Combination of quantitative features from H&E biopsies and CT scans predicts response to chemotherapy and overall survival in small cell lung cancer (SCLC).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 8572-8572
Author(s):  
Cristian Barrera ◽  
Mohammadhadi Khorrami ◽  
Prantesh Jain ◽  
Pingfu Fu ◽  
Kate Butler ◽  
...  
Keyword(s):  
Overall Survival ◽  
Regression Model ◽  
Risk Score ◽  
Risk Group ◽  
Image Features ◽  
Small Cell ◽  
Ct Scans ◽  
Small Cell Lung ◽  
Kaplan Meier ◽  
Response To Chemotherapy

8572 Background: Small Cell Lung Cancer (SCLC) is an aggressive malignancy with a rapid growth, and Chemotherapy remains mainstay of treatment. Identifying therapeutic targets in SCLC presents a challenge, partially due to a lack of accurate and consistently predictive biomarkers. In this study we sought to evaluate the utility of a combination of computer-extracted radiographic and pathology features from pretreatment baseline CT and H&E biopsy images to predict sensitivity to platinum-based chemotherapy and overall survival (OS) in SCLC. Methods: Seventy-eight patients with extensive and limited-stage SCLC who received platinum-doublet chemotherapy were selected. Objective response to chemotherapy (RECIST criteria) and overall survival (OS) as clinical endpoints were available for 51 and 78 patients respectively. The patients were divided randomly into two sets (Training (Sd), Validation (Sv)) with a constraint (equal number of responders and nonresponders in Sd)—Sd comprised twenty-one patients with SCLC. Sv included thirty patients. CT scans and digitized Hematoxylin Eosin-stained (H&E) biopsy images were acquired for each patient. A set of CT derived (46%) and tissue derived (53%) image features were captured. These included shape and textural patterns of the tumoral and peritumoral regions from CT scans and of tumor regions on H&E images. A random forest feature selection and linear regression model were used to identify the most predictive CT and H&E derived image features associated with chemotherapy response from Sd. A Cox proportional hazard regression model was used with these features to compute a risk score for each patients in Sd. Patients in Sv were stratified into high and low-risk groups based on the median risk score. Kaplan-Meier survival analysis was used to assess the prognostic ability of the risk score on Sv. Results: The risk score comprised nine CT (intra and peri-tumoral texture) and six H&E derived (cancer cell texture and shape) features. A linear regression model in conjunction with these 15 features was significantly associated with chemo-sensitivity in Sv (AUC = 0.76, PRC = 0.81). A multivariable model with these 15 features was significantly associated with OS in Sv (HR = 2.5, 95% CI: 1.3-4.9, P = 0.0043). Kaplan-Meier survival analysis revealed a significantly reduced OS in the high-risk group compared to the low-risk group. Conclusions: A combined CT and H&E tissue derived image signature model predicted response to chemotherapy and improved OS in SCLC patients. Image features from baseline CT scans and H&E tissue slide images may help in better risk stratification of SCLC patients. Additional independent validation of these quantitative image-based biomarkers is warranted.

scholarly journals Eight-Gene Metabolic Signature Related with Tumor-Associated Macrophages Predicting Overall Survival for Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Junyu Huo ◽  
Yunjin Zang ◽  
Hongjing Dong ◽  
Xiaoqiang Liu ◽  
Fu He ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Survival Analysis ◽  
Risk Score ◽  
Risk Group ◽  
Cancer Genome ◽  
Metabolic Reprogramming ◽  
Tumor Associated Macrophages ◽  
Kaplan Meier ◽  
The Relationship

