Pre-operative stereotactic radiosurgery and peri-operative dexamethasone for resectable brain metastases: a two-arm pilot study evaluating clinical outcomes and immunological correlates.

Enhancing the efficacy of immunotherapy in brain metastases (BrM) requires an improved understanding of the immune composition of BrM and how this is affected by radiation and dexamethasone. Our two-arm pilot study (NCT04895592) allocated 26 patients with BrM to either low (Arm A) or high (Arm B) dose peri-operative dexamethasone followed by pre-operative stereotactic radiosurgery (pSRS) and resection (n= 13 per arm). The primary endpoint, a safety analysis at 4 months, was met. The secondary clinical endpoints of overall survival, distant brain failure, leptomeningeal disease and local recurrence at 12-months were 66%, 37.3%, 6%, and 0% respectively and were not significantly different between arms (p= 0.7739, p= 0.3884, p= 0.3469). Immunological data from two large retrospective BrM datasets and confirmed by correlates from both arms of this pSRS prospective trial revealed that BrM CD8 T cells were composed of predominantly PD1+ TCF1+ stem-like and PD1+ TCF1-TIM3+ effector-like cells. Clustering of TCF1+ CD8 T cells with antigen presenting cells in immune niches was prognostic for local control, even without pSRS. Following pSRS, CD8 T cell and immune niche density were transiently reduced compared to untreated BrM, followed by a rebound 6+ days post pSRS with an increased frequency of TCF1- effector-like cells. In sum, pSRS is safe and therapeutically beneficial, and these data provide a framework for how pSRS may be leveraged to maximize intracranial CD8 T cell responses.

Using a patient-specific diffusion model to generate CBCT-based synthetic CTs for CBCT-guided adaptive radiotherapy.

BACKGROUND: Cone beam computed tomography (CBCT) can be used to evaluate the inter-fraction anatomical changes during the entire course for image-guided radiotherapy (IGRT). However, CBCT artifacts from various sources restrict the full application of CBCT-guided adaptive radiation therapy (ART). PURPOSE: Inter-fraction anatomical changes during ART, including variations in tumor size and normal tissue anatomy, can affect radiation therapy (RT) efficacy. Acquiring high-quality CBCT images that accurately capture patient- and fraction-specific (PFS) anatomical changes is crucial for successful IGRT. METHODS: To enhance CBCT image quality, we proposed PFS lung diffusion models (PFS-LDMs). The proposed PFS models use a pre-trained general lung diffusion model (GLDM) as a baseline, which is trained on historical deformed CBCT (dCBCT)-planning CT (pCT) paired data. For a given patient, a new PFS model is fine-tuned on a CBCT-deformed pCT (dpCT) pair after each fraction to learn the PFS knowledge for generating personalized synthetic CT (sCT) with quality comparable to pCT or dpCT. The learned PFS knowledge is the specific mapping relationships, including personal inter-fraction anatomical changes between personalized CBCT-dpCT pairs. The PFS-LDMs were evaluated on an institutional lung cancer dataset, quantified by mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), and structural similarity index measure (SSIM) metrics. We also compared our PFS-LDMs with a mainstream GAN-based model, demonstrating that our PFS fine-tuning strategy could be applied to existing generative models. RESULTS: Our models showed remarkable improvements across all four evaluation metrics. The proposed PFS-LDMs outperformed the GLDM, demonstrating the effectiveness of our proposed fine-tuning strategy. The PFS model fine-tuned with CBCT images from four prior fractions, reduced the MAE from 103.95 to 15.96 Hounsfield units (HU), and increased the mean PSNR, NCC, and SSIM from 25.36 dB to 33.57 dB, 0.77 to 0.98, and 0.75 to 0.97, respectively. Applying our PFS fine-tuning strategy to a Cycle GAN model also showed improvements, with all four fine-tuned PFS Cycle GAN (PFS-CG) models outperforming the general Cycle GAN model. Overall, our proposed PFS fine-tuning strategy improved CBCT image quality compared to both the pre-correction and non-fine-tuned general models, with our proposed PFS-LDMs yielding better performance than the GAN-based model across all metrics. CONCLUSIONS: Our proposed PFS-LDMs significantly improve CBCT image quality with increased HU accuracy and fewer artifacts, thus better capturing inter-fraction anatomical changes. This lays the groundwork for enabling CBCT-based ART, which could enhance clinical efficiency and achieve personalized high-precision treatment by accounting for inter-fraction anatomical changes.

