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Publications relating parotid dose-volume characteristics to radiotherapy-induced salivary toxicity were reviewed. Late salivary dysfunction has been correlated to the mean parotid gland dose, with recovery occurring with time. Severe xerostomia (defined as long-term salivary function of <25% of baseline) is usually avoided if at least one parotid gland is spared to a mean dose of less than approximately 20 Gy or if both glands are spared to less than approximately 25 Gy (mean dose). For complex, partial-volume RT patterns (e.g., intensity-modulated radiotherapy), each parotid mean dose should be kept as low as possible, consistent with the desired clinical target volume coverage. A lower parotid mean dose usually results in better function. Submandibular gland sparing also significantly decreases the risk of xerostomia. The currently available predictive models are imprecise, and additional study is required to identify more accurate models of xerostomia risk. [ABSTRACT FROM AUTHOR]

Clinical studies of the dependence of normal tissue response on dose-volume factors are often confusingly inconsistent, as the QUANTEC reviews demonstrate. A key opportunity to accelerate progress is to begin storing high-quality datasets in repositories. Using available technology, multiple repositories could be conveniently queried, without divulging protected health information, to identify relevant sources of data for further analysis. After obtaining institutional approvals, data could then be pooled, greatly enhancing the capability to construct predictive models that are more widely applicable and better powered to accurately identify key predictive factors (whether dosimetric, image-based, clinical, socioeconomic, or biological). Data pooling has already been carried out effectively in a few normal tissue complication probability studies and should become a common strategy. [ABSTRACT FROM AUTHOR]

The three-dimensional dose, volume, and outcome data for lung are reviewed in detail. The rate of symptomatic pneumonitis is related to many dosimetric parameters, and there are no evident threshold "tolerance dose-volume" levels. There are strong volume and fractionation effects. [ABSTRACT FROM AUTHOR]

Biomarkers are of interest for predicting or monitoring normal tissue toxicity of radiation therapy. Advances in molecular radiobiology provide novel leads in the search for normal tissue biomarkers with sufficient sensitivity and specificity to become clinically useful. This article reviews examples of studies of biomarkers as predictive markers, as response markers, or as surrogate endpoints for radiation side effects. Single nucleotide polymorphisms are briefly discussed in the context of candidate gene and genomewide association studies. The importance of adjusting for radiation dose distribution in normal tissue biomarker studies is underlined. Finally, research priorities in this field are identified and discussed. [ABSTRACT FROM AUTHOR]

The 16 clinical articles in this issue review the dose-volume dependence of toxicities of external beam radiotherapy. They are limited by the difficulty of synthesizing results from different publications. The major problems stem from incomplete reporting of results and use of incompatible or ambiguous endpoints. Here we specify these problems; give recommendations to authors, editors, and reviewers on standards of reporting; and provide methods of defining endpoints suitable for the dose-volume analysis of toxicity. Adopting these recommendations will facilitate meta-analysis and increase the utility of individual studies of the dependence of complications on dose distributions. [ABSTRACT FROM AUTHOR]

The dose, volume, and clinical outcome data for penile bulb are reviewed for patients treated with external-beam radiotherapy. Most, but not all, studies find an association between impotence and dosimetric parameters (e.g., threshold doses) and clinical factors (e.g., age, comorbid diseases). According to the data available, it is prudent to keep the mean dose to 95% of the penile bulb volume to <50 Gy. It may also be prudent to limit the D70 and D90 to 70 Gy and 50 Gy, respectively, but coverage of the planning target volume should not be compromised. It is acknowledged that the penile bulb may not be the critical component of the erectile apparatus, but it seems to be a surrogate for yet to be determined structure(s) critical for erectile function for at least some techniques. [ABSTRACT FROM AUTHOR]

The available dose/volume/outcome data for rectal injury were reviewed. The volume of rectum receiving >or=60 Gy is consistently associated with the risk of Grade >or=2 rectal toxicity or rectal bleeding. Parameters for the Lyman-Kutcher-Burman normal tissue complication probability model from four clinical series are remarkably consistent, suggesting that high doses are predominant in determining the risk of toxicity. The best overall estimates (95% confidence interval) of the Lyman-Kutcher-Burman model parameters are n = 0.09 (0.04-0.14); m = 0.13 (0.10-0.17); and TD(50) = 76.9 (73.7-80.1) Gy. Most of the models of late radiation toxicity come from three-dimensional conformal radiotherapy dose-escalation studies of early-stage prostate cancer. It is possible that intensity-modulated radiotherapy or proton beam dose distributions require modification of these models because of the inherent differences in low and intermediate dose distributions. [ABSTRACT FROM AUTHOR]

Current prognostic scores in multiple myeloma (MM) currently rely on disease burden and a limited set of genomic alterations. Some studies have suggested gene expression panels may predict clinical outcomes, but none are presently utilized in clinical practice. The tyrosine kinase WEE1 is a critical cell cycle regulator during the S-phase and G2M checkpoint. Abnormal WEE1 expression has been implicated in multiple cancers including breast, ovarian, and gastric cancers, but its prognostic signal in MM has not been thoroughly reported. We, therefore, analyzed the MMRF CoMMpass dataset (N = 659) and identified a high-risk group (top tertile) and a low-risk group (bottom tertile) based on WEE1 expression sorted in descending order. PFS was significantly different (p < 1e-9) between the groups, which was validated in two independent microarray gene expression profiling (GEP) datasets from the Total Therapy 2 (N = 341) and 3 (N = 214) trials. Our results show that WEE1 expression is prognostic independent of known biomarkers, differentiates outcomes associated with known markers, is upregulated independently of its interacting neighbors, and is associated with dysregulated P53 pathways. This suggests that WEE1 expression levels may have clinical utility in prognosticating outcomes in newly diagnosed MM and may support the application of WEE1 inhibitors to MM preclinical models. Determining the causes of abnormal WEE1 expression may uncover novel therapeutic pathways., Competing Interests: Competing interests: SZU: research funding: Amgen, BMS/Celgene, GSK, Janssen, Merck, Pharmacyclics, Sanofi, Seattle Genetics, Takeda. Consulting/Advisory Board: AbbVie, Amgen, BMS, Celgene, Genentech, Gilead, GSK, Janssen, Sanofi, Seattle Genetics, SecuraBio, SkylineDX, Takeda, TeneoBio., (© 2025. The Author(s).)