Abstract Background: In recent years, the relationship between tumor associated macrophages (TAMs) and solid tumors has become a research hotspot. The study aims at exploring the close relationship of TAMs with metabolic reprogramming genes in hepatocellular carcinoma(HCC), in order to provide a new way of treatment for HCC.Materials and methods: The study selected 343 HCC patients with complete survival information(survival time >= 1month) in the Cancer Genome Atlas (TCGA) as the study objects. Kaplan-Meier survival analysis assisted in figuring out the relationship between macrophage infiltration level and overall survival (OS), and Pearson correlation test to identify metabolic reprogramming genes(MRGs) related to tumor macrophage abundance. Lasso regression algorithm were conducted on prognosis related MRGs screened by Univariate Cox regression analysis and Kaplan-Meier survival analysis to construct the riskscore, another independent cohort (including 228 HCC patients) from the International Cancer Genome Consortium (ICGC) were used for external validation regarding the prognostic signature.Results: A risk score composed of 8 metabolic genes can accurately predict the OS of training cohort(TCGA) and testing cohort(ICGC). It is important that the risk score could widely used for people with different clinical characteristics, and is an independent predictor independent of other clinical factors affecting prognosis. As expected, high-risk group exhibited an obviously higher macrophage abundance relative to low-risk group, and the risk score presented a positive relation to the expression level of three commonly used immune checkpoints(PD1,PDL1,CTLA4).Conclusion: Our study constructed and validated a novel eight‑gene signature for predicting HCC patients’ OS, which possibly contributed to making clinical treatment decisions.

scholarly journals Pretreatment plasma d-dimer levels as an independent prognostic factor for overall survival among patients with advanced non-small-cell lung cancer

2020 ◽  
Vol 48 (10) ◽  
pp. 030006052096266
Author(s):  
Qianfei Liu ◽  
Jianbo He ◽  
Ruiling Ning ◽  
Liping Tan ◽  
Aiping Zeng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Prognostic Factor ◽  
Small Cell Lung Cancer ◽  
Advanced Nsclc ◽  
Multivariate Analyses ◽  
Small Cell ◽  
Small Cell Lung ◽  
Kaplan Meier ◽  
D Dimer

Objective To evaluate the prognostic accuracy of d-dimer levels for advanced non-small-cell lung cancer (NSCLC). Methods This retrospective cohort study included 651 patients initially diagnosed with advanced NSCLC. Patients with d-dimer levels ≥0.5 mg/L were included in the high d-dimer group, whereas patients with lower levels were included in the normal group. Cumulative survival was estimated using Kaplan–Meier curves and compared using the log-rank test. Multivariate analyses were performed using the Cox proportional hazards model. Results The median plasma d-dimer level in the study cohort was 0.61 ± 0.49 mg/L. d-dimer levels were elevated in 60.98% of patients, and 80.1% of such patients had adenocarcinoma. Univariate and multivariate analyses identified d-dimer content as an independent factor for the prognosis of NSCLC (hazard ratio [HR] = 1.54, 95% confidence interval [CI] = 1.19–1.98). Kaplan–Meier analysis revealed that high plasma d-dimer levels were associated with shorter overall survival (HR = 1.48, 95% CI = 1.19–1.84). In addition, the receipt of <2 lines of treatment was associated with a higher risk of death than the receipt of >2 lines. Conclusion The present results imply that pretreatment plasma d-dimer levels could represent a prognostic factor for advanced NSCLC.

Time from stereotactic body radiotherapy to immunotherapy as a predictor for outcome in metastatic non small cell lung cancer.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 9024-9024
Author(s):  
Rodney E Wegner ◽  
Stephen Abel ◽  
Shaakir Hasan ◽  
Richard White ◽  
Gene Grant Finley ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Small Cell Lung Cancer ◽  
Median Survival ◽  
Stereotactic Body Radiotherapy ◽  
Cox Regression ◽  
Stage Iv ◽  
Small Cell ◽  
Small Cell Lung ◽  
Kaplan Meier

