Aging (Albany NY). 2025 Aug 8;17. doi: 10.18632/aging.206295. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a condition predominantly affecting the elderly and leading to a decline in lung function. Our study investigates the aging-related mechanisms in IPF using artificial intelligence (AI) approaches. We developed a pathway-aware proteomic aging clock using UK Biobank data and applied it alongside a specialized version of Precious3GPT (ipf-P3GPT) to demonstrate an AI-driven mode of IPF research. The aging clock shows great performance in cross-validation (R2=0.84) and its utility is validated in an independent dataset to show that severe cases of COVID-19 are associated with an increased aging rate. Computational analysis using ipf-P3GPT revealed distinct but overlapping molecular signatures between aging and IPF, suggesting that IPF represents a dysregulation rather than mere acceleration of normal aging processes. Our findings establish novel connections between aging biology and IPF pathogenesis while demonstrating the potential of AI-guided approaches in therapeutic development for age-related diseases.

PMID:40782333 | DOI:10.18632/aging.206295

Nat Commun. 2025 Aug 9;16(1):7373. doi: 10.1038/s41467-025-61699-w.

ABSTRACT

Monkeypox virus (MPXV) has re-emerged globally since May 2022, posing a significant public health threat. To address this, we develop two multivalent mRNA vaccine candidates-AAL, encoding three MPXV antigens, and AALI, which combines AAL with an immune-enhancing IFN-α protein. Both vaccines are delivered via mannose-modified lipid nanoparticles to target dendritic cells. Here we show that these vaccines elicit strong antibody responses against vaccinia virus and multiple MPXV clades, induce robust memory B-cell and T-cell responses, and promote dendritic cell maturation. In mouse challenge models, both vaccines provide protection against clade IIb MPXV and vaccinia virus, significantly reducing viral loads and preventing lung damage. Immune profiling reveals enhanced B- and T-cell receptor diversity and distinct CDR3 motifs post-vaccination. These findings demonstrate the potential of using mRNA-based multivalent vaccines as an effective strategy for preventing mpox and related Orthopoxvirus infections.

PMID:40783493 | DOI:10.1038/s41467-025-61699-w

Trends Parasitol. 2025 Aug 8:S1471-4922(25)00195-3. doi: 10.1016/j.pt.2025.07.006. Online ahead of print.

ABSTRACT

Parasitology has long relied on genomics and transcriptomics to explore gene function, diversity, and host-parasite interactions, yet functional insight often requires deeper molecular resolution. This forum highlights advances in proteomics, metabolomics, lipidomics, and emerging technologies. We advocate an integrative multiomics approach to better understand parasite biology in context.

PMID:40783337 | DOI:10.1016/j.pt.2025.07.006

J Ethnopharmacol. 2025 Aug 7:120359. doi: 10.1016/j.jep.2025.120359. Online ahead of print.

ABSTRACT

AIM: This article explores the modern significance of ethnopharmacology in overcoming drug discovery challenges and highlights its potential when combined with cutting-edge technologies to drive innovative therapeutic developments.

MATERIAL AND METHODS: A comprehensive literature search was conducted across multiple databases, including PubMed, ScienceDirect, Scopus, utilizing keywords such as ethnopharmacology, traditional medicine, drug discovery and development, reverse pharmacology, modern techniques of drug discovery, ethnopharmacology and antibiotic resistance or metabolic disease or ageing.

RESULTS: Drug discovery faces significant challenges, including lengthy timelines, exorbitant costs, high risk of failure, and the potential for adverse drug reactions. Ethnopharmacology bridges traditional medicinal knowledge with modern scientific methodologies, uncovering novel drug candidates and emphasizing multi-target therapeutic approaches. The discipline offers promising solutions for metabolic diseases, antimicrobial resistance, and chronic conditions by leveraging bioactive compounds from natural sources. Furthermore, advancements in reverse pharmacology, Ayurgenomics and systems biology enhance the discovery and development, enabling personalized and safer treatments.

CONCLUSION: Ethnopharmacology's integration with cutting-edge technologies presents a transformative path toward synergistic formulation discovery and personalized therapeutics.

PMID:40783108 | DOI:10.1016/j.jep.2025.120359

Cell Chem Biol. 2025 Aug 5:S2451-9456(25)00225-9. doi: 10.1016/j.chembiol.2025.07.002. Online ahead of print.

