Search Results (265)

Background/Objectives: Differentiating recurrent disease from postsurgical changes on ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) remains challenging in malignant pleural mesothelioma (MPM). This study aimed to characterize the temporal patterns of postsurgical FDG uptake and evaluate the diagnostic performance of FDG PET/CT for detecting recurrent disease after radical surgery. Methods: We retrospectively analyzed 91 postsurgical PET/CT scans from 45 patients with MPM who underwent extrapleural pneumonectomy (EPP; n = 29) or pleurectomy/decortication (P/D; n = 16). Scans were stratified into four postoperative time intervals: <6 months, 6 to <12 months, 12 to <24 months, and ≥24 months. FDG uptake in the postsurgical bed and local recurrent lesions was quantified using maximum standardized uptake value ratios normalized to the mediastinal blood pool and liver. Recurrence was confirmed by histopathology or follow-up imaging. Results: Postsurgical FDG uptake showed a time-dependent decline, with significantly lower uptake beyond 24 months postoperatively (p < 0.05). EPP patients demonstrated significantly higher postsurgical FDG uptake than P/D patients (p < 0.01). FDG PET/CT identified occult recurrence in 23.4% of CT-negative scans. Local recurrent lesions showed significantly higher FDG uptake than postsurgical changes across all postoperative intervals (p < 0.001). Conclusions: Postsurgical FDG uptake in MPM demonstrates a time-dependent decline, and surgical extent is an important determinant of background metabolic activity. Despite this variable background, FDG PET/CT demonstrated high diagnostic accuracy for detecting recurrent disease, including CT-occult recurrences. Incorporating surgical type and postoperative interval into PET/CT interpretation may improve diagnostic accuracy in postoperative MPM surveillance. Full article

Primary pulmonary artery sarcoma (PPAS) is a mesenchymal tumor originating from the pulmonary artery, accounting for approximately 0.001–0.003% of all sarcomas. The early clinical symptoms are atypical, and diagnosis is often delayed, making the management of this disease challenging. The widespread availability of multidetector computed tomography (MDCT), ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT), and high-resolution echocardiography has significantly improved the diagnostic capability for PPAS. We herein report a 74-year-old female patient who presented with a 3-week history of exertional dyspnea without an apparent trigger. She had received anti-inflammatory therapy at another hospital for one week. Five days before admission, she experienced right-sided chest pain without apparent cause, which was respiratory-related. On the day of admission, laboratory tests revealed a slight elevation in D-dimer levels. Echocardiography showed an irregular, moderately echogenic mass at the origin of the right pulmonary artery. Enhanced computed tomography (CT) of the chest revealed a filling defect in the right pulmonary artery accompanied by bilateral pleural effusion. The patient was given heparin anticoagulation therapy. To confirm the nature of these lesions, a PET/CT scan was conducted five days after admission, which indicated hypermetabolism in the right pulmonary artery, suggesting primary pulmonary artery sarcoma. Due to the poor efficacy of anticoagulation therapy, the patient continued to experience breath-holding after physical activity. Subsequently, catheter-guided interventional angiography was carried out for pulmonary artery thrombectomy and biopsy, and histopathological examination revealed pulmonary artery sarcoma. Given the patient’s respiratory failure and heart failure, as well as the uncertain efficacy of radiotherapy and chemotherapy, interventional pulmonary artery thrombectomy alleviated the chest pain. Currently, the patient’s overall condition is stable. Full article

Recent advances in medical image segmentation have introduced a wide spectrum of deep learning paradigms, including 2D/3D convolutional neural networks (CNNs), transformer-based architectures, vision-language models (VLMs), prompt-driven foundation models such as Segment Anything Model (SAM), and diffusion-based approaches. Although these methods have demonstrated remarkable performance across MRI, CT, PET, ultrasound, and endoscopic imaging, the rapid proliferation of architectures has created methodological uncertainty regarding optimal model selection under varying clinical and data constraints. Existing surveys primarily focus on architectural categorization, yet provide limited guidance for decision-oriented model selection. This study presents a comprehensive and decision-guided survey that systematically analyzes segmentation paradigms across imaging modalities, task types, dataset characteristics, and evaluation protocols. Beyond taxonomy, we propose a practical model selection framework that links clinical scenarios, such as small lesion detection, multi-organ 3D segmentation, limited-data regimes, and domain shift, to appropriate segmentation strategies. Furthermore, robustness, generalization, annotation variability, and benchmarking reproducibility are critically examined. By integrating architectural taxonomy, cross-modal comparative analysis, and a structured decision framework, this work provides a clinically oriented roadmap for selecting segmentation methods and highlights future research directions toward reliable and reproducible medical AI systems. Full article

