scholarly journals Differentiation between immune checkpoint inhibitor‐related and radiation pneumonitis in lung cancer by CT radiomics and machine learning

2022 ◽  
Author(s):  
Jun Cheng ◽  
Yi Pan ◽  
Wei Huang ◽  
Kun Huang ◽  
Yanhai Cui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Radiation Pneumonitis ◽  
Checkpoint Inhibitor

scholarly journals Machine learning-based predictors for immune checkpoint inhibitor therapy of non-small-cell lung cancer

2019 ◽  
Vol 30 (4) ◽  
pp. 655-657 ◽  
Author(s):  
M. Wiesweg ◽  
F. Mairinger ◽  
H. Reis ◽  
M. Goetz ◽  
R.F.H. Walter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Checkpoint Inhibitor ◽  
Small Cell ◽  
Small Cell Lung ◽  
Checkpoint Inhibitor Therapy

scholarly journals Radiation pneumonitis in patients with non-small-cell lung cancer receiving chemoradiotherapy and an immune checkpoint inhibitor: a retrospective study

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Jeong Yun Jang ◽  
Su Ssan Kim ◽  
Si Yeol Song ◽  
Yeon Joo Kim ◽  
Sung-woo Kim ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Retrospective Study ◽  
Lung Volume ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Radiation Pneumonitis ◽  
Checkpoint Inhibitor ◽  
Significant Risk ◽  
Small Cell ◽  
Small Cell Lung

Abstract Background Immunotherapy has been administered to many patients with non-small-cell lung cancer (NSCLC). However, only few studies have examined toxicity in patients receiving an immune checkpoint inhibitor (ICI) after concurrent chemoradiotherapy (CCRT). Therefore, we performed a retrospective study to determine factors that predict radiation pneumonitis (RP) in these patients. Methods We evaluated the size of the planning target volume, mean lung dose (MLD), and the lung volume receiving more than a threshold radiation dose (VD) in 106 patients. The primary endpoint was RP ≥ grade 2, and toxicity was evaluated. Results After CCRT, 51/106 patients were treated with ICI. The median follow-up period was 11.5 months (range, 3.0–28.2), and RP ≥ grade 2 occurred in 47 (44.3%) patients: 27 and 20 in the ICI and non-ICI groups, respectively. Among the clinical factors, only the use of ICI was associated with RP (p = 0.043). Four dosimetric variables (MLD, V20, V30, and V40) had prognostic significance in univariate analysis for occurrence of pneumonitis (hazard ratio, p-value; MLD: 2.3, 0.009; V20: 2.9, 0.007; V30: 2.3, 0.004; V40: 2.5, 0.001). Only V20 was a significant risk factor in the non-ICI group, and MLD, V30, and V40 were significant risk factors in the ICI group. The survival and local control rates were superior in the ICI group than in the non-ICI group, but no significance was observed. Conclusions Patients receiving ICI after definitive CCRT were more likely to develop RP, which may be related to the lung volume receiving high-dose radiation. Therefore, several factors should be carefully considered for patients with NSCLC.

scholarly journals Radiation Pneumonitis in Patients with Non-Small-Cell Lung Cancer Receiving Chemoradiotherapy and an Immune Checkpoint Inhibitor: A Retrospective Study

2021 ◽  
Author(s):  
Jeong Yun Jang ◽  
Su Ssan Kim ◽  
Si Yeol Song ◽  
Yeon Joo Kim ◽  
Sung-woo Kim ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Retrospective Study ◽  
Lung Volume ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Radiation Pneumonitis ◽  
Checkpoint Inhibitor ◽  
Significant Risk ◽  
Small Cell ◽  
Small Cell Lung

