Citations

Abstract Objective . Phase gating is a critical technique to mitigate tumor motion during radiotherapy, particularly in spot-scanned particle therapy where internal motion can interfere with dynamic spot scanning patterns and, simultaneously, introducing substantial range uncertainties. However, the current commercial state of the art in real-time phase prediction is challenged by patient-specific breathing variability as well as detection and delivery system latencies. This leads to suboptimal efficiency, mis-timed radiation delivery and requires frequent manual intervention. This study aims to improve phase prediction accuracy using deep learning (DL)-based time series forecasting to enable more accurate dose delivery. Approach . Retrospective breathing waveforms from 69 proton therapy patients, sampled at 30 Hz, were labeled with inspiratory peaks and assigned subjective regularity scores (four levels). DL models with various architectures were trained using waveform amplitude to predict current or future breathing phases. Model performance was evaluated using mean squared error, phase binning accuracy, and timing deviation for radiation on/off events. Bayesian optimization was used for hyperparameter tuning. Results were compared between models and against a commercial algorithm currently in clinical use. Main Results . The curated waveform dataset included 165 242 s for training, 24 057 s for validation, and 30 322 s for testing, with an additional 40 604 s from separate patients for extended validation. The long short-term memory and temporal fusion transformer models significantly outperformed the commercial algorithm, improving phase prediction accuracy by nearly 20% and reducing timing deviations across all regularity levels. Significance . DL-based time series forecasting may substantially improve breathing phase prediction accuracy over current clinically available methods, offering a more precise and reliable approach to phase-gated radiation delivery.

Abstract Context Prior reports of plasma estradiol concentrations and their reference ranges in postmenopausal women identified marked variation, in part due to assay method and body mass index (BMI). Objective To develop an international reference range for plasma estradiol in postmenopausal women, support accurate assessment of concentrations during systemic and local (eg, vaginal) estradiol therapy, and improve risk prediction for diseases, including osteoporosis, breast, and endometrial cancers. Methodology Estradiol concentrations were measured using primarily liquid chromatography-tandem mass spectrometry (LC-MS/MS) assays in 5 international cohorts of community-dwelling, postmenopausal women of primarily European ancestry aged 38-100 years who were not using exogenous estrogens (n = 7206). Measurements were harmonized to the Centers for Disease Control and Prevention (CDC) reference measurement procedure (RMP) by re-assaying a subset of samples for each cohort and generating recalibration equations. Reference intervals were determined for all women, non-obese women, and specific BMI categories. Results Estradiol concentrations from separate LC-MS/MS assays correlated closely with the CDC RMP, and harmonization minimized inter-assay variability. Estradiol concentrations correlated with BMI but not with chronological age. The reference range (2.5th-97.5th percentile) was 1.1-18.2 pg/mL (4.0-66.8 pmol/L) with median of 4.9 pg/mL (18.0 pmol/L) for all postmenopausal women and 1.1-12.5 pg/mL (4.0-45.9 pmol/L) with median of 4.1 pg/mL (15.1 pmol/L) for non-obese women (BMI < 30). Reference ranges increased progressively with higher BMI categories. Conclusion Cross-calibration of estradiol measurements with the CDC RP enabled the development of harmonized plasma estradiol reference ranges for postmenopausal women who were not using exogenous estrogens, suitable for future studies to establish clinical utility. Increasing obesity in postmenopausal women was associated with increasing estradiol concentration and BMI category-specific reference ranges.

Background: Post-treatment prognosis and monitoring are critical for determining the timing of salvage treatment in glioblastoma patients but has been challenging due to difficulties differentiating progression from treatment effects in conventional images. This exploratory study aimed to establish the correlation of radiomics image features from time series of amino acid tracer 18F-DOPA PET images, with outcomes, using machine learning and dimension reduction analysis. Methods: 18F-DOPA PET images were collected for a patient cohort with wild-type IDH and unmethylated MGMT who underwent dose-escalated radiation therapy. Quantitative features were derived from the high uptake region (T/N > 2.0) in pre- and post-radiation therapy follow-up 18F-DOPA PET images. A customized workflow was utilized for pre-selecting predictive features, followed by manifold learning. Machine learning algorithms were employed to establish associations between imaging features and remaining survival (RS), defined as the time between a follow-up scan and date of death. Results: The ML models exhibited 81–83% ROC_AUC in predicting RS evaluated on an independent test dataset. A RS map is proposed for monitoring tumor alterations through serial 18F-DOPA PET scans, demonstrating superior sensitivity and better correlation with survival compared to the RANO criteria. Conclusions: Our study demonstrates that ML models utilizing FU 18F-DOPA PET images have the potential to effectively predict future survival outcomes in patients with glioblastoma treated with dose-escalated radiation therapy. The capability to assess changes in tumor over time through imaging can potentially assist in patient stratification and the selection of salvage treatments, while also aiding in distinguishing treatment effects from genuine tumor progression.