ABSTRACT

C-terminal cyclic imides are posttranslational modifications (PTMs) on proteins that are recognized and removed by the E3 ligase substrate adapter cereblon (CRBN). Despite the observation of these modifications across the proteome by mass spectrometry-based proteomics, an orthogonal and generalizable method to visualize the C-terminal cyclic imide would enhance detection, sensitivity, and throughput of endogenous CRBN substrate characterization. Here, we develop an antibody-like reagent, termed "cerebody," for visualizing and enriching C-terminal cyclic imide-modified proteins. We describe the engineering of CRBN derivatives to produce cerebody and use it to identify CRBN substrates by western blot and enrichment from whole-cell and tissue lysates. CRBN substrates identified by cerebody enrichment are mapped, validated, and further characterized for dependence on the C-terminal cyclic imide modification. These methods will accelerate the characterization of endogenous CRBN substrates and their regulation.

PMID:40782806 | DOI:10.1016/j.chembiol.2025.07.002

Biomed Pharmacother. 2025 Aug 8;190:118437. doi: 10.1016/j.biopha.2025.118437. Online ahead of print.

ABSTRACT

Triple-negative breast cancer (TNBC) is characterized by aggressive metastatic behaviour and limited therapeutic options. Here, we present a multi-tiered systems biology framework to investigate the anti-metastatic potential of enterolactone (EL), integrating computational, transcriptomic, and experimental approaches in the MDA-MB-231 TNBC model. Network pharmacology identified 78 EL-metastatic TNBC (mTNBC) overlapping targets as potential therapeutic targets of EL against mTNBC, with PPI and GMFA network enrichment uncovering key metastasis-associated pathways including Notch, TGF-β, TNF, and ErbB signaling. Molecular docking and 100 ns molecular dynamics simulations revealed stable binding of EL to several core oncogenic proteins (e.g., EGFR, PARP1, AURKB, SMAD4, CDK4), suggesting poly-pharmacological engagement. Genome-wide transcriptomic profiling of EL-treated cells coupled with GSEA revealed coordinated downregulation of oncogenic programs including E2F, G2/M checkpoint, MYC targets, EMT, and metabolic plasticity, alongside induction of NRF2 signaling and ferroptosis. This study reports, for the first time, transcriptome-wide effects of EL in MDA-MB-231 cells, linking its activity to the repression of stemness, invasion, and immune-evasive traits. Experimental validation via qPCR confirmed EL-mediated suppression of key molecular targets of TGF-β and Notch signaling pathways. EL also impaired cortactin expression and disrupted cytoskeletal remodeling, validated by immunofluorescence and flow cytometry studies, indicating attenuation of invasive machinery. Furthermore, EL reduced metastatic dissemination in a zebrafish xenograft model, reinforcing its in vivo anti-metastatic potential. Together, our integrative study elucidates EL's multitarget mechanism against metastatic TNBC and highlights its translational promise as a systems-level modulator of oncogenic signaling. These findings warrant further in-depth mechanistic investigations to validate EL's therapeutic potential.

PMID:40782436 | DOI:10.1016/j.biopha.2025.118437

BMJ Case Rep. 2025 Aug 9;2009:bcr0620080205. doi: 10.1136/bcr.06.2008.0205.

ABSTRACT

Aripiprazole (APZ) is a novel antipsychotic agent which does not block dopamine (DA) receptors but is rather a partial DA agonist. Thus, it has been proposed that APZ may not induce tardive dyskinesia (TD), a disfiguring and sometimes disabling and irreversible side effect of neuroleptics. Our patient had Lewy body dementia (LBD) and developed severe worsening of parkinsonism over 1 month of APZ treatment. Within days of discontinuation of APZ dramatic orobuccal dyskinesias emerged. Treatment emergent worsening of parkinsonism improved but orobuccal dyskinesias persisted unchanged until his death 8 months later. Others have reported severe extrapyramidal reactions including neuroleptic malignant syndrome and TD with APZ. APZ has been suggested as a treatment for TD but treatment benefit may reflect "masked" dyskinesia. We conclude that, despite an attractive in vitro profile and promising animal data, APZ can induce serious extrapyramidal side effects, including TD.

PMID:40783219 | DOI:10.1136/bcr.06.2008.0205

Clin J Gastroenterol. 2025 Aug 9. doi: 10.1007/s12328-025-02197-w. Online ahead of print.

