Computed tomography patterns and clinical outcomes of radiation pneumonitis in non-small-cell lung cancer patients.

Background: Radiation pneumonitis (RP) is not an uncommon complication in lung cancer patients undergoing radiation therapy (RT) and symptomatic RP can affect their quality of life. Purpose: To investigate the CT findings of RP in non-small cell lung cancer (NSCLC) patients and their relationship with clinical outcomes. Materials and methods: We reviewed data from 240 NSCLC patients who underwent RT between 2014 and 2022. CT findings of RP were evaluated for parenchymal abnormalities and distribution, which were then classified into three patterns: localized pneumonia (LP), cryptogenic organizing pneumonia (COP), and acute interstitial pneumonia (AIP). Clinical outcomes of RP were evaluated based on Common Terminology Criteria for Adverse Events (CTCAE) grade. Results: Of the 153 patients, 135 developed RP. The most common pattern was LP (n = 78), followed by COP (n = 30) and AIP (n = 25). Among the three CT patterns, CTCAE grade and days between the start of RT and the onset of RP (RT-RP days) were statistically significantly different (p < 0.05). The patients with AIP patterns exhibited higher CTCAE grade, and fewer RT-RP days compared to those with non-AIP patterns (p < 0.05). In these patients, lung-to-lung metastasis and underlying interstitial lung abnormality were observed more frequently (p < 0.05). Underlying pulmonary fibrosis, the AIP pattern, and higher CT extent scores were more frequently observed in higher CTCAE grade group (p < 0.001). In multiple regression analysis, age, bilateral distribution, RT-RP days, and CT extent score ≥3 were independent predicting factors for higher CTCAE grade. Conclusions: RP in NSCLC patients can be classified into LP, COP, and AIP patterns and they exhibit different severities in clinical outcomes.

Oxytocin: A Shield against Radiation-Induced Lung Injury in Rats.

BACKGROUND: Radiation-induced lung injury (RILI), a serious side effect of thoracic radiotherapy, can lead to acute radiation pneumonitis (RP) and chronic pulmonary fibrosis (PF). Despite various interventions, no effective protocol exists to prevent pneumonitis. Oxytocin (OT), known for its anti-inflammatory, antiapoptotic, and antioxidant properties, has not been explored for its potential in mitigating RILI. MATERIALS AND METHODS: This study involved 24 female Wistar albino rats, divided into three groups: control group, radiation (RAD) + saline, and RAD + OT. The RAD groups received 18 Gy of whole-thorax irradiation. The RAD + OT group was treated with OT (0.1 mg/kg/day) intraperitoneally for 16 weeks. Computerizing tomography (CT) imaging and histopathological, biochemical, and blood gas analyses were performed to assess lung tissue damage and inflammation. RESULTS: Histopathological examination showed significant reduction in alveolar wall thickening, inflammation, and vascular changes in the RAD + OT group compared to the RAD + saline group. Biochemical analysis revealed decreased levels of TGF-beta, VEGF, and PDGF, and increased BMP-7 and prostacyclin in the RAD + oxytocin group (p < 0.05). Morphometric analysis indicated significant reductions in fibrosis, edema, and immune cell infiltration. CT imaging demonstrated near-normal lung parenchyma density in the RAD + oxytocin group (p < 0.001). CONCLUSION: Oxytocin administration significantly mitigates radiation-induced pneumonitis in rats, implying that is has potential as a therapeutic agent for preventing and treating RILI.

Predictive Value of Simulated CT Radiomics Combined with Ipsilateral Lung Dosimetry Parameters for Radiation Pneumonitis in Patients with Esophageal Cancer: A Machine Learning-Based Retrospective Study.