Abstract Background Conventional proton dose calculation methods are either time‐ and resource‐intensive, like Monte Carlo (MC) simulations, or they sacrifice accuracy, as seen with analytical methods. This trade‐off between computational efficiency and accuracy highlights the need for improved dose calculation approaches in clinical settings. Purpose This study aims to develop a deep‐learning‐based model that calculates dose‐to‐water ( D W ) and dose‐to‐medium ( D M ) using patient anatomy and proton spot map (PSM), achieving approaching MC‐level accuracy with significantly reduced computation time. Additionally, the study seeks to generalize the model to different treatment sites using transfer learning. Methods A SwinUNetr model was developed using 259 four‐field prostate proton stereotactic body radiation therapy (SBRT) plans to calculate patient‐specific D W and D M distributions from CT and projected PSM (PPSM). The PPSM was created by projecting PSM into the CT scans using spot coordinates, stopping power ratio, beam divergence, and water‐equivalent thickness. Fine‐tuning was then performed for the central nervous system (CNS) site using 84 CNS plans. The model's accuracy was evaluated against MC simulation benchmarks using mean absolute error (MAE), gamma analysis (2% local dose difference, 2‐mm distance‐to‐agreement, 10% low dose threshold), and relevant clinical indices on the test dataset. Results The trained model achieved a single‐field dose calculation time of 0.07 s on a Nvidia‐A100 GPU, over 100 times faster than MC simulators. For the prostate site, the best‐performing model showed an average MAE of 0.26 ± 0.17 Gy and a gamma index of 92.2% ± 3.1% in dose regions above 10% of the maximum dose for D W calculations, and an MAE of 0.30 ± 0.19 Gy with a gamma index of 89.7% ± 3.9% for D M calculations. After transfer learning for CNS plans, the model achieved an MAE of 0.49 ± 0.24 Gy and a gamma index of 90.1% ± 2.7% for D W computations, and an MAE of 0.47 ± 0.25 Gy with a gamma index of 85.4% ± 7.1% for D M computations. Conclusions The SwinUNetr model provides an efficient and accurate method for computing dose distributions in proton therapy. It also opens the possibility of reverse‐engineering PSM from D W , potentially speeding up treatment planning while maintaining accuracy.

Objectives Black/African American (B/AA) pancreatic ductal adenocarcinoma (PDAC) patients have worse clinical outcomes than White patients and are underrepresented in genomic databases. We aimed to expand our understanding of the PDAC somatic landscape from a diverse cohort. Materials and Methods Formalin-fixed paraffin-embedded specimens from 24 surgically resected PDAC cases were collected, with self-reported race/ethnicity. Whole exome sequencing was performed on malignant and benign tissue. Bioinformatics analysis included deduction of genetic ancestry and somatic mutational analysis, with comparisons to public datasets. Results Out of 24 cases, 17 identified as B/AA race; genetic ancestry analysis confirmed proportions of Sub-Saharan African ancestry greater than 47%. The most commonly mutated genes included KRAS , TP53 , SMAD4 , and CDKN2A . Comparison of mutations in our cohort versus publicly available, predominantly White datasets showed higher mutation frequencies of ATM, RREB1, BRCA1/2, KDM6A, ARID1A, BRAF, and MYC ( P < 0.04). When cohorts were combined and analyzed by race, no mutation frequencies differences were observed, including KRAS . Conclusions Genomic analysis of PDAC tumors from B/AA and White patients demonstrate similarities in mutation frequencies. Larger studies are needed to further understand molecular characterizations across continental subpopulations. This study provides further rationale for equitable representation of diverse patients in genomic databases and clinical trials.

