Promising Biomarkers of Radiation-Induced Lung Injury: A Review

Liu, Xinglong; Shao, Chunlin; Fu, Jiayi

doi:10.3390/biomedicines9091181

Cited by 24 publications

(18 citation statements)

References 170 publications

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“…Our results indicated that an integrated model combined with radiomics features and clinical factors may help to differ CIP from RP in NSCLC. Thirdly, there were no clinical and laboratory parameters involved, which have been proved to reveal the mechanism of two kinds of pneumonitis to some extent ( 32 , 33 ).…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Radiomics Nomogram Using Computed Tomography for Differentiating Immune Checkpoint Inhibitor-Related Pneumonitis From Radiation Pneumonitis for Patients With Non-Small Cell Lung Cancer

et al. 2022

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BackgroundThe combination of immunotherapy and chemoradiotherapy has become the standard therapeutic strategy for patients with unresected locally advance-stage non-small cell lung cancer (NSCLC) and induced treatment-related adverse effects, particularly immune checkpoint inhibitor-related pneumonitis (CIP) and radiation pneumonitis (RP). The aim of this study is to differentiate between CIP and RP by pretreatment CT radiomics and clinical or radiological parameters.MethodsA total of 126 advance-stage NSCLC patients with pneumonitis were enrolled in this retrospective study and divided into the training dataset (n =88) and the validation dataset (n = 38). A total of 837 radiomics features were extracted from regions of interest based on the lung parenchyma window of CT images. A radiomics signature was constructed on the basis of the predictive features by the least absolute shrinkage and selection operator. A logistic regression was applied to develop a radiomics nomogram. Receiver operating characteristics curve and area under the curve (AUC) were applied to evaluate the performance of pneumonitis etiology identification.ResultsThere was no significant difference between the training and the validation datasets for any clinicopathological parameters in this study. The radiomics signature, named Rad-score, consisting of 11 selected radiomics features, has potential ability to differentiate between CIP and RP with the empirical and α-binormal-based AUCs of 0.891 and 0.896. These results were verified in the validation dataset with AUC = 0.901 and 0.874, respectively. The clinical and radiological parameters of bilateral changes (p < 0.001) and sharp border (p = 0.001) were associated with the identification of CIP and RP. The nomogram model showed good performance on discrimination in the training dataset (AUC = 0.953 and 0.950) and in the validation dataset (AUC = 0.947 and 0.936).ConclusionsCT-based radiomics features have potential values for differentiating between patients with CIP and patients with RP. The addition of bilateral changes and sharp border produced superior model performance on classifying, which could be a useful method to improve related clinical decision-making.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of a Radiomics Nomogram Using Computed Tomography for Differentiating Immune Checkpoint Inhibitor-Related Pneumonitis From Radiation Pneumonitis for Patients With Non-Small Cell Lung Cancer

et al. 2022

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“…Additionally, activated fibroblasts promote cytoskeletal remodeling, which suggests that TGF-β is significantly involved in late-onset RILI. Although there have been studies on RILI-related cytokine levels, it is difficult to identify significant factors due to variations in the evaluation timings and patient characteristics ( 1 , 4 ). A recent study reported a significant increase in G-CSF, IL-6, and interferon-gamma levels at 3 weeks after chemoradiotherapy for NSCLC.…”

Section: Discussionmentioning

confidence: 99%

Changes in serum transforming growth factor-beta concentration as a predictive factor for radiation-induced lung injury onset in radiotherapy-treated patients with locally advanced lung cancer

Seto¹,

Kaneko²,

Miyamae³

et al. 2022

Transl Lung Cancer Res

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Background: Radiation-induced lung injury (RILI) occurs after chest radiation therapy, which ranges from acute radiation pneumonia to subsequent radiation pulmonary fibrosis. However, they are difficult to predict.The study aimed to examine the predictive utility of serum levels of transforming growth factor-beta (TGF-β) for radiation-induced lung injury.Methods: This single-center prospective observational study enrolled 21 patients with locally advanced lung cancer who underwent chest radiation therapy. We measured the serum levels of TGF-β, Krebs von Denlungen-6, and granulocyte colony-stimulating factor (GCSF) eight times immediately before irradiation.Results: Seven, four, eight, and one patient had Grade 0, 1, 2, and 3 radiation-induced lung injury, respectively. Compared with the Grade 0 and 1 RILI groups (RP− group), the Grade 2 and 3 RILI groups (RP+ group) had a significantly higher relative ratio of TGF-β values from immediately before irradiation to the time of 30-48 Gy irradiation (P=0.011). The cut-off value of the TGF-β relative ratio of the RP+ group measured from the receiver operating characteristic curve was 1.31; moreover, the sensitivity, specificity, and positive predictive value were 75%, 100%, and 75%, respectively. There was no significant between-group difference in the levels of the other cytokines.Conclusions: For patients undergoing radiation therapy for locally advanced lung cancer, the ratio of TGF-β levels before and after 30-48 Gy irradiation may predict the onset of RILI. Our findings may facilitate the identification of predictors of the onset of radiation-induced lung injury.

