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.

[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.

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.

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).

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.

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.

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.

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.

Associations Between Mean Lung Dose and Prevalence of Radiation Pneumonitis in Elderly Lung Cancer Patients.

BACKGROUND/AIM: Pneumonitis is a serious radiotherapy complication. This study, which is a prerequisite for a prospective trial, aimed to identify the prevalence of pneumonitis and risk factors in elderly patients with lung cancer. PATIENTS AND METHODS: Ninety-eight lung cancer patients aged ≥65 years were included. Seventeen factors were investigated regarding grade ≥2 pneumonitis at 24 weeks following radiotherapy. RESULTS: The prevalence of grade ≥2 pneumonitis at 24 weeks was 27.3%. On univariate analysis, a significant association was observed for mean (ipsilateral) lung dose (MLD; ≤13.0 vs. 13.1-20.0 vs. >20.0 Gy; 0% vs. 24.9% vs. 48.7%). Results were significant also for ≤13.0 vs. >13.0 Gy (0% vs. 37.1%) or ≤20.0 vs. >20.0 Gy (13.4% vs. 48.7%). MLD achieved significance on multivariate analysis. CONCLUSION: Elderly patients receiving MLDs >13.0 Gy, particularly >20.0 Gy, have a high risk of grade ≥2 pneumonitis. These results are important for designing a prospective trial.

Hydrogen-rich solution alleviates acute radiation pneumonitis by regulating oxidative stress and macrophages polarization.

This study was aimed to investigate the effect of hydrogen-rich solution (HRS) on acute radiation pneumonitis (ARP) in rats. The ARP model was induced by X-ray irradiation. Histopathological changes were assessed using HE and Masson stains. Inflammatory cytokines were detected by ELISA. Immunohistochemistry and flow cytometry were performed to quantify macrophage (CD68) levels and the M2/M1 ratio. Western blot analysis, RT-qPCR, ELISA and flow cytometry were used to evaluate mitochondrial oxidative stress injury indicators. Immunofluorescence double staining was performed to colocalize CD68/LC3B and p-AMPK-α/CD68. The relative expression of proteins associated with autophagy activation and the adenosine 5'-monophosphate-activated protein kinase/mammalian target of rapamycin/Unc-51-like kinase 1 (AMPK/mTOR/ULK1) signaling pathway were detected by western blotting. ARP decreased body weight, increased the lung coefficient, collagen deposition and macrophage infiltration and promoted M1 polarization in rats. After HRS treatment, pathological damage was alleviated, and M1 polarization was inhibited. Furthermore, HRS treatment reversed the ARP-induced high levels of mitochondrial oxidative stress injury and autophagy inhibition. Importantly, the phosphorylation of AMPK-α was inhibited, the phosphorylation of mTOR and ULK1 was activated in ARP rats and this effect was reversed by HRS treatment. HRS inhibited M1 polarization and alleviated oxidative stress to activate autophagy in ARP rats by regulating the AMPK/mTOR/ULK1 signaling pathway.

DNA Repair Genetics and the Risk of Radiation Pneumonitis in Patients With Lung Cancer: A Systematic Review and Meta-analysis.

AIMS: ERCC1 rs11615 and ERCC2 rs238406 single nuclear polymorphism (SNPs) are known for their association with treatment outcome, likely related to radiosensitivity of both tumor and normal tissue in patients with non-small-cell lung cancer. This study aimed to review the effect of 1) these ERCC1/2 SNPs and 2) other SNPs of DNA repair genes on radiation pneumonitis (RP) in patients with lung cancer. MATERIALS AND METHODS: SNPs of our interest included ERCC1 rs11615 and ERCC2 rs238406 and other genes of DNA repair pathways that are functional and biologically active. DNA repair SNPs reported by at least two independent studies were pooled for meta-analysis. The study endpoint was radiation pneumonitis (RP) after radiotherapy. Recessive, dominant, homozygous, heterozygous, and allelic genotype models were used where appropriate. RESULTS: A total of 16 studies (3080 patients) were identified from the systematic review and 12 studies (2090 patients) on 11 SNPs were included in the meta-analysis. The SNPs were ATM rs189037, ATM rs373759, NEIL1 rs4462560, NEIL1 rs7402844, APE1 rs1130409, XRCC3 rs861539, ERCC1 rs11615, ERCC1 rs3212986, ERCC2 rs238406, ERCC2 rs13181, and XRCC1 rs25487. ERCC1 rs11615 (236 patients) and ERCC2 rs238406 (254 patients) were not significantly associated with RP. Using the allelic model, the G allele for NEIL1 gene was significantly associated with a reduced odds of developing symptomatic (grade ≥2) RP compared to the C allele for rs7402844 (OR 0.70, 95% CI: 0.49, 0.99, P = 0.04). Similarly, the T allele for APE1 gene was significantly associated with a reduced odds of developing symptomatic (grade ≥2) RP compared to the G allele for rs1130409 (OR 0.59, 95% CI: 0.43, 0.81, P = 0.001). CONCLUSION: Genetic variation in the DNA repair pathway genes may play a significant role in the risk of developing radiation pneumonitis in patients with lung cancer. Further studies are needed on genotypic features of DNA repair pathway genes and their association with treatment sensitivity, as such knowledge may guide personalized radiation dose prescription.

