Ashman, Jonathan B.

Abstract We describe a 46-year-old male with Neurofibromatosis type 1 (NF1) and a previously known parenchymal medullary mass identified in 2022, initially managed with surveillance. In late 2023, he developed a progressive brainstem syndrome prompting presentation to our center. MRI showed a T2/FLAIR hyperintense, heterogeneously enhancing ventral medullary mass with leptomeningeal involvement, a non-enhancing right pontine lesion, right optic nerve glioma, and nodular leptomeningeal foci at T4 and L4–L5. He received IMRT to the brainstem prior to tissue diagnosis with good local response. Due to progression at T4, biopsy revealed a hypercellular, mitotically active glioma without necrosis or microvascular proliferation. Molecular profiling demonstrated IDH1/2-wildtype, H3K27M-negative, retained ATRX, intact 1p/19q, and CDKN2A/B homozygous deletion. DNA methylation profiling classified the tumor as high-grade astrocytoma with piloid features (HGAP). Spinal lesions responded to proton therapy, but new metastatic foci emerged in the subependymal lateral ventricles, leptomeninges, and cervical spinal cord. He underwent whole-brain radiation, oral temozolomide, and intrathecal cytarabine/etoposide. In June 2024, cell-free DNA analysis showed a similar molecular profile with newly identified 1p/19q codeletion, diverging from tissue findings. The patient remains alive >3 years from initial presentation. HGAP is a novel molecular class of IDH-wildtype gliomas defined by DNA methylation profiling and MAPK pathway alterations (commonly NF1, BRAF, FGFR1), CDKN2A/B codeletion, and ATRX alterations. These tumors often resemble glioblastoma or diffuse midline gliomas on imaging and are typically infratentorial or spinal, complicating biopsy. Prognostic data are limited, with <50% 5-year overall survival and no clear prognostic value of MGMT promoter methylation. This case illustrates multifocal HGAP in a patient with NF1 with favorable survival. The acquired 1p/19q codeletion raises questions about tumor mosaicism, subclonal evolution, and prognostic implications.

Abstract Zanubrutinib is a second-generation Bruton Tyrosine Kinase (BTK) inhibitor, FDA-approved for Mantle Cell Lymphoma (MCL), designed to be more selective and potentially better tolerated than first-generation agents like Ibrutinib. Progressive multifocal leukoencephalopathy (PML), a rare but severe complication, has not previously been reported with Zanubrutinib. We report a 74-year-old woman with a germline CHEK2 mutation and asymptomatic MCL (CD5+, cyclin D1+, Ki67 10–20%) diagnosed in 2016, managed with surveillance. In 2023, she was also diagnosed with localized breast cancer treated with lumpectomy and exemestane. Later that year, she developed new headaches. Brain MRI revealed diffuse bilateral pachymeningeal enhancement, suspicious for lymphomatous spread, without parenchymal involvement. By early 2024, follow-up imaging showed stable pachymeningeal enhancement with new scattered punctate right frontal subcortical lesions, coinciding with systemic MCL progression. Spinal imaging revealed epidural and bone marrow involvement; CSF was negative for malignancy. Zanubrutinib was initiated in March 2024. By August 2024, new bilateral frontal and parietal fuzzy hyperintense and faintly enhancing white matter lesions emerged, while prior punctate lesions regressed. Biopsy confirmed PML with JC virus DNA by in situ hybridization and absence of B-cell clonality. Zanubrutinib was discontinued. Follow-up imaging showed regression of lesions without new enhancement, consistent with immune reconstitution. The initial subcortical punctate lesions remained stable. In 2025, she was diagnosed with localized pancreatic adenocarcinoma, precluding cytotoxic therapy initially due to concern for PML reactivation. However, as of May 2025, neuroimaging remains stable, and patient was started on palliative gemcitabine/paclitaxel. This is the first reported case of PML associated with Zanubrutinib. It underscores the importance of vigilance for PML even in patients without overt immunosuppression, especially those with overlapping malignancies and treatment exposures in the correct clinical context.

