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8 papers

The Newton-X platform for mixed quantum-classical dynamics.

Barbatti M et al. · Jul 8, 2026

Mixed quantum-classical dynamics (MQCD) methods are effective models for excited-state processes in quasi-classical molecular systems, in which nuclear motion is described by classical trajectories while electronic populations undergo quantum nonadiabatic transitions. This article presents Newton-X 26, a new generation of the Newton-X platform that consolidates two decades of development into a modular ecosystem for generating spectra and initial conditions, propagating dynamics, and analyzing, postprocessing, and archiving data. Newton-X 26 supports multiple MQCD strategies, including surface hopping, decoherence-corrected Ehrenfest dynamics, and ab initio multiple spawning, and connects to a range of electronic-structure engines through dedicated interfaces. The platform emphasizes efficient execution for large trajectory ensembles, enabling systematic convergence analyses and uncertainty estimation. Complementary tools support automated data curation, machine-learning-assisted workflows, and reproducible FAIR-oriented reporting and sharing. Taken together, Newton-X 26 provides an open-source environment for routine MQCD applications and continued method development across multiple electronic-structure levels.

Physics and Astronomy

Latent density discrepancies in commercial lung-equivalent inserts and their clinical dosimetric impact.

Nakao M et al. · Jul 1, 2026

Background Accurate heterogeneity correction in high-precision radiotherapy relies on precise computed tomography (CT) number-to-density conversion via the Hounsfield unit look-up table (HLUT). While physical properties of tissue-equivalent materials are generally assumed consistent with manufacturer specifications, an independent audit identified clinically significant density discrepancies in commercially available lung-equivalent phantom inserts. Purpose This study evaluates the physical properties of nonconforming lung inserts through mass measurements and stoichiometric analysis, and assesses the clinical dosimetric impact of the associated density discrepancies. Methods Five lung-inhale inserts manufactured in 2010, 2015, and 2024 (10A, 10B, 15A, 15B, and 24A) were analyzed. Mass and physical dimensions were measured in triplicate using a precision balance (1 mg resolution) and vernier calipers. Stoichiometric analysis was conducted using reference materials to evaluate the tissue-equivalence of the inserts and quantify deviations from the theoretical baseline. A nonconforming table and a conforming reference table (RT) were established, derived from inserts 10A and 24A, respectively. For clinical impact assessment, volumetric modulated arc therapy (VMAT) plans for three clinical cases involving centrally located lung tumors (utilizing both inspiration breath-hold (IBH) and free-breathing) were optimized for stereotactic body radiotherapy (SBRT) and recalculated with the RT using the Acuros XB algorithm. Differences in gross tumor volume (GTV) mean dose and planning target volume (PTV) D95% were evaluated to quantify the dosimetric consequences. Results The 2010 inserts (10A and 10B) exhibited a 17.1% mass reduction and lower CT numbers compared to the reference 24A insert. Dimensional variations were negligible (≤ 0.2 mm) across all samples. Clinical recalculation revealed maximum dose reductions of 2.1% for the GTV mean dose and 3.0% for the PTV D95% in the worst-case scenario. These errors exceed the 2% clinical tolerance, propagated by HLUT interpolation across the low-density range. Conclusions Substantial inter-lot density variations in commercial calibration phantoms can lead to dosimetric errors that exceed established clinical limits, particularly for centrally located tumors treated with IBH. Medical physicists must not implicitly rely on nominal manufacturer values; independent audits and initial mass screening at acceptance are highly recommended for maintaining dose calculation accuracy.

Physics and Astronomy

Uncertainty quantification of U-Net based segmentation tool using conformal prediction.

Borden BJ et al. · Jul 1, 2026

Background The radiation therapy treatment process is very labour intensive, and artificial intelligence (AI) based auto contouring tools are increasingly being adopted to improve efficiency. However, current acceptance testing of AI auto contouring algorithms relies primarily on area- and distance-based metrics, with limited assessment of model uncertainty. Purpose To demonstrate conformal prediction as a complementary error analysis technique for AI auto contouring algorithms, providing spatially localized uncertainty information that traditional metrics do not capture. Methods A U-Net architecture with a ResNet-34 encoder was trained on BC Cancer breast data to segment the left lung, right lung, and the heart. Initial testing was performed on a subset of 376 computed tomography (CT) scans using both area-based (IoU) and distance-based (HD95) metrics. Conformal prediction using adaptive prediction sets was then performed on 138 CT scans. The change in the derivative of the intersection over union (IoU) between the original predictions and the conformal predictions was observed with respect to the selected confidence level. Results U-Net achieved a mean IoU of 0.924 and a mean HD95 of 11.35. When conformal prediction was applied using a 90% confidence threshold, the percent differences between the conformal prediction IoUs and the U-Net prediction IoUs were 1.01%, 0.89%, and 1.46% for the left lung, right lung, and the heart, respectively. The IoU derivatives differed significantly between true positive and false positive structure predictions (P Conclusions Conformal prediction provides an additional tool for acceptance testing of AI auto contouring algorithms. Beyond traditional area- and distance-based metrics, it spatially localizes uncertain predictions and offers a mechanism for identifying false positives.