Metabolic modulation of melanoma enhances the therapeutic potential of immune checkpoint inhibitors.

Introduction: Lactate is a pivotal molecule with diverse functions in the metabolic reprogramming of cancer cells. Beyond its role in metabolism, lactate exerts a modulatory effect within the tumor microenvironment; it is utilized by stromal cells and has been implicated in the suppression of the immune response against the tumor. Methods: Using in vitro assays (including flow cytometry, live-cell imaging and metabolic analyses), the impact of lactate dehydrogenase inhibitors (LDHIs) on melanoma cells were assessed. The therapeutic potential of LDHIs with immune checkpoint inhibitors (ICIs) were tested in vivo in murine models of melanoma tumors. Results: A potent anti-proliferative effect (via both cell cycle alterations and enhanced apoptosis) of LDHIs, Oxamate (Oxa) and methyl 1-hydroxy-6-phenyl-4-(trifluoromethyl)-1H-indole-2-carboxylate (NHI-2), was found upon treatment of melanoma cell lines. Using a combination of Oxa and NHI-2, a synergistic effect to inhibit proliferation, glycolysis, and ATP production was observed. Metabolic analysis revealed significant alteration in glycolysis and oxidative phosphorylation, while metabolite profiling emphasized consequential effects on lactate metabolism and induced energy depletion by LDHIs. Detection of increased RANTES and MCP-1, with Oxa and NHI-2 treatment, prompted the consideration of combining LDHIs with ICIs. In vivo studies using a murine B78 melanoma tumor model revealed a significant improvement in treatment efficacy when LDHIs were combined with ICIs. Conclusions: These findings propose the potential of targeting lactate metabolism to enhance the efficacy of ICI treatments in patients with melanoma.

A lung SBRT treatment planning technique to focus high dose on gross disease.

This study investigated a straightforward treatment planning technique for definitive stereotactic body radiation therapy (SBRT) for patients with early-stage lung cancer aimed at increasing dose to gross disease by strategically penalizing the normal tissue objective (NTO) in the EclipseTM treatment planning system. Twenty-five SBRT cases were replanned to 50 Gy in 5 fractions using static and dynamic NTO methods (50 plans total). The NTO had a start dose of 100% at the target border, end dose of 20%, fall-off rate of 0.4/mm, and a priority of 150. For the static NTO plans, a lower planning target volume (PTV) objective was placed at 52 Gy with a priority of 100. Maximum dose was not penalized. Optimization was performed without user interaction. In contrast, the planner incrementally increased the priority of the NTO on the dynamic NTO plans until 95% of the target volume was covered by the prescription dose. Further, the dynamic NTO plans used both PTV lower and upper objectives at 63-64 Gy with priorities of 50. Maximum dose was penalized to ensure that the hot spot was within ± 2% of the static NTO global maximum dose. Following optimization, all plans were normalized so that the prescription dose covered 95% of the PTV. Plans were scored based on RTOG 0813 criteria, and dose to the internal target volume (ITV) and PTV was evaluated. The Wilcoxon signed-rank test (threshold = 0.05) was used to evaluate differences between the static and dynamic NTO plans. All plans met RTOG 0813 planning guidelines. In comparison to the static NTO plans, the dynamic NTO plans exhibited statistically significant increases in PTV mean dose, ITV mean dose, and PTV-ITV mean dose. Notably, the dynamic NTO plans more effectively concentrated the high dose on gross disease at the center of the PTV. As compared to the static NTO plans, the mean dose was 4.6 Gy higher in the ITV while only 1.3 Gy higher in the PTV-ITV rind of the dynamic NTO plans. Global maximum doses were similar. There were some small yet statistically significant differences in dose conformity between plan types. Furthermore, the dynamic NTO plans demonstrated a significant reduction in total monitor units (MU). This study demonstrated an efficient optimization strategy for lung SBRT plans that concentrates the highest dose in the gross disease, which may improve local control.

