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.

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.

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.

OBJECTIVE: 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.
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.
CONCLUSIONS: Prophylactic administration of CAM may reduce Grade ≥2 RP in patients treated with IMRT for lung cancer. Therefore, further clinical trials are warranted.

OBJECTIVE: 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.
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.
CONCLUSIONS: AIQCT identified CT features associated with SRP. A predictive model for SRP was proposed based on AI-detected Br-volume and the NL-Dmean.

OBJECTIVE: 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.
BACKGROUND: The protocol of this study was registered on PROSPERO (CRD42023426565).

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.

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-β, 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-β, 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.
CONCLUSIONS: Normal mouse serum ameliorates radiation pneumonitis in mice by inhibiting the expressions of key proteins in the Focal adhesion signaling pathway.

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.
CONCLUSIONS: 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.

In order to study how to use pulmonary functional imaging obtained through 4D-CT fusion for radiotherapy planning, and transform traditional dose volume parameters into functional dose volume parameters, a functional dose volume parameter model that may reduce level 2 and above radiation pneumonia was obtained. 41 pulmonary tumor patients who underwent 4D-CT in our department from 2020 to 2023 were included. MIM Software (MIM 7.0.7; MIM Software Inc., Cleveland, OH, USA) was used to register adjacent phase CT images in the 4D-CT series. The three-dimensional displacement vector of CT pixels was obtained when changing from one respiratory state to another respiratory state, and this three-dimensional vector was quantitatively analyzed. Thus, a color schematic diagram reflecting the degree of changes in lung CT pixels during the breathing process, namely the distribution of ventilation function strength, is obtained. Finally, this diagram is fused with the localization CT image. Select areas with Jacobi > 1.2 as high lung function areas and outline them as fLung. Import the patient\'s DVH image again, fuse the lung ventilation image with the localization CT image, and obtain the volume of fLung different doses (V60, V55, V50, V45, V40, V35, V30, V25, V20, V15, V10, V5). Analyze the functional dose volume parameters related to the risk of level 2 and above radiation pneumonia using R language and create a predictive model. By using stepwise regression and optimal subset method to screen for independent variables V35, V30, V25, V20, V15, and V10, the prediction formula was obtained as follows: Risk = 0.23656-0.13784 * V35 + 0.37445 * V30-0.38317 * V25 + 0.21341 * V20-0.10209 * V15 + 0.03815 * V10. These six independent variables were analyzed using a column chart, and a calibration curve was drawn using the calibrate function. It was found that the Bias corrected line and the Apparent line were very close to the Ideal line, The consistency between the predicted value and the actual value is very good. By using the ROC function to plot the ROC curve and calculating the area under the curve: 0.8475, 95% CI 0.7237-0.9713, it can also be determined that the accuracy of the model is very high. In addition, we also used Lasso method and random forest method to filter out independent variables with different results, but the calibration curve drawn by the calibration function confirmed poor prediction performance. The function dose volume parameters V35, V30, V25, V20, V15, and V10 obtained through 4D-CT are key factors affecting radiation pneumonia. Establishing a predictive model can provide more accurate lung restriction basis for clinical radiotherapy planning.