ABSTRACT

Malignant triton tumor (MTT) is a rare subset (5%) of malignant peripheral nerve sheath tumors (MPNSTs), classified as soft-tissue sarcomas. MTT is an orphan disease characterized by rhabdomyoblastic differentiation, therapeutic resistance, and a sinister prognosis. The neoplasms classically arise at the trunk, head and neck region, and extremities. In 50% of the cases, MTT is associated with neurofibromatosis type 1 (NF1), a relatively common autosomal dominant cancer-prone disorder of the central nervous system. Few cases of MTT in the gastrointestinal tract have been published, including esophagus, duodenum, and rectum. In this article, we present what we believe to be the first report of MTT in the common bile duct. A multidisciplinary approach was the key in establishing this particular diagnosis, and workup included endoscopic ultrasound, endoscopic retrograde cholangiopancreatography, pathological staining, and genetic testing. Literature focusing on MTT remains scarce, and patients with MTT are often included with other subtypes in broader studies of MPNST. Therefore, our literature review covers MPNST and focusses on MTT where appropriate. It provides the current understanding of tumor epidemiology, genetics, and diagnostic workup, and discusses therapeutic challenges and future perspectives. Our case report underlines the value of cholangioscopy-guided biopsies, and honoring patient's autonomy in end-of-life setting.

PMID:40782265 | DOI:10.1007/s12328-025-02197-w

Commun Biol. 2025 Aug 8;8(1):1184. doi: 10.1038/s42003-025-08630-7.

ABSTRACT

Founder events influenced the genetic diversity within the province of Quebec, increasing the frequency of certain rare pathogenic variants in regional populations. Some regions, such as Beauce, remain understudied despite evidence of a regional founder effect. Leveraging extensive genealogical data, we observe a specific regional structure emerging in Beauce following the initial settlement. It is characterised by a gradual increase in inbreeding and kinship coefficients and a low diversity of ancestors. Taking advantage of the region's genetic distinctiveness, we describe 36 rare pathogenic variants with higher carrier rates in Beauce than in urban regions, likely due to the regional founder effect. This provides the first in-depth study of Beauce's genetic and genealogical landscape, revealing a distinct structure and suggesting that other overlooked regions, in Quebec and elsewhere, could benefit from fine-scale population structure studies to improve the understanding and management of rare diseases.

PMID:40781540 | DOI:10.1038/s42003-025-08630-7

Tumori. 2025 Aug 9:3008916251353420. doi: 10.1177/03008916251353420. Online ahead of print.

ABSTRACT

BACKGROUND: Women carriers of BRCA1/2 mutations face a very high lifetime risk (penetrance) of developing breast and/or ovarian cancer. A sizeable proportion of carriers, however, does not develop cancer at all or develop it only late in life, thus suggesting a potential modulation of this risk. Epidemiological studies have suggested that other genetic (polymorphisms) and environmental factors (lifestyle) affect penetrance. However, data regarding these associations mainly come from retrospective case-control analyses and the results are likely to be distorted by bias.

AIMS: The e-BRAVE (Brca, ReseArch, Virtual, Education) study aims to create a web-based prospective cohort and biological bank of unaffected women carriers of BRCA1/2 mutations to investigate the role of polymorphisms and environmental factors, and their interaction, in the occurrence of primary BRCA-related cancers.

METHODS: An innovative digital platform (including a mobile App) will be used to empower the synergy between participants and researchers, supporting engagement with women, adherence to intervention plan, self-empowerment, flanked by activities tracking and monitoring.

RESULTS: Based on the incidence data in previous studies, we estimate to observe an overall incidence of ~3.7% year.

CONCLUSION: The success of this study will ensure the definition of further predictive risk models and comprehensive recommendations aimed at improving management and health of BRCA women.

PMID:40782011 | DOI:10.1177/03008916251353420

Eur J Clin Pharmacol. 2025 Aug 8. doi: 10.1007/s00228-025-03893-9. Online ahead of print.

ABSTRACT

PURPOSE: The relevance of CYP2D6 activity on serum levels and clinical response of fluoxetine remains unclear. The present study aim was to evaluate the impact of CYP2D6 genotype on i) fluoxetine and norfluoxetine exposure, and ii) treatment switch from fluoxetine to alternative antidepressants in adults and children/adolescents.

