from the Chinese multicenter lung cancer database diagnosed between 2011 and 2021 were considered as the validation cohort. Results: We established a nomogram based on each independent prognostic factor analysis for 1-, 3-, and 5-year overall survival (OS) . For the training cohort, the area under the curves (AUCs) for predicting the 1-, 3-, and 5-year OS were 0.806, 0.856, and 0.886. For the testing cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.804, 0.849, and 0.873. For the validation cohort, AUCs for predicting the 1-, 3-, and 5-year OS were 0.86, 0.874, and 0.861. The calibration curves were observed to be closer to the ideal 45° dotted line with regard to 1-, 3-, and 5-year OS in the training cohort, the testing cohort, and the validation cohort. The decision curve analysis (DCA) plots indicated that the established nomogram had greater net benefits in comparison with the Tumor-Node-Metastasis (TNM) staging system for predicting 1-, 3-, and 5-year OS of lung adenocarcinoma patients. The Kaplan-Meier curves indicated that patients' survival in the low-risk group was better than that in the high-risk group (P < .001). Conclusion: The nomogram performed very well with excellent predictive ability in both the US population and the Chinese population.
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Affiliation(s)
- Zhiqiang Wang
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Fan Hu
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Ruijie Chang
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Xiaoyue Yu
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Chen Xu
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Yujie Liu
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Rongxi Wang
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Hui Chen
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Shangbin Liu
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Danni Xia
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Yingjie Chen
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Xin Ge
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Tian Zhou
- Dongfang
Hospital, Beijing University of Chinese
Medicine, Beijing, China
| | - Shuixiu Zhang
- Dongfang
Hospital, Beijing University of Chinese
Medicine, Beijing, China
| | - Haoyue Pang
- Dongfang
Hospital, Beijing University of Chinese
Medicine, Beijing, China
| | - Xueni Fang
- Dongfang
Hospital, Beijing University of Chinese
Medicine, Beijing, China
| | - Yushuang Zhang
- The Fourth
Hospital of Hebei Medical University,
Shijiazhuang, China
| | - Jin Li
- The Fourth
Hospital of Hebei Medical University,
Shijiazhuang, China
| | - Kaiwen Hu
- Dongfang
Hospital, Beijing University of Chinese
Medicine, Beijing, China
| | - Yong Cai
- School of Public Health, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
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9
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Wang Y, Lin X, Sun D. A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models? ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1597. [PMID: 34790803 PMCID: PMC8576716 DOI: 10.21037/atm-21-4733] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 10/02/2021] [Indexed: 12/18/2022]
Abstract
Objective To discover potential predictors and explore how to build better models by summarizing the existing prognostic prediction models of non-small cell lung cancer (NSCLC). Background Research on clinical prediction models of NSCLC has experienced explosive growth in recent years. As more predictors of prognosis are discovered, the choice of predictors to build models is particularly important, and in the background of more applications of next-generation sequencing technology, gene-related predictors are widely used. As it is more convenient to obtain samples and follow-up data, the prognostic model is preferred by researchers. Methods PubMed and the Cochrane Library were searched using the items “NSCLC”, “prognostic model”, “prognosis prediction”, and “survival prediction” from 1 January 1980 to 5 May 2021. Reference lists from articles were reviewed and relevant articles were identified. Conclusions The performance of gene-related models has not obviously improved. Relative to the innovation and diversity of predictors, it is more important to establish a highly stable model that is convenient for clinical application. Most of the prevalent models are highly biased and referring to PROBAST at the beginning of the study may be able to significantly control the bias. Existing models should be validated in a large external dataset to make a meaningful comparison.