Lung SBRT: Dose gradient optimization based on target size.

This study investigated optimization settings that steepen the dose gradient as a function of target size for lung stereotactic body radiation therapy (SBRT). Sixty-eight lung SBRT patients with planning target volumes (PTVs) ranging from 2-203 cc were categorized into small (<20 cc), medium (20-50 cc), and large (>50 cc) groups. VMAT plans were generated using the normal tissue objective (NTO) to penalize the dose gradient at progressively steeper NTO fall-off values (0.1, 0.2, 0.3, 0.4, 0.5 mm-1). Dose was calculated using the AcurosXB algorithm and was normalized so the prescription dose covered 95% of the PTV. Mann-Whitney, Kruskal-Wallis and ANOVA tests were used to assess for statistical differences in the Conformity Index at the 50% isodose level (CI50%), global maximum dose (Dmax), and monitor units (MU) across the various NTO settings. All plans adhered to institutional criteria and met the guidelines of the Radiation Therapy Oncology Group 0813. Steeper NTO fall-off values significantly increased Dmax and MUs across all groups (p < 0.05). CI50% significantly differed with fall-off values in small (0.3 mm-1) and medium (0.2 mm-1) targets, indicating steeper NTO fall-off values improve CI50% for small and medium targets (p < 0.05). Large targets showed no significant CI50% difference across these fall-off values. As target size increases, the importance of fall-off values in achieving an acceptable CI50% diminishes. Smaller targets benefit from steeper fall-off values despite increased Dmax and MUs. Consideration of fall-off value relative to target size is crucial to limit dose spillage outside the target.

CBCT-based synthetic CT image generation using a diffusion model for CBCT-guided lung radiotherapy.

BACKGROUND: Although cone beam computed tomography (CBCT) has lower resolution compared to planning CTs (pCT), its lower dose, higher high-contrast resolution, and shorter scanning time support its widespread use in clinical applications, especially in ensuring accurate patient positioning during the image-guided radiation therapy (IGRT) process. PURPOSE: While CBCT is critical to IGRT, CBCT image quality can be compromised by severe stripe and scattering artifacts. Tumor movement secondary to respiratory motion also decreases CBCT resolution. In order to improve the image quality of CBCT, we propose a Lung Diffusion Model (L-DM) framework. METHODS: Our proposed algorithm is based on a conditional diffusion model trained on pCT and deformed CBCT (dCBCT) image pairs to synthesize lung CT images from dCBCT images and benefit CBCT-based radiotherapy. dCBCT images were used as the constraint for the L-DM. The image quality and Hounsfield unit (HU) values of the synthetic CTs (sCT) images generated by the proposed L-DM were compared to three selected mainstream generation models. RESULTS: We verified our model in both an institutional lung cancer dataset and a selected public dataset. Our L-DM showed significant improvement in the four metrics of mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), and structural similarity index measure (SSIM). In our institutional dataset, our proposed L-DM decreased the MAE from 101.47 to 37.87 HU and increased the PSNR from 24.97 to 29.89 dB, the NCC from 0.81 to 0.97, and the SSIM from 0.80 to 0.93. In the public dataset, our proposed L-DM decreased the MAE from 173.65 to 58.95 HU, while increasing the PSNR, NCC, and SSIM from 13.07 to 24.05 dB, 0.68 to 0.94, and 0.41 to 0.88, respectively. CONCLUSIONS: The proposed L-DM significantly improved sCT image quality compared to the pre-correction CBCT and three mainstream generative models. Our model can benefit CBCT-based IGRT and other potential clinical applications as it increases the HU accuracy and decreases the artifacts from input CBCT images.

Immune System Dose With Proton Versus Photon Radiotherapy for Treatment of Locally Advanced NSCLC.