Pulmonary nodules detected in patients with a history of malignancy are often clinically presumed to represent metastatic disease until proven otherwise. Granular cell tumor (GCT) is an uncommon, usually benign neoplasm of presumed Schwannian origin, which rarely occurs in the lung. Our aim is to emphasize the diagnostic challenges and the crucial role of histopathology in preventing overtreatment in oncology patients. Herein, we report the case of a 56-year-old woman with a previous history of papillary renal cell carcinoma diagnosed one year earlier, staged as pT1, WHO/ISUP grade 2, and treated with partial nephrectomy, with no evidence of residual disease or distant metastases at follow-up. During routine surveillance, she developed a solitary pulmonary nodule. Chest computed tomography (CT) showed a 12 mm solid nodule in the left upper lobe which was then further investigated with a positron emission tomography with 2-[¹⁸F] fluoro-2-deoxy-D-glucose [(¹⁸F)-FDG PET/CT, revealing a low glucidic uptake (SUVmax 4 and SUV mean 1.4). Endobronchial ultrasound-guided biopsy was non-diagnostic. Given the patient’s oncological history, the solid appearance on CT, and the mild FDG uptake, metastatic disease could not be excluded, and a parenchyma-sparing diagnostic wedge resection was performed. Histology showed a well-circumscribed proliferation of epithelioid cells with abundant granular eosinophilic cytoplasm and bland nuclei. Immunohistochemistry demonstrated diffuse S100 and CD68 positivity, supporting the diagnosis of primary pulmonary granular cell tumor. This case underscores the critical role of histopathological evaluation in the assessment of solitary pulmonary nodules, emphasizing that lesions identified during oncologic surveillance are not invariably indicative of malignancy. Full article

Multifunctional scaffolds that combine structural support with the controlled delivery of bioactive agents remain a major challenge in tissue engineering. To extend the use of these devices in biomedicine, 3D printing is presented as an alternative that enables the manufacture of complex devices tailored to each patient, thereby solving specific problems in a timely and efficient manner. In this study, porous 3D scaffolds were fabricated via digital light processing (DLP) using a PET-RAFT resin composed of 2-(dimethylamino)ethyl methacrylate (DMAEMA) and poly(ethylene glycol) diacrylate (PEGDA₅₇₅). Sodium chloride (NaCl) was incorporated as a porogen, while insulin-loaded poly(lactic-co-glycolic acid) (PLGA) nanoparticles were embedded as osteoinductive agents. The printed constructs exhibited high-resolution, reproducible trabecular-like architectures, as confirmed by micro-computed tomography (micro-CT), with interconnected pores averaging 70.7 ± 24.7 μm and a total porosity of 57.0 ± 6.98%. Thermal and chemical analyses confirmed scaffold stability and controlled degradability. Cytocompatibility assays using MC3T3-E1, C2C12, hGMSCs, and C166-GFP cells showed viability above 80% after 7 days (ISO 10993-5). Insulin-loaded nanoparticles enabled sustained release, characterized by an initial burst followed by gradual release up to 72 h. Dynamic bioreactor culture enhanced cell adhesion and RUNX2 expression, confirming the osteoinductive potential of the hybrid scaffold for advanced BTE applications. This study introduces an innovative PET-RAFT-derived resin that combines structural reinforcement with spatiotemporal regulation of insulin release, offering a potential strategy for enhanced biomaterial tissue engineering and tailored therapeutic interventions. Full article

Background: Positron emission tomography (PET) provides physiologic information central to oncologic staging and treatment assessment, but its availability is limited by cost, radiation exposure, and scanner access. Synthesizing PET from computed tomography (CT) is attractive but challenging, as tracer uptake is only partially constrained by anatomy, making the mapping inherently one-to-many. Methods: We propose a conditional 3D latent diffusion framework (3D-LDM) for CT-to-PET synthesis in the head–neck and thoracic region. The pipeline localizes anatomy by segmenting lungs in CT and restricting the volume to reduce irrelevant variability. PET volumes are encoded into a compact latent space using a KL-regularized 3D autoencoder, and a conditional 3D diffusion U-Net learns to generate PET latents conditioned on CT via a denoising diffusion process. The model was trained and evaluated on 900 paired PET/CT studies. Performance was assessed in SUV space using MAE, PSNR, and SSIM, and compared against transformer-, CNN-, and GAN-based baselines. Results: On the held-out test cohort, 3D-LDM achieved the best overall quantitative fidelity (MAE = 303.05 ± 22.16 SUV units, PSNR = 32.64 ± 1.79, SSIM = 0.86 ± 0.03), outperforming all baselines with statistically significant differences (p < 0.001). At the lesion level, the model achieved a precision of 0.76 (95% CI: 0.71, 0.81) and recall of 0.76 (95% CI: 0.72, 0.80), detecting an average of 3.19 lesions per scan with a false-positive rate of 0.72/scan. Lesion-wise NMSE was 11.37%, significantly outperforming GAN and transformer baselines. Conclusions: 3D-LDM enables efficient, high-fidelity PET synthesis in the head–neck and thoracic regions, substantially improving lesion-level accuracy over state-of-the-art baselines. While it is not a replacement for diagnostic PET, these results support the model’s potential as a clinical decision support tool. Full article