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Affiliation(s)
- Yuhang Wang
- Graduate School, Tianjin Medical University, Tianjin, China
| | | | - Daqiang Sun
- Graduate School, Tianjin Medical University, Tianjin, China.,Department of Thoracic Surgery, Tianjin Chest Hospital of Nankai University, Tianjin, China
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10
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Liao Y, Yin G, Fan X. The Positive Lymph Node Ratio Predicts Survival in T 1-4N 1-3M 0 Non-Small Cell Lung Cancer: A Nomogram Using the SEER Database. Front Oncol 2020; 10:1356. [PMID: 32903785 PMCID: PMC7438846 DOI: 10.3389/fonc.2020.01356] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Accepted: 06/29/2020] [Indexed: 12/14/2022] Open
Abstract
Background: An increasing number of studies have shown that the positive lymph node ratio (pLNR) can be used to evaluate the prognosis of non-small cell lung cancer (NSCLC) patients. To determine the predictive value of the pLNR, we collected data from the Surveillance, Epidemiology, and End Results (SEER) database and performed a retrospective analysis. Methods: We collected survival and clinical information on patients with T1-4N1-3M0 NSCLC diagnosed between 2010 and 2016 from the SEER database and screened them according to inclusion and exclusion criteria. X-tile software was used to obtain the best cut-off value for the pLNR. Then, we randomly divided patients into a training set and a validation set at a ratio of 7:3. Pearson's correlation coefficient, tolerance and the variance inflation factor (VIF) were used to detect collinearity between variables. Univariate and multivariate Cox regression analyses were used to identify significant prognostic factors, and nomograms was constructed to visualize the results. The concordance index (C-index), calibration curves, and decision curve analysis (DCA) were used to assess the predictive ability of the nomogram. We divided the patient scores into four groups according to the interquartile interval and constructed a survival curve using Kaplan-Meier analysis. Results: A total of 6,245 patients were initially enrolled. The best cut-off value for the pLNR was determined to be 0.55. The nomogram contained 13 prognostic factors, including the pLNR. The pLNR was identified as an independent prognostic factor for both overall survival (OS) and cancer-specific survival (CSS). The C-index was 0.703 (95% CI, 0.695-0.711) in the training set and 0.711 (95% CI, 0.699-0.723) in the validation set. The calibration curves and DCA also indicated the good predictability of the nomogram. Risk stratification revealed a statistically significant difference among the four groups of patients divided according to quartiles of risk score. Conclusion: The nomogram containing the pLNR can accurately predict survival in patients with T1-4N1-3M0 NSCLC.
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Affiliation(s)
- Yi Liao
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China.,Inflammation & Allergic Diseases Research Unit, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Guofang Yin
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China.,Inflammation & Allergic Diseases Research Unit, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xianming Fan
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China.,Inflammation & Allergic Diseases Research Unit, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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11
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Yin G, Xiao H, Liao Y, Huang C, Fan X. Construction of a Nomogram After Using Propensity Score Matching to Reveal the Prognostic Benefit of Tumor Resection of Stage IV M1a Nonsmall Cell Lung Cancer Patients. Cancer Invest 2020; 38:277-288. [PMID: 32267175 DOI: 10.1080/07357907.2020.1753761] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The aim of this work was to determine whether tumor resection could improve the prognosis of M1a nonsmall-cell lung cancer (NSCLC) patients. We obtained patient data from the Surveillance, Epidemiology, and End Results (SEER) database and used propensity score matching (PSM) to reduce the influence of confounding variables. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors, and the prediction results were visualized using the nomogram. A total of 772 patients with and without tumor resection were enrolled after PSM, and the nomogram combined with independent prognostic factors including age, sex, histological type, grade, T stage, N stage, chemotherapy, and surgery showed great prediction and discriminatory ability. Tumor resection is possibly a better choice for these patients.
