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Introduction: Biology-guided radiotherapy (BgRT) is a novel radiation delivery approach utilizing fluorodeoxyglucose (FDG) activity on positron emission tomography (PET) imaging performed in real-time to track and direct RT. Our institution recently acquired the RefleXion X1 BgRT system and sought to assess the feasibility of targeting metastatic sites in various organs, including the liver. However, in order for BgRT to function appropriate, adequate contrast in FDG activity between the tumor and the background tissue, referred to as the normalized SUV (NSUV), is necessary for optimal functioning of BgRT., Methods: We reviewed the charts of 50 lung adenocarcinoma patients with liver metastases. The following variables were collected: SUVmax and SUVmean for each liver metastasis, SUVmean and SUVmax at 5 and 10 mm radially from the lesion, and NSUV at 5 mm and 10 mm (SUVmax of the liver metastasis divided by SUV mean at 5 mm at 10 mm respectively)., Results: 82 measurable liver metastases were included in the final analysis. The average SUVbackground of liver was 2.26 (95% confidence interval [CI] 2.17-2.35); average SUVmean for liver metastases was 5.31 (95% CI 4.87-5.75), and average SUVmax of liver metastases was 9.19 (95% CI 7.59-10.78). The average SUVmean at 5 mm and 10 mm radially from each lesion were 3.08 (95% CI 3.00-2.16) and 2.60 (95% CI 2.52-2.68), respectively. The mean NSUV at 5 mm and 10 mm were 3.13 (95% CI 2.53-3.73) and 3.69 (95% CI 3.00-4.41) respectively. Furthermore, 90% of lesions had NSUV greater than 1.45 at 5 mm and greater than 1.77 at 10 mm., Conclusions: This is the first study to comprehensively characterize FDG contrast between the liver tumor and background, referred to as NSUV. Due to the high background SUV normally found in the liver, this work will be valuable for guiding optimization of BgRT for treating liver metastases in the future using the RefleXion ® X1 and potentially other similar BgRT platforms., (© 2024. The Author(s).)

Despite lower rates and intensity of smoking, Black men experience a higher incidence of lung cancer compared to white men. The racial disparity in lung cancer is particularly pronounced in Chicago, a highly segregated urban city. Neighborhood conditions, particularly social stress, may play a role in lung tumorigenesis. Preliminary studies indicate that Black men residing in neighborhoods with higher rates of violent crime have significantly higher levels of hair cortisol, an indicator of stress response. To examine the relationship between social stress exposure and gene expression in lung tumors, we investigated glucocorticoid receptor (GR) binding in 15 lung tumor samples in relation to GR target gene expression levels and zip code level residential violent crime rates. Spatial transcriptomics and a version of ChIP sequencing known as CUT&RUN were used. Heatmap of genes, pathway analysis, and motif analysis were conducted at the statistical significance of P < 0.05. GR recruitment to chromatin was correlated with zip code level residential violent crime rate and overall GR binding increased with higher violent crime rates. Our findings suggest that exposure to residential violent crime may influence tumor biology via reprogramming GR recruitment. Prioritizing lung cancer screening in neighborhoods with increased social stress, such as high levels of violent crime, may reduce racial disparities in lung cancer., Significance: Exposure to neighborhood violent crime is correlated with glucocorticoid signaling and lung tumor gene expression changes associated with increased tumor aggressiveness, suggesting social conditions have downstream biophysical consequences that contribute to lung cancer disparities., (©2024 The Authors; Published by the American Association for Cancer Research.)

Talimogene laherparepvec (TVEC) is a genetically modified oncolytic herpes simplex virus (HSV-1) that is used for the intralesional treatment of advanced or metastatic melanoma. Given that TVEC produces the granulocyte-macrophage colony-stimulating factor (GM-CSF), recent reports have suggested that radiation treatment (RT) given in conjunction with TVEC may provide synergistic immune activation at the site, and possibly systemically. However, studies on combining RT with TVEC remain limited. We conducted a retrospective review of melanoma patients from a single cancer center who received TVEC and RT in the same region of the body and compared them to patients who received TVEC with RT at another site (other than the site of TVEC injection). Between January 2015 and September 2022, we identified twenty patients who were treated with TVEC and RT; fourteen patients received TVEC and RT in the same region, and six had treatments in separate regions. Regions were determined at the time of analysis and were based on anatomic sites (such as arm, leg, torso, etc.). Kaplan-Meier analysis of progression-free survival (PFS), analyses of time to distant metastasis (DM), overall survival (OS), and locoregional control (LRC), and the corresponding log-rank test were performed. With a median follow-up of 10.5 months [mos] (range 1.0-58.7 mos), we found an improvement in PFS with TVEC and RT in the same region compared to different regions, which were 6.4 mos (95% CI, 2.4-NR mos) and 2.8 mos (95% CI, 0.7-4.4 mos), respectively; p = 0.005. There was also a significant improvement in DM when TVEC and RT were used in the same region compared to different regions: 13.8 mos (95% CI, 4.6-NR mos) and 2.8 mos (95% CI, 0.7-4.4 mos), respectively ( p = 0.001). However, we found no difference in overall survival (OS) between patients who had TVEC and RT in the same region (19.0 mos, 95% confidence interval [CI], 4.1-not reached [NR] mos) and those who received treatments in different regions (18.5 mos, 95% CI, 1.0-NR mos); p = 0.366. There was no statistically significant improvement in locoregional control (LRC) in patients who had TVEC and RT in the same region was 26.0 mos (95% CI, 6.4-26.0 mos) compared to patients who received TVEC and RT in different regions (4.4 mos) (95% CI, 0.7-NR mos) ( p = 0.115). No grade 3 or higher toxicities were documented in either group. Overall, there were improvements in PFS and DM when TVEC and RT were delivered to the same region of the body compared to when they were used in different regions. However, we did not find a significant difference in locoregional recurrence or OS. Future studies are needed to assess the sequence and timing of combining RT and TVEC to potentially enhance the immune response both locally and distantly.