METHODS: Patients were included retrospectively from a therapeutic drug monitoring service. Patients were subgrouped by age, i.e. < 18 yrs (young) and ≥ 18 yrs (adult), and divided into CYP2D6 genotype-predicted phenotype subgroups of poor metabolizers (PMs), intermediate metabolizers (IMs), normal metabolizers (NMs) and ultrarapid metabolizers (UMs).

RESULTS: Among 1027 adult patients, the metabolic ratio was lowest in PMs and highest in UMs (p ≤ 0.04), but there was no difference in active moiety (fluoxetine + norfluoxetine) across CYP2D6 phenotype groups (p ≥ 0.1). Similar trends were observed in 196 young patients, both for metabolic ratio and active moiety. In adult patients, switching from fluoxetine to an alternative antidepressant had odds ratio of 2.9 in UMs (p = 0.004) and 2.3 in PMs (p = 0.007) compared with NMs. The number of switches per genotype group was too low for meaningful comparisons in young patients.

CONCLUSION: The fluoxetine and norfluoxetine active moiety was unaffected by CYP2D6 genotype in adults and children/adolescents. Still, adult CYP2D6 PMs and UMs switched antidepressant treatment two to three times more often than NMs, indicating that relative levels of fluoxetine and norfluoxetine may affect treatment outcome rather than active moiety in relation to CYP2D6 phenotype.

PMID:40781498 | DOI:10.1007/s00228-025-03893-9

Abdom Radiol (NY). 2025 Aug 9. doi: 10.1007/s00261-025-05151-z. Online ahead of print.

ABSTRACT

PURPOSE: To compare the lesion detectability of hypovascular liver metastases between 70-keV and 40-keV images from dual energy-computed tomography (CT) reconstructed with deep-learning image reconstruction (DLIR).

METHODS: This multi-institutional, retrospective study included adult patients both pre- and post-treatment for gastrointestinal adenocarcinoma. All patients underwent contrast-enhanced CT with reconstruction at 40-keV and 70-keV. Liver metastases were confirmed using gadoxetic acid-enhanced magnetic resonance imaging. Four radiologists independently assessed lesion conspicuity (per-patient and per-lesion) using a 5-point scale. A radiologic technologist measured image noise, tumor-to-liver contrast, and contrast-to-noise ratio (CNR). Quantitative and qualitative results were compared between 70-keV and 40-keV images.

RESULTS: The study included 138 patients (mean age, 69 ± 12 years; 80 men) with 208 liver metastases. Seventy-one patients had liver metastases, while 67 did not. Primary cancer sites included 68 cases of pancreas, 50 colorectal, 12 stomach, and 8 gallbladder/bile duct. No significant difference in per-patient lesion detectability was found between 70-keV images (sensitivity, 71.8-90.1%; specificity, 61.2-85.1%; accuracy, 73.9-79.7%) and 40-keV images (sensitivity, 76.1-90.1%; specificity, 53.7-82.1%; accuracy, 71.7-79.0%) (p = 0.18-> 0.99). Similarly, no significant difference in per-lesion lesion detectability was observed between 70-keV (sensitivity, 67.3-82.2%) and 40-keV images (sensitivity, 68.8-81.7%) (p = 0.20-> 0.99). However, Image noise was significantly higher at 40 keV, along with greater tumor-to-liver contrast and CNRs for both hepatic parenchyma and tumors (p < 0.01).

CONCLUSION: There was no significant difference in hypovascular liver metastases detectability between 70-keV and 40-keV images using the DLIR technology.

PMID:40782256 | DOI:10.1007/s00261-025-05151-z

Jpn J Radiol. 2025 Aug 9. doi: 10.1007/s11604-025-01849-8. Online ahead of print.

ABSTRACT

PURPOSE: To assess the efficacy of super-resolution deep learning reconstruction (SR-DLR) with motion reduction algorithm (SR-DLR-M) in mitigating aorta motion artifacts compared to SR-DLR and deep learning reconstruction with motion reduction algorithm (DLR-M).

MATERIALS AND METHODS: This retrospective study included 86 patients (mean age, 65.0 ± 14.1 years; 53 males) who underwent contrast-enhanced CT including the chest region. CT images were reconstructed with SR-DLR-M, SR-DLR, and DLR-M. Circular or ovoid regions of interest were placed on the aorta, and the standard deviation of the CT attenuation was recorded as quantitative noise. From the CT attenuation profile along a line region of interest that intersected the left common carotid artery wall, edge rise slope and edge rise distance were calculated. Two readers assessed the images based on artifact, sharpness, noise, structure depiction, and diagnostic acceptability (for aortic dissection).