Background: Voxel-based analysis (VBA) for population level radiotherapy (RT) outcomes modeling requires topology preserving inter-patient deformable image registration (DIR) that preserves tumors on moving images while avoiding unrealistic deformations due to tumors occurring on fixed images., Purpose: We developed a tumor-aware recurrent registration (TRACER) deep learning (DL) method and evaluated its suitability for VBA., Methods: TRACER consists of encoder layers implemented with stacked 3D convolutional long short term memory network (3D-CLSTM) followed by decoder and spatial transform layers to compute dense deformation vector field (DVF). Multiple CLSTM steps are used to compute a progressive sequence of deformations. Input conditioning was applied by including tumor segmentations with 3D image pairs as input channels. Bidirectional tumor rigidity, image similarity, and deformation smoothness losses were used to optimize the network in an unsupervised manner. TRACER and multiple DL methods were trained with 204 3D computed tomography (CT) image pairs from patients with lung cancers (LC) and evaluated using (a) Dataset I (N = 308 pairs) with DL segmented LCs, (b) Dataset II (N = 765 pairs) with manually delineated LCs, and (c) Dataset III with 42 LC patients treated with RT., Results: TRACER accurately aligned normal tissues. It best preserved tumors, indicated by the smallest tumor volume difference of 0.24%, 0.40%, and 0.13 % and mean square error in CT intensities of 0.005, 0.005, 0.004, computed between original and resampled moving image tumors, for Datasets I, II, and III, respectively. It resulted in the smallest planned RT tumor dose difference computed between original and resampled moving images of 0.01 and 0.013 Gy when using a female and a male reference., Conclusions: TRACER is a suitable method for inter-patient registration involving LC occurring in both fixed and moving images and applicable to voxel-based analysis methods., (© 2024 American Association of Physicists in Medicine.)

Using a novel unsupervised method to integrate multi-omic data, we previously identified a breast cancer group with a poor prognosis. In the current study, we characterize the biological features of this subgroup, defined as the high-risk group, using various data sources. Assessment of three published hypoxia signatures showed that the high-risk group exhibited higher hypoxia scores (p < 0.0001 in all three signatures), compared to the low-risk group. Our analysis of the immune cell composition using CIBERSORT and leukocyte fraction showed significant differences between the high and low-risk groups across the entire cohort, as well as within PAM50 subtypes. Within the basal subtype, the low-risk group had a statistically significantly higher spatial fraction of tumor-infiltrating lymphocytes (TILs) compared to the high-risk group (p = 0.0362). Our findings indicate that this subgroup with poor prognosis is driven by a distinct biological signature with high activation of hypoxia-related genes as well as a low number of TILs., Competing Interests: Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Purpose: The relative biological effectiveness (RBE) of tumor control for proton beam therapy (PBT) compared to photon radiotherapy (RT) is typically assumed to be independent of fractionation. To test this, we modeled published PBT outcome results for early-stage non-small cell lung cancer (NSCLC) treatments across a range of fractionation schedules., Materials and Methods: All published and analyzable cohorts were included (399 patients, 413 treated lesions). Two models were used to fit the data: a previously published tumor simulation model that fits photon RT results of NSCLC across all fractionation regimes and the Fowler LQ model with a kick-off time term. The treatment effect of each cohort was referenced to the photon equivalent dose through mechanistic model simulations in a 2 Gy/weekday scenario, with radiobiological parameters determined to simultaneously best-fit all fractionation results. The tumor control RBE of each published treatment schedule, compared to the modeled photon RT effect of the same schedule, was then estimated., Results: For cohorts whose treatments lasted less than three weeks (i.e., 12 fractions or less), the RBE of PBT was in the range of 1.08 to 1.11. However, for fractionated treatments stretching over four weeks or more (20-25 fractions), the relative effectiveness was much lower, with RBEs in the range of 0.82-0.89. This conclusion was unchanged using the simpler Fowler LQ + time model., Conclusions: The proton RBE for hypo-fractionated schedules was 20-30% higher than for conventional schedules. The derived radiobiological parameters of PBT differ significantly from those of photon RT, indicating that PBT is influenced differentially by radiobiological mechanisms which require further investigation.

This work introduces a user-friendly, cloud-based software framework for conducting Artificial Intelligence (AI) analyses of medical images. The framework allows users to deploy AI-based workflows by customizing software and hardware dependencies. The components of our software framework include the Python-native Computational Environment for Radiological Research (pyCERR) platform for radiological image processing, Cancer Genomics Cloud (CGC) for accessing hardware resources and user management utilities for accessing images from data repositories and installing AI models and their dependencies. GNU-GPL copyright pyCERR was ported to Python from MATLAB-based CERR to enable researchers to organize, access, and transform metadata from high dimensional, multi-modal datasets to build cloud-compatible workflows for AI modeling in radiation therapy and medical image analysis. pyCERR provides an extensible data structure to accommodate metadata from commonly used medical imaging file formats and a viewer to allow for multi-modal visualization. Analysis modules are provided to facilitate cloud-compatible AI-based workflows for image segmentation, radiomics, DCE MRI analysis, radiotherapy dose-volume histogram-based features, and normal tissue complication and tumor control models for radiotherapy. Image processing utilities are provided to help train and infer convolutional neural network-based models for image segmentation, registration and transformation. The framework allows for round-trip analysis of imaging data, enabling users to apply AI models to their images on CGC and retrieve and review results on their local machine without requiring local installation of specialized software or GPU hardware. The deployed AI models can be accessed using APIs provided by CGC, enabling their use in a variety of programming languages. In summary, the presented framework facilitates end-to-end radiological image analysis and reproducible research, including pulling data from sources, training or inferring from an AI model, utilities for data management, visualization, and simplified access to image metadata.

