Physics in medicine and biology最新文献

Objective Accurate segmentation of the prostate and dominant intraprostatic lesions (DILs) on magnetic resonance imaging (MRI) is important for prostate cancer radiation therapy treatment planning and targeted dose escalation. However, DIL segmentation remains challenging due to small datasets, institutional bias, and variable imaging protocols. Although the Segment Anything Model (SAM) has shown promise in medical image segmentation, most prior work depends on manual prompts. This study developed a fully automated pipeline that combines localization with a fine-tuned SAM model to segment the prostate and DIL. Approach Two datasets were utilized: the PI-CAI dataset, comprising 1,476 patients, and the Cancer Imaging Archive dataset, comprising 803 patients. The pipeline consisted of two stages: (1) a reinforcement learning-based localization network predicted bounding boxes as segmentation inputs, and (2) a fine-tuned SAM model performed segmentation. Model performance was evaluated using the Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and detection rates, with additional analysis based on lesion volumes. Main Results The proposed method achieved a mean and median DSC of 0.896±0.070 and 0.915, and an IoU of 0.818±0.100 and 0.844 for prostate segmentation. For DIL segmentation, the mean and median DSC were 0.592±0.192 and 0.636, IoU of 0.446±0.190 and 0.466, with a detection rate of 89%. Four DIL groups were created based on lesion volume percentile. The mean/median DSC and IoU for each volume group is as follows: 0.5-1.0 cubic centimeters (cc): 0.555±0.201/0.562 & 0.414±0.205/0.391; 1.0-1.8 cc: 0.603±0.185/0.660 & 0.454±0.180/0.492; 1.8-4.0 cc: 0.588±0.183/0.627 & 0.439±0.174/0.456; >4.0 cc: 0.621±0.197/0.669 & 0.477±0.197/0.503. Significance This study presented a fully automated prostate and DIL segmentation framework on MRI by integrating a localization network with fine-tuned SAM. The method achieved robust performance across large multi-institutional datasets and diverse lesion shapes. It shows strong potential for application to clinical workflows for prostate cancer radiation therapy planning and treatment.

Objective: Tumor Treating Fields (TTFields) is an emerging cancer therapy whose efficacy is closely linked to the electric field (EF) intensity delivered to the tumor. However, current computational workflows for simulating the EF and planning treatment rely on time-consuming manual segmentation and proprietary software, hindering efficiency, reproducibility, and accessibility.

Approach: We introduce AutoSimTTF, a fully automatic pipeline for personalized EF simulation and optimized treatment planning for TTFields.The end-to-end workflow utilizes advanced deep learning model for automated tumor segmentation, conducts finite element method (FEM)-based EF simulation, and determines a computationally optimized treatment plan via a novel, physics-based parameter optimization method.

Main results: The automated segmentation module achieved high precision, yielding a Dice Similarity Coefficient of 0.91 for the whole tumor. In terms of efficiency, the active planning workflow was completed in approximately 12 minutes, significantly outperforming conventional multi-day manual processes. The pipeline's simulation accuracy was validated against a conventional semi-automated workflow, demonstrating deviations of less than 14.1% for most tissues. Critically, the parameter optimization generated personalized transducer montages that produced a significantly higher EF intensity at the tumor site (up to 111.9% higher) and substantially improved field focality (19.4% improvement) compared to traditional fixed-array configurations.

Significance: AutoSimTTF addresses major challenges in efficiency and reproducibility, paving the way for data-driven personalized TTFields therapy and large-scale computational research.

Objective.Incorporating iodine into deoxyribonucleic acid (DNA) bases offers a strategy to enhance radiotherapy. The iodine increases the photoabsorption cross-section and can promote DNA disruption and cell death in cancerous tissue. In this study, we investigate the local fragmentation mechanisms of iodinated DNA and the spatial extent of damage propagation following photoactivation.Approach.Single-stranded DNA oligonucleotides consisting of 2-5 bases, in which the methyl group of thymine is substituted with an iodine atom, were irradiated with synchrotron x-rays above the iodine L-shell ionisation threshold (4900 eV). Fragmentation patterns were extracted by subtracting background spectra obtained below the threshold (4500 eV), and the results were complemented by Born-Oppenheimer molecular dynamics simulations to resolve bond breaking at the atomic level.Main results.We find that longer oligonucleotide chains predominantly generate larger, high-m/zfragments, while shorter sequences produce a wider variety of small fragments. Backbone cleavage is observed in all sequences, with phosphate- and sugar-based ions dominating the spectra. Bond scission extends up to five bases from the iodination site, with the heaviest stable fragment containing two bases.Significance.Suppose this effect is extrapolated to genomic DNA, which includes about 29.5% thymine. In that case, the amount of thymine replaced by iodinated uracil can help estimate the extent of DNA damage that might occur during radiation therapy using iodine as a radiosensitiser.