Objective: To explore how non-surgical esophageal cancer patients can identify high-risk factors for radiation-induced pneumonitis after receiving radiotherapy. Methods: We retrospectively included 228 esophageal cancer patients who were unable to undergo surgical treatment but received radiotherapy for the first time. By retrospective analysis and identifying potential risk factors for symptomatic radiation-induced pneumonitis (ie ≥grade 2), as well as delineating the affected lung as an area of interest on localized CT and extracting radiomics features, along with extracting dosimetric parameters from the affected lung area. After feature screening, patients were randomly divided into training and testing sets in a 7-to-3 ratio, and a prediction model was established using machine learning algorithms. Finally, the receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to validate the predictive performance of the model. Results: A total of 54 cases of symptomatic radiation pneumonitis occurred in this study, with a total incidence rate of 23.68%. The results of multivariate analysis showed that the occurrence of symptomatic radiation pneumonitis was significantly correlated with the mean lung dose (MLD), esophageal PTVD90, esophageal PTVV50, V5, V10, V15, and V20 in patients. The machine learning prediction model constructed based on candidate prediction variables has a prediction performance interval between 0.751 (95% CI: 0.700-0.802) and 0.891 (95% CI: 0.840-0.942) in the training and validation sets, respectively. Among them, the RFM algorithm has the best prediction performance for radiation-induced pneumonitis, with 0.891 (95% CI: 0.840-0.942) and 0.887 (95% CI: 0.836-0.938) in the training and validation sets, respectively. Conclusion: The combination of localization CT radiomics features and diseased lung dosimetry parameters has good predictive value for radiation-induced pneumonitis in esophageal cancer patients after radiotherapy. Especially, the radiation-induced pneumonitis prediction model constructed using RF algorithm can be more effectively used to guide clinical decision-making in esophageal cancer patients.

Integrative prediction model for radiation pneumonitis incorporating genetic and clinical-pathological factors using machine learning.

Purpose: We aimed to develop a machine learning-based prediction model for severe radiation pneumonitis (RP) by integrating relevant clinicopathological and genetic factors, considering the associations of clinical, dosimetric parameters, and single nucleotide polymorphisms (SNPs) of genes in the TGF-ß1 pathway with RP. Methods: We prospectively enrolled 59 primary lung cancer patients undergoing radiotherapy and analyzed pretreatment blood samples, clinicopathological/dosimetric variables, and 11 functional SNPs in TGFß pathway genes. Using the Synthetic Minority Over-sampling Technique (SMOTE) and nested cross-validation, we developed a machine learning-based prediction model for severe RP (grade ≥ 2). Feature selection was conducted using four methods (filtered-based, wrapper-based, embedded, and logistic regression), and performance was evaluated using three machine learning models. Results: Severe RP occurred in 20.3 % of patients with a median follow-up of 39.7 months. In our final model, age (>66 years), smoking history, PTV volume (>300 cc), and AG/GG genotype in BMP2 rs1979855 were identified as the most significant predictors. Additionally, incorporating genomic variables for prediction alongside clinicopathological variables significantly improved the AUC compared to using clinicopathological variables alone (0.822 vs. 0.741, p = 0.029). The same feature set was selected using both the wrapper-based method and logistic model, demonstrating the best performance across all machine learning models (AUC: XGBoost 0.815, RF 0.805, SVM 0.712, respectively). Conclusion: We successfully developed a machine learning-based prediction model for RP, demonstrating age, smoking history, PTV volume, and BMP2 rs1979855 genotype as significant predictors. Notably, incorporating SNP data significantly enhanced predictive performance compared to clinicopathological factors alone.

A novel predictor for dosimetry data of lung and the radiation pneumonitis incidence prior to SBRT in lung cancer patients.

Normal tissue complication probability (NTCP) models for radiation pneumonitis (RP) in lung cancer patients with stereotactic body radiation therapy (SBRT), which based on dosimetric data from treatment planning, are limited to patients who have already received radiation therapy (RT). This study aims to identify a novel predictive factor for lung dose distribution and RP probability before devising actionable SBRT plans for lung cancer patients. A comprehensive correlation analysis was performed on the clinical and dose parameters of lung cancer patients who underwent SBRT. Linear regression models were utilized to analyze the dosimetric data of lungs. The performance of the regression models was evaluated using mean squared error (MSE) and the coefficient of determination (R2). Correlational analysis revealed that most clinical data exhibited weak correlations with dosimetric data. However, nearly all dosimetric variables showed "strong" or "very strong" correlations with each other, particularly concerning the mean dose of the ipsilateral lung (MI) and the other dosimetric parameters. Further study verified that the lung tumor ratio (LTR) was a significant predictor for MI, which could predict the incidence of RP. As a result, LTR can predict the probability of RP without the need to design an elaborate treatment plan. This study, as the first to offer a comprehensive correlation analysis of dose parameters, explored the specific relationships among them. Significantly, it identified LTR as a novel predictor for both dose parameters and the incidence of RP, without the need to design an elaborate treatment plan.

CT-based different regions of interest radiomics analysis for acute radiation pneumonitis in patients with locally advanced NSCLC after chemoradiotherapy.