Purpose: We hypothesized that an in-house developed system using megavoltage and kilovoltage image guidance (MKIG) to ensure correct prostate positioning during stereotactic body radiation therapy (SBRT) could potentially avoid unwanted doses to nontarget tissues, leading to reduced toxicities., Methods and Materials: We built a 3-dimensional MKIG platform that accurately tracks prostate implanted fiducials in real time and clinically translated the system to replace a commercial approach, intrafraction motion review (IMR), which only tracks fiducials in the 2-dimensional kilovoltage views. From 2017 to 2019, 150 patients with prostate cancer were treated with SBRT and monitored using MKIG. The motion trace of the fiducials alerts therapists to interrupt and reposition the prostate when displacement exceeds a 1.5 mm threshold. A comparison cohort of 121 patients was treated with the same dose regimen and treatment technique but managed by IMR. Statistics of intrafractional patient shifts and delivery time were collected to evaluate the workflow efficacy. The incidence of grade ≥2 urinary toxicities was analyzed to assess clinical complications. The median follow-up time was 3.7 years (0.2-8.2 years)., Results: MKIG treatments had more treatment shifts (1.09 vs 0.28) and a longer average delivery time per fraction (579 ± 205 seconds vs 357 ± 117 seconds) than IMR treatments. Three-quarters (75%) of shifts resulting from MKIG were ≤3 mm, versus 51% in IMR, indicating that MKIG detected and corrected smaller deviations. The incidence of grade ≥2 urinary toxicity was lower in the MKIG than the IMR cohort: 10.7% versus 19.8% (P = .047). On multivariate analysis of late urinary toxicity, only high (>7) preradiation therapy international prostate symptom score (P < .043) and the use of MKIG were selected (P < .029)., Conclusions: Automated and quantitative MKIG introduced minimal workflow impact and was superior to IMR in localizing the prostate during SBRT, which correlated with a clinically significant reduction in late urinary toxicity. Further clinical testing using randomized trials will be required to validate the impact on outcomes., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Cancer transcriptional patterns reflect both unique features and shared hallmarks across diverse cancer types, but whether differences in these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that these shared transcriptomic signatures reflect repurposed versions of functional tasks performed by normal tissues. Starting with normal tissue transcriptomic profiles, we use non-negative matrix factorization to derive six distinct transcriptomic phenotypes, called archetypes, which combine to describe both normal tissue patterns and variations across a broad spectrum of malignancies. We show that differential enrichment of these signatures correlates with key tumor characteristics, including overall patient survival and drug sensitivity, independent of clinically actionable DNA alterations. Additionally, we show that in HR+/HER2- breast cancers, metastatic tumors adopt transcriptomic signatures consistent with the invaded tissue. Broadly, our findings suggest that cancer often arrogates normal tissue transcriptomic characteristics as a component of both malignant progression and drug response. This quantitative framework provides a strategy for connecting the diversity of cancer phenotypes and could potentially help manage individual patients., (© 2024. The Author(s).)

Although recent advanced sequencing technologies have improved the resolution of genomic and proteomic data to better characterize molecular phenotypes, efficient computational tools to analyze and interpret the large-scale omic data are still needed. To address this, we have developed a network-based bioinformatic tool called Ollivier-Ricci curvature-omics (ORCO). ORCO incorporates gene interaction information with omic data into a biological network, and computes Ollivier-Ricci curvature (ORC) values for individual interactions. ORC, an edge-based measure, indicates network robustness and captures global gene signaling changes in functional cooperation using a consistent information passing measure, thereby helping identify therapeutic targets and regulatory modules in biological systems. This tool can be applicable to any data that can be represented as a network. ORCO is an open-source Python package and publicly available on GitHub at https://github.com/aksimhal/ORC-Omics.

Radiotherapy aims to achieve a high tumor control probability while minimizing damage to normal tissues. Personalizing radiotherapy treatments for individual patients, therefore, depends on integrating physical treatment planning with predictive models of tumor control and normal tissue complications. Predictive models could be improved using a wide range of rich data sources, including tumor and normal tissue genomics, radiomics, and dosiomics. Deep learning will drive improvements in classifying normal tissue tolerance, predicting intra-treatment tumor changes, tracking accumulated dose distributions, and quantifying the tumor response to radiotherapy based on imaging. Mechanistic patient-specific computer simulations ('digital twins') could also be used to guide adaptive radiotherapy. Overall, we are entering an era where improved modeling methods will allow the use of newly available data sources to better guide radiotherapy treatments., (Copyright © 2024. Published by Elsevier Inc.)

Current prognostic scores in multiple myeloma (MM) currently rely on disease burden and a limited set of genomic alterations. Some studies have suggested gene expression panels may predict clinical outcomes, but none are presently utilized in clinical practice. We therefore analyzed the MMRF CoMMpass dataset (N=659) and identified a high-risk group (top tertile) and a low-risk group ( bottom tertile) based on WEE1 expression sorted in descending order. The tyrosine kinase WEE1 is a critical cell cycle regulator during the S-phase and G2M-checkpoint. Abnormal WEE1 expression has been implicated in multiple cancers including breast, ovarian, and gastric cancers, but has not until this time been implicated in MM. PFS was significantly different (p <1e-9) between the groups, which was validated in two independent microarray gene expression profiling (GEP) datasets from the Total Therapy 2 (N=341) and 3 (N=214) trials. Our results show WEE1 expression is prognostic independent of known biomarkers, differentiates outcomes associated with known markers, is upregulated independently of its interacting neighbors, and is associated with dysregulated P53 pathways. This suggests that WEE1 expression levels may have clinical utility in prognosticating outcomes in newly diagnosed MM and may support the application of WEE1 inhibitors to MM preclinical models. Determining the causes of abnormal WEE1 expression may uncover novel therapeutic pathways., Competing Interests: Competing Interests SZU: Research funding: Amgen, BMS/Celgene, GSK, Janssen, Merck, Pharmacyclics, Sanofi, Seattle Genetics, Takeda. Consulting/Advisory Board: Abbvie, Amgen, BMS, Celgene, Genentech, Gilead, GSK, Janssen, Sanofi, Seattle Genetics, SecuraBio, SkylineDX, Takeda, TeneoBio.

