Locality and Singularity for Store-Atomic Memory Models

Robustness is a correctness notion for concurrent programs running under relaxed consistency models. The task is to check that the relaxed behavior coincides (up to traces) with sequential consistency (SC). Although computationally simple on paper (robustness has been shown to be PSPACE-complete for TSO, PGAS, and Power), building a practical robustness checker remains a challenge. The problem is that the various relaxations lead to a dramatic number of computations, only few of which violate robustness. In the present paper, we set out to reduce the search space for robustness checkers. We focus on store-atomic consistency models and establish two completeness results. The first result, called locality, states that a non-robust program always contains a violating computation where only one thread delays commands. The second result, called singularity, is even stronger but restricted to programs without lightweight fences. It states that there is a violating computation where a single store is delayed. As an application of the results, we derive a linear-size source-to-source translation of robustness to SC-reachability. It applies to general programs, regardless of the data domain and potentially with an unbounded number of threads and with unbounded buffers. We have implemented the translation and verified, for the first time, PGAS algorithms in a fully automated fashion. For TSO, our analysis outperforms existing tools

Is Proton Therapy for Synchronous Bilateral Breast Cancer a Medical Necessity?

Synchronous bilateral breast cancer (sBBC) is rare, more common in younger patients, and associated with median overall survival of about 5 years. sBBC presents a challenge for radiotherapy (RT) planning due to the large and complex target volumes. Traditional tangential techniques result in hot spots due to field overlap, low coverage of lymph nodes, and high dose to cardiopulmonary structures. Although intensity-modulated radiation therapy (IMRT) dosimetry has improved, IMRT doses to heart and lungs in sBBC consistently exceed standard dose constraints. We hypothesize intensity-modulated proton therapy (IMPT) may offer dosimetric advantages over IMRT plans that are dramatic enough to position IMPT as the RT standard of care for synchronous bilateral breast cancer. We retrospectively reviewed the radiation plans of all 17 consecutive sBBC patients treated at our institution with IMPT from 2020 to 2023. IMPT prescriptions were for 45-50 Gray (Gy) in 25-28 fractions with 95% of the target volumes covered by 95% of the prescription dose and dose constraints to OARs per published prospective trials. IMRT comparison plans were designed for 11 patients. Treatment was delivered to the bilateral reconstructed chest wall and regional nodes (n = 8), bilateral intact breasts and regional nodes (n = 5) or to the bilateral intact breasts without nodes (n = 4). Of all the proton plans, heart mean dose was 0.7 Gy, left anterior descending artery (LAD) mean dose was 4.7 Gy, lung mean dose was 5.3 Gy, lung V20 was 8.3%, and lung V5 was 28.8%. Among the comparison plans using the same anatomy (Table 1), IMPT significantly decreased the average mean heart dose from 6.5 Gy to 0.67 Gy and LAD dose from 11 Gy to 3 Gy, as compared to IMRT. IMPT also decreased the bilateral lung V5 from 83% to 28% and V20 from 22% to 7%, as compared to IMRT. In the challenging case of sBBC, IMPT allows for conformal treatment of bilateral breast cancer while dramatically sparing the heart and lungs. In contrast, IMRT provides unacceptable plans that can lead to critical lung and heart toxicity- unable to meet standard heart mean, lung mean, or lung V5 constraints. This stark difference in OAR sparing, even in a small sample, argues that IMPT should be incorporated into CMS guidelines as a medical necessity for sBBC

Prognostic Value of Weekly Delta-Radiomics during MR-Linac Radiotherapy of Glioblastoma

PURPOSE/OBJECTIVE(S)MRI after chemoradiotherapy (chemoRT) shows areas of presumed tumor growth in ≤ 50% of glioblastoma (GBM) patients, which can be true progression (TP) - tumor growth with poor treatment response, or pseudoprogression (PP) - edema and tumor necrosis with favorable treatment response. Patients with TP have median overall survival (OS) of only 7 months, while patients with PP have median OS of 36 months. However, on imaging, TP and PP are usually not discernible during treatment, making it difficult to adapt radiation for poor responders. The purpose of this study was to investigate the prognostic value of delta radiomic features from MR-Linac for GBM. MATERIALS/METHODSUsing an IRB-approved prospective cohort of GBM patients undergoing 30 fractions of chemoRT to 60 Gy on a 0.35T MR-Linac, 2 regions of interest (ROI) were contoured on daily T2-weighted treatment set-up scans: 1) tumor/edema (lesion) and 2) post-surgical resection cavity (RC). The lesion ROI were used to calculate texture features: second order radiomics features based on the gray-level co-occurrence matrix (GLCM), gray-level size zone matrix (GLSZM), gray-level run length matrix (GLRLM), and neighborhood gray-tone difference matrix (NGTDM). Each of these describe the probability of spatial relationships of gray levels occurring within the ROI. Features from fraction 1 (pre-radiation) were subtracted from fractions 5, 10, 15, 25, and 30 to create delta features at 5 timepoints (D5-D30). Patient response was retrospectively defined as no progression (NP), TP, or PP. Supervised machine learning was utilized using a 500-tree random forest (RF) classification model with TP or PP as the outcome. Variable importance analysis was conducted by calculating the out-of-bag errors with multiple bootstrapped data sets. The most prognostic features were selected using the RF importance scores. RESULTSThirty-six patients were screened for inclusion: 9 were excluded due to no T2 lesion (RC ROI only). Of the remaining 27 patients: 10 had NP, 11 had TP, and 6 had PP. Thirty-nine texture features, plus lesion volume and mean lesion intensity (for a total of 41 variables per time point) were calculated and included in the model. Of the 10 most prognostic features, 6 were from D10, suggesting that prognostic changes in the underlying lesion microenvironment are occurring within the first 10 fractions of treatment. The model selected GLSZM high gray-level zone emphasis (HGZE) D10, IBSI code 5GN9, as the most prognostic feature. The receiver operator characteristic (ROC) area under the curve (AUC) for GLSZM HGZE D10 was 0.94 (95% CI = 0.81-1.00). CONCLUSIONDelta radiomic features extracted from MR-Linac imaging may predict between PP and TP in GBM patients during treatment, which is earlier than current methods. This could allow physicians to adapt/intensify treatment in real time for poorly responding patients. Future directions include analysis with a larger patient cohort and with additional MRI contrasts (MR-Linac multiparametric MRI)