Purpose: To establish a radiomics model using radiomics features from different region of interests (ROI) based on dosimetry-related regions in enhanced computed tomography (CT) simulated images to predict radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC). Methods: Our retrospective study was conducted based on a cohort of 236 NSCLC patients (59 of them with RP≥2) who were treated in 2 institutions and divided into the primary cohort (n = 182,46 of them with RP≥2) and external validation cohort (n = 54,13 of them with RP≥2). Radiomic features extracted from three ROIs were defined as the whole lung (WL), the dose volume histogram (DVH) of the lung V20 (V20_Lung) and the DVH of the V30 of lung minus the planning target volume (PTV) (V30 Lung-PTV). A total of 107 radiomics features were extracted from each ROIs. The U test, correlation coefficient and least absolute shrinkage and selection operator (LASSO) were performed for features selection. Six models based on different classification algorithms were developed to select the best radiomics model (R model).In addition, we built a dosimetry model then combined it with the best R model to create a mixed model (R+D model) The receiver operating characteristic (ROC) curve was delineated to assess the predictive efficacy of the models. Decision curve analysis could benefit from the model proposals through the assessment of clinical utility. Results: Among the three ROIs, the best R model constructed from the LightGBM algorithm demonstrated the strongest discriminative ability in the ROI of V30 Lung-PTV. The corresponding area under the curve (AUC) value was 0.930 (95 % confidence interval (CI): 0.829-0.941). The D model, R model and R+D model achieved AUC values of 0.798 (95 %CI: 0.732-0.865), 0.930 (95 %CI: 0.829-0.941) and 0.940 (95 %CI: 0.906-0.974) in primary cohort, and in external validation cohort, the AUC values were 0.793 (95 %CI:0.637-0.949), 0.887 (95 %CI:0.810-0.993), 0.951 (95CI%:0.891-1.000). Decision curve demonstrate that R+D model could benefit for patients through the assessment of clinical utility. Conclusion: The radiomics model was able to predict the acute RP more effectively in comparison with the traditional dosimetry model. Especially the radiomics model based on the V30 Lung-PTV region was able to achieve a higher accuracy when compared to the other regions.

PTEN Depletion Increases Radiosensitivity in Response to Ataxia Telangiectasia-Related-3 (ATR) Inhibition in Non-Small Cell Lung Cancer (NSCLC).

Radiotherapy (RT) treatment is an important strategy for the management of non-small cell lung cancer (NSCLC). Local recurrence amongst patients with late-stage NSCLC remains a challenge. The loss of PTEN has been associated with radio-resistance. This study aimed to examine the efficacy of RT combined with ataxia telangiectasia-mutated Rad3-related (ATR) inhibition using Ceralasertib in phosphatase and tensin homolog (PTEN)-depleted NSCLC cells and to assess early inflammatory responses indicative of radiation pneumonitis (RP) after combined-modality treatment. Small hairpin RNA (shRNA) transfections were used to generate H460 and A549 PTEN-depleted models. Ceralasertib was evaluated as a single agent and in combination with RT in vitro and in vivo. Histological staining was used to assess immune cell infiltration in pneumonitis-prone C3H/NeJ mice. Here, we report that the inhibition of ATR in combination with RT caused a significant reduction in PTEN-depleted NSCLC cells, with delayed DNA repair and reduced cell viability, as shown by an increase in cells in Sub G1. Combination treatment in vivo significantly inhibited H460 PTEN-depleted tumour growth in comparison to H460 non-targeting PTEN-expressing (NT) cell-line-derived xenografts (CDXs). Additionally, there was no significant increase in infiltrating macrophages or neutrophils except at 4 weeks, whereby combination treatment significantly increased macrophage levels relative to RT alone. Overall, our study demonstrates that ceralasertib and RT combined preferentially sensitises PTEN-depleted NSCLC models in vitro and in vivo, with no impact on early inflammatory response indicative of RP. These findings provide a rationale for evaluating ATR inhibition in combination with RT in NSCLC patients with PTEN mutations.

Nintedanib for the treatment of symptomatic radiation pneumonitis. A Case Report.

Radiation induced pneumonitis is a common side effect of thoracic radiotherapy. We report a case of a patient diagnosed with symptomatic radiation pneumonitis and a serious contra-indication for corticosteroids. For that reason, the patient was treated with nintedanib instead. After several weeks of treatment her symptoms and chest CT improved significantly. This case shows that nintedanib might be an effective treatment of radiation pneumonitis if corticosteroids are contra-indicated.

Radiation pneumonitis prediction with dual-radiomics for esophageal cancer underwent radiotherapy.