9024 Background: Immunotherapy has changed the face of treatment for stage IV non small cell lung cancer (NSCLC), quickly becoming the standard of care. The appropriate timing of immunotherapy in the setting of other ablative therapies, namely stereotactic radiosurgery (SRS) and stereotactic body radiotherapy (SBRT), remains to be determined. We sought to use the National Cancer Database to examine trends in immunotherapy use as well as timing as it relates to SBRT/SRS in stage IV NSCLC patients. Methods: We queried the NCDB for patients with Stage IV NSCLC diagnosed between 2004-2015 that were treated with SRS or SBRT techniques (to any site) and had at least three months of follow up. Multivariable logistic regression was used to identify predictors of immunotherapy use. Receiver operator curve analysis was used to identify the optimal timepoint between SBRT and immunotherapy correlating with overall survival. Kaplan-meier curves were generated to determine overall survival. Multivariable cox regression was used to determine factors predictive of survival. A propensity score was generated and incorporated into Kaplan-meier and cox regressions to account for indication bias. Results: We identified 13,862 patients meeting the above eligibility criteria, 371 being treated with immunotherapy. The vast majority (75%) had chemotherapy as well. Patients with adenocarcinoma, treatment with chemotherapy, and more recent year of treatment were more likely to receive immunotherapy. Univariable Kaplan-meier analysis showed improved median survival with immunotherapy, 17 months vs. 13 months, p < 0.0001. On multivariable propensity-adjusted cox regression significant predictors for improved overall survival were younger age, lower comorbidity score, lower grade, private insurance, and female gender. Using a cutoff of 21 days after start of SBRT, patients treated thereafter were more likely to survive longer, median survival of 19 months vs 15 months, p = 0.0335. Conclusions: Immunotherapy use in Stage IV NSCLC after SBRT has increased over time, mostly in patients with adenocarcinoma and in the setting of chemotherapy. In this analysis, outcomes were improved when immunotherapy was given at least three weeks after start of SBRT.

scholarly journals Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer

2017 ◽  
Vol 117 (5) ◽  
pp. 744-751 ◽  
Author(s):  
Marliese Alexander ◽  
Rory Wolfe ◽  
David Ball ◽  
Matthew Conron ◽  
Robert G Stirling ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Small Cell Lung Cancer ◽  
Risk Score ◽  
Cell Lung Cancer ◽  
Prognostic Index ◽  
Small Cell ◽  
Small Cell Lung

scholarly journals Identification of a Pyroptosis-Related Gene Signature for Predicting Overall Survival and Response to Immunotherapy in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Susu Zheng ◽  
Xiaoying Xie ◽  
Xinkun Guo ◽  
Yanfang Wu ◽  
Guobin Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Regression Model ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Related Gene

Pyroptosis is a novel kind of cellular necrosis and shown to be involved in cancer progression. However, the diverse expression, prognosis and associations with immune status of pyroptosis-related genes in Hepatocellular carcinoma (HCC) have yet to be analyzed. Herein, the expression profiles and corresponding clinical characteristics of HCC samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then a pyroptosis-related gene signature was built by applying the least absolute shrinkage and selection operator (LASSO) Cox regression model from the TCGA cohort, while the GEO datasets were applied for verification. Twenty-four pyroptosis-related genes were found to be differentially expressed between HCC and normal samples. A five pyroptosis-related gene signature (GSDME, CASP8, SCAF11, NOD2, CASP6) was constructed according to LASSO Cox regression model. Patients in the low-risk group had better survival rates than those in the high-risk group. The risk score was proved to be an independent prognostic factor for overall survival (OS). The risk score correlated with immune infiltrations and immunotherapy responses. GSEA indicated that endocytosis, ubiquitin mediated proteolysis and regulation of autophagy were enriched in the high-risk group, while drug metabolism cytochrome P450 and tryptophan metabolism were enriched in the low-risk group. In conclusion, our pyroptosis-related gene signature can be used for survival prediction and may also predict the response of immunotherapy.

scholarly journals A Multi-parametric Prognostic Model Based on Clinical Features and Serological Markers Predicts Overall Survival in Non-small Cell Lung Cancer Patients With Chronic Hepatitis B Viral Infection

2020 ◽  
Author(s):  
Shulin Chen ◽  
Hanqing Huang ◽  
Yijun Liu ◽  
Changchun Lai ◽  
Songguo Peng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Risk Score ◽  
Prognostic Model ◽  
Tnm Staging ◽  
Small Cell ◽  
Clinical Treatment ◽  
Serological Markers ◽  
Small Cell Lung ◽  
Model Risk