Synergizing the interaction of single nucleotide polymorphisms with dosiomics features to build a dual-omics model for the prediction of radiation pneumonitis.

OBJECTIVE: Radiation pneumonitis (RP) is the major dose-limiting toxicity of thoracic radiotherapy. This study aimed to developed a dual-omics (single nucleotide polymorphisms, SNP and dosiomics) prediction model for symptomatic RP. MATERIALS AND METHODS: The potential SNPs, which are of significant difference between the RP grade ≥ 3 group and the RP grade ≤ 1 group, were selected from the whole exome sequencing SNPs using the Fisher's exact test. Patients with lung cancer who received thoracic radiotherapy at our institution from 2009 to 2016 were enrolled for SNP selection and model construction. The factorization machine (FM) method was used to model the SNP epistasis effect, and to construct the RP prediction model (SNP-FM). The dosiomics features were extracted, and further selected using the minimum redundancy maximum relevance (mRMR) method. The selected dosiomics features were added to the SNP-FM model to construct the dual-omics model. RESULTS: For SNP screening, peripheral blood samples of 28 patients with RP grade ≥ 3 and the matched 28 patients with RP grade ≤ 1 were sequenced. 81 SNPs were of significant difference (P < 0.015) and considered as potential SNPs. In addition, 21 radiation toxicity related SNPs were also included. For model construction, 400 eligible patients (including 108 RP grade ≥ 2) were enrolled. Single SNP showed no strong correlation with RP. On the other hand, the SNP-SNP interaction (epistasis effect) of 19 SNPs were modeled by the FM method, and achieved an area under the curve (AUC) of 0.76 in the testing group. In addition, 4 dosiomics features were selected and added to the model, and increased the AUC to 0.81. CONCLUSIONS: A novel dual-omics model by synergizing the SNP epistasis effect with dosiomics features was developed. The enhanced the RP prediction suggested its promising clinical utility in identifying the patients with severe RP during thoracic radiotherapy.

Enhancing the prediction of symptomatic radiation pneumonitis for locally advanced non-small-cell lung cancer by combining 3D deep learning-derived imaging features with dose-volume metrics: a two-center study.

OBJECTIVE: This study aims to examine the ability of deep learning (DL)-derived imaging features for the prediction of radiation pneumonitis (RP) in locally advanced non-small-cell lung cancer (LA-NSCLC) patients. MATERIALS AND METHODS: The study cohort consisted of 90 patients from the Fudan University Shanghai Cancer Center and 59 patients from the Affiliated Hospital of Jiangnan University. Occurrences of RP were used as the endpoint event. A total of 512 3D DL-derived features were extracted from two regions of interest (lung-PTV and PTV-GTV) delineated on the pre-radiotherapy planning CT. Feature selection was done using LASSO regression, and the classification models were built using the multilayered perceptron method. Performances of the developed models were evaluated by receiver operating characteristic curve analysis. In addition, the developed models were supplemented with clinical variables and dose-volume metrics of relevance to search for increased predictive value. RESULTS: The predictive model using DL features derived from lung-PTV outperformed the one based on features extracted from PTV-GTV, with AUCs of 0.921 and 0.892, respectively, in the internal test dataset. Furthermore, incorporating the dose-volume metric V30Gy into the predictive model using features from lung-PTV resulted in an improvement of AUCs from 0.835 to 0.881 for the training data and from 0.690 to 0.746 for the validation data, respectively (DeLong p < 0.05). CONCLUSION: Imaging features extracted from pre-radiotherapy planning CT using 3D DL networks could predict radiation pneumonitis and may be of clinical value for risk stratification and toxicity management in LA-NSCLC patients. CLINICAL RELEVANCE STATEMENT: Integrating DL-derived features with dose-volume metrics provides a promising noninvasive method to predict radiation pneumonitis in LA-NSCLC lung cancer radiotherapy, thus improving individualized treatment and patient outcomes.