Objective.To design and validate a single, reconfigurable gamma probe that overcomes the static compromise between spatial resolution and sensitivity in radio-guided surgery, enabling both rapid lesion detection and precise margin delineation.Approach.A dual-layer lead collimator was designed for a LaBr₃(Ce)-SiPM detector. A validated analytical model coupled with a multi-objective genetic algorithm (NSGA-II) was used to explore the theoretical performance limits and identify optimal geometries. A two-phase computational search identified a single, universal geometry that can be switched intraoperatively between a high-sensitivity (HS) mode and a high-resolution (HR) mode by adjusting collimator positions.Main results.The universal design, at a 30 mm distance, achieves a spatial resolution of 6.41 mm full width at half maximum (FWHM) in HR mode and a sensitivity of 1483 counts per second (cps)/MBq in HS mode. The optimization framework identified specialized, distance-specific theoretical designs with resolutions as fine as 3.26 mm FWHM. The underlying detector's energy resolution is sufficient to distinguish between ⁹⁹_mTc (140.5 keV) and¹²³I (159 keV).Significance.This work presents a practical, single-instrument solution that offers surgeons the intraoperative flexibility to prioritize either rapid detection or precise delineation. The developed design methodology provides a robust framework for creating next-generation, application-specific surgical guidance tools.

Objective.Adaptive radiotherapy aimed to account for inter- and intra-fractional anatomical changes to improve target coverage and spare normal tissues. Commercial softwares could quantify anatomical and setup differences between planning computed tomography (pCT) and image-guidance cone-beam computed tomography (CBCT) using density gamma passing rate (dGPR). This study evaluated the utility of dGPR as an automated, site-specific trigger for offline adaptive workflows, using statistical process control (SPC) to define tolerance thresholds and correlating dGPR with setup errors, planning target volume (PTV) coverage, and longitudinal stability.Approach.240 patients across six anatomical sites were retrospectively analysed. First-fraction CBCTs were compared to pCTs using MobiusCB to compute dGPR. SPC analysis was performed to establish site-specific tolerance limits. Rigid phantom studies were conducted to quantify dGPR sensitivity to setup errors. Correlation between dGPR and PTVV_100%was assessed in patients with repeat CTs, and longitudinal stability of dGPR across fractions was evaluated.Main results.SPC-derived lower action limits (Al) ranged from 97.3% (brain) to 82.2% (breast), reflecting site-specific anatomical variability and imaging protocols. Phantom studies verified dGPR sensitivity due to rigid shifts, with head dGPR decreasing to as low as 83.5% at 8 mm translation, while pelvis dGPR remained above 93.2% for the same shift, reflecting greater tolerance due to its larger volume. In head-and-neck patients, dGPR correlated moderately with changes in PTVV_100%(r= 0.56), with no coverage losses >10% observed when dGPR exceeded 90%. Longitudinal analysis showed stable dGPR for most sites, but gradual declines in head-and-neck patients, with some values falling below SPC-derived threshold of 94.3% towards the end of treatment.Significance.dGPR offered a practical, automated, and site-specific metric for detecting anatomical changes and setup errors during radiotherapy. SPC-derived thresholds provided robust action levels tailored to each site, and MobiusCB enabled automated alerts when thresholds were exceeded, reducing reliance on subjective image inspection.