Abstract Purpose Lattice radiation therapy (LRT) is a form of spatially fractionated radiation therapy that allows increased total dose delivery aiming for improved treatment response without an increase in toxicities, commonly utilized for palliation of bulky tumors. The LRT treatment planning process is complex, while eligible patients often have an urgent need for expedited treatment start. In this study, we aimed to develop a simulation‐free workflow for volumetric modulated arc therapy (VMAT)‐based LRT planning via deep learning‐predicted synthetic CT (sCT) to expedite treatment initiation. Methods Two deep learning models were initially trained using 3D U‐Net architecture to generate sCT from diagnostic CTs (dCT) of the thoracic and abdomen regions using a training dataset of 50 patients. The models were then tested on an independent dataset of 15 patients using image similarity analysis assessing mean absolute error (MAE) and structural similarity index measure (SSIM) as metrics. VMAT‐based LRT plans were generated based on sCT and recalculated on the planning CT (pCT) for dosimetric accuracy comparison. Differences in dose volume histogram (DVH) metrics between pCT and sCT plans were assessed using the Wilcoxon signed‐rank test. Results The final sCT prediction model demonstrated high image similarity to pCT, with a MAE and SSIM of 38.93 ± 14.79 Hounsfield Units (HU) and 0.92 ± 0.05 for the thoracic region, and 73.60 ± 22.90 HU and 0.90 ± 0.03 for the abdominal region, respectively. There were no statistically significant differences between sCT and pCT plans in terms of organ‐at‐risk and target volume DVH parameters, including maximum dose (Dmax), mean dose (Dmean), dose delivered to 90% (D90%) and 50% (D50%) of target volume, except for minimum dose (Dmin) and (D10%). Conclusion With demonstrated high image similarity and adequate dose agreement between sCT and pCT, our study is a proof‐of‐concept for using deep learning predicted sCT for a simulation‐free treatment planning workflow for VMAT‐based LRT.

e14551 Background: Extracellular vesicles (EVs) are microscopic particles released by all cell types with a distinct fingerprint reflecting the cell of origin. In patients with prostate cancer, PSMA+EVs have been associated with clinical outcomes. In this study, we investigated tumor-derived EVs (tdEVs) using the tumor-specific CD326 marker as a novel biomarker for pancreas ductal adenocarcinoma (PDAC). Methods: The study included patients with localized PDAC treated on a prospective phase 2 clinical trial evaluating a 15-fraction chemoradiation (CRT) regimen. Blood samples were obtained after induction chemotherapy (C) (prior to CRT) and in follow-up. 20 healthy controls were included. High-resolution flow cytometry enumerated levels of fluorescent antibody labelled tumor-specific CD326 (EpCAM)-positive EVs. All samples were de-identified and randomly coded. The technologist responsible for EV analysis was blinded to patient information and clinical data. Each sample was run in triplicate. Flow cytometry data was analyzed with custom-built software to automatically determine and apply gates and generate reports with scatter plots and EV concentrations for each combination of markers and data summaries. Results: tdEV data were analyzed from 70 patients with pre-CRT samples. Patients had mean age 66 years (9.0 standard deviation), with 60% ECOG 0, 43% borderline resectable and 48% locally advanced. Patients had a median 8 cycles of C prior to CRT, and 70% proceeded to surgery after CRT. 2-year overall survival (OS) was 54%. Patient samples showed elevated total CD326 EVs compared to the healthy controls (p = 0.0005). Furthermore, patient CD326 EVs revealed two distinct subpopulations: small (120-1,000nm) and large (1,000-3,000nm) size CD326 EVs. No large CD326 subpopulation was found in healthy donor samples. Higher pre-CRT levels of total CD326 and large CD326 EVs were associated with decreased progression-free survival (PFS) (hazard ratio (HR) = 2.38, p = 0.013 and HR = 2.017, p = 0.021) on multivariable models with surgical resection. Resection was associated with increased PFS (p < 0.0001), and the interaction terms did not meet significance (p = 0.083 and p = 0.21). No differences in pre-CRT total or large CD326 EVs were identified between patients with or without surgical resection (p = 0.72 and p = 0.83). CA19-9 levels were not predictive of clinical outcomes in this trial and showed no association with EV levels. Conclusions: We present an association between clinical outcomes and tdEVs in PDAC, highlighting their potential as a novel, independent biomarker. If validated, tdEVs may offer an opportunity to personalize care and improve risk stratification.