Abstract Background To develop and validate a multi-parametric prognostic model based on clinical features and serological markers to estimate overall survival (OS) in non-small cell lung cancer (NSCLC) patients with chronic hepatitis B viral (HBV) infection. Methods The prognostic model was generated by using Lasso regression in training cohort. The incremental predictive value of the model to traditional TNM staging and clinical treatment for individualized survival was evaluated by concordance index (C-index), time-dependent ROC (tdROC), and decision curve analysis (DCA). A model risk score nomogram for OS was built by combining TNM staging and clinical treatment. Then we stratified patients into high and low risk subgroups according to the model risk score. Difference in survival between subgroups was analyzed using Kaplan–Meier survival analysis. Furthermore, correlations between the prognostic model and TNM staging or treatment were analysed. Results The C-index values of the model for predicting OS were 0.769 and 0.676 in the training and validation cohorts, respectively, which were higher than that of TNM staging, and treatment, the tdROC curve and DCA also showed the model had good predictive accuracy and discriminatory power than TNM staging and treatment. And the nomogram shown some clinical net benefit. According to the model risk score, we divided the patients into low risk and high risk subgroups. The differences of OS rates were significant in the subgroups. Furthermore, the model was positive correlation with TNM staging and treatment. Conclusions The proposed prognostic model showed favorable performance than traditional TNM staging and clinical treatment for estimating OS in NSCLC (HBV+) patients.

Systemic immune-inflammation index predicted overall survival and radiosensitivity in advanced non-small-cell lung cancer

2020 ◽  
Vol 16 (5) ◽  
pp. 103-115 ◽  
Author(s):  
Xixi Li ◽  
Pingping Hu ◽  
Jing Liu ◽  
Jiandong Zhang ◽  
Qiqi Liu
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Area Under The Curve ◽  
Small Cell ◽  
Small Cell Lung ◽  
Kaplan Meier ◽  
Cox Proportional Hazard Models ◽  
Immune Inflammation

Aim: To evaluate the predictive significance of systemic immune-inflammation index (SII) on overall survival (OS) and radiosensitivity in advanced non-small-cell lung cancer. Materials & methods: Kaplan–Meier analysis and Cox proportional hazard models were used to assess the prognostic value of SII. Results: The optimal cutoff for SII was 555.59, with an area under the curve of 0.782 (sensitivity: 76.6%, specificity: 71.9%, 95% CI: 0.730–0.833), respectively. Median OS (p < 0.001) in the low SII group (32.8 months) was better than the OS in the high SII group (8.5 months). SII-low group statistically exhibited a better radiosensitivity. Conclusion: SII was an independent prognostic factor for OS and predictive factor for radiosensitivity. Higher level of SII associated with poorer OS and poorer radiosensitivity.

scholarly journals 365 Computational pathology delivers objective and quantitative PD-L1 expression analysis for enrichment of responders to durvalumab in non-small cell lung cancer (NSCLC)

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A392-A393
Author(s):  
Guenter Schmidt ◽  
Ansh Kapil ◽  
Lina Meinecke ◽  
Farzad Sekhavati ◽  
Jan Lesniak ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trial ◽  
Overall Survival ◽  
Tumor Cell ◽  
Tumor Cells ◽  
Visual Assessment ◽  
Small Cell ◽  
Small Cell Lung ◽  
Kaplan Meier ◽  
Nsclc Patients