RESULTS: Quantitative noise was 7.4/5.4/8.3 Hounsfield unit (HU) in SR-DLR-M/SR-DLR/DLR-M. Significant differences were observed between SR-DLR-M vs. SR-DLR and DLR-M (p < 0.001). Edge rise slope and edge rise distance were 107.1/108.8/85.8 HU/mm and 1.6/1.5/2.0 mm, respectively, in SR-DLR-M/SR-DLR/DLR-M. Statistically significant differences were detected between SR-DLR-M vs. DLR-M (p ≤ 0.001 for both). Two readers scored artifacts in SR-DLR-M as significantly better than those in SR-DLR (p < 0.001). Scores for sharpness, noise, and structure depiction in SR-DLR-M were significantly better than those in DLR-M (p ≤ 0.005). Diagnostic acceptability in SR-DLR-M was significantly better than that in SR-DLR and DLR-M (p < 0.001).

CONCLUSIONS: SR-DLR-M provided significantly better CT images in diagnosing aortic dissection compared to SR-DLR and DLR-M.

PMID:40782239 | DOI:10.1007/s11604-025-01849-8

Skeletal Radiol. 2025 Aug 9. doi: 10.1007/s00256-025-04991-6. Online ahead of print.

ABSTRACT

OBJECTIVE: To develop and validate a deep learning model for automated 3D segmentation of rotator cuff muscles on longitudinal CT scans to quantify muscle volume and fat fraction in patients undergoing total shoulder arthroplasty (TSA).

METHODS: The proposed segmentation models adopted DeepLabV3 + with ResNet50 as the backbone. The models were trained, validated, and tested on preoperative or minimum 2-year follow-up CT scans from 53 TSA subjects. 3D Dice similarity scores, average symmetric surface distance (ASSD), 95th percentile Hausdorff distance (HD95), and relative absolute volume difference (RAVD) were used to evaluate the model performance on hold-out test sets. The trained models were applied to a cohort of 172 patients to quantify rotator cuff muscle volumes and fat fractions across preoperative and minimum 2- and 5-year follow-ups.

RESULTS: Compared to the ground truth, the models achieved mean Dice of 0.928 and 0.916, mean ASSD of 0.844 mm and 1.028 mm, mean HD95 of 3.071 mm and 4.173 mm, and mean RAVD of 0.025 and 0.068 on the hold-out test sets for the pre-operative and the minimum 2-year follow-up CT scans, respectively.

CONCLUSION: This study developed accurate and reliable deep learning models for automated 3D segmentation of rotator cuff muscles on clinical CT scans in TSA patients. These models substantially reduce the time required for muscle volume and fat fraction analysis and provide a practical tool for investigating how rotator cuff muscle health relates to surgical outcomes. This has the potential to inform patient selection, rehabilitation planning, and surgical decision-making in TSA and RCR.

PMID:40782188 | DOI:10.1007/s00256-025-04991-6

Clin Transplant. 2025 Aug;39(8):e70268. doi: 10.1111/ctr.70268.

ABSTRACT

BACKGROUND: In regions with limited donor availability, optimizing efficiency in lung transplant decision-making is crucial. Preoperative prediction of 1-year graft failure can enhance candidate selection and clinical decision-making.

METHODS: We utilized data from the Korean Organ Transplantation Registry to develop and validate a deep learning-based model for predicting 1-year graft failure after lung transplantation. A total of 240 cases were analyzed using 5-fold cross-validation. Among 25 preoperative factors associated with 1-year graft failure, we selected the top 9 variables with coefficients ≥ 0.25 for model development.

RESULTS: Of the 240 lung transplant recipients, 55 (22.92%) developed graft failure within 1 year, while 185 survived. The final predictive model incorporated nine key pretransplant factors: age, bronchiolitis obliterans syndrome after hematopoietic cell transplantation, pretransplant bacteremia, bronchiectasis, creatinine, diabetes, positive human leukocyte antigen crossmatch, panel reactive antibody 1 peak mean fluorescence intensity, and pretransplant steroid use. The multilayer perceptron model demonstrated strong predictive performance, achieving an area under the curve of 0.780 and an accuracy of 0.733.