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Affiliation(s)
- Guofang Yin
- Department of Respiratory and Critical Care Medicine II, The Affiliated Hospital of Southwest Medical University, Luzhuo, Sichuan Province, People's Republic of China
| | - Hua Xiao
- Department of Respiratory and Critical Care Medicine II, The Affiliated Hospital of Southwest Medical University, Luzhuo, Sichuan Province, People's Republic of China
| | - Yi Liao
- Department of Respiratory and Critical Care Medicine II, The Affiliated Hospital of Southwest Medical University, Luzhuo, Sichuan Province, People's Republic of China
| | - Chengliang Huang
- Department of Respiratory and Critical Care Medicine II, The Affiliated Hospital of Southwest Medical University, Luzhuo, Sichuan Province, People's Republic of China
| | - Xianming Fan
- Department of Respiratory and Critical Care Medicine II, The Affiliated Hospital of Southwest Medical University, Luzhuo, Sichuan Province, People's Republic of China
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Bhargava A, Mishra DK, Tiwari R, Lohiya NK, Goryacheva IY, Mishra PK. Immune cell engineering: opportunities in lung cancer therapeutics. Drug Deliv Transl Res 2020; 10:1203-1227. [PMID: 32172351 DOI: 10.1007/s13346-020-00719-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Engineered immune cells offer a prime therapeutic alternate for some aggressive and frequently occurring malignancies like lung cancer. These therapies were reported to result in tumor regression and overall improvement in patient survival. However, studies also suggest that the presence of cancer cell-induced immune-suppressive microenvironment, off-target toxicity, and difficulty in concurrent imaging are some prime impendent in the success of these approaches. The present article reviews the need and significance of the currently available immune cell-based strategies for lung cancer therapeutics. It also showcases the utility of incorporating nanoengineered strategies and details the available formulations of nanocarriers. In last, it briefly discussed the existing methods for nanoparticle fuctionalization and challenges in translating basic research to the clinics. Graphical Abstract.
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Affiliation(s)
- Arpit Bhargava
- Department of Molecular Biology, ICMR-National Institute for Research in Environmental Health, Kamla Nehru Hospital,, Building (Gandhi Medical College Campus), Bhopal, Madhya Pradesh, 462001, India
| | | | - Rajnarayan Tiwari
- Department of Molecular Biology, ICMR-National Institute for Research in Environmental Health, Kamla Nehru Hospital,, Building (Gandhi Medical College Campus), Bhopal, Madhya Pradesh, 462001, India
| | | | - Irina Yu Goryacheva
- Department of General and Inorganic Chemistry, Saratov State University, Saratov, Russian Federation
| | - Pradyumna Kumar Mishra
- Department of Molecular Biology, ICMR-National Institute for Research in Environmental Health, Kamla Nehru Hospital,, Building (Gandhi Medical College Campus), Bhopal, Madhya Pradesh, 462001, India.
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Huang Z, Xing S, Zhu Y, Qu Y, Jiang L, Sheng J, Wang Q, Xu S, Xue N. Establishment and Validation of Nomogram Model Integrated With Inflammation-Based Factors for the Prognosis of Advanced Non-Small Cell Lung Cancer. Technol Cancer Res Treat 2020; 19:1533033820971605. [PMID: 33191854 PMCID: PMC7675852 DOI: 10.1177/1533033820971605] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 08/30/2020] [Accepted: 09/30/2020] [Indexed: 12/15/2022] Open
Abstract
OBJECTS Inflammation is one of the hallmarks of cancer. Tumor-associated inflammatory response plays a crucial role in enhancing tumorigenesis. This study aimed to establish an effective predictive nomogram based on inflammation factors in patients with advanced non-small cell lung cancer (NSCLC). METHODS We retrospectively evaluated 887 patients with advanced NSCLC between November 2004 and December 2015 and randomly divided them into primary (n = 520) and validation cohorts (n = 367). Cox regression analysis was used to identify prognostic factors for building the nomogram. The predictive accuracy and discriminative ability of the nomogram were determined using a concordance index (C-index), calibration plot, and decision curve analysis and were compared to the TNM staging system. RESULTS The nomogram was established using independent risk factors (P < 0.05): age, TNM stage, C reaction protein-to-albumin ratio (CAR), and neutrophils (NEU). The C-index of the model for predicting OS had a superior discrimination power compared to that of the TNM staging system both in the primary [0.711 (95% CI: 0.675-0.747) vs 0.531 (95% CI: 0.488-0.574), P < 0.01] and validation cohorts [0.703, 95% CI: 0.671 -0.735 vs 0.582, 95% CI: 0.545-0.619, P < 0.01]. Decision curves also demonstrated that the nomogram had higher overall net benefits than that of the TNM staging system. Subgroup analyses revealed that the nomogram was a favorable prognostic parameter in advanced NSCLC (P < 0.05). The results were internally validated using the validation cohorts. CONCLUSIONS The proposed nomogram with inflammatory factors resulted in an accurate prognostic prediction in patients with advanced NSCLC.