Purpose: Emerging data have illuminated the impact of effective radiation dose to immune cells (EDIC) on outcomes in patients with locally advanced, unresectable non-small cell lung cancer (NSCLC) treated with intensity-modulated radiotherapy (IMRT). Hypothesizing that intensity-modulated proton therapy (IMPT) may reduce EDIC versus IMRT, we conducted a dosimetric analysis of patients treated at our institution. Materials and Methods: Data were retrospectively collected for 12 patients with locally advanced, unresectable NSCLC diagnosed between 2019 and 2021 who had physician-approved IMRT and IMPT plans. Data to calculate EDIC from both Jin et al (PMID: 34944813) and Ladbury et al's (PMID: 31175902) models were abstracted. Paired t tests were utilized to compare the difference in mean EDIC between IMPT and IMRT plans. Results: IMPT decreased EDIC for 11 of 12 patients (91.7%). The mean EDIC per the Jin model was significantly lower with IMPT than IMRT (3.04 GyE vs 4.99 Gy, P < .001). Similarly, the mean EDIC per the Ladbury model was significantly lower with IMPT than IMRT (4.50 GyE vs 7.60 Gy, P < .002). Modeled 2-year overall survival was significantly longer with IMPT than IMRT (median 71% vs 63%; P = .03). Conclusion: IMPT offers a statistically significant reduction in EDIC compared to IMRT. Given the emergence of EDIC as a modifiable prognostic factor in treatment planning, our dosimetric study highlights a potential role for IMPT to address an unmet need in improving oncologic outcomes in patients with locoregionally advanced NSCLC.

Cardiac MRI segmentation using shifted-window multilayer perceptron mixer networks.

Objectives. In this work, we proposed a deep-learning segmentation algorithm for cardiac magnetic resonance imaging to aid in contouring of the left ventricle, right ventricle, and Myocardium (Myo).Approach.We proposed a shifted window multilayer perceptron (Swin-MLP) mixer network which is built upon a 3D U-shaped symmetric encoder-decoder structure. We evaluated our proposed network using public data from 100 individuals. The network performance was quantitatively evaluated using 3D volume similarity between the ground truth contours and the predictions using Dice score coefficient, sensitivity, and precision as well as 2D surface similarity using Hausdorff distance (HD), mean surface distance (MSD) and residual mean square distance (RMSD). We benchmarked the performance against two other current leading edge networks known as Dynamic UNet and Swin-UNetr on the same public dataset.Results.The proposed network achieved the following volume similarity metrics when averaged over three cardiac segments: Dice = 0.952 ± 0.017, precision = 0.948 ± 0.016, sensitivity = 0.956 ± 0.022. The average surface similarities were HD = 1.521 ± 0.121 mm, MSD = 0.266 ± 0.075 mm, and RMSD = 0.668 ± 0.288 mm. The network shows statistically significant improvement in comparison to the Dynamic UNet and Swin-UNetr algorithms for most volumetric and surface metrics withp-value less than 0.05. Overall, the proposed Swin-MLP mixer network demonstrates better or comparable performance than competing methods.Significance.The proposed Swin-MLP mixer network demonstrates more accurate segmentation performance compared to current leading edge methods. This robust method demonstrates the potential to streamline clinical workflows for multiple applications.

Radiation and anti-PD-L1 synergize by stimulating a stem-like T cell population in the tumor-draining lymph node.

Radiotherapy (RT) and anti-PD-L1 synergize to enhance local and distant (abscopal) tumor control. However, clinical results in humans have been variable. With the goal of improving clinical outcomes, we investigated the underlying synergistic mechanism focusing on a CD8+ PD-1+ Tcf-1+ stem-like T cell subset in the tumor-draining lymph node (TdLN). Using murine melanoma models, we found that RT + anti-PD-L1 induces a novel differentiation program in the TdLN stem-like population which leads to their expansion and differentiation into effector cells within the tumor. Our data indicate that optimal synergy between RT + anti-PD-L1 is dependent on the TdLN stem-like T cell population as either blockade of TdLN egress or specific stem-like T cell depletion reduced tumor control. Together, these data demonstrate a multistep stimulation of stem-like T cells following combination therapy which is initiated in the TdLN and completed in the tumor.

Deformable lung 4DCT image registration via landmark-driven cycle network.