Background/aims: Tumor response to radiation is thought to depend on the direct killing of tumor cells. Our laboratory has called this into question. Firstly, we showed that the biology of the host, specifically the endothelial expression of acid sphingomyelinase (ASMase), was critical in determining tumor radiocurability. Secondly, we have shown that the immune system can enhance radiation response by allowing a complete tumor control in hemi-irradiated tumors. In this paper, we focus on the integration of these two findings., Methods: We used Lewis Lung Carcinoma (LLC) cells, injected in the flank of either: (i) ASMase knockout or (ii) WT of matched background (sv129xBl/6) or (iii) C57Bl/6 mice. Radiation therapy (RT) was delivered to 50% or 100% of the LLC tumor volume. Tumor response, immune infiltration (CD8 + T cells), ICAM-1, and STING activation were measured. Radiotherapy was also combined with methyl-cyclodextrin, to inhibit the ASMase-mediated formation of ceramide-enriched lipid rafts., Results: We recapitulated our previous finding, namely that tumor hemi-irradiation was sufficient for tumor control in the LLC/C57Bl/6 model. However, in ASMase KO mice hemi-irradiation was ineffective. Likewise, pharmacological inhibition of ASMase significantly reduced the tumor response to hemi-irradiation. Further, we demonstrated elevated ICAM-1 expression, increased levels of CD8 + T cells, ICAM-1, and STING activation in tumors growing in C57Bl/6 mice, as well as the ASMase WT strain. However, no such changes were seen in tumors growing in ASMase KO mice., Conclusion: ASMase and ceramide generation are necessary to mediate a radiation-induced anti-tumor immune response via STING activation., Competing Interests: The authors have no conflicts of interest to declare., (© Copyright by the Author(s). Published by Cell Physiol Biochem Press.)

Importance: Given high rates of locoregional control after definitive management of head and neck squamous cell carcinoma (HNSCC), better methods are needed to project distant metastasis (DM) risk. Tumor hypoxia on 18F-fluoromisonidazole (FMISO) positron emission tomography (PET) is associated with locoregional failure, but data demonstrating an association with DM are limited., Objective: To determine whether tumor hypoxia on FMISO PET is associated with DM risk after chemoradiotherapy (CRT) for HNSCC., Design, Setting, and Participants: This cohort study assessed patients with HNSCC enrolled in 2 prospective clinical trials at a single academic referral center from 2004 to 2021 in which participants received FMISO PET before and during CRT. Data analysis occurred from May 2023 to May 2024., Exposures: FMISO PET scans before and 1 to 2 weeks after starting CRT were evaluated for tumor hypoxia by nuclear medicine physicians., Main Outcomes and Measures: The primary outcome was DM, defined as biopsy-proven HNSCC outside the primary site and regional lymph nodes. Time to DM was modeled with competing risk regression, with death as a competing risk. Overall survival (OS) was assessed secondarily and modeled with Cox regression., Results: Among 281 patients (median [range] age at CRT, 58.7 [25.5-85.6] years; 251 male [89.3%]) included in this study, 242 (86.1%) had oropharyngeal primary cancer, and 266 (94.7%) had human papillomavirus-positive disease. Of all patients, 217 (77.2%) had T stage 1 or 2, and 231 patients (82.2%) had N stage 2b or less. De-escalated 30 Gy CRT was delivered to 144 patients (51.2%), and the remainder received standard 70 Gy CRT. On FMISO PET examination, 73 patients (26.0%) had hypoxia-negative disease before CRT, 138 patients (49.1%) had hypoxia-positive disease before CRT and then hypoxia-negative disease during CRT, and 70 patients (24.9%) persistently had hypoxia-positive disease before and during CRT. At a median (IQR) 58 (46-91) months of follow-up, 12 DM events and 22 deaths were observed. Persistent intratreatment hypoxia was associated with increased DM risk (hazard ratio, 3.51; 95% CI, 1.05-11.79; P = .04) and worse OS (hazard ratio, 2.66; 95% CI, 1.14-6.19; P = .02). No patients with hypoxia-negative disease before CRT experienced DM., Conclusions and Relevance: In this cohort study using pooled analysis of prospective nonrandomized clinical trials incorporating FMISO PET in the definitive management of HNSCC, persistent intratreatment hypoxia was associated with increased risk of DM and worse OS. Conversely, all patients with hypoxia-negative disease before treatment remained free of DM. These findings suggest that pretreatment and intratreatment FMISO PET results may serve as biomarkers for DM risk and aid in identifying candidates for escalated therapeutic strategies.

Purpose: The genetic intratumoral heterogeneity observed in human osteosarcomas poses challenges for drug development and the study of cell fate, plasticity, and differentiation, which are processes linked to tumor grade, cell metastasis, and survival., Experimental Design: To pinpoint errors in osteosarcoma differentiation, we transcriptionally profiled 31,527 cells from a tissue-engineered model that directs mesenchymal stem cells toward adipogenic and osteoblastic fates. Incorporating preexisting chondrocyte data, we applied trajectory analysis and non-negative matrix factorization to generate the first human mesenchymal differentiation atlas., Results: This "roadmap" served as a reference to delineate the cellular composition of morphologically complex osteosarcoma tumors and quantify each cell's lineage commitment. Projecting a bulk RNA-sequencing osteosarcoma dataset onto this roadmap unveiled a correlation between a stem-like transcriptomic phenotype and poorer survival outcomes., Conclusions: Our study quantifies osteosarcoma differentiation and lineage, a prerequisite to better understanding lineage-specific differentiation bottlenecks that might someday be targeted therapeutically., (©2024 American Association for Cancer Research.)