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“…Thus, it is of great significance to predict RILT, particularly severe RILT, in advance of radiation. Despite emerging attempts to build predictive models, there is still no effective model available for RILT in clinical practice [ 6 , 8 – 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…TGF- β is another classical candidate; however, some studies failed to find the independent predictive value of TGF- β 1 for RILT, mainly because of improper sample handling, indicating it is a less practical biomarker for RILT in the clinical setting [ 22 , 23 ]. Other cytokines related to lung injury, such as tumor necrosis factor (TNF), interleukin (IL)-1, and IL-6, were not reliable either because some of the elevations happened only after RT [ 8 ]. Therefore, it would be preferable to determine a novel and stable biomarker indicating the risk of RILT before the initiation of RT in a larger cohort.…”

Section: Introductionmentioning

confidence: 99%

IDO1 Activity Predicts Lung Toxicity in Patients with Unresectable Stage III NSCLC and Chemoradiotherapy

Gao

Wang

et al. 2023

Journal of Oncology

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Objectives. Indoleamine 2,3-dioxygenase 1 (IDO1) acts as the key rate-limiting enzyme that converts tryptophan (Trp) to kynurenine (Kyn). Its activity was primarily induced by interferon-γ (IFN-γ), which was reported to play a role in the development of acute radiation-induced pneumonitis. In this study, we aimed to investigate the correlation between IDO1 activity and radiation-induced lung toxicity (RILT) in stage III nonsmall cell lung cancer (NSCLC) patients who were treated with chemoradiotherapy (CRT). Materials and Methods. Systemic IDO1 activity was reflected by Kyn : Trp ratio. Plasma levels of Kyn and Trp in 113 stage III NSCLC patients were measured by high-performance liquid chromatography (HPLC) before the initiation of radiotherapy. Dynamic change of IDO1 activity was followed in 23 patients before, during, and after radiotherapy. We also used RNA sequencing (RNA-seq) data from the Cancer Genome Atlas Program (TCGA) database and performed gene set enrichment analysis (GSEA) to explore how IDO1 was involved in the development of RILT. Results. 9.7% (11/113) of the whole group developed G 3 + (greater than or equal to Grade 3) RILT. Preradiation IDO1 activity was significantly higher in patients who developed G 3 + RILT than in non G 3 + RILT patients. ( P = 0.029 , AUC = 0.70). Univariate and multivariate analyses showed that high IDO1 activity was independently associated with the risk of G 3 + RILT ( P = 0.034 ). A predictive model combining both IDO1 activity and FEV1 was established for severe RILT and displayed a moderate predictive value (AUC = 0.83, P < 0.001 ). The incidence of G 3 + RILT was 2.6% (1/38) in patients with an IDO activity ≤0.069 and FEV1 > 59.4%, and 50.0% (6/12) in those with an IDO activity >0.069 and FEV1 ≤ 59.4%. Of 23 patients with dynamic tracking, the IDO1 activity of postradiation was significantly lower than midradiation ( P = 0.021 ), though no significant differences among the three time points were observed ( P = 0.070 ). Bioinformatic analysis using RNA-seq data from 1014 NSCLC patients revealed that IDO mainly functioned in the inflammatory response instead of the late fibrosis process in NSCLC patients. Conclusion. High baseline IDO1 activity combined with unfavorable baseline FEV1 was predictive of severe RILT in unresectable stage III NSCLC patients. IDO1 might play a role in the acute inflammatory response. Finding effective interventions to alleviate RILT using IDO inhibitors is warranted in the future.

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Promising Biomarkers of Radiation-Induced Lung Injury: A Review

Cited by 24 publications

References 170 publications

Development and Validation of a Radiomics Nomogram Using Computed Tomography for Differentiating Immune Checkpoint Inhibitor-Related Pneumonitis From Radiation Pneumonitis for Patients With Non-Small Cell Lung Cancer

Development and Validation of a Radiomics Nomogram Using Computed Tomography for Differentiating Immune Checkpoint Inhibitor-Related Pneumonitis From Radiation Pneumonitis for Patients With Non-Small Cell Lung Cancer

Changes in serum transforming growth factor-beta concentration as a predictive factor for radiation-induced lung injury onset in radiotherapy-treated patients with locally advanced lung cancer

IDO1 Activity Predicts Lung Toxicity in Patients with Unresectable Stage III NSCLC and Chemoradiotherapy

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