BACKGROUND: An automated, accurate, and efficient lung four-dimensional computed tomography (4DCT) image registration method is clinically important to quantify respiratory motion for optimal motion management. PURPOSE: The purpose of this work is to develop a weakly supervised deep learning method for 4DCT lung deformable image registration (DIR). METHODS: The landmark-driven cycle network is proposed as a deep learning platform that performs DIR of individual phase datasets in a simulation 4DCT. This proposed network comprises a generator and a discriminator. The generator accepts moving and target CTs as input and outputs the deformation vector fields (DVFs) to match the two CTs. It is optimized during both forward and backward paths to enhance the bi-directionality of DVF generation. Further, the landmarks are used to weakly supervise the generator network. Landmark-driven loss is used to guide the generator's training. The discriminator then judges the realism of the deformed CT to provide extra DVF regularization. RESULTS: We performed four-fold cross-validation on 10 4DCT datasets from the public DIR-Lab dataset and a hold-out test on our clinic dataset, which included 50 4DCT datasets. The DIR-Lab dataset was used to evaluate the performance of the proposed method against other methods in the literature by calculating the DIR-Lab Target Registration Error (TRE). The proposed method outperformed other deep learning-based methods on the DIR-Lab datasets in terms of TRE. Bi-directional and landmark-driven loss were shown to be effective for obtaining high registration accuracy. The mean and standard deviation of TRE for the DIR-Lab datasets was 1.20 ± 0.72 mm and the mean absolute error (MAE) and structural similarity index (SSIM) for our datasets were 32.1 ± 11.6 HU and 0.979 ± 0.011, respectively. CONCLUSION: The landmark-driven cycle network has been validated and tested for automatic deformable image registration of patients' lung 4DCTs with results comparable to or better than competing methods.

Safety and efficacy of durvalumab after concurrent chemoradiation in Black patients with locally advanced non-small cell lung cancer.

BACKGROUND: The PACIFIC trial established consolidative durvalumab after concurrent chemoradiation as standard-of-care in patients with stage III or unresectable non-small cell lung cancer (NSCLC). Black patients, however, comprised just 2% (n = 14) of randomized patients in this trial, warranting real-world evaluation of the PACIFIC regimen in these patients. METHODS: This single-institution, multi-site study included 105 patients with unresectable stage II/III NSCLC treated with concurrent chemoradiation followed by durvalumab between 2017 and 2021. Overall survival (OS), progression-free survival (PFS), and grade ≥3 pneumonitis-free survival (PNFS) were compared between Black and non-Black patients using Kaplan-Meier and Cox regression analyses. RESULTS: A total of 105 patients with a median follow-up of 22.8 months (interquartile range, 11.3-37.3 months) were identified for analysis, including 57 Black (54.3%) and 48 (45.7%) non-Black patients. The mean radiation prescription dose was higher among Black patients (61.5 ± 2.9 Gy vs. 60.5 ± 1.9 Gy; p = .031), but other treatment characteristics were balanced between groups. The median OS (not-reached vs. 39.7 months; p = .379) and PFS (31.6 months vs. 19.3 months; p = .332) were not statistically different between groups. Eight (14.0%) Black patients discontinued durvalumab due to toxicity compared to 13 (27.1%) non-Black patients (p = .096). The grade ≥3 pneumonitis rate was similar between Black and non-Black patients (12.3% vs. 12.5%; p = .973), and there was no significant difference in time to grade ≥3 PNFS (p = .904). Three (5.3%) Black patients and one (2.1%) non-Black patient developed grade 5 pneumonitis. CONCLUSIONS: The efficacy and tolerability of consolidative durvalumab after chemoradiation appears to be comparable between Black and non-Black patients.

Deep learning-based fast volumetric imaging using kV and MV projection images for lung cancer radiotherapy: A feasibility study.