BackgroundThe pathologist’s visual assessment of tumor proportion score (TPS) with 25% cutoff on PD-L1 stained tissue samples is an established method to select metastatic NSCLC patients that are likely to respond to an anti-PD-L1 monotherapy.1 However, manual scoring is often subject to subjectivity in human perception2 and there remains a critical need for more objective and quantitative methods to assess PD-L1 expression in immuno-oncology.MethodsWe used deep learning (DL) based image analysis (IA) to generate a novel PD-L1 Quantitative Continuous Score (QCS)3 in tumor cells. PD-L1 QCS consists of two DL models to first segment epithelial regions and second detect membranes, cytoplasm and nuclei of each tumor cell in PD-L1 immunohistochemically (IHC) stained tissue slides. The PD-L1 expression of each tumor cell compartment was estimated by the respective optical density (OD) of DAB, and tumor cells with a membrane OD greater than ODmin are considered as PD-L1-positive. A slide comprising at greater percentage of PD-L1-positive tumor cells than a cutoff value (CoV) is considered QCS-positive. The ODmin and CoV parameters were linked to patient overall survival (OS), by minimizing the Kaplan Meier log-rank p-values and keeping at least 50% prevalence in the QCS-positive subgroup.Fully supervised QCS-IA models were extensively trained using pathologists’ annotations and the performance was validated on unseen data to ensure its generalization and robustness.3 4 The QCS IA was locked and blindly applied on clinical trial data (NCT01693562, durvalumab-treated late-stage NSCLC cohort) without further refinement.ResultsData analytics techniques were used to determine optimal PD-L1 QCS parameters for the clinical trial cohort of N=162 late-stage NSCLC patients. A PD-L1 QCS algorithm (ODmin=8, CoV=57%) is able to stratify durvalumab-treated NSCLC patients at a higher prevalence and more significant log rank p-value (64%, p=0.0001) for OS (figure 1) compared to pathologist TPS (59%, p=0.01). Median OS times of (19.2 months vs 7.9 months) was observed in the QCS-positive vs negative subgroups, respectively. The box plots (figure 2) indicate an overall good agreement (72% concordance) of the fully automated QCS with the pathologists TPS, which quantitatively supports the positive visual assessment of the cell segmentation accuracy.Abstract 365 Figure 1Kaplan Meier (KM) curves for OS stratification. KM curves for Overall Survival (OS) stratification with (left) manual PD-L1 TPS score (25% cutoff), and (right) automated QCS (57% cutoff).Abstract 365 Figure 2QCS scores within TPS positive and negative groups. Box plot indicating percent positive cells (OD≥8) as measured by PD-L1 QCS within the PD-L1 high (red) and low (blue) groups as per pathologist assessment by TPS.ConclusionsThe novel Quantitative Continuous Scoring (QCS) provides an objective way of correlating a quantitative estimate of PD-L1 IHC expression on tumor cells with survival of durvalumab-treated NSCLC patients. This data establishes a first proof-of-concept demonstrating the potential utility of PD-L1 QCS towards precision medicine in immuno-oncology.ReferencesRebelatto M, et al. Development of a programmed cell death ligand-1 immunohistochemical assay validated for analysis of non-small cell lung cancer and head and neck squamous cell carcinoma. Diagnostic Pathology 2016.Tsao M S, et al. PD-L1 immunohistochemistry comparability study in real-life clinical samples: results of blueprint phase 2 project. Journal of Thoracic Oncology 2018.Gustavson M, et al. Novel approach to HER2 quantification: digital pathology coupled with AI-based image and data analysis delivers objective and quantitative HER2 expression analysis for enrichment of responders to trastuzumab deruxtecan (T-DXd; DS-8201), specifically in HER2-low patients. (2021) DOI: 10.1158/1538-7445.SABCS20-PD6-01Kapil A, et al. Domain adaptation-based deep learning for automated tumor cell (TC) scoring and survival analysis on PD-L1 stained tissue images. IEEE Transactions on Medical Imaging DOI: 10.1109/TMI.2021.3081396Ethics ApprovalClinical study NCT01693562, from which data in this report were obtained, was carried out in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. The study protocol, amendments, and participant informed consent document were approved by the appropriate institutional review boards.

scholarly journals Molecular Identification of Immunity- and Ferroptosis-related Gene Signature in Non-small Cell Lung Cancer