Physics and Astronomy

Conditional sparing in FLASH radiotherapy: Conformal dosimetry and ALARA remain essential.

Chang CC et al. · Jul 1, 2026

FLASH radiotherapy shows promise in reducing normal tissue toxicity while maintaining tumor control. However, preclinical and early clinical evidence reveal biological and technical uncertainties. We review preclinical and veterinary studies, highlight unexpected toxicities, and argue that the fundamental principles of precision and safety in radiotherapy-particularly conformal treatment and ALARA-remain crucial.

Physics and Astronomy

Automated Pharmacometric Model Development by Leveraging Low-Dimensional Neural ODEs and LASSO Regression.

Bräm DS et al. · Jul 1, 2026

Current pharmacometrics (PMX) model development is a manual process with iterative model building, fitting, and evaluation, which can be resource-intensive and time-consuming. Existing automated model development approaches utilize algorithms that still rely on iterative processes and perform model selection based on goodness-of-fit criteria. Recent advances in machine learning and artificial intelligence, particularly neural ordinary differential equations (NODEs), have demonstrated strong potential for characterizing complex pharmacokinetic (PK) and pharmacodynamic (PD) dynamics directly from data. However, NODEs are inherently black-box models, which limits their interpretability and their ability to provide mechanistic insights, both of which are essential in PMX. We recently presented a promising concept that proposes interpretable ODE-based structural models from NODEs but relied on manually identifying functional relationships, which can be challenging and prevents full automation. In this work, we present an automated model development approach that combines NODEs with least absolute shrinkage and selection operator (LASSO) regression to automatically propose structural models based on the dynamics learned by NODEs. The approach leverages LASSO's feature selection capability, thereby linking data-driven modeling with interpretable mechanistic structures. We demonstrate the applicability of this automated NODE-LASSO model development approach in three different scenarios: neonatal weight development, bi-exponential PK data, and warfarin PK/PD data. The results indicate that our automated NODE-LASSO model development approach can recover meaningful, mechanism-based structures while reducing the need for extensive iterative and manual model development. This highlights its potential as a resource-efficient and interpretable modeling strategy for PMX and its applications in model-informed drug development and clinical research.

Physics and Astronomy

Explainable machine learning for patient-specific quality assurance in intensity-modulated radiotherapy based on anatomical structures.

Zhang X et al. · Jul 1, 2026

Background Patient-specific quality assurance (PSQA) plays a pivotal role in intensity-modulated radiotherapy (IMRT) to ensure accurate dose delivery. However, conventional measurement-based PSQA approaches are labor-intensive and provide limited insight into the underlying factors contributing to variations in gamma passing rates (GPRs). Anatomical characteristics of the planning target volume (PTV) and organs at risk (OARs) may contain predictive information relevant to GPR performance, yet their potential has not been fully explored within interpretable machine learning frameworks. Purpose This study aimed to develop an interpretable machine learning (ML) framework for predicting GPRs in IMRT based on anatomical features extracted from the PTV and OARs. Methods A retrospective cohort of 243 clinical chest IMRT plans was analyzed. Radiomic and dosimetric features were extracted for each anatomical structure. Two ML regression models-Random Forest (RF) and eXtreme Gradient Boosting (XGBoost)-were developed to predict GPRs for the PTV and OARs under four gamma criteria (3%/3 mm, 3%/2 mm, 2%/3 mm, and 2%/2 mm). The GPR obtained by comparing the dose distribution reconstructed using the independent Monte Carlo (MC) dose calculation software ArcherQA (Wisdom Technology Company Limited, Hefei, China)-based on linear accelerator delivery log files-with the original planned dose distribution was used as the reference standard, and calculated using global gamma analysis with a 10% dose threshold. Model performance was evaluated using the mean absolute error (MAE), root mean square error (RMSE), and Spearman's rank correlation coefficient. Shapley Additive Explanations (SHAP) were applied to interpret feature contributions in the best-performing model. Results Both models demonstrated robust predictive performance across different anatomical structures and gamma criteria. As the gamma criteria became less stringent, prediction errors decreased accordingly. Prediction accuracy was relatively high for OARs; for example, under the 3%/3 mm criterion, the test-set MAE was 0.06% ± 0.01% for the heart and 0.26% ± 0.04% for the whole lung. In contrast, the prediction error was relatively larger for the PTV, with a test-set MAE of 1.98% ± 0.31% under the same criterion. SHAP analysis revealed that texture-related radiomic features contributed most substantially to model predictions. Moreover, feature importance patterns varied according to organ type and gamma-criterion stringency. Conclusions Multi-omics descriptors derived from anatomical structures can reliably predict GPRs in IMRT. The proposed interpretable ML framework not only achieves accurate prediction but also enhances mechanistic understanding through SHAP-based explanations. These findings provide valuable insights into dose verification variability and offer a practical, transparent tool for IMRT patient-specific quality assurance.