PURPOSE: The long acquisition time of CBCT discourages repeat verification imaging, therefore increasing treatment uncertainty. In this study, we present a fast volumetric imaging method for lung cancer radiation therapy using an orthogonal 2D kV/MV image pair. METHODS: The proposed model is a combination of 2D and 3D networks. The proposed model consists of five major parts: (1) kV and MV feature extractors are used to extract deep features from the perpendicular kV and MV projections. (2) The feature-matching step is used to re-align the feature maps to their projection angle in a Cartesian coordinate system. By using a residual module, the feature map can focus more on the difference between the estimated and ground truth images. (3) In addition, the feature map is downsized to include more global semantic information for the 3D estimation, which is useful to reduce inhomogeneity. By using convolution-based reweighting, the model is able to further increase the uniformity of image. (4) To reduce the blurry noise of generated 3D volume, the Laplacian latent space loss calculated via the feature map that is extracted via specifically-learned Gaussian kernel is used to supervise the network. (5) Finally, the 3D volume is derived from the trained model. We conducted a proof-of-concept study using 50 patients with lung cancer. An orthogonal kV/MV pair was generated by ray tracing through CT of each phase in a 4D CT scan. Orthogonal kV/MV pairs from nine respiratory phases were used to train this patient-specific model while the kV/MV pair of the remaining phase was held for model testing. RESULTS: The results are based on simulation data and phantom results from a real Linac system. The mean absolute error (MAE) values achieved by our method were 57.5 HU and 77.4 HU within body and tumor region-of-interest (ROI), respectively. The mean achieved peak-signal-to-noise ratios (PSNR) were 27.6 dB and 19.2 dB within the body and tumor ROI, respectively. The achieved mean normalized cross correlation (NCC) values were 0.97 and 0.94 within the body and tumor ROI, respectively. A phantom study demonstrated that the proposed method can accurately re-position the phantom after shift. It is also shown that the proposed method using both kV and MV is superior to current method using kV or MV only in image quality. CONCLUSION: These results demonstrate the feasibility and accuracy of our proposed fast volumetric imaging method from an orthogonal kV/MV pair, which provides a potential solution for daily treatment setup and verification of patients receiving radiation therapy for lung cancer.

The mitochondrial pyruvate carrier complex potentiates the efficacy of proteasome inhibitors in multiple myeloma.

Multiple myeloma (MM) is a hematological malignancy that emerges from antibody-producing plasma B cells. Proteasome inhibitors, including the US Food and Drug Administration-approved bortezomib (BTZ) and carfilzomib (CFZ), are frequently used for the treatment of patients with MM. Nevertheless, a significant proportion of patients with MM are refractory or develop resistance to this class of inhibitors, which represents a significant challenge in the clinic. Thus, identifying factors that determine the potency of proteasome inhibitors in MM is of paramount importance to bolster their efficacy in the clinic. Using genome-wide CRISPR-based screening, we identified a subunit of the mitochondrial pyruvate carrier (MPC) complex, MPC1, as a common modulator of BTZ response in 2 distinct human MM cell lines in vitro. We noticed that CRISPR-mediated deletion or pharmacological inhibition of the MPC complex enhanced BTZ/CFZ-induced MM cell death with minimal impact on cell cycle progression. In fact, targeting the MPC complex compromised the bioenergetic capacity of MM cells, which is accompanied by reduced proteasomal activity, thereby exacerbating BTZ-induced cytotoxicity in vitro. Importantly, we observed that the RNA expression levels of several regulators of pyruvate metabolism were altered in advanced stages of MM for which they correlated with poor patient prognosis. Collectively, this study highlights the importance of the MPC complex for the survival of MM cells and their responses to proteasome inhibitors. These findings establish mitochondrial pyruvate metabolism as a potential target for the treatment of MM and an unappreciated strategy to increase the efficacy of proteasome inhibitors in the clinic.

Automated lung tumor delineation on positron emission tomography/computed tomography via a hybrid regional network.