Abstract Background This study aimed to determine whether proton craniospinal irradiation (CSI) decreased the dose to normal tissue and resulted in less toxicity than photon CSI for adult patients. Methods This single-institution retrospective analyzed differences in radiation doses, acute toxicity, and cost between proton and CSI for adult medulloblastoma patients. Results Of 39 total patients, 20 were treated with photon CSI prior to 2015, and 19 were treated with proton CSI thereafter. Median age was 28 years (range 18–66). The molecular subtype was most commonly sonic hedgehog (68%). Patients most commonly received 36 Gy CSI in 20 fractions with a boost to 54–55.8 Gy (92%). Proton CSI delivered significantly lower mean doses to cochleae, lacrimal glands, lens, parotid glands, pharyngeal constrictors, esophagus, lungs, liver, and skin (all P &lt; .001). Patients receiving proton CSI had significantly lower rates of acute dysphagia of any grade (5% versus 35%, P = .044) and decreased median weight loss during radiation (+1.0 versus –2.8 kg, P = .011). Weight loss was associated with acute hospitalization (P = .009). Median follow-up was 2.9 and 12.9 years for proton and photon patients, respectively, limiting late toxicity and outcome comparisons. At the last follow-up, 5 photon patients had died (2 of progressive disease, 3 without recurrence ages 41–63) and 21% had experienced major cardiovascular events. At 10 years, 89% were alive and 82% were recurrence free. Conclusions This study demonstrates dosimetric improvements with proton CSI, potentially leading to decreased acute toxicity including dysphagia and weight loss during treatment.

In this study, we investigated whether radiomics features from pre-treatment positron emission tomography (PET) images could be used to predict disease progression in patients with HPV-positive oropharyngeal cancer treated with definitive proton or x-ray radiotherapy. Machine learning models were built using a dataset from Mayo Clinic, Rochester, Minnesota (n = 72) and tested on a dataset from Mayo Clinic, Phoenix, Arizona (n = 22). A total of 71 clinical and radiomics features were considered. The Mann–Whitney U test was used to identify the top 2 clinical and top 20 radiomics features that were significantly different between progression and progression-free patients. Two dimensionality reduction methods were used to define two feature sets (manually filtered or machine-driven). A forward feature selection scheme was conducted on each feature set to build models of increased complexity (number of input features from 1 to 6) and evaluate model robustness and overfitting. The machine-driven features had superior performance and were less prone to overfitting compared to the manually filtered features. The four-variable Gaussian Naïve Bayes model using the ‘Radiation Type’ clinical feature and three machine-driven features achieved a training accuracy of 79% and testing accuracy of 77%. These results demonstrate that radiomics features can provide risk stratification beyond HPV-status to formulate individualized treatment and follow-up strategies.

Abstract BACKGROUND Interpretation of serial magnetic resonance imaging (MRI) for glioblastoma following radiation therapy (RT) is complicated by difficulty differentiating tumor from treatment-related changes, even using updated RANO criteria. The incorporation of novel imaging such as 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (18F-DOPA) PET/CT to post-treatment serial imaging may improve prognostication and better facilitate future treatment decisions. METHODS The secondary analysis of a recent phase II prospective clinical trial of 18F-DOPA PET/CT-directed dose-escalated RT included patients with IDH-wildtype, MGMT-unmethylated glioblastoma who underwent post-treatment serial 18F-DOPA PET/CT surveillance. Quantitative features were extracted from pre-RT and post-RT serial PET/CT images, and robust prognostic features were selected using an in-house workflow. Both an automated machine learning (ML) algorithm and an interpretable ML algorithm were utilized to correlate surveillance PET image features with subsequent survival of greater than 12 months versus less than 12 months from the surveillance timepoint. Changes from pre-RT to post-RT PET/CT (delta model) were also assessed for association with post-RT survival and validated with a separate cohort. RESULTS Thirty-five patients with IDH-wildtype, MGMT-unmethylated glioblastoma who had at least one available (range: 1-14) post-treatment 18F-DOPA PET/CT were included. Twenty-four were used for model training, while 11 were used for validation. Ultimately, a five-feature post-RT model utilizing two shape, two texture, and one first-order radiomic feature was selected. For the delta model, five texture, two shape, and one first order radiomic feature were selected. The models show 90% accuracy in predicting survival &lt; 12 months post-surveillance on the training set, and 68%-73% accuracy (AUC 0.64-0.73) for the validation cohort. Delta features were significantly associated with overall survival (p &lt; 0.05). CONCLUSIONS Post-RT serial 18F-DOPA PET/CT imaging can help distinguish true tumor progression in patients with glioblastoma using a radiomics model. Tumor response evaluated for changes from pre-RT to post-RT 18F-DOPA PET/CT also predicted subsequent overall survival.