Clinical outcomes of interstitial lung abnormalities: a systematic review and meta-analysis.

Interstitial lung abnormalities (ILA), incidental findings on computed tomography scans, have raised concerns due to their association with worse clinical outcomes. Our meta-analysis, which included studies up to April 2023 from PubMed/MEDLINE, Embase, and Cochrane Library, aimed to clarify the impact of ILA on mortality, lung cancer development, and complications from lung cancer treatments. Risk ratios (RR) with 95% confidence intervals (CI) were calculated for outcomes. Analyzing 10 studies on ILA prognosis and 9 on cancer treatment complications, we found that ILA significantly increases the risk of overall mortality (RR 2.62, 95% CI 1.94-3.54; I2 = 90%) and lung cancer development (RR 3.85, 95% CI 2.64-5.62; I2 = 22%). Additionally, cancer patients with ILA had higher risks of grade 2 radiation pneumonitis (RR 2.28, 95% CI 1.71-3.03; I2 = 0%) and immune checkpoint inhibitor-related interstitial lung disease (RR 3.05, 95% CI 1.37-6.77; I2 = 83%) compared with those without ILA. In conclusion, ILA significantly associates with increased mortality, lung cancer risk, and cancer treatment-related complications, highlighting the necessity for vigilant patient management and monitoring.

Patterns and Incidence of Pneumonitis and Initial Treatment Outcomes with Durvalumab Consolidation Therapy after Radical Chemoradiotherapy for Stage III Non-Small Cell Lung Cancer.

Durvalumab consolidation after chemoradiotherapy for stage III non-small cell lung cancer (NSCLC) has become the standard of care. Single-center results were examined for treatment outcomes and patterns of pneumonitis in clinical practice. Patients with stage III NSCLC who underwent chemoradiotherapy at our institution (n = 150) were included. The patients were treated with chemoradiotherapy and durvalumab consolidation (Group D, n = 69) or chemoradiotherapy alone (Group N, n = 81). The overall survival (OS), progression-free survival (PFS), and the incidence of and risk factors for 12-month pneumonitis grade ≥ 2 (G2) were investigated. Two-year OS rates were 71.6% in Group D and 52.7% in Group N (p = 0.052). Two-year PFS rates were 43.0% in Group D and 26.5% in Group N (p = 0.010), although a propensity score matched analysis showed no significant difference. The incidence of 12-month pneumonitis ≥ G2 tended to be higher in Group D than in Group N (41.9% vs. 26.3%, p = 0.080). However, there was no difference in pneumonitis ≥ G3 rates (10.5% vs. 12.6%, p = 0.657). A multivariate analysis showed that the lung volume spared from 5 Gy (VS5) < 1800 cm3 was a risk factor for pneumonitis ≥ G2 in Group D. Durvalumab consolidation showed the potential to prolong PFS without increasing the severity of pneumonitis.

From pixels to prognosis: unveiling radiomics models with SHAP and LIME for enhanced interpretability.

Radiomics-based prediction models have shown promise in predicting Radiation Pneumonitis (RP), a common adverse outcome of chest irradiation. Τhis study looks into more than just RP: it also investigates a bigger shift in the way radiomics-based models work. By integrating multi-modal radiomic data, which includes a wide range of variables collected from medical images including cutting-edge PET/CT imaging, we have developed predictive models that capture the intricate nature of illness progression. Radiomic features were extracted using PyRadiomics, encompassing intensity, texture, and shape measures. The high-dimensional dataset formed the basis for our predictive models, primarily Gradient Boosting Machines (GBM)-XGBoost, LightGBM, and CatBoost. Performance evaluation metrics, including Multi-Modal AUC-ROC, Sensitivity, Specificity, and F1-Score, underscore the superiority of the Deep Neural Network (DNN) model. The DNN achieved a remarkable Multi-Modal AUC-ROC of 0.90, indicating superior discriminatory power. Sensitivity and specificity values of 0.85 and 0.91, respectively, highlight its effectiveness in detecting positive occurrences while accurately identifying negatives. External validation datasets, comprising retrospective patient data and a heterogeneous patient population, validate the robustness and generalizability of our models. The focus of our study is the application of sophisticated model interpretability methods, namely SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations), to improve the clarity and understanding of predictions. These methods allow clinicians to visualize the effects of features and provide localized explanations for every prediction, enhancing the comprehensibility of the model. This strengthens trust and collaboration between computational technologies and medical competence. The integration of data-driven analytics and medical domain expertise represents a significant shift in the profession, advancing us from analyzing pixel-level information to gaining valuable prognostic insights.