BACKGROUND: Multimodality positron emission tomography/computed tomography (PET/CT) imaging combines the anatomical information of CT with the functional information of PET. In the diagnosis and treatment of many cancers, such as non-small cell lung cancer (NSCLC), PET/CT imaging allows more accurate delineation of tumor or involved lymph nodes for radiation planning. PURPOSE: In this paper, we propose a hybrid regional network method of automatically segmenting lung tumors from PET/CT images. METHODS: The hybrid regional network architecture synthesizes the functional and anatomical information from the two image modalities, whereas the mask regional convolutional neural network (R-CNN) and scoring fine-tune the regional location and quality of the output segmentation. This model consists of five major subnetworks, that is, a dual feature representation network (DFRN), a regional proposal network (RPN), a specific tumor-wise R-CNN, a mask-Net, and a score head. Given a PET/CT image as inputs, the DFRN extracts feature maps from the PET and CT images. Then, the RPN and R-CNN work together to localize lung tumors and reduce the image size and feature map size by removing irrelevant regions. The mask-Net is used to segment tumor within a volume-of-interest (VOI) with a score head evaluating the segmentation performed by the mask-Net. Finally, the segmented tumor within the VOI was mapped back to the volumetric coordinate system based on the location information derived via the RPN and R-CNN. We trained, validated, and tested the proposed neural network using 100 PET/CT images of patients with NSCLC. A fivefold cross-validation study was performed. The segmentation was evaluated with two indicators: (1) multiple metrics, including the Dice similarity coefficient, Jacard, 95th percentile Hausdorff distance, mean surface distance (MSD), residual mean square distance, and center-of-mass distance; (2) Bland-Altman analysis and volumetric Pearson correlation analysis. RESULTS: In fivefold cross-validation, this method achieved Dice and MSD of 0.84 ± 0.15 and 1.38 ± 2.2 mm, respectively. A new PET/CT can be segmented in 1 s by this model. External validation on The Cancer Imaging Archive dataset (63 PET/CT images) indicates that the proposed model has superior performance compared to other methods. CONCLUSION: The proposed method shows great promise to automatically delineate NSCLC tumors on PET/CT images, thereby allowing for a more streamlined clinical workflow that is faster and reduces physician effort.

Compact portable sources of high-LET radiation: Validation and potential application for galactic cosmic radiation countermeasure discovery.

Implementation of a systematic program for galactic cosmic radiation (GCR) countermeasure discovery will require convenient access to ground-based space radiation analogs. The current gold standard approach for GCR simulation is to use a particle accelerator for sequential irradiation with ion beams representing different GCR components. This has limitations, particularly for studies of non-acute responses, strategies that require robotic instrumentation, or implementation of complex in vitro models that are emerging as alternatives to animal experimentation. Here we explore theoretical and practical issues relating to a different approach to provide a high-LET radiation field for space radiation countermeasure discovery, based on use of compact portable sources to generate neutron-induced charged particles. We present modeling studies showing that DD and DT neutron generators, as well as an AmBe radionuclide-based source, generate charged particles with a linear energy transfer (LET) distribution that, within a range of biological interest extending from about 10 to 200 keV/µm, resembles the LET distribution of reference GCR radiation fields experienced in a spacecraft or on the lunar surface. We also demonstrate the feasibility of using DD neutrons to induce 53BP1 DNA double-strand break repair foci in the HBEC3-KT line of human bronchial epithelial cells, which are widely used for studies of lung carcinogenesis. The neutron-induced foci are larger and more persistent than X ray-induced foci, consistent with the induction of complex, difficult-to-repair DNA damage characteristic of exposure to high-LET (>10 keV/µm) radiation. We discuss limitations of the neutron approach, including low fluence in the low LET range (<10 keV/µm) and the absence of certain long-range features of high charge and energy particle tracks. We present a concept for integration of a compact portable source with a multiplex microfluidic in vitro culture system, and we discuss a pathway for further validation of the use of compact portable sources for countermeasure discovery.

Impact of radiation dose to the immune cells in unresectable or stage III non-small cell lung cancer in the durvalumab era.