Objectives: Identifying patients’ advantageous radiotherapy modalities prior to CT simulation is challenging. This study aimed to develop a workflow using deep learning (DL)-predicted synthetic CT (sCT) for treatment modality comparison based solely on a diagnostic CT (dCT). Methods: A DL network, U-Net, was trained utilizing 46 thoracic cases from a public database to generate sCT images predicting planning CT (pCT) scans based on the latest dCT, and tested on 15 institutional patients. The sCT accuracy was evaluated against the corresponding pCT and a commercial algorithm deformed CT (MdCT) based on Mean Absolute Error (MAE) and Universal Quality Index (UQI). To determine advantageous treatment modality, clinical dose-volume histogram (DVH) metrics and Normal Tissue Complication Probability (NTCP) differences between proton and photon treatment plans were analyzed on the sCTs via concordance correlation coefficient (CCC). Results: The AI-generated sCTs closely resembled those of the commercial deformation algorithm in the tested cases. The differences in MAE and UQI values between the sCT-vs-pCT and MdCT-vs-pCT were 19.38 HU and 0.06, respectively. The mean absolute NTCP deviation between sCT and pCT was 1.54%, 0.21%, and 2.36% for esophagus perforation, lung pneumonitis, and heart pericarditis, respectively. The CCC between sCT and pCT was 0.90 for DVH metrics and 0.97 for NTCP, indicating moderate agreement for DVH metrics and substantial agreement. Conclusions: Radiation oncologists can potentially utilize this personalized sCT based approach as a clinical support tool to rapidly compare the treatment modality benefit during patient consultation and facilitate in-depth discussion on potential toxicities at a patient-specific level.

Abstract Background Minimal clinically important differences (MCIDs) quantify the clinical relevance of quality of life results at the individual patient and group level. The aim of this study was to estimate the MCID for the Brief Fatigue Inventory (BFI) and the Worst and Usual Fatigue items in patients with brain or CNS cancer undergoing curative radiotherapy. Methods Data from a multi-site prospective registry was used. The MCID was calculated using distribution-based and anchor-based approaches. For the anchor-based approach, the fatigue item from the PROMIS-10 served as the anchor to determine if a patient improved, deteriorated, or had no change from baseline to end of treatment (EOT). We compared the unadjusted means on the BFI for the 3 groups to calculate the MCID. For the distribution-based approaches, we calculated the MCID as 0.5 SD of the scores and as 1.96 times the standard error of measurement. Results Three-hundred and fifty nine patients with brain or CNS tumors undergoing curative radiotherapy filled out the 9-item BFI at baseline and EOT. The MCID for the BFI was 1.33 (ranging from 0.99 to 1.70 across the approaches), 1.51 (ranging from 1.16 to 2.02) and 1.76 (ranging from 1.38 to 2.14) for the usual and worst fatigue items, respectively. Conclusions This study provides the MCID ranges for the BFI and Worst and Usual fatigue items, which will allow clinically meaningful conclusions to be drawn from BFI scores. These results can be used to select optimal treatments for patients with brain or CNS cancer or to interpret BFI scores from clinical trials.