CONCLUSIONS: Our machine learning-based model effectively predicts 1-year graft failure in lung transplant recipients using a minimal set of pretransplant variables. Further validation is needed to confirm its clinical applicability.

PMID:40782091 | DOI:10.1111/ctr.70268

Int J Artif Organs. 2025 Aug 9:3913988251359522. doi: 10.1177/03913988251359522. Online ahead of print.

ABSTRACT

Lung cancer (LC) is one of the leading causes of cancer related deaths worldwide and early recognition is critical for enhancing patient outcomes. However, existing LC detection techniques face challenges such as high computational demands, complex data integration, scalability limitations, and difficulties in achieving rigorous clinical validation. This research proposes an Enhanced Hyper Tuning Deep Learning (EHTDL) model utilizing bioinspired algorithms to overcome these limitations and improve accuracy and efficiency of LC detection and classification. The methodology begins with the Smooth Edge Enhancement (SEE) technique for preprocessing CT images, followed by feature extraction using GLCM-based Texture Analysis. To refine the features and reduce dimensionality, a Hybrid Feature Selection approach combining Grey Wolf optimization (GWO) and Differential Evolution (DE) is employed. Precise lung segmentation is performed using Mask R-CNN to ensure accurate delineation of lung regions. A Deep Fractal Edge Classifier (DFEC) is introduced, consisting of five fractal blocks with convolutional layers and pooling to progressively learn LC characteristics. The proposed EHTDL model achieves remarkable performance metrics, including 99% accuracy, 100% precision, 98% recall, and 99% F1-score, demonstrating its robustness and effectiveness. The model's scalability and efficiency make it suitable for real-time clinical application offering a promising solution for early LC detection and significantly enhancing patient care.

PMID:40781973 | DOI:10.1177/03913988251359522

Med Phys. 2025 Aug;52(8):e17976. doi: 10.1002/mp.17976.

ABSTRACT

BACKGROUND: Quantitative measures of dopamine transporter (DaT) uptake in the caudate, putamen, and globus pallidus (GP) derived from DaT-single-photon emission computed tomography (SPECT) images are being investigated as biomarkers to diagnose, assess disease status, and track the progression of Parkinsonism. Reliable quantification from DaT-SPECT images requires performing attenuation compensation (AC), typically with a separate x-ray CT scan. Such CT-based AC (CTAC) has multiple challenges, a key one being the non-availability of x-ray CT components on many clinical SPECT systems. Even when a CT is available, the additional CT scan leads to increased radiation dose, costs, and complexity; potential quantification errors due to SPECT-CT misalignment; and higher training and regulatory requirements.

PURPOSE: To overcome the challenges with the requirement of a CT scan for AC in DaT SPECT, we develop a transmission-less AC method for DaT SPECT and validate the method in a clinically realistic setting using an in silico imaging trial.

METHOD: Integrating concepts from physics and deep learning (DL), we propose a DL-based transmission-less AC method for DaT-SPECT (DaT-CTLESS). In this method, an initial attenuation map reconstructed from scatter-energy window projection is segmented into different regions using a U-net-based network trained on CT scans. Each region is assigned a predefined attenuation coefficient, yielding an attenuation map for AC. An in silico imaging trial, titled ISIT-DaT, was designed to evaluate the performance of DaT-CTLESS on the regional uptake quantification task. In this trial, DaT SPECT scans of a virtual patient population, curated from CT and MR images of real patients, were generated with simulated SPECT scanners from two vendors. The Society of Nuclear Medicine (SNM) guidelines suggest using a uniform attenuation map for AC (UAC) when a CT scan is unavailable. Thus, the primary objective of ISIT-DaT was to assess whether the correlation between the activity uptake obtained using DaT-CTLESS and CTAC, as quantified using the intraclass correlation coefficient (ICC), was higher than the correlation between UAC and CTAC. Secondary objectives included evaluating DaT-CTLESS on the task of distinguishing patients with normal versus reduced DaT-specific binding ratio (SBR) of putamen, evaluating the repeatability of DaT-CTLESS in a test-retest study, assessing the generalizability across two SPECT scanners, evaluating DaT-CTLESS using fidelity-based figures of merit (FoMs), and evaluating the sensitivity of DaT-CTLESS to intra-regional uptake heterogeneity. Finally, we compared DaT-CTLESS with two other deep-learning transmission-less AC methods on regional uptake quantification across different training dataset sizes.