Purpose: Dosimetric predictors of toxicity in patients treated with definitive chemoradiation for locally advanced non-small cell lung cancer are often identified through trial and error. This study used machine learning (ML) and explainable artificial intelligence to empirically characterize dosimetric predictors of toxicity in patients treated as part of a prospective clinical trial., Methods and Materials: A secondary analysis of the Radiation Therapy Oncology Group (RTOG) 0617 trial was performed. Multiple ML models were trained to predict grade ≥3 pulmonary, cardiac, and esophageal toxicities using clinical and dosimetric features. Model performance was evaluated using the area under the curve (AUC). The best performing model for each toxicity was explained using the Shapley Additive Explanation (SHAP) framework; SHAP values were used to identify relevant dosimetric thresholds and were converted to odds ratios (ORs) with confidence intervals (CIs) generated using bootstrapping to obtain quantitative measures of risk. Thresholds were validated using logistic regression., Results: The best-performing models for pulmonary, cardiac, and esophageal toxicities, outperforming logistic regression, were extreme gradient boosting (AUC, 0.739), random forest (AUC, 0.706), and naive Bayes (AUC, 0.721), respectively. For pulmonary toxicity, thresholds of a mean dose >18 Gy (OR, 2.467; 95% CI, 1.049-5.800; P = .038) and lung volume receiving ≥20 Gy (V20) > 37% (OR, 2.722; 95% CI, 1.034-7.163; P = .043) were identified. For esophageal toxicity, thresholds of a mean dose >34 Gy (OR, 4.006; 95% CI, 2.183-7.354; P < .001) and V20 > 37% (OR, 3.725; 95% CI, 1.308-10.603; P = .014) were identified. No significant thresholds were identified for cardiac toxicity., Conclusions: In this data set, ML approaches validated known dosimetric thresholds and outperformed logistic regression at predicting toxicity. Furthermore, using explainable artificial intelligence, clinically useful dosimetric thresholds might be identified and subsequently externally validated., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: The role of post-operative radiotherapy (PORT) for completely resected N2 non-small-cell lung cancer (NSCLC) is controversial in light of recent randomized data. We sought to utilize machine learning to identify a subset of patients who may still benefit from PORT based on extent of nodal involvement., Materials/methods: Patients with completely resected N2 NSCLC were identified in the National Cancer Database. We trained a machine-learning based model of overall survival (OS). SHapley Additive exPlanation (SHAP) values were used to identify prognostic and predictive thresholds of number of positive lymph nodes (LNs) involved and lymph node ratio (LNR). Cox proportional hazards regression was used for confirmatory analysis., Results: A total of 16,789 patients with completely resected N2 NSCLC were identified. Using the SHAP values, we identified thresholds of 3+ positive LNs and a LNR of 0.34+. On multivariate analysis, PORT was not significantly associated with OS (p = 0.111). However, on subset analysis of patients with 3+ positive LNs, PORT improved OS (HR: 0.91; 95% CI: 0.86-0.97; p = 0.002). On a separate subset analysis in patients with a LNR of 0.34+, PORT improved OS (HR: 0.90; 95% CI: 0.85-0.96; p = 0.001). Patients with 3+ positive lymph nodes had a 5-year OS of 38% with PORT compared to 31% without PORT. Patient with positive lymph node ratio 0.34+ had a 5-year OS of 38% with PORT compared to 29% without PORT., Conclusions: Patients with a high lymph node burden or lymph node ratio may present a subpopulation of patients who could benefit from PORT. To our knowledge, this is the first study to use machine learning algorithms to address this question with a large national dataset. These findings address an important question in the field of thoracic oncology and warrant further investigation in prospective studies., Competing Interests: Conflicts of interest notification The authors have no conflicts of interest to report., (Copyright © 2022 Elsevier B.V. All rights reserved.)