Abstract PURPOSE 18F-DOPA is sensitive/specific for identifying areas of biologically aggressive disease for glioblastoma. We report the impact of 18F-DOPA on radiation treatment planning from a phase II study. METHODS Gross target volume (GTV) was defined by the boolean of high-risk PET (tumor/normal brain SUV &gt; 2.0) and MRI T1 contrast enhancement (MRI-CE) including surgical cavity. The clinical target volume (CTV) included the GTV with 1cm margin. Using proton beam therapy, GTV ≤ 65 cc was treated with 25 (CTV), 30 (MRI-CE), and 35 (PET) GyE over 5 fractions with a simultaneous integrated boost. GTV &gt; 65 cc received 30 (CTV), 35 (MRI-CE), and 40 (PET) GyE over 10 fractions. Radiation field design was compared to traditional 1.5cm CTV margin on MRI-CE. RESULTS Between May 2019 and October 2021, 39 patients were treated. The median MRI-CE tumor diameter was 5.2cm. The median PET volume was 7.9cc (range 0–64.9cc). The median MRI-CE volume was 35.1cc (range 8.6–114cc). High-risk PET was contained within: MRI-CE 18/39 (46%), MRI-CE + T2W FLAIR 13/39 (33%), MRI-CE + T2W FLAIR + outside GTV 4/39 (10%), outside GTV 2/39 (5%) and no uptake 2/39 (5%). Utilizing this planning technique, there was a 26% volume reduction in CTV to traditional 1.5 cm margin on MRI-CE. PET imaging in treatment planning allowed inclusion of high-risk disease that would have been excluded on traditional CTV in 23% of cases (range 1.6-33.8cc). PET was more predictive [HR 1.03 (95% CI 1.01, 1.05) p = 0.002] than MRI [HR 1.01 (95% CI 1.00, 1.03) p = 0.02] for death with each cc increasing risk of death by 3.1%. CONCLUSIONS The incorporation of 18F-DOPA in treatment planning yields a more targeted approach with reductions in both normal brain treated and risk of geographic miss compared to traditional CTV margins.

2002 Background: Elderly patients with glioblastoma have a poor prognosis with phase III trials reporting median overall survival (OS) and global time to deterioration of 6–9 and 1.2 months respectively. 18F-DOPA (3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine) positron emission tomography (PET) is sensitive and specific for identifying regions of high density and biologically aggressive glioblastoma. Proton beam therapy (PBT) can spare more healthy tissue that may improve quality of life for this patient population. With improved targeting of disease along with dosimetric advantages of PBT, this phase II trial tested whether hypofractionated PBT can offer both improved survival and quality of life. Methods: Patients with newly diagnosed glioblastoma aged ≥65 years without contraindications to 18F-DOPA were eligible. Target volumes were defined by PET (tumor/normal brain SUV ≥2.0) and MRI areas of contrast enhancement including surgical cavity. Patients received dose escalated hypofractionated PBT of 35-40 GyE to PET/MRI over 5-10 treatments with 10 mm margin. Patients received concurrent/adjuvant temozolomide. With an alpha of 0.1, 18 of 39 patients would be required to be alive at 12 months for the study to be considered a success. Kaplan-Meier/Cox regression analysis was performed for progression free survival (PFS) and OS. Toxicities were evaluated with CTCAE v4.0. Patients completed EORTC BN20, QLQ-C30, and MMSE questionnaires. Time to deterioration, defined as a 10-point decrease in the score of the function domain or 10-point increase in score of symptom domain, was evaluated. Results: Between 5/2019-6/2021, 43 patients were enrolled, with 4 patients withdrawing due to progression/insurance denial prior to beginning protocol therapy. The median age was 70.2 yrs. and median follow-up was 12.5 months. MGMT methylation was present in 13 (33%). Tumors were multifocal in 10 (26%) and unifocal in 29 (74%). The primary endpoint was met with median PFS/OS was 6.5/12.9 months for all patients and for MGMT methylated patients 11.9/29.8 months respectively. Grade 3 baseline adjusted treatment related adverse events included 1 (3%) confusion, 2 (5%) fatigue, and 1 (3%) seizure. There were no grade 4/5 events. Multivariate analysis revealed MGMT unmethylated, ECOG 2 and biopsy only patients had worse survival. PET volume was more predictive than MRI volume for OS with PET volume>26 cc having a hazard ratio of 3.7. The median time to global deterioration per QLC-C30 was 6.3 months. Conclusions: This phase II trial incorporating 18F-DOPA PET-guided dose escalated hypofractionated proton beam treatment for elderly patients with glioblastoma met its primary endpoint for improved OS and patient reported quality of life compared to historical controls. Further investigation is warranted. Clinical trial information: NCT03778294 .