Background/Objectives: Assessing treatment response in neuroendocrine tumors (NETs) remains challenging. The multifactorial mechanism of action of peptide receptor radionuclide therapy (PRRT) further complicates response evaluation, particularly in the absence of standardized criteria. This study aimed to compare different imaging-based response assessment methods after PRRT and to explore their relationship with clinical outcomes, particularly progression-free survival (PFS). Methods: In this single-center retrospective study, we analyzed NET patients treated with PRRT between 2020 and 2024 who underwent [⁶⁸Ga]Ga-DOTATOC PET/CT before and after therapy, with a minimum follow-up of 12 months. Five response criteria were evaluated: (a) Krenning Score changes; (b) adapted PERCIST criteria (MORE); (c) ZP-normalized parameter; (d) qualitative visual PET assessment; and (e) RECIST-based morphological response on contrast-enhanced CT. Imaging findings were correlated with clinical outcomes. Nonparametric analyses were performed using the MedCalc^® software. Results: Thirty-one patients (median age 63 years; 17 males) with NET were evaluated after PRRT. Post-PRRT PET/CT was performed at a median of 3 months. Response rates varied across methods, with higher rates noted using functional imaging (MORE 66%, Visual PET 68%, ZP 70%) compared to RECIST (40%) and Krenning score (21%). After a median follow-up of 37 months, 58% of patients experienced disease progression. Although no significant association was found between the response criteria and progression (p > 0.05), functional imaging showed a trend toward better correlation with longer progression-free survival. Conclusions: Functional PET-based responsecriteria suggest association with progression-free survival compared to RECIST criteria and Krenning score changes. However, given the exploratory nature of the study and its methodological limitations, these observations should be considered hypothesis-generating only. They do not provide definitive evidence of superiority over established assessment methods and therefore should not be interpreted as practice-changing. Full article

Background/Objectives: Psoriatic disease is a systemic inflammatory condition associated with increased cardiometabolic risk, but the impact of contemporary systemic therapies and narrowband ultraviolet B (NB-UVB) phototherapy on vascular and systemic inflammatory markers remains incompletely characterized. We aimed to systematically synthesize prospective evidence on treatment-associated changes in vascular inflammation and systemic inflammatory biomarkers in adults with moderate-to-severe psoriatic disease. Specifically, we evaluated changes assessed by 18F-FDG PET/CT imaging and circulating biomarkers following biologic therapies or NB-UVB phototherapy. Methods: We systematically searched MEDLINE, Embase, Web of Science, Scopus, and CENTRAL from inception to 31 January 2026 for prospective interventional and observational studies in adults with psoriasis or psoriatic arthritis treated with biologic agents targeting TNF-α, IL-12/23, IL-17, or IL-23, or with NB-UVB phototherapy. Eligible studies were required to report serial assessments of vascular inflammation by 18F-FDG PET/CT (typically aortic target-to-background ratio) and/or systemic inflammatory markers (high-sensitivity C-reactive protein, interleukin-6, TNF-α, GlycA, or hematologic indices such as the neutrophil-to-lymphocyte ratio) over at least 8 weeks of follow-up. We imposed no language restrictions and included only full-text, peer-reviewed prospective studies. Risk of bias was evaluated using RoB 2 for randomized trials and ROBINS-I for nonrandomized studies. Random-effects meta-analyses were prespecified for outcomes reported by at least two clinically comparable studies; however, because of substantial heterogeneity in reporting and methodology, effect estimates were summarized using a structured narrative synthesis. Results: Thirteen prospective studies (n ≈ 900 adults, published 2015–2025) met inclusion criteria, including four studies with serial 18F-FDG PET/CT imaging and one additional PET/CT study providing baseline observational data on vascular inflammation, as well as eight biomarker-focused prospective cohorts. Across randomized mechanistic trials and observational studies, biologic therapies reduced aortic target-to-background ratio by approximately 6–12% over 12–24 weeks (e.g., mean change from 2.42 to 2.18 with TNF-α inhibition and from 2.51 to 2.20 with IL-17 blockade), and no study reported worsening of PET-derived vascular indices under effective systemic treatment. Biologic and other systemic therapies produced concordant reductions in hs-CRP (typically by 30–50%), IL-6, TNF-α, GlycA, and blood-count-derived indices including neutrophil-to-lymphocyte ratio, with biomarker improvements frequently