Objective.This study introduces a novel non-invasive wound healing method that generates Lorentz fields (LFs) in the wound area using ultrasonic transducers under a static magnetic field, enabling localized stimulation without direct electrode contact.Approach.Theoretical derivations of the governing equations, supported by numerical simulations, demonstrate the feasibility and potential effectiveness of this technique. The model includes the two-dimensional geometry of the wound, skin layers, gel, a single-element ultrasonic probe, or a 16-element linear phased array (LPA) transducer. The pressure and velocity current density distributions in the wound area were analyzed under three different excitation configurations: (i) excitation using a single-element ultrasonic probe, (ii) beam steering of the LPA transducer at 5^∘intervals between-30∘and+30∘at 13 different angles, and (iii) focusing of the LPA transducer at 0^∘. In each configuration, distinct pressure distributions and velocity current density patterns were obtained in the wound region. In addition,in vivoanimal experiments were conducted using the single-element ultrasonic probe to evaluate the biological effects of LF-based stimulation on wound healing. The study included four experimental groups: a static magnetic field (SMF) group, an ultrasound (US) group, a combined LF group, and a control group without any stimulation.Main results.In the single-element probe configuration, the simulated velocity current density reached approximately 4.51μAcm-2, corresponding to a pressure of 0.17 MPa. These values remained within the established safety limits while being sufficient to promote wound healing. For the LPA transducer, electronic beam steering enabled a uniform distribution of acoustic pressure and induced current density over a wider wound area. The pressure ranged between ±(0.118-0.203)  MPa, and the corresponding velocity current density varied between ±(2.33-2.69) μAcm-2. In the focusing configuration (0^∘), the maximum pressure in the wound region reached 0.285 MPa, while the peak absolute velocity current density was 6.72 μAcm-2, both remaining within safe limits. Animal experiments were conducted for 14 d, with each group receiving a 5 min daily treatment. The Lorentz-field group exhibited the fastest wound closure, followed by the US and magnetic-field groups, whereas the control group showed the least improvement.Significance.The proposed method offers an innovative and safe alternative for accelerating wound healing by combining US and SMFs to generate Lorentz-induced current densities in the wound, providing localized and non-invasive therapeutic stimulation.

Objective.Patients with locally advanced non-small cell lung cancer (LA-NSCLC) exhibit heterogeneous prognoses despite receiving standard treatments, highlighting the need for more reliable prognostic biomarkers. This study aims to develop and validate OmicsMap model, a deep radiomics biomarkers derived from computed tomography images for the prediction of progression-free survival (PFS) in LA-NSCLC patients.Approach.We retrospectively analyzed data from 329 LA-NSCLC patients who underwent definitive radiotherapy. The cohort was randomly divided into development (N= 220) and independent testing set (N= 109). The prognostic signature was derived from integrated radiomics features extracted from both the primary tumor and involved lymph nodes, and inter-patient radiomics feature interactions. To achieve this, high-dimensional radiomics data from all patients were transformed into structured two-dimensional representations, termed OmicsMap, wherein radiomics feature interactions were encoded within the pixelated configuration. Deep radiomics features from the OmicsMaps were then extracted using a convolutional neural network for prognostic prediction. Model performance was evaluated by time-dependent area under the receiver operating characteristic curves area under the curve (AUC). Kaplan-Meier curves were plotted and hazard ratios (HR) were calculated via Cox proportional hazards model.Main results.The OmicsMap model achieved time-dependent AUCs of 0.76, 0.78 and 0.76 at 1, 2 and 3 years in the independent testing set, significantly outperforming the clinical model (AUC: 0.57, 0.57, 0.64;p< 0.05). The proposed model improved predictive discrimination with 7.69% increase in C-index over conventional radiomics approaches. It effectively stratified patients into high-risk and low-risk subgroups for both PFS (p< 0.001, HR = 0.380) and overall survival (p= 0.0021, HR = 0.525) in the testing set.Significance.The proposed OmicsMap model provides a novel paradigm for enhancing prognostic prediction in patients with LA-NSCLC. By improving risk stratification, the framework may help inform clinical decision-making and support future efforts toward more individualized management strategies.