BACKGROUND: To integrate radiomics and dosiomics features from multiple regions in the radiation pneumonia (RP grade ≥ 2) prediction for esophageal cancer (EC) patients underwent radiotherapy (RT). METHODS: Total of 143 EC patients in the authors' hospital (training and internal validation: 70%:30%) and 32 EC patients from another hospital (external validation) underwent RT from 2015 to 2022 were retrospectively reviewed and analyzed. Patients were dichotomized as positive (RP+) or negative (RP-) according to CTCAE V5.0. Models with radiomics and dosiomics features extracted from single region of interest (ROI), multiple ROIs and combined models were constructed and evaluated. A nomogram integrating radiomics score (Rad_score), dosiomics score (Dos_score), clinical factors, dose-volume histogram (DVH) factors, and mean lung dose (MLD) was also constructed and validated. RESULTS: Models with Rad_score_Lung&Overlap and Dos_score_Lung&Overlap achieved a better area under curve (AUC) of 0.818 and 0.844 in the external validation in comparison with radiomics and dosiomics models with features extracted from single ROI. Combining four radiomics and dosiomics models using support vector machine (SVM) improved the AUC to 0.854 in the external validation. Nomogram integrating Rad_score, and Dos_score with clinical factors, DVH factors, and MLD further improved the RP prediction AUC to 0.937 and 0.912 in the internal and external validation, respectively. CONCLUSION: CT-based RP prediction model integrating radiomics and dosiomics features from multiple ROIs outperformed those with features from a single ROI with increased reliability for EC patients who underwent RT.

Clinical predictors of severe radiation pneumonitis in patients undergoing thoracic radiotherapy for lung cancer.

Background: Severe radiation pneumonitis (RP), one of adverse events in patients with lung cancer receiving thoracic radiotherapy, is more likely to lead to more mortality and poor quality of life, which could be predicted by clinical information and treatment scheme. In this study, we aimed to explore the clinical predict model for severe RP. Methods: We collected information on lung cancer patients who received radiotherapy from August 2020 to August 2022. Clinical features were obtained from 690 patients, including baseline and treatment data as well as radiation dose measurement parameters, including lung volume exceeding 5 Gy (V5), lung volume exceeding 20 Gy (V20), lung volume exceeding 30 Gy (V30), mean lung dose (MLD), etc. Among them, 621 patients were in the training cohort, and 69 patients were in the test cohort. Three models were built using different screening methods, including multivariate logistics regression (MLR), backward stepwise regression (BSR), and random forest regression (RFR), to evaluate their predictive power. Overoptimism in the training cohorts was evaluated by four validation methods, including hold-out, 10-fold, leave-one-out, and bootstrap methods, and test cohort was used to evaluate the predictive performance of the model. Model calibration, decision curve analysis (DCA), and evaluation of the nomograms for the three models were completed. Results: Severe RP was up to 9.4%. The results of multivariate analysis of logistics regression in all patients showed that patients with subclinical (untreated and asymptomatic) interstitial lung disease (ILD) could increase the risk of severe RP, and patients with a better lung diffusion function and received standardized steroids treatment could decrease the risk of severe RP. The three models built by MLR, BSR, and RFR all had good accuracy (>0.850) and moderate κ value (>0.4), and the model 2 built by BSR had the highest area under the receiver operating characteristic (ROC) curve (AUC) in three models, which was 0.958 [95% confidence interval (CI): 0.932-0.985]. The calibration curve showed good agreement between the predicted and actual values, and the DCA showed a positive net benefit for the model 2 which drew the nomogram. The model 2 included subclinical ILD, diffusing capacity of the lung for carbon monoxide (DLCO), ipsilateral lung V20, and standardized steroid treatment, which could affect the incidence of severe RP. Conclusions: Subclinical ILD, DLCO, ipsilateral lung V20, and with or not standardized steroid treatment could affect the incidence of severe RP. Strict lung dose limitation and standardized steroid treatment could contribute to a decrease in severe RP.

Prophylactic Effect of Clarithromycin on Radiation Pneumonitis in IMRT for Lung Cancer.