RESULTS: In the ISIT-DaT trial, data from 150 virtual patients were used to train, and another 47 were used to evaluate the DaT-CLTESS method. We observed that DaT-CTLESS yielded a significantly higher correlation with CTAC than the correlation between UAC and CTAC on the regional DaT uptake quantification task. Further, DaT-CLTESS had an excellent agreement with CTAC (ICC: 0.96, 95% CI: [0.94, 0.97], p < 0.05) on this task, significantly outperformed UAC in distinguishing patients with normal versus reduced putamen SBR, and on fidelity-based FoMs, yielded good generalizability across two scanners, was generally insensitive to intra-regional uptake heterogeneity, demonstrated good repeatability in the test-retest study, exhibited robust performance even as the size of the training data was reduced, and generally outperformed the other considered DL methods on the task of quantifying regional uptake across different training dataset sizes.

CONCLUSION: The proposed DaT-CTLESS method, as evaluated in ISIT-DaT trial, was observed to yield reliable performance for transmission-less AC in DaT-SPECT, providing a strong motivation for further clinical evaluation.

PMID:40781836 | DOI:10.1002/mp.17976

Med Phys. 2025 Aug;52(8):e17990. doi: 10.1002/mp.17990.

ABSTRACT

BACKGROUND: CT lung imaging protocols need to be optimized. This claim is especially important due to the possible introduction of low-dose CT (LDCT) for lung cancer screening. Given the incorporation of non-linear reconstructions and post-processing, the use of phantoms that consider task-based evaluation is needed. This is also true for acceptance and continuous QC use.

PURPOSE: To present and validate a lung-CT hybrid phantom composed of two setups, one for task-based image quality metrics and the other anthropomorphic.

METHODS: The task-based metrics setup was based on the well-known Mercury phantom and the anthropomorphic setup named Freddie (from Figure of Merit Performance evaluation of Detectability in Diagnostic CT Imaging Equipment) was designed with the same basic dimensions of the Mercury phantom, but including pieces and materials for mimicking chest structures, such as tracheobronchial tree and lung parenchyma. This setup allows the inclusion of pieces of different sizes to mimic ground-glass opacities, and sub-solid and solid lung nodules. The validation of the phantom adopted three methods: comparative evaluation of the attenuation properties and the corresponding Hounsfield Units (HU) values of the selected materials; image assessment according to five chest radiologists and eight non-radiologists' observations (reader study), and measurement of task-based metrics. Images of both setups were acquired using two clinical thorax protocols, both using automatic tube current modulation (TCM). Two x-ray filter combinations were adopted. The images were reconstructed using a deep learning-based algorithm.

RESULTS: The agreement of nominal and observed HU values in the task-based setup was within 15%, except for three (TangoBlack+, VeroClear, and HIPS) of the materials employed in the phantom construction, at some beam energies. In the reader study, synthetic solid nodules printed in VeroClear received average Likert scores 4.0 (range 3.0-4.0) from radiologists and 3 (range 2.6-3.8) from non-radiologists, printed in TangoBlack+ received an average Likert score of 3.9 (range 3.8-4.2) from radiologists and 4.0 (range 3.8-4.4) from non-radiologists, while those printed in HIPS scored an average Likert of 3.8 (range 3.3-3.9) from radiologists and 3.3 (range 3.1-3.3) from non-radiologists. The synthetic ground-glass opacities (GGO) nodules manufactured in EVA received an average Likert score of 3.8 (range 2.8-4.6) from radiologists and 4.3 (range 3.6-4.8) from non-radiologists. The task-based setup demonstrated detectability index variations across protocols influenced by the dose levels, voltage, and x-ray beam filtration used.

CONCLUSIONS: The novelty of the proposed design is concentrated on the possibility of associating the response of the task-based setup (Mercury) with a patient-based setup (Freddie) in a unique phantom. This hybrid design enhances the potential to apply the detectability index for optimizing CT protocols in clinical scenarios.

PMID:40781832 | DOI:10.1002/mp.17990

Med Phys. 2025 Aug;52(8):e18002. doi: 10.1002/mp.18002.