Spatially fractionated radiotherapy (SFRT) includes historical grid therapy approaches but more recently encompasses the controlled introduction of one or more cold dose regions using intensity modulation delivery techniques. The driving hypothesis behind SFRT is that it may allow for an increased immune response that is otherwise suppressed by radiation effects. With both two- and three-dimensional SFRT approaches, SFRT dose distributions typically include multiple dose cold spots or valleys. Despite its unconventional methods, reported clinical experience shows that SFRT can sometimes induce marked tumor regressions, even in patients with large hypoxic tumors. Preclinical models using extreme dose distributions (i.e., half-sparing) have been shown to nevertheless result in full tumor eradications, a more robust immune response, and systemic anti-tumor immunity. SFRT takes advantage of the complementary immunomodulatory features of low- and high-dose radiotherapy to integrate the delivery of both into a single target. Clinical trials using three-dimensional SFRT (i.e., lattice-like dose distributions) have reported both promising tumor and toxicity results, and ongoing clinical trials are investigating synergy between SFRT and immunotherapies., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background and Purpose: Radiation pneumonitis (RP) is a common side effect of thoracic radiotherapy and often has a long course characterized by acute exacerbations and progression to permanent lung fibrosis. There are no validated biomarkers of prognosis in patients diagnosed with RP., Materials and Methods: We analyzed a time course of serum chemokines, cytokines, and other proteins from patients with grade 2+ RP in a randomized clinical trial of a steroid taper plus nintedanib, a multiple tyrosine kinase inhibitor, versus placebo plus a steroid taper for the treatment of RP. Weighted gene correlation network analysis (WGCNA) and univariable zero inflated Poisson models were used to identify groups of correlated analytes and their associations with clinical outcomes., Results: Thirty enrolled patients had biomarker data available, and 17 patients had enough analytes tested for network analysis. WGNCA identified ten analytes, including transforming growth factor beta-1 (TGF-β1), monocyte chemoattractant protein-1 (MCP-1), and platelet-derived growth factor (PDGF), that in aggregate were correlated with the occurrence of pulmonary exacerbations (p = 0.008), the total number of acute pulmonary exacerbations (p = 0.002), and treatment arm (p = 0.036). By univariable analysis, an increase in rate of change of two components of the RP module were associated with an increased incidence rate of pulmonary exacerbations: interleukin 5 (IL-5, incidence rate ratio (IRR) 1.02, 95% CI 1.01-1.04, p = 0.002), and tumor necrosis factor superfamily 12 (TNFSF12, IRR 1.06, CI 1-1.11, p = 0.036). An increased slope of epidermal growth factor (EGF) was associated with a decreased incidence rate of exacerbations (IRR 0.94, CI 0.89-1, p = 0.036)., Conclusion: We identified a panel of serum biomarkers that showed association with nintedanib treatment and acute pulmonary exacerbations in patients with RP. A confirmatory study will be needed to validate this panel for use as a prognostic tool in patients with RP., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: AR previously consulted for Boehringer Ingelheim. CMR has consulted regarding oncology drug development with AbbVie, Amgen, AstraZeneca, Epizyme, Genentech/Roche, Ipsen, Jazz, Lilly, and Syros, and serves on the scientific advisory boards of Bridge Medicines, Earli, and Harpoon Therapeutics. DRJ has consulted for AstraZeneca and serves on the clinical trial steering committee for Merck. All other authors declare no competing interest., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Purpose: Larger tumors are underrepresented in most prospective trials on stereotactic body radiation therapy (SBRT) for inoperable non-small cell lung cancer (NSCLC). We performed this phase 1 trial to specifically study the maximum tolerated dose (MTD) of SBRT for NSCLC >3 cm., Methods and Materials: A 3 + 3 dose-escalation design (cohort A) with an expansion cohort at the MTD (cohort B) was used. Patients with inoperable NSCLC >3 cm (T2-4) were eligible. Select ipsilateral hilar and single-station mediastinal nodes were permitted. The initial SBRT dose was 40 Gy in 5 fractions, with planned escalation to 50 and 60 Gy in 5 fractions. Adjuvant chemotherapy was mandatory for cohort A and optional for cohort B, but no patients in cohort B received chemotherapy. The primary endpoint was SBRT-related acute grade (G) 4+ or persistent G3 toxicities (Common Terminology Criteria for Adverse Events version 4.03). Secondary endpoints included local failure (LF), distant metastases, disease progression, and overall survival., Results: The median age was 80 years; tumor size was >3 cm and ≤5 cm in 20 (59%) and >5 cm in 14 patients (41%). In cohort A (n = 9), 3 patients treated to 50 Gy experienced G3 radiation pneumonitis (RP), thus defining the MTD. In the larger dose-expansion cohort B (n = 25), no radiation therapy-related G4+ toxicities and no G3 RP occurred; only 2 patients experienced G2 RP. The 2-year cumulative incidence of LF was 20.2%, distant failure was 34.7%, and disease progression was 54.4%. Two-year overall survival was 53%. A biologically effective dose (BED) <100 Gy was associated with higher LF (P = .006); advanced stage and higher neutrophil/lymphocyte ratio were associated with greater disease progression (both P = .004)., Conclusions: Fifty Gy in 5 fractions is the MTD for SBRT to tumors >3 cm. A higher BED is associated with fewer LFs even in larger tumors. Cohort B appears to have had less toxicity, possibly due to the omission of chemotherapy., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background and Purpose: Volume regression during radiotherapy can indicate patient-specific treatment response. We aimed to identify pre-treatment multimodality imaging (MMI) metrics from positron emission tomography (PET), magnetic resonance imaging (MRI), and computed tomography (CT) that predict rapid tumor regression during radiotherapy in human papilloma virus (HPV) associated oropharyngeal carcinoma., Materials and Methods: Pre-treatment FDG PET-CT, diffusion-weighted MRI (DW-MRI), and intra-treatment (at 1, 2, and 3 weeks) MRI were acquired in 72 patients undergoing chemoradiation therapy for HPV+ oropharyngeal carcinoma. Nodal gross tumor volumes were delineated on longitudinal images to measure intra-treatment volume changes. Pre-treatment PET standardized uptake value (SUV), CT Hounsfield Unit (HU), and non-gaussian intravoxel incoherent motion DW-MRI metrics were computed and correlated with volume changes. Intercorrelations between MMI metrics were also assessed using network analysis. Validation was carried out on a separate cohort (N = 64) for FDG PET-CT., Results: Significant correlations with volume loss were observed for baseline FDG SUV mean (Spearman ρ = 0.46, p < 0.001), CT HU mean (ρ = 0.38, p = 0.001), and DW-MRI diffusion coefficient, D mean (ρ = -0.39, p < 0.001). Network analysis revealed 41 intercorrelations between MMI and volume loss metrics, but SUV mean remained a statistically significant predictor of volume loss in multivariate linear regression (p = 0.01). Significant correlations were also observed for SUV mean in the validation cohort in both primary (ρ = 0.30, p = 0.02) and nodal (ρ = 0.31, p = 0.02) tumors., Conclusions: Multiple pre-treatment imaging metrics were correlated with rapid nodal gross tumor volume loss during radiotherapy. FDG-PET SUV in particular exhibited significant correlations with volume regression across the two cohorts and in multivariate analysis., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Nancy Lee receives consulting fees from Shanghai Joanne Medical Ltd, Yingming Consulting, and Varian, has support from a Varian travel grant, and is on the advisory board for Merck, Merck Serono, Merck EMD, Nanobiotix, and Regeneron. Nadeem Riaz receives research support from Invitae, Pfizer, and Repare Therapuetics., (© 2024 The Authors. Published by Elsevier B.V. on behalf of European Society of Radiotherapy & Oncology.)