AbstractPurposeThe Leksell Gamma Knife Icon unit (IU) was introduced recently as an upgrade to the Perfexion unit (PU) at our Gamma Knife practice. In the current study, we sought mainly to characterize dosimetry and targeting accuracy of the IU treatment deliveries using both invasive frame and frameless treatment workflows.MethodsRelative output factors were measured by delivering single‐shot 4, 8 and 16 mm radiation profiles in the manufacturer's acrylonitrile butadiene styrene spherical phantom in coronal and sagittal planes using EBT3 film. Resultant dosimetry was compared with the manufacturer's dose calculation and derived output factors were compared with the manufacturer's published value. Geometric consistency of stereotactic coordinates based on cone‐beam computed tomography (CBCT) versus the traditional conventional CT‐based method was characterized using a rigid phantom containing nine fiducial indicators over four separate trials. End‐to‐end (E2E) testing using EBT3 film was designed to evaluate both dosimetric and geometric accuracy for hypothetical framed and frameless workflows.ResultsRelative output factors as measured by the manufacturer were independently confirmed using EBT3 film measurements to within 2%. The mean 3D radial discrepancy in stereotactic space between CBCT and CT‐based definition over the sampled locations in our rigid geometry phantom was demonstrated to be between 0.40 mm and 0.56 mm over the set of trials, larger than prior reported values. E2E performed in 2D demonstrates sub‐mm (and typically < 0.5 mm) accuracy for framed and frameless workflows; geometric accuracy of framed treatments using CBCT‐defined stereotactic coordinates was shown to be slightly improved in comparison with those defined using conventional CT. Furthermore, in phantom, frameless workflows exhibited better accuracy than framed workflows for fractionated treatments, despite large magnitudes of introduced interfraction setup error. Accuracy of dosimetric delivery was confirmed in terms of qualitative comparisons of dose profiles and in terms of 2D gamma pass rates based on 1%/1 mm criteria.ConclusionThe IU was commissioned for clinical use of frameless and framed treatment protocols. The present study outlines an extensive E2E methodology for confirmation of dosimetric and geometric treatment accuracy.

Abstract Medulloblastoma is a posterior fossa tumor rarely diagnosed in adults. Treatment includes craniospinal irradiation (CSI). Proton CSI is increasingly utilized to decrease radiation dose to normal tissues, despite the lack of randomized data demonstrating decreased toxicity compared to photon CSI. This single institution retrospective study of 39 adult medulloblastoma patients includes nineteen patients treated with photon CSI prior to 2015, and twenty treated with proton CSI thereafter. Median age was 28 years (range 18-66). Molecular subtype was most commonly sonic hedgehog (68%). The most common fractionation schedule was 36 Gy CSI in 20 fractions (85% of photon and 58% of proton patients) with a boost to 54-55.8 Gy (92%). Proton CSI delivered significantly lower mean doses to the cochleae (median 32 Gy vs. 44 Gy), lacrimal glands (8 vs. 36 Gy), lens (2 vs. 28 Gy), parotid glands (3 vs. 26 Gy), pharyngeal constrictors (6 vs. 15 Gy), esophagus (2 vs. 29 Gy), heart (0 vs. 14 Gy), lungs (1 vs. 7 Gy), liver (0.1 vs. 7 Gy), and skin (38 vs. 51 Gy) (all p&lt; 0.001). Patients receiving proton CSI had significantly lower rates of acute dysphagia of any grade (5% vs. 35%, p= 0.044) and decreased median weight loss during radiation (+1.0 vs. -2.8 kg, p= 0.011). Acute hospitalization was associated with increased weight loss (p= 0.009). Median follow-up was 2.9 and 12.9 years for proton and photon patients, respectively, limiting late toxicity and outcome comparisons. At last follow-up five photon patients had died (two of progressive disease, three without recurrence ages 41-63) and 21% had experienced major cardiovascular events. At 10 years, 89% were alive and 82% were recurrence free. In conclusion, this study demonstrates dosimetric improvements with proton CSI, potentially leading to decreased acute toxicity including dysphagia and weight loss during treatment.