BACKGROUND/PURPOSE: Higher estimated radiation doses to immune cells (EDIC) have correlated with worse overall survival (OS) in patients with locally-advanced non-small cell lung cancer (NSCLC) prior to the PACIFIC trial, which established consolidative durvalumab as standard-of-care. Here, we examine the prognostic impact of EDIC in the durvalumab era. MATERIALS/METHODS: This single-institution, multi-center study included patients with unresectable stage II/III NSCLC treated with chemoradiation followed by durvalumab. Associations between EDIC [analyzed continuously and categorically (≤6 Gy vs > 6 Gy)] and OS, progression-free survival (PFS), and locoregional control (LRC) were evaluated by Kaplan-Meier and Cox proportional methods. RESULTS: 100 patients were included with median follow-up of 23.7 months. The EDIC > 6 Gy group had a significantly greater percentage of stage IIIB/IIIC disease (76.0 % vs 32.6 %; p < 0.001) and larger tumor volumes (170 cc vs 42 cc; p < 0.001). There were no differences in early durvalumab discontinuation from toxicity (24.1 % vs 15.2 %; p = 0.27). Median OS was shorter among the EDIC > 6 Gy group (29.6 months vs not reached; p < 0.001). On multivariate analysis, EDIC > 6 Gy correlated with worse OS (HR: 4.15, 95 %CI: 1.52-11.33; p = 0.006), PFS (HR: 3.79; 95 %CI: 1.80-8.0; p < 0.001), and LRC (HR: 2.66, 95 %CI: 1.15-6.18; p = 0.023). Analyzed as a continuous variable, higher EDIC was associated with worse OS (HR: 1.34; 95 %CI: 1.16-1.57; p < 0.001), PFS (HR: 1.52; 95 %CI: 1.29-1.79; p < 0.001), and LRC (HR: 1.34, 95 %CI: 1.13-1.60; p = 0.007). CONCLUSIONS: In the immunotherapy era, EDIC is an independent predictor of OS and disease control in locally advanced NSCLC, warranting investigation into techniques to reduce dose to the immune compartment.

Metabolic response to radiation therapy in cancer.

Tumor metabolism has emerged as a hallmark of cancer and is involved in carcinogenesis and tumor growth. Reprogramming of tumor metabolism is necessary for cancer cells to sustain high proliferation rates and enhanced demands for nutrients. Recent studies suggest that metabolic plasticity in cancer cells can decrease the efficacy of anticancer therapies by enhancing antioxidant defenses and DNA repair mechanisms. Studying radiation-induced metabolic changes will lead to a better understanding of radiation response mechanisms as well as the identification of new therapeutic targets, but there are few robust studies characterizing the metabolic changes induced by radiation therapy in cancer. In this review, we will highlight studies that provide information on the metabolic changes induced by radiation and oxidative stress in cancer cells and the associated underlying mechanisms.

Definitive intensity modulated proton re-irradiation for lung cancer in the immunotherapy era.

Introduction: As immunotherapy has improved distant metastasis-free survival (DMFS) in Non-Small Cell Lung Cancer (NSCLC), isolated locoregional recurrences have increased. However, management of locoregional recurrences can be challenging. We report our institutional experience with definitive intent re-irradiation using Intensity Modulated Proton Therapy (IMPT). Method: Retrospective cohort study of recurrent or second primary NSCLC or LS-SCLC treated with IMPT. Kaplan-Meier method and log-rank test were used for time-to-event analyses. Results: 22 patients were treated from 2019 to 2021. After first course of radiation (median 60 Gy, range 45-70 Gy), 45% received adjuvant immunotherapy. IMPT re-irradiation began a median of 28.2 months (8.8-172.9 months) after initial radiotherapy. The median IMPT dose was 60 GyE (44-60 GyE). 36% received concurrent chemotherapy with IMPT and 18% received immunotherapy after IMPT. The median patient's IMPT lung mean dose was 5.3 GyE (0.9-13.9 GyE) and 5 patients had cumulative esophagus max dose >100 GyE with 1-year overall survival (OS) 68%, 1-year local control 80%, 1-year progression free survival 45%, and 1-year DMFS 60%. Higher IMPT (HR 1.4; 95% CI 1.1-1.7, p=0.01) and initial radiotherapy mean lung doses (HR 1.3; 95% CI 1.0-1.6, p=0.04) were associated with worse OS. Two patients developed Grade 3 pneumonitis or dermatitis, one patient developed Grade 2 pneumonitis, and seven patients developed Grade 1 toxicity. There were no Grade 4 or 5 toxicities. Discussion: Definitive IMPT re-irradiation for lung cancer can prolong disease control with limited toxicity, particularly in the immunotherapy era.