BACKGROUND/AIM: To evaluate the association between prophylactic administration of clarithromycin (CAM) and the development of radiation pneumonitis (RP) in patients treated with intensity modulated radiation therapy (IMRT) for lung cancer. PATIENTS AND METHODS: A total of 89 patients who underwent definitive or salvage IMRT for lung cancer were retrospectively evaluated. The median total and daily doses were 60 Gy and 2 Gy, respectively. A total of 39 patients (44%) received CAM for a median of three months after the start of IMRT. The relationship between the development of RP and certain clinical factors was analyzed. RESULTS: RP of Grade ≥2 was recognized in 10 (11%) patients; Grade 2 in six patients and Grade 3 in four patients. The incidence of Grade ≥2 RP was 3% (1/39) in patients treated with CAM, which was significantly lower than that of 18% (9/50) in patients without CAM. The median lung V20 and V5 in the 10 patients with RP Grade ≥2 were 24% and 46%, respectively, compared with 18% and 37% in the 79 patients with RP Grade 0-1, and the differences were significant. Durvalumab administration after IMRT was also a significant factor for RP Grade ≥2. CONCLUSION: Prophylactic administration of CAM may reduce Grade ≥2 RP in patients treated with IMRT for lung cancer. Therefore, further clinical trials are warranted.

[Normal mouse serum alleviates radiation pneumonitis in mice by inhibiting the focal adhesion signaling pathway].

OBJECTIVE: To evaluate the therapeutic effect of normal mouse serum on radiation pneumonitis in mice and explore the possible mechanism. METHODS: Mouse models of radiation pneumonitis induced by thoracic radiation exposure were given intravenous injections of 100 µL normal mouse serum or normal saline immediately after the exposure followed by injections once every other day for a total of 8 injections. On the 15th day after irradiation, histopathological changes of the lungs of the mice were examined using HE staining, the levels of TNF-α, TGF-ß, IL-1α and IL-6 in the lung tissue and serum were detected using ELISA, and the percentages of lymphocytes in the lung tissue were analyzed with flow cytometry. Highth-roughput sequencing of exosome miRNA was carried out to explore the changes in the signaling pathways. The mRNA expression levels of the immune-related genes were detected by qRT-PCR, and the protein expressions of talin-1, tensin2, FAK, vinculin, α-actinin and paxillin in the focal adhesion signaling pathway were detected with Western blotting. RESULTS: In the mouse models of radiation pneumonitis, injections of normal mouse serum significantly decreased the lung organ coefficient, lowered the levels of TNF-α, TGF-ß, IL-1α and IL-6 in the serum and lung tissues, and ameliorated infiltration of CD45+, CD4+ and Treg lymphocytes in the lung tissue (all P < 0.05). The expression levels of Egfr and Pik3cd genes at both the mRNA and protein levels and the protein expressions of talin-1, tensin2, FAK, vinculin, α?actinin and paxillin were all significantly down-regulated in the mouse models after normal mouse serum treatment. CONCLUSION: Normal mouse serum ameliorates radiation pneumonitis in mice by inhibiting the expressions of key proteins in the Focal adhesion signaling pathway.

Multi-omics deep learning for radiation pneumonitis prediction in lung cancer patients underwent volumetric modulated arc therapy.

BACKGROUND AND OBJECTIVE: To evaluate the feasibility and accuracy of radiomics, dosiomics, and deep learning (DL) in predicting Radiation Pneumonitis (RP) in lung cancer patients underwent volumetric modulated arc therapy (VMAT) to improve radiotherapy safety and management. METHODS: Total of 318 and 31 lung cancer patients underwent VMAT from First Affiliated Hospital of Wenzhou Medical University (WMU) and Quzhou Affiliated Hospital of WMU were enrolled for training and external validation, respectively. Models based on radiomics (R), dosiomics (D), and combined radiomics and dosiomics features (R+D) were constructed and validated using three machine learning (ML) methods. DL models trained with CT (DLR), dose distribution (DLD), and combined CT and dose distribution (DL(R+D)) images were constructed. DL features were then extracted from the fully connected layers of the best-performing DL model to combine with features of the ML model with the best performance to construct models of R+DLR, D+DLD, R+D+DL(R+D)) for RP prediction. RESULTS: The R+D model achieved a best area under curve (AUC) of 0.84, 0.73, and 0.73 in the internal validation cohorts with Support Vector Machine (SVM), XGBoost, and Logistic Regression (LR), respectively. The DL(R+D) model achieved a best AUC of 0.89 and 0.86 using ResNet-34 in training and internal validation cohorts, respectively. The R+D+DL(R+D) model achieved a best performance in the external validation cohorts with an AUC, accuracy, sensitivity, and specificity of 0.81(0.62-0.99), 0.81, 0.84, and 0.67, respectively. CONCLUSIONS: The integration of radiomics, dosiomics, and DL features is feasible and accurate for the RP prediction to improve the management of lung cancer patients underwent VMAT.

Radiomics-based hybrid model for predicting radiation pneumonitis: A systematic review and meta-analysis.

