Lifetimes of the $2_1^{+}$ states in ${}^{66,68}$Ge were measured using the recoil distance Doppler shift method. Excited states in ${}^{66}$Ge and ${}^{68}$Ge were populated using the ${}^{58}$Ni(${}^{10}$B, $p$2$n$) and ${}^{58}$Ni(${}^{12}$C, 2$p$) reactions, respectively. Lifetimes were extracted from coincidence data using the differential decay curve method. The resulting $B$($E$2; $2_1^{+}\to 0_1^{+}$) transition strengths are compared with large-scale shell-model calculations.

Objective. This study aims to address the limitations of traditional methods for calculating linear energy transfer (LET), a critical component in assessing relative biological effectiveness (RBE). Currently, Monte Carlo (MC) simulation, the gold-standard for accuracy, is resource-intensive and slow for dose optimization, while the speedier analytical approximation has compromised accuracy. Our objective was to prototype a deep-learning-based model for calculating dose-averaged LET (LET_d) using patient anatomy and dose-to-water (D_W) data, facilitating real-time biological dose evaluation and LET optimization within proton treatment planning systems. Approach. 275 4-field prostate proton Stereotactic Body Radiotherapy plans were analyzed, rendering a total of 1100 fields. Those were randomly split into 880, 110, and 110 fields for training, validation, and testing. A 3D Cascaded UNet model, along with data processing and inference pipelines, was developed to generate patient-specific LET_d distributions from CT images and D_W. The accuracy of the LET_d of the test dataset was evaluated against MC-generated ground truth through voxel-based mean absolute error (MAE) and gamma analysis. Main results. The proposed model accurately inferred LET_d distributions for each proton field in the test dataset. A single-field LET_d calculation took around 100 ms with trained models running on a NVidia A100 GPU. The selected model yielded an average MAE of 0.94 ± 0.14 MeV cm⁻¹ and a gamma passing rate of 97.4% ± 1.3% when applied to the test dataset, with the largest discrepancy at the edge of fields where the dose gradient was the largest and counting statistics was the lowest. Significance. This study demonstrates that deep-learning-based models can efficiently calculate LET_d with high accuracy as a fast-forward approach. The model shows great potential to be utilized for optimizing the RBE of proton treatment plans. Future efforts will focus on enhancing the model’s performance and evaluating its adaptability to different clinical scenarios.

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.

The breakpoint for a 360° radiotherapy gantry is typically positioned at 180°. This arbitrary setting has not been systematically evaluated for efficiency and may cause redundant gantry rotation and extended setup times. Our study aimed to identify an optimal gantry breakpoint angle for a full-gantry proton therapy system, with the goal of minimizing gantry movement. We analyzed 70 months of clinically delivered proton therapy plans (9152 plans, 131 883 fractions), categorizing them by treatment site and mapping the fields from a partial-gantry to full-gantry orientation. For each delivered fraction, we computed the minimum total gantry rotation angle as a function of gantry breakpoint position, which was varied between 0° and 360° in 1° steps. This analysis was performed separately within the entire plan cohort and individual treatment sites, both with and without the capability of over-rotating 10° past the breakpoint from either direction (20° overlap). The optimal gantry breakpoint was identified as one which resulted in a low average gantry rotation per fraction. Considering mechanical constraints, 130° was identified as a reasonable balance between increased gantry-rotation efficiency and practical treatment considerations. With a 20° overlap, this selection reduced the average gantry rotation by 41.4° per fraction when compared to the standard 180° breakpoint. Disease site subgroups showed the following reduction in average gantry rotation: gastrointestinal 192.2°, thoracic 56.3°, pediatric 44.9°, genitourinary 19.9°, central nervous system 10.7°, breast 2.8°, and head and neck 0.1°. For a full-gantry system, a breakpoint of 130° generally outperforms the conventional 180° breakpoint. This reduction is particularly impactful for gastrointestinal, pediatric, and thoracic sites, which constitute a significant proportion of cases at our center. The adjusted breakpoint could potentially streamline patient delivery, alleviate mechanical wear, and enhance treatment precision by reducing the likelihood of patient movement during delivery.

Background/objectives: ¹⁸F-DOPA is an amino acid radiotracer with high uptake in glioblastoma and low uptake in normal brain. Patients underwent pre-radiation and post-radiation ¹⁸F-DOPA PET scans on a prospective clinical trial. This analysis investigates quantitative image features correlated with prognosis and treatment response to identify patients who benefit the most from dose-escalated therapy.Methods: Quantitative image features from ¹⁸F-DOPA PET scans of 58 glioblastoma patients were extracted from the high uptake region (TBR>2.0) in both pre-RT and early post-RT follow-up PET images, which were then refined using Pearson pair correlation. To explore the possibility to identify patients who benefit the most from dose-escalated therapy, pre-irradiation features were identified with univariate Cox regression analysis. Classifications with simple threshold or with Decision Tree models were carried out to categorize patients into distinct survival groups. Additionally, the features with notable changes before and after RT were identified and the temporal patterns of these changes between the survival groups were compared. Multivariates cox analysis was performed to assess the prognostic value of delta features in survival analysis.Results: The pre-irradiation features demonstrated predictive capability in distinguishing survival groups, yielding an accuracy of 0.78 on the reserved test dataset. We also pinpointed eight quantitative features that exhibited a significant difference before and after radiotherapy in patients with MGMT-unmethylated glioblastoma. The change of the features presented different patterns between the survival groups separated by median overall survival and the inclusion of delta features can enhance the accuracy of survival analysis. Conversely, for patients with methylated MGMT, no feature displayed such significant changes between preRT and early postRT.Conclusions: Our study showcased the potential of employing quantitative features derived from ¹⁸F-DOPA images to refine the stratification of patients with unmethylated MGMT for dose escalated therapy. Moreover, the change of these features can serve as valuable tools for monitoring treatment responses following radiotherapy.