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Affiliation(s)
- Zhiliang Huang
- Department of Thoracic Surgery, Xiamen Branch, Zhongshan Hospital, Fudan
University, Xiamen, Fujian, China
- Department of thoracic surgery, State Key Laboratory of Oncology in
South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer
Center, Guangzhou, China
| | - Shan Xing
- Department of Clinical Laboratory, State Key Laboratory of Oncology
in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer
Center, Guangzhou, China
| | - Yuanying Zhu
- Department of Clinical Laboratory, State Key Laboratory of Oncology
in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer
Center, Guangzhou, China
| | - Yuanye Qu
- Department of Clinical Laboratory, Affiliated Tumor Hospital of Zhengzhou
University, Henan Tumor Hospital, Zhengzhou Key Laboratory of Digestive
Tumor Markers, Zhengzhou, China
| | - Lina Jiang
- Department of Radiology, Affiliated Tumor Hospital of Zhengzhou
University, Henan Tumor Hospital, Zhengzhou Key Laboratory of Digestive
Tumor Markers, Zhengzhou, China
| | - Jiahe Sheng
- Department of Clinical Laboratory, Affiliated Tumor Hospital of Zhengzhou
University, Henan Tumor Hospital, Zhengzhou Key Laboratory of Digestive
Tumor Markers, Zhengzhou, China
| | - Qian Wang
- Department of Radiation Oncology, China-Japan Union Hospital of Jilin
University, Changchun, China
| | - Songtao Xu
- Department of Thoracic Surgery, Xiamen Branch, Zhongshan Hospital, Fudan
University, Xiamen, Fujian, China
- Department of Thoracic Surgery, Zhongshan Hospital, Fudan
University, Shanghai, China
| | - Ning Xue
- Department of Clinical Laboratory, State Key Laboratory of Oncology
in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer
Center, Guangzhou, China
- Department of Clinical Laboratory, Affiliated Tumor Hospital of Zhengzhou
University, Henan Tumor Hospital, Zhengzhou Key Laboratory of Digestive
Tumor Markers, Zhengzhou, China
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Non-SMC Condensin I Complex Subunit H (NCAPH) Is Associated with Lymphangiogenesis and Drug Resistance in Oral Squamous Cell Carcinoma. J Clin Med 2019; 9:jcm9010072. [PMID: 31892156 PMCID: PMC7019401 DOI: 10.3390/jcm9010072] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 12/23/2019] [Accepted: 12/24/2019] [Indexed: 12/24/2022] Open
Abstract
Background: Head and neck cancer, including oral squamous cell carcinoma (OSCC), is the sixth most common malignancy. OSCC has strong invasive ability, and its malignant potential is closely associated with local expansion and lymph node metastasis. Furthermore, local or nodal recurrence worsens OSCC prognosis. In our previous cDNA microarray analysis, non-structural maintenance of chromosome (SMC) condensin I complex subunit H (NCAPH) was identified as an upregulated gene in recurrent OSCC. Although NCAPH has several functions in tumors, its role in OSCC is unknown. Methods: In this study, we examined NCAPH expression in OSCC and performed a functional analysis of human OSCC cells. Results: NCAPH expression was higher in OSCC than in normal oral mucosa. In immunohistochemistry using 142 OSCC specimens, the immunostaining of NCAPH was strongly associated with nodal metastasis and lymphatic infiltration. In multivariate analysis using the Cox proportional hazards model, NCAPH expression was an independent poor prognostic indicator for OSCC. Moreover, NCAPH promoted the migration and adhesion of endothelial cells to OSCC cells and promoted the resistance to platinum anticancer drugs. Conclusions: Our present findings suggest that NCAPH is a novel diagnostic and therapeutic target in OSCC.
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