PURPOSE: This study reviewed and meta-analyzed evidence on radiomics-based hybrid models for predicting radiation pneumonitis (RP). These models are crucial for improving thoracic radiotherapy plans and mitigating RP, a common complication of thoracic radiotherapy. We examined and compared the RP prediction models developed in these studies with the radiomics features employed in RP models. METHODS: We systematically searched Google Scholar, Embase, PubMed, and MEDLINE for studies published up to April 19, 2024. Sixteen studies met the inclusion criteria. We compared the RP prediction models developed in these studies and the radiomics features employed. RESULTS: Radiomics, as a single-factor evaluation, achieved an area under the receiver operating characteristic curve (AUROC) of 0.73, accuracy of 0.69, sensitivity of 0.64, and specificity of 0.74. Dosiomics achieved an AUROC of 0.70. Clinical and dosimetric factors showed lower performance, with AUROCs of 0.59 and 0.58. Combining clinical and radiomic factors yielded an AUROC of 0.78, while combining dosiomic and radiomics factors produced an AUROC of 0.81. Triple combinations, including clinical, dosimetric, and radiomics factors, achieved an AUROC of 0.81. The study identifies key radiomics features, such as the Gray Level Co-occurrence Matrix (GLCM) and Gray Level Size Zone Matrix (GLSZM), which enhance the predictive accuracy of RP models. CONCLUSIONS: Radiomics-based hybrid models are highly effective in predicting RP. These models, combining traditional predictive factors with radiomic features, particularly GLCM and GLSZM, offer a clinically feasible approach for identifying patients at higher RP risk. This approach enhances clinical outcomes and improves patient quality of life. PROTOCOL REGISTRATION: The protocol of this study was registered on PROSPERO (CRD42023426565).

Quantitative analysis of interstitial lung abnormalities on computed tomography to predict symptomatic radiation pneumonitis after lung stereotactic body radiotherapy.

BACKGROUND AND PURPOSE: Symptomatic radiation pneumonitis (SRP) is a complication of thoracic stereotactic body radiotherapy (SBRT). As visual assessments pose limitations, artificial intelligence-based quantitative computed tomography image analysis software (AIQCT) may help predict SRP risk. We aimed to evaluate high-resolution computed tomography (HRCT) images with AIQCT to develop a predictive model for SRP. MATERIALS AND METHODS: AIQCT automatically labelled HRCT images of patients treated with SBRT for stage I lung cancer according to lung parenchymal pattern. Quantitative data including the volume and mean dose (Dmean) were obtained for reticulation + honeycombing (Ret + HC), consolidation + ground-glass opacities, bronchi (Br), and normal lungs (NL). After associations between AIQCT's quantified metrics and SRP were investigated, we developed a predictive model using recursive partitioning analysis (RPA) for the training cohort and assessed its reproducibility with the testing cohort. RESULTS: Overall, 26 of 207 patients developed SRP. There were significant between-group differences in the Ret + HC, Br-volume, and NL-Dmean in patients with and without SRP. RPA identified the following risk groups: NL-Dmean ≥ 6.6 Gy (high-risk, n = 8), NL-Dmean < 6.6 Gy and Br-volume ≥ 2.5 % (intermediate-risk, n = 13), and NL-Dmean < 6.6 Gy and Br-volume < 2.5 % (low-risk, n = 133). The incidences of SRP in these groups within the training cohort were 62.5, 38.4, and 7.5 %; and in the testing cohort 50.0, 27.3, and 5.0 %, respectively. CONCLUSION: AIQCT identified CT features associated with SRP. A predictive model for SRP was proposed based on AI-detected Br-volume and the NL-Dmean.

The Mitochondrial-Derived Peptide MOTS-c Alleviates Radiation Pneumonitis via an Nrf2-Dependent Mechanism.

Radiation pneumonitis (RP) is a prevalent and fatal complication of thoracic radiotherapy due to the lack of effective treatment options. RP primarily arises from mitochondrial injury in lung epithelial cells. The mitochondrial-derived peptide MOTS-c has demonstrated protective effects against various diseases by mitigating mitochondrial injury. C57BL/6 mice were exposed to 20 Gy of lung irradiation (IR) and received daily intraperitoneal injections of MOTS-c for 2 weeks. MOTS-c significantly ameliorated lung tissue damage, inflammation, and oxidative stress caused by radiation. Meanwhile, MOTS-c reversed the apoptosis and mitochondrial damage of alveolar epithelial cells in RP mice. Furthermore, MOTS-c significantly inhibited oxidative stress and mitochondrial damage in MLE-12 cells and primary mouse lung epithelial cells. Mechanistically, MOTS-c increased the nuclear factor erythroid 2-related factor (Nrf2) level and promoted its nuclear translocation. Notably, Nrf2 deficiency abolished the protective function of MOTS-c in mice with RP. In conclusion, MOTS-c alleviates RP by protecting mitochondrial function through an Nrf2-dependent mechanism, indicating that MOTS-c may be a novel potential protective agent against RP.