Physics and Astronomy

Dual-scan conformal cone-beam CT for targeted image-quality improvement using dynamic collimation.

Liu Y et al. · Jul 1, 2026

Background Cone-beam computed tomography (CBCT) is routinely used for image guidance in radiation therapy, but conventional full-field CBCT acquisition may expose anatomically irrelevant regions and generate substantial scatter, which degrades soft-tissue contrast and limits the accuracy of target localization and adaptive radiotherapy workflows. A clinically practical imaging strategy should improve image quality in the region of clinical interest while preserving sufficient full-field anatomical information for patient setup and dose-related assessment. Purpose To improve image quality within clinically relevant regions without increasing the total imaging dose, this study developed a dual-scan conformal cone-beam CT method using dynamic collimation. Methods A dual-scan conformal acquisition strategy using dynamic collimation was designed. Dynamic collimation was defined as angle-dependent shaping of the X-ray field around a preplanned target region; it was implemented with a multi-leaf collimator model in the digital phantom and patient-data simulations and with the movable kV collimator in the physical phantom measurement. Collimator positions were calculated from forward projections of the target region, and a target-region-based intensity-optimization model was used to allocate more photons to projection angles and detector regions contributing to the target region. The dual-scan protocol, consisting of a low-dose full-field-of-view (FOV) scan and a higher-dose conformal scan, was reconstructed using a regularized weighted least-squares algorithm with anisotropic total-variation regularization. The method was evaluated using a FORBILD head phantom, patient planning CT datasets, and an anthropomorphic head phantom measured on a Varian Edge on-board imaging system. Results The proposed method achieved the best overall image quality among the compared methods. In the FORBILD phantom, for target region 1 (VOI1), SSIM increased from 0.702 to 0.855, CNR increased from 0.514 to 1.463, and SNR1/SNR2 increased from 33.8/32.8 to 113.1/114.6 compared with conventional FDK reconstruction. In the prostate cancer case, CNR increased from 0.44 to 1.94, and SNR1/SNR2 increased from 25.6/23.3 to 74.6/82.4. In the measured anthropomorphic head phantom, CNR improved from 1.036 to 2.379, and SNR1/SNR2 improved from 42.8/50.1 to 89.8/98.0. Conclusion The proposed dual-scan conformal CBCT strategy using dynamic collimation improves local image quality in the clinically relevant target region while maintaining full-FOV information through the two-scan protocol. This method may provide a practical route toward patient-specific CBCT guidance for adaptive radiotherapy.

Physics and Astronomy

Controlling the synchronization and symmetry breaking of coupled bacterial pili on active biofilm carpets.

Altın B et al. · Jun 25, 2026

In the low Reynolds number regime, active biological systems utilize nonreciprocal cyclic activities to achieve motility, as seen in the spinning of bacterial flagella and the beating of cilia. Coupling among these active mechanical components leads to synchronization and emergence of metachronal waves. Here, we report that biofilms of Pseudomonas nitroreducens form active carpet-like surfaces textured with diverse topological defects, generating Mexican-wave-like collective behavior in which bacteria periodically lift up. On these active surfaces, non-reciprocally coupled extension and retraction activities of bacterial pili drive these collective oscillations. Surprisingly, this collective behavior exhibits left-right asymmetry across the biofilm driving unidirectionally propagating waves. We discover that this directionality is primarily governed by an aging-related frequency gradient across the biofilm. Leveraging these insights, we further demonstrate the ability to control the collective dynamics of these waves, including symmetry breaking, transitions from spiral waves into target and propagating plane waves by manipulating the elastic properties of biofilms. Overall, our findings illuminate the fundamental role of nonreciprocally interacting active components in regulating synchronization, collective dynamics, and symmetry-breaking phenomena in biological systems.

Physics and Astronomy