Purpose: In radiation therapy (RT), if an immobilization device is lost or damaged, the patient may need to be brought back for resimulation, device fabrication, and treatment planning, causing additional imaging radiation exposure, inconvenience, cost, and delay. We describe a simulation-free method for replacing lost or damaged RT immobilization devices., Methods and Materials: Replacement immobilization devices were fabricated using existing simulation scans as design templates by computer numerical control (CNC) milling of molds made from extruded polystyrene (XPS). XPS material attenuation and bolusing properties were evaluated, a standard workflow was established, and 12 patients were treated. Setup reproducibility was analyzed postfacto using Dice similarity coefficient (DSC) and mean distance to agreement (MDA) calculations comparing onboard treatment imaging with computed tomography (CT) simulations., Results: Results showed that XPS foam material had less dosimetric impact (attenuation and bolusing) than materials used for our standard immobilization devices. The average direct cost to produce each replacement mold was $242.17, compared with over $2000 for standard resimulation. Hands-on time to manufacture was 86.3 minutes, whereas molds were delivered in as little as 4 hours and mostly within 24 hours, compared with a week or more required for standard resimulation. Each mold was optically scanned after production and was measured to be within 2-mm tolerance (pointwise displacement) of design input. All patients were successfully treated using the CNC-milled foam mold replacements, and pretreatment imaging verified satisfactory clinical setup reproduction for each case. The external body contours from the setup cone beam CT and the original CT simulation with matching superior-inferior extent were compared by calculating the DSC and MDA. DSC average was 0.966 (SD, 0.011), and MDA average was 2.694 mm (SD, 0.986)., Conclusions: CNC milling of XPS foam is a quicker and more convenient solution than traditional resimulation for replacing lost or damaged RT immobilization devices. Satisfactory patient immobilization, low dosimetric impact compared with standard immobilization devices, and strong correlation of onboard contours with CT simulations are shown. We share our clinical experience, workflow, and manufacturing guide to help other clinicians who may want to adopt this solution., (© 2024 The Authors.)

Radiation pneumonitis (RP) that develops early (i.e., within 3 mo) (RP Early ) after completion of concurrent chemoradiation (cCRT) leads to treatment discontinuation and poorer survival for patients with stage III non-small cell lung cancer. Since no RP Early risk model exists, we explored whether published RP models and pretreatment 18 F-FDG PET/CT-derived features predict RP Early Methods: One hundred sixty patients with stage III non-small cell lung cancer treated with cCRT and consolidative immunotherapy were analyzed for RP Early Three published RP models that included the mean lung dose (MLD) and patient characteristics were examined. Pretreatment 18 F-FDG PET/CT normal-lung SUV featured included the following: 10th percentile of SUV (SUV P10 ), 90th percentile of SUV (SUV P90 ), SUV max , SUV mean , minimum SUV, and SD. Associations between models/features and RP Early were assessed using area under the receiver-operating characteristic curve (AUC), P values, and the Hosmer-Lemeshow test (pHL). The cohort was randomly split, with similar RP Early rates, into a 70%/30% derivation/internal validation subset. Results: Twenty (13%) patients developed RP Early Predictors for RP Early were MLD alone (AUC, 0.72; P = 0.02; pHL, 0.87), SUV P10 , SUV P90, and SUV mean (AUC, 0.70-0.74; P = 0.003-0.006; pHL, 0.67-0.70). The combined MLD and SUV P90 model generalized in the validation subset and was deemed the final RP Early model (RP Early risk = 1/[1+e (- x ) ]; x = -6.08 + [0.17 × MLD] + [1.63 × SUV P90 ]). The final model refitted in the 160 patients indicated improvement over the published MLD-alone model (AUC, 0.77 vs. 0.72; P = 0.0001 vs. 0.02; pHL, 0.65 vs. 0.87). Conclusion: Patients at risk for RP Early can be detected with high certainty by combining the normal lung's MLD and pretreatment 18 F-FDG PET/CT SUV P90 This refined model can be used to identify patients at an elevated risk for premature immunotherapy discontinuation due to RP Early and could allow for interventions to improve treatment outcomes., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Melanoma response to immune-modulating therapy remains incompletely characterized at the molecular level. In this study, we assess melanoma immunotherapy response using a multi-scale network approach to identify gene modules with coordinated gene expression in response to treatment. Using gene expression data of melanoma before and after treatment with nivolumab, we modeled gene expression changes in a correlation network and measured a key network geometric property, dynamic Ollivier-Ricci curvature, to distinguish critical edges within the network and reveal multi-scale treatment-response gene communities. Analysis identified six distinct gene modules corresponding to sets of genes interacting in response to immunotherapy. One module alone, overlapping with the nuclear factor kappa-B pathway (NFkB), was associated with improved patient survival and a positive clinical response to immunotherapy. This analysis demonstrates the usefulness of dynamic Ollivier-Ricci curvature as a general method for identifying information-sharing gene modules in cancer., (© 2024. The Author(s).)

Cancer transcriptional patterns exhibit both shared and unique features across diverse cancer types, but whether these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that cancer transcriptional diversity mirrors patterns in normal tissues optimized for distinct functional tasks. Starting with normal tissue transcriptomic profiles, we use non-negative matrix factorization to derive six distinct transcriptomic phenotypes, called archetypes, which combine to describe both normal tissue patterns and variations across a broad spectrum of malignancies. We show that differential enrichment of these signatures correlates with key tumor characteristics, including overall patient survival and drug sensitivity, independent of clinically actionable DNA alterations. Additionally, we show that in HR+/HER2- breast cancers, metastatic tumors adopt transcriptomic signatures consistent with the invaded tissue. Broadly, our findings suggest that cancer often arrogates normal tissue transcriptomic characteristics as a component of both malignant progression and drug response. This quantitative framework provides a strategy for connecting the diversity of cancer phenotypes and could potentially help manage individual patients., Competing Interests: AUTHORS’ DISCLOSURES The authors declare no competing interests.