The incidence of and risk factors for radiation pneumonitis in patients treated with simultaneous bevacizumab and thoracic radiotherapy.

BACKGROUND: First-line chemotherapy combined with bevacizumab is one of the standard treatment modes for patients with advanced non-small cell lung cancer (NSCLC). Thoracic radiotherapy (TRT) can provide significant local control and survival benefits to patients during the treatment of advanced NSCLC. However, the safety of adding TRT has always been controversial, especially because of the occurrence of radiation pneumonia (RP) during bevacizumab treatment. Therefore, in this study, we used an expanded sample size to evaluate the incidence of RP when using bevacizumab in combination with TRT. PATIENTS AND METHODS: Using an institutional query system, all medical records of patients with NSCLC who received TRT during first-line chemotherapy combined with bevacizumab from 2017 to 2020 at Shandong Cancer Hospital and Institute were reviewed. RP was diagnosed via computed tomography and was classified according to the RTOG toxicity scoring system. The risk factors for RP were identified using univariate and multivariate analyses. The Kaplan-Meier method was used to calculate progression-free survival (PFS) and overall survival (OS). RESULTS: Ultimately, 119 patients were included. Thirty-eight (31.9%) patients developed Grade ≥ 2 RP, of whom 27 (68.1%) had Grade 2 RP and 11 (9.2%) had Grade 3 RP. No patients developed Grade 4 or 5 RP. The median time for RP occurrence was 2.7 months (range 1.2-5.4 months). In univariate analysis, male, age, KPS score, V20 > 16.9%, V5 > 33.6%, PTV (planning target volume)-dose > 57.2 Gy, and PTV-volume > 183.85 cm3 were correlated with the occurrence of RP. In multivariate analysis, male, V20 > 16.9%, and PTV-volume > 183.85 cm3 were identified as independent predictors of RP occurrence. The mPFS of all patients was 14.27 (95% CI, 13.1-16.1) months. The one-year and two-year PFS rates were 64.9% and 20.1%, respectively. The mOS of all patients was 37.09 (95% CI, 33.8-42.0) months. The one-year survival rate of all patients was 95%, and the two-year survival rate was 71.4%. CONCLUSIONS: The incidence of Grade ≥ 2 RP in NSCLC patients who received both bevacizumab and TRT was 31.9%. Restricting factors such as V20 and PTV will help reduce the risk of RP in these patients. For patients who receive both bevacizumab and TRT, caution should be exercised when increasing TRT, and treatment strategies should be optimized to reduce the incidence of RP.

Optimal management of radiation pneumonitis: Findings of an international Delphi consensus study.

PURPOSE: Radiation pneumonitis (RP) is a dose-limiting toxicity for patients undergoing radiotherapy (RT) for lung cancer, however, the optimal practice for diagnosis, management, and follow-up for RP remains unclear. We thus sought to establish expert consensus recommendations through a Delphi Consensus study. METHODS: In Round 1, open questions were distributed to 31 expert clinicians treating thoracic malignancies. In Round 2, participants rated agreement/disagreement with statements derived from Round 1 answers using a 5-point Likert scale. Consensus was defined as ≥ 75 % agreement. Statements that did not achieve consensus were modified and re-tested in Round 3. RESULTS: Response rate was 74 % in Round 1 (n = 23/31; 17 oncologists, 6 pulmonologists); 82 % in Round 2 (n = 19/23; 15 oncologists, 4 pulmonologists); and 100 % in Round 3 (n = 19/19). Thirty-nine of 65 Round 2 statements achieved consensus; a further 10 of 26 statements achieved consensus in Round 3. In Round 2, there was agreement that risk stratification/mitigation includes patient factors; optimal treatment planning; the basis for diagnosis of RP; and that oncologists and pulmonologists should be involved in treatment. For uncomplicated radiation pneumonitis, an equivalent to 60 mg oral prednisone per day, with consideration of gastroprotection, is a typical initial regimen. However, in this study, no consensus was achieved for dosing recommendation. Initial steroid dose should be administered for a duration of 2 weeks, followed by a gradual, weekly taper (equivalent to 10 mg prednisone decrease per week). For severe pneumonitis, IV methylprednisolone is recommended for 3 days prior to initiating oral corticosteroids. Final consensus statements included that the treatment of RP should be multidisciplinary, the uncertainty of whether pneumonitis is drug versus radiation-induced, and the importance risk stratification, especially in the scenario of interstitial lung disease. CONCLUSIONS: This Delphi study achieved consensus recommendations and provides practical guidance on diagnosis and management of RP.