Objective.Current dosimetry protocols typically recommend multiple measurements to determine recombination correction factors (ks), increasing the time required for dose measurements in the quality assurance workflow. We propose a novel dual-gap ionization chamber (DGIC) design for reference dosimetry featuring two air gaps of different thicknesses within a single device. This design enables the determination ofksdirectly from the same measurements required to determine absorbed dose-to-water. Thus, eliminating the need for separate measurements to correct for recombination losses. The approach relies on analyzing the charge ratio between the two gaps, under ultra high dose rates (UHDR) and dose per pulse (DPP) under ultra high DPP (UHDPP) conditions.Approach.A DGIC prototype with electrode distances of 1 and 0.6 mm was developed and tested using different beam qualities: (1) a 240 MeV u^-1clinical carbon ion beam at conventional field dose rates of 25 Gy min^-1, (2) a 226 MeV continuous proton beam with a current between 5 and 800 nA at the cyclotron exit, where the maximum approximately corresponds to 200 Gy s^-1in the treatment room and (3) a 9 MeV electron beam with a DPP from 0.03 to 4.2 Gy, a frequency of 60 Hz and a pulse duration between 0.7-3.9μs.ks-factors were derived for the top cavity of 1 mm gap using the DGIC method and compared against the following: for proton and carbon ions, comparisons were made with the Jaffé plot method. For the electron beam, it was compared with a dose rate independent device, a flashDiamond detector, and the integrated current transformer of the LINAC.Main results.A DGIC prototype was able to successfully correct for recombination losses under different beam modalities: for initial recombination in a clinical carbon ion beam, volume recombination in UHDR proton beam with field dose rates of 200 Gy s^-1and in UHDPP electron beams, where four pulses were delivered with DPP up to 4.2 Gy (this DPP corresponds to an effective pulse duration of 3.9μs).Significance.A DGIC design and its inherent method provides a practical and accurate way of determining dose and dose rate in emerging radiotherapy treatment modalities.

Objective: Bone scan imaging for the detection of bone metastasis of breast cancer has been widely adopted; however, noise, anatomy superimposition, and small size for early lesions will severely affect its prediction performance. In this work, we propose a new framework with two major contributions to solve the main problems existing in current deep-learning-based approaches. Approach: In this study, we put forward a new model called the Dual Branch Feature Alignment Network (DBFANet) for automated breast cancer bone metastases detection in bone scintigraphy. DBFA-net adopts a dual-branch CNN-transformer structure: the CNN branch focuses on the local details, while the transformer branch learns the global context. In addition, we design a feature alignment module (FRAT), which employs the bi-directional cross-attention mechanism for the complementary feature from two branches. Moreover, we propose an enhanced multi-scale attention module (EMSA) based on the squeeze-and-excitation (SE) block for stronger multi-scale lesion representations with less background noise suppression. Main results: We validated our proposed model based on a bone scintigraphy dataset containing 5,092 images. In terms of bone metastasis prediction, DBFANet achieved an accuracy, precision, and recall value of 93.1%, 84.6%, and 84.7%, respectively, all superior to previous models (such as ResNet-50, EfficientNet-V2, and MaxViT). The ablation study has shown that both FRAT and EMSA have individual effectiveness and complementary benefits. Finally, additional external validation was performed on a publicly available bone scintigraphy dataset (BS-80K). Significance: DBFANet shows the highest detection performance for bone metastasis detection from multiview bone scintigraphy images with imbalanced classes and noise in the image, and the feature alignment with enhanced multiscale attention of DBFANet provides a useful and precise tool for bone metastasis diagnosis in a nuclear medicine imaging scenario.

Purpose: Radiation pneumonitis occurs in approximately 10-30% of lung cancer patients treated with radiation therapy, posing a significant dose-limiting factor. The assessment of regional ventilation changes from functional ventilation data can provide essential information regarding treatment response. However, this task can be challenging since ventilation maps contain noisy measurements and artifacts. Methods: We introduce a framework that estimates physiological changes from a set of longitudinal ventilation scans. Our method identifies changes as more plausible if they follow a monotonic trend while attributing smaller confidence in regions where large fluctuations are observed. Our algorithm outputs the estimated volumes of significant function increase and decline. The proposed framework was calibrated and validated using synthetic datasets. We also applied our model to a dataset comprising 11 lung cancer patients for whom multiple 4DCT scans were obtained during the course of radiotherapy treatment. CT-derived ventilation maps were generated and used as input to the proposed framework. In order to create a control dataset where no functional changes were expected, we also shuffled the time points for the 11 patients in every possible way that discarded as much temporal information as possible resulting in 128 functional map sequences. Results: In the patient dataset, 3/11 patients were identified with significant functional decline and 4/11 with functional increase that was associated with tumor regression. Finally, in the control dataset the frequency of occurrence of significant changes was 1.6% (4/256) compared to 32% (7/22) for the original patient dataset. Conclusion: We have developed a framework for analyzing functional ventilation changes from longitudinal data. The results of the lung cancer patient dataset indicate that significant functional increase and decline can occur during the course of radiotherapy treatment. More generally, the developed framework can be used to assess ventilation changes with the potential of guiding adaptive treatment strategies. .