Abstract Background: Immunotherapy has been administered to many patients with non-small-cell lung cancer (NSCLC). However, only few studies have examined toxicity in patients receiving an immune checkpoint inhibitor (ICI) after concurrent chemoradiotherapy (CCRT). Therefore, we performed a retrospective study to determine factors that predict radiation pneumonitis (RP) in these patients.Methods: We evaluated the size of the planning target volume, mean lung dose (MLD), and the lung volume receiving more than a threshold radiation dose (VD) in 106 patients. The primary endpoint was RP ≥ grade 2, and toxicity was evaluated. Results: After CCRT, 51/106 patients were treated with ICI. The median follow-up period was 11.5 months (range, 3.0–28.2), and RP ≥ grade 2 occurred in 47 (44.3%) patients: 27 and 20 in the ICI and non-ICI groups, respectively. None of the examined clinical factors were associated with RP. Four dosimetric variables (MLD, V20, V30, and V40) had prognostic significance in univariate analysis for occurrence of pneumonitis (odds ratio, p-value; MLD: 3.0, 0.026; V20: 4.1, 0.001; V30: 3.1, 0.006; V40: 3.7, 0.002). Only V20 was a significant risk factor in the non-ICI group, and V30 and V40 were significant risk factors in the ICI group. The survival and local control rates were superior in the ICI group than in the non-ICI group, but no significance was observed.Conclusions: Patients receiving ICI after definitive CCRT were more likely to develop RP, which may be related to the lung volume receiving high-dose radiation. Therefore, several factors should be carefully considered for patients with NSCLC.

scholarly journals Identification of NOTCH4 mutation as a response biomarker for immune checkpoint inhibitor therapy

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Junyu Long ◽  
Dongxu Wang ◽  
Xu Yang ◽  
Anqiang Wang ◽  
Yu Lin ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Bladder Cancer ◽  
Cancer Patients ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Checkpoint Inhibitor ◽  
Small Cell ◽  
Small Cell Lung ◽  
Esophagogastric Cancer ◽  
Clinical Benefits

Abstract Background Immune checkpoint inhibitor (ICI) therapy elicits durable antitumor responses in patients with many types of cancer. Genomic mutations may be used to predict the clinical benefits of ICI therapy. NOTCH homolog-4 (NOTCH4) is frequently mutated in several cancer types, but its role in immunotherapy is still unclear. Our study is the first to study the association between NOTCH4 mutation and the response to ICI therapy. Methods We tested the predictive value of NOTCH4 mutation in the discovery cohort, which included non-small cell lung cancer, melanoma, head and neck squamous cell carcinoma, esophagogastric cancer, and bladder cancer patients, and validated it in the validation cohort, which included non-small cell lung cancer, melanoma, renal cell carcinoma, colorectal cancer, esophagogastric cancer, glioma, bladder cancer, head and neck cancer, cancer of unknown primary, and breast cancer patients. Then, the relationships between NOTCH4 mutation and intrinsic and extrinsic immune response mechanisms were studied with multiomics data. Results We collected an ICI-treated cohort (n = 662) and found that patients with NOTCH4 mutation had better clinical benefits in terms of objective response rate (ORR: 42.9% vs 25.9%, P = 0.007), durable clinical benefit (DCB: 54.0% vs 38.1%, P = 0.021), progression-free survival (PFS, hazard ratio [HR] = 0.558, P < 0.001), and overall survival (OS, HR = 0.568, P = 0.006). In addition, we validated the prognostic value of NOTCH4 mutation in an independent ICI-treated cohort (n = 1423). Based on multiomics data, we found that NOTCH4 mutation is significantly associated with enhanced immunogenicity, including a high tumor mutational burden, the expression of costimulatory molecules, and activation of the antigen-processing machinery, and NOTCH4 mutation positively correlates activated antitumor immunity, including infiltration of diverse immune cells and various immune marker sets. Conclusions Our findings indicated that NOTCH4 mutation serves as a novel biomarker correlated with a better response to ICI therapy.

scholarly journals Development and Validation of a Prognostic Model for Patients with Advanced Lung Cancer Treated with the Immune Checkpoint Inhibitor Atezolizumab

2020 ◽  
Vol 26 (13) ◽  
pp. 3280-3286 ◽  
Author(s):  
Ashley M. Hopkins ◽  
Ganessan Kichenadasse ◽  
Elizabeth Garrett-Mayer ◽  
Christos S. Karapetis ◽  
Andrew Rowland ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Immune Checkpoint ◽  
Prognostic Model ◽  
Immune Checkpoint Inhibitor ◽  
Checkpoint Inhibitor ◽  
Advanced Lung Cancer ◽  
Development And Validation
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