ABSTRACT

BACKGROUND: Cherenkov imaging enables real-time visualization of megavoltage X-ray or electron beam delivery to the patient during radiation therapy (RT). Bio-morphological features, such as vasculature, seen in these images are patient-specific signatures that can be used for verification of positioning and motion management that are essential to precise RT treatment. However, no concerted analysis of this biological feature-based tracking has been utilized until now because of the slow speed and accuracy of conventional image processing for feature segmentation.

PURPOSE: This study aims to demonstrate the first deep learning framework for such an application, achieving video frame rate processing.

MATERIALS AND METHODS: To address the challenge of limited annotation of bio-morphological features in Cherenkov images, a transfer learning strategy was applied. A fundus photography dataset including 20,529 patch retina images with ground-truth vessel annotation was used to pre-train a ResNet based segmentation framework. Subsequently, a small Cherenkov dataset (1483 images from 212 treatment fractions of 19 breast cancer patients) with known annotated vasculature masks was used to fine-tune the model for accurate segmentation prediction.

RESULTS: The well-trained model was tested on clinical Cherenkov dataset which was not used in fine-tune steps. This deep learning framework achieved consistent and rapid segmentation of Cherenkov-imaged bio-morphological features on a test dataset containing 19 patients (179 images), including subcutaneous veins, scars, and pigmented skin. The average segmentation by the model achieved a Dice score of 0.85 and required less than 0.7 ms processing time per instance.

CONCLUSIONS: The model demonstrated outstanding consistency against input image variances and speed compared to conventional manual segmentation methods, laying the foundation for online segmentation in real-time monitoring in a prospective setting.

PMID:40781822 | DOI:10.1002/mp.18002

Med Phys. 2025 Aug;52(8):e18016. doi: 10.1002/mp.18016.

ABSTRACT

BACKGROUND: One-tissue compartment model (1TCM) kinetic parameters calculated from dynamic [11C]aceta te cardiac PET/CT imaging can assess cardiac function and assist clinical diagnosis. However, the long acquisition time of dynamic data hinders its clinical application.

PURPOSE: This study proposed a deep learning-based method for the generation of [11C]acetate 1TCM kinetic parametric images with shortened dynamic PET data, aiming to explore the feasibility of reducing the time required for parametric analysis.

METHODS: A spatial-temporal cascaded network (STCN), consisting of two convolutional modules and one Transformer module, was proposed to generate parametric images K1, k2, and vb. The STCN was trained and tested on [11C]acetate dataset (training/testing: 40 subjects/17 subjects) using 10 frames of dynamic data acquired within the first 10 min of scanning. The parametric images fitted from 40 min of dynamic data using non-linear least squares (NLLS) are considered the reference standard (RS). A temporal loss was incorporated into the training process by integrating the kinetic model. The performance of the STCN was evaluated using normalized root-mean-square error (NRMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). Weighted Akaike information criterion (WAIC) and coefficient of variance (CoV) were calculated within the myocardial region to evaluate the model's goodness-of-fit and the parameter's degree of variability. The proposed method was compared with NLLS and multilinear least squares fitted on 10 min of dynamic data (CM_10 and MLM_10). Three deep learning-based methods, that is, U-Net, Pix2pix, and CycleGAN, were also trained for comparison. Furthermore, ablation experiments were performed to assess the contribution of individual components of the STCN to the generation of parametric images.

RESULTS: The STCN achieved the best PSNR and SSIM for k2 and vb parametric images (PSNR: 25.718 ± 2.635 and 32.230 ± 4.090; SSIM: 0.864 ± 0.056 and 0.944 ± 0.041, respectively). The PSNR for the K1 images generated by STCN was lower than that generated by the Pix2pix model (28.927 ± 2.956 vs. 28.930 ± 2.705). The 1TCM parameters obtained by STCN achieved an average WAIC of 635.64 ± 38.44 in the myocardial region. No significant difference in CoV within the myocardium was found between RS and parametric images derived from STCN. The ablation study results demonstrated that our proposed model architecture and specialized loss functions could improve the quality of the generated parametric images in NRMSE, PSNR and SSIM.

CONCLUSIONS: The result of the present study shows that the proposed STCN can generate 1TCM parametric images using only 10 min of dynamic [¹¹C]acetate PET data, demonstrating its potential for calculating cardiac [11C]acetate PET 1TCM kinetic parameters in clinical practice.

PMID:40781790 | DOI:10.1002/mp.18016