2021 ◽  
Author(s):  
Taisheng Liu ◽  
Jinye Zhang ◽  
Xiaoshan Hu ◽  
Tao Xie ◽  
Jian Zhang
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Risk Group ◽  
Risk Groups ◽  
Gene Signature ◽  
Small Cell ◽  
Related Gene ◽  
Small Cell Lung

Abstract Background: Lung cancer is one of the dominant causes of cancer-related deaths worldwide. Ferroptosis, an iron-dependent regulated cell death, plays an important role in the cancer immunotherapy. However, the role of immunity- and ferroptosis-related gene signature in non-small cell lung cancer (NSCLC) remains unknown.Method: The RNA sequencing (RNA-seq) expression data and clinical information of NSCLC were downloaded from The Cancer Genome Atlas (TCGA) database and performed differential analysis. Univariate and multivariate cox regressions were used to identify the ferroptosis-related gene, and receiver operating characteristic (ROC) model was established using the independent risk factors. GO and KEGG enrichment analyses were performed to investigate the biological functions of differential genes.Results: A 5-gene signature was constructed to stratify patients into high- and low-risk groups. Compared with patients in the low-risk group, patients in the high-risk group showed significantly poor overall survival (P < 0.001 in the TCGA cohort and P = 0.001 in the GSE13213 cohort). The risk score was an independent predictor for overall survival in multivariate Cox regression analyses (HR > 1, P < 0.01). The 1 year-, 2 year- and 3 year-ROCs were 0.792, 0.644 and 0.641 in TCGA and 0.623, 0.636 and 0.631 in GSE13213, respectively. Functional analysis revealed that immune-related pathways were enriched, and immune status were different between two risk groups. Conclusions: We identified differently expressed immunity- and ferroptosis-related genes that may involve in NSCLC. These genes may predict the overall survival in NSCLC and targeting ferroptosis may be an alternative for clinical therapy.

scholarly journals Identification and Prognostic Value Exploration of Radiotherapy Sensitivity-Associated Genes in Non-Small-Cell Lung Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Qing Ma ◽  
Kai Geng ◽  
Ping Xiao ◽  
Lili Zeng
Keyword(s):  
Lung Cancer ◽  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Small Cell ◽  
Small Cell Lung ◽  
Cox Regression Analysis ◽  
Nsclc Patients

Background. Non-small-cell lung cancer (NSCLC) is a prevalent malignancy with high mortality and poor prognosis. The radiotherapy is one of the most common treatments of NSCLC, and the radiotherapy sensitivity of patients could affect the individual prognosis of NSCLC. However, the prognostic signatures related to radiotherapy response still remain limited. Here, we explored the radiosensitivity-associated genes and constructed the prognostically predictive model of NSCLC cases. Methods. The NSCLC samples with radiotherapy records were obtained from The Cancer Genome Atlas database, and the mRNA expression profiles of NSCLC patients from the GSE30219 and GSE31210 datasets were obtained from the Gene Expression Omnibus database. The Weighted Gene Coexpression Network Analysis (WGCNA), univariate, least absolute shrinkage and selection operator (LASSO), multivariate Cox regression analysis, and nomogram were conducted to identify and validate the radiotherapy sensitivity-related signature. Results. WGCNA revealed that 365 genes were significantly correlated with radiotherapy response. LASSO Cox regression analysis identified 8 genes, including FOLR3, SLC6A11, ALPP, IGFN1, KCNJ12, RPS4XP22, HIST1H2BH, and BLACAT1. The overall survival (OS) of the low-risk group was better than that of the high-risk group separated by the Risk Score based on these 8 genes for the NSCLC patients. Furthermore, the immune infiltration analysis showed that monocytes and activated memory CD4 T cells had different relative proportions in the low-risk group compared with the high-risk group. The Risk Score was correlated with immune checkpoints, including CTLA4, PDL1, LAG3, and TIGIT. Conclusion. We identified 365 genes potentially correlated with the radiotherapy response of NSCLC patients. The Risk Score model based on the identified 8 genes can predict the prognosis of NSCLC patients.

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