Objectives: Auto-segmentation promises greater speed and lower inter-reader variability than manual segmentations in radiation oncology clinical practice. This study aims to implement and evaluate the accuracy of the auto-segmentation algorithm, "Masked Image modeling using the vision Transformers (SMIT)," for neck nodal metastases on longitudinal T 2 -weighted (T 2 w) MR images in oropharyngeal squamous cell carcinoma (OPSCC) patients., Methods: This prospective clinical trial study included 123 human papillomaviruses (HPV-positive [+]) related OSPCC patients who received concurrent chemoradiotherapy. T 2 w MR images were acquired on 3 T at pre-treatment (Tx), week 0, and intra-Tx weeks (1-3). Manual delineations of metastatic neck nodes from 123 OPSCC patients were used for the SMIT auto-segmentation, and total tumor volumes were calculated. Standard statistical analyses compared contour volumes from SMIT vs manual segmentation (Wilcoxon signed-rank test [WSRT]), and Spearman's rank correlation coefficients ( ρ ) were computed. Segmentation accuracy was evaluated on the test data set using the dice similarity coefficient (DSC) metric value. P -values <0.05 were considered significant., Results: No significant difference in manual and SMIT delineated tumor volume at pre-Tx (8.68 ± 7.15 vs 8.38 ± 7.01 cm 3 , P  = 0.26 [WSRT]), and the Bland-Altman method established the limits of agreement as -1.71 to 2.31 cm 3 , with a mean difference of 0.30 cm 3 . SMIT model and manually delineated tumor volume estimates were highly correlated ( ρ = 0.84-0.96, P  < 0.001). The mean DSC metric values were 0.86, 0.85, 0.77, and 0.79 at the pre-Tx and intra-Tx weeks (1-3), respectively., Conclusions: The SMIT algorithm provides sufficient segmentation accuracy for oncological applications in HPV+ OPSCC., Advances in Knowledge: First evaluation of auto-segmentation with SMIT using longitudinal T 2 w MRI in HPV+ OPSCC., Competing Interests: The authors declare that they have no conflict of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology.)

Directionally sensitive radiomic features including the histogram of oriented gradient (HOG) have been shown to provide objective and quantitative measures for predicting disease outcomes in multiple cancers. However, radiomic features are sensitive to imaging variabilities including acquisition differences, imaging artifacts and noise, making them impractical for using in the clinic to inform patient care. We treat the problem of extracting robust local directionality features by mapping via optimal transport a given local image patch to an iso-intense patch of its mean. We decompose the transport map into sub-work costs each transporting in different directions. To test our approach, we evaluated the ability of the proposed approach to quantify tumor heterogeneity from magnetic resonance imaging (MRI) scans of brain glioblastoma multiforme, computed tomography (CT) scans of head and neck squamous cell carcinoma as well as longitudinal CT scans in lung cancer patients treated with immunotherapy. By considering the entropy difference of the extracted local directionality within tumor regions, we found that patients with higher entropy in their images, had significantly worse overall survival for all three datasets, which indicates that tumors that have images exhibiting flows in many directions may be more malignant. This may seem to reflect high tumor histologic grade or disorganization. Furthermore, by comparing the changes in entropy longitudinally using two imaging time points, we found patients with reduction in entropy from baseline CT are associated with longer overall survival (hazard ratio = 1.95, 95% confidence interval of 1.4-2.8, p = 1.65e-5). The proposed method provides a robust, training free approach to quantify the local directionality contained in images.

Background and Purpose: Objective assessment of delivered radiotherapy (RT) to thoracic organs requires fast and accurate deformable dose mapping. The aim of this study was to implement and evaluate an artificial intelligence (AI) deformable image registration (DIR) and organ segmentation-based AI dose mapping (AIDA) applied to the esophagus and the heart., Materials and Methods: AIDA metrics were calculated for 72 locally advanced non-small cell lung cancer patients treated with concurrent chemo-RT to 60 Gy in 2 Gy fractions in an automated pipeline. The pipeline steps were: (i) automated rigid alignment and cropping of planning CT to week 1 and week 2 cone-beam CT (CBCT) field-of-views, (ii) AI segmentation on CBCTs, and (iii) AI-DIR-based dose mapping to compute dose metrics. AIDA dose metrics were compared to the planned dose and manual contour dose mapping (manual DA)., Results: AIDA required ∼2 min/patient. Esophagus and heart segmentations were generated with a mean Dice similarity coefficient (DSC) of 0.80±0.15 and 0.94±0.05, a Hausdorff distance at 95th percentile (HD95) of 3.9±3.4 mm and 14.1±8.3 mm, respectively. AIDA heart dose was significantly lower than the planned heart dose (p = 0.04). Larger dose deviations (>=1Gy) were more frequently observed between AIDA and the planned dose (N = 26) than with manual DA (N = 6)., Conclusions: Rapid estimation of RT dose to thoracic tissues from CBCT is feasible with AIDA. AIDA-derived metrics and segmentations were similar to manual DA, thus motivating the use of AIDA for RT applications., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: This research was partially supported by the NCI R01CA258821-01 as well as the Memorial Sloan Kettering (MSK) Cancer Center Support Grant/Core Grant NCI P30CA008748. This research was also supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI19C1234)., (© 2024 The Author(s).)