Deep-Learning Model Prediction of Radiation Pneumonitis Using Pretreatment Chest Computed Tomography and Clinical Factors.

Objectives: This study aimed to build a comprehensive deep-learning model for the prediction of radiation pneumonitis using chest computed tomography (CT), clinical, dosimetric, and laboratory data. Introduction: Radiation therapy is an effective tool for treating patients with lung cancer. Despite its effectiveness, the risk of radiation pneumonitis limits its application. Although several studies have demonstrated models to predict radiation pneumonitis, no reliable model has been developed yet. Herein, we developed prediction models using pretreatment chest CT and various clinical data to assess the likelihood of radiation pneumonitis in lung cancer patients. Methods: This retrospective study analyzed 3-dimensional (3D) lung volume data from chest CT scans and 27 features including dosimetric, clinical, and laboratory data from 548 patients who were treated at our institution between 2010 and 2021. We developed a neural network, named MergeNet, which processes lung 3D CT, clinical, dosimetric, and laboratory data. The MergeNet integrates a convolutional neural network with subsequent fully connected layers. A support vector machine (SVM) and light gradient boosting machine (LGBM) model were also implemented for comparison. For comparison, the convolution-only neural network was implemented as well. Three-dimensional Resnet-10 network and 4-fold cross-validation were used. Results: Classification performance was quantified by using the area under the receiver operative characteristic curve (AUC) metrics. MergeNet showed the AUC of 0.689. SVM, LGBM, and convolution-only networks showed AUCs of 0.525, 0.541, and 0.550, respectively. Application of DeLong test to pairs of receiver operating characteristic curves respectively yielded P values of .001 for the MergeNet-SVM pair and 0.001 for the MergeNet-LGBM pair. Conclusion: The MergeNet model, which incorporates chest CT, clinical, dosimetric, and laboratory data, demonstrated superior performance compared to other models. However, since its prediction performance has not yet reached an efficient level for clinical application, further research is required. Contribution: This study showed that MergeNet may be an effective means to predict radiation pneumonitis. Various predictive factors can be used together for the radiation pneumonitis prediction task via the MergeNet.

Dosimetric analysis of patients receiving volumetric-modulated arc palliative radiotherapy to the thoracic spine: A comparison with conventional mono-isocentric parallel opposed pair treatment.

PURPOSE: Volumetric modulated arc therapy (VMAT) has allowed for dose escalation and a decrease in radiation-induced toxicities for a variety of treatment sites, including spinal metastases. This article will compare the dosimetric impacts on normal lung tissue in patients treated with both VMAT and conventional treatment to the thoracic spine and determine if any significant difference exists among patient reported Edmonton Symptom Assessment System (ESAS) scores. METHODS: This retrospective quality assurance study identified 288 patients who received palliative radiotherapy to the thoracic spine using VMAT or conventional planning techniques with various palliative dose fractionation schemes. V5 lung dose levels, treated planning target volume (PTV) cord length, patient-reported ESAS scores at the time of radiation oncology consultation, 3 months' post-treatment, and 6 months' post-treatment were analyzed. All symptoms on the ESAS survey were investigated, but shortness of breath (SOB) scores were the main focus of this study. Date of death for each patient was also included for analysis. RESULTS: Patients treated with a VMAT technique had significantly higher V5 lung dose levels compared to those treated conventionally (right lung: p = 1.67e-14; left lung: p = 1.33e-6). Despite this, no significant differences were observed for SOB scores at all time points between groups and conventionally treated patients reported significantly worse pain, tiredness, depression, and wellbeing scores. A moderate correlation was observed between PTV length and nausea, SOB, appetite, and drowsiness scores in the VMAT group. Treatment technique was not found to have a significant impact on patient lifespan. CONCLUSIONS: Despite higher V5 lung dose levels associated with a VMAT technique, no significant differences were found in patient-reported ESAS scores compared to patients treated with conventional techniques. This demonstrates that palliation of thoracic spinal metastases is feasible and safe using a VMAT technique.