Network properties account for the complex relationship between genes, making it easier to identify complex patterns in their interactions. In this work, we leveraged these network properties for dual purposes. First, we clustered pediatric sarcoma tumors using network information flow as a similarity metric, computed by the Wasserstein distance. We demonstrate that this approach yields the best concordance with histological subtypes, validated against three state-of-the-art methods. Second, to identify molecular targets that would be missed by more conventional methods of analysis, we applied a novel unsupervised method to cluster gene interactomes represented as networks in pediatric sarcoma. RNA-Seq data were mapped to protein-level interactomes to construct weighted networks that were then subjected to a non-Euclidean, multi-scale geometric approach centered on a discrete notion of curvature. This provides a measure of the functional association among genes in the context of their connectivity. In confirmation of the validity of this method, hierarchical clustering revealed the characteristic EWSR1-FLI1 fusion in Ewing sarcoma. Furthermore, assessing the effects of in silico edge perturbations and simulated gene knockouts as quantified by changes in curvature, we found non-trivial gene associations not previously identified., (© 2024. The Author(s).)

Purpose: Radiotherapy is traditionally given in equally spaced weekday fractions. We hypothesize that heterogeneous interfraction intervals can increase radiosensitivity via reoxygenation. Through modeling, we investigate whether this minimizes local failures and toxicity for early-stage non-small cell lung cancer (NSCLC)., Methods: Previously, a tumor dose-response model based on resource competition and cell-cycle-dependent radiosensitivity accurately predicted local failure rates for early-stage NSCLC cohorts. Here, the model mathematically determined non-uniform inter-fraction intervals minimizing local failures at similar normal tissue toxicity risk, i.e., iso-BED3 (iso-NTCP) for fractionation schemes 18Gyx3, 12Gyx4, 10Gyx5, 7.5Gyx8, 5Gyx12, 4Gyx15. Next, we used these optimized schedules to reduce toxicity risk (BED3) while maintaining stable local failures (TCP)., Results: Optimal schedules consistently favored a "primer shot" fraction followed by a 2-week break, allowing tumor reoxygenation. Increasing or decreasing the assumed baseline hypoxia extended or shortened this optimal break by up to one week. Fraction sizes of 7.5 Gy and up required a single primer shot, while smaller fractions needed one or two extra fractions for full reoxygenation. The optimized schedules, versus consecutive weekday fractionation, predicted absolute LF reductions of 4.6%-7.4%, except for the already optimal LF rate seen for 18Gyx3. Primer shot schedules could also reduce BED3 at iso-TCP with the biggest improvements for the shortest schedules (94.6Gy reduction for 18Gyx3)., Conclusion: A validated simulation model clearly supports non-standard "primer shot" fractionation, reducing the impact of hypoxia-induced radioresistance. A limitation of this study is that primer-shot fractionation is outside prior clinical experience and therefore will require clinical studies for definitive testing., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Purpose: We assessed the association of cardiac radiation dose with cardiac events and survival post-chemoradiation therapy (CRT) in patients with locally advanced non-small cell lung cancer (LA-NSCLC) after adoption of modern radiation therapy (RT) techniques, stricter cardiac dose constraints, and immune checkpoint inhibitor (ICI) consolidation., Methods and Materials: This single-institution, multi-site retrospective study included 335 patients with LA-NSCLC treated with definitive, concurrent CRT between October 2017 and December 2021. All patients were evaluated for ICI consolidation. Planning dose constraints included heart mean dose < 20 Gy (<10 Gy if feasible) and heart volume receiving ≥ 50 Gy (V50Gy) < 25 %. Twenty-one dosimetric parameters for three different cardiac structures (heart, left anterior descending coronary artery [LAD], and left ventricle) were extracted. Primary endpoint was any major adverse cardiac event (MACE) post-CRT, defined as acute coronary syndrome, heart failure, coronary revascularization, or cardiac-related death. Secondary endpoints were: grade ≥ 3 cardiac events (per CTCAE v5.0), overall survival (OS), lung cancer-specific mortality (LCSM), and other-cause mortality (OCM)., Results: Median age was 68 years, 139 (41 %) had baseline coronary heart disease, and 225 (67 %) received ICI consolidation. Proton therapy was used in 117 (35 %) and intensity-modulated RT in 199 (59 %). Median LAD V15Gy was 1.4 % (IQR 0-22) and median heart mean dose was 8.7 Gy (IQR 4.6-14.4). Median follow-up was 3.3 years. Two-year cumulative incidence of MACE was 9.5 % for all patients and 14.3 % for those with baseline coronary heart disease. Two-year cumulative incidence of grade ≥ 3 cardiac events was 20.4 %. No cardiac dosimetric parameter was associated with an increased risk of MACE or grade ≥ 3 cardiac events. On multivariable analysis, cardiac dose (LAD V15Gy and heart mean dose) was associated with worse OS, driven by an association with LCSM but not OCM., Conclusions: With modern RT techniques, stricter cardiac dose constraints, and ICI consolidation, cardiac dose was associated with LCSM but not OCM or cardiac events in patients with LA-NSCLC., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Purpose: Disease progression after definitive stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (NSCLC) occurs in 20-40% of patients. Here, we explored published and novel pre-treatment CT and PET radiomics features to identify patients at risk of progression., Materials/methods: Published CT and PET features were identified and explored along with 15 other CT and PET features in 408 consecutively treated early-stage NSCLC patients having CT and PET < 3 months pre-SBRT (training/set-aside validation subsets: n = 286/122). Features were associated with progression-free survival (PFS) using bootstrapped Cox regression (Bonferroni-corrected univariate predictor: p ≤ 0.002) and only non-strongly correlated predictors were retained (|Rs|<0.70) in forward-stepwise multivariate analysis., Results: Tumor diameter and SUV max were the two most frequently reported features associated with progression/survival (in 6/20 and 10/20 identified studies). These two features and 12 of the 15 additional features (CT: 6; PET: 6) were candidate PFS predictors. A re-fitted model including diameter and SUV max presented with the best performance (c-index: 0.78; log-rank p-value < 0.0001). A model built with the two best additional features (CT spiculation1 and SUV entropy ) had a c-index of 0.75 (log-rank p-value < 0.0001)., Conclusions: A re-fitted pre-treatment model using the two most frequently published features - tumor diameter and SUVmax - successfully stratified early-stage NSCLC patients by PFS after receiving SBRT., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships, which may be considered as potential competing interests: All authors: NIH/NCI Cancer Center Support Grant P30 CA008748. Additional support: J. Deasy: NIH/NCI R01 CA198121, Support from PAIGE AI outside the submitted work., (Copyright © 2023 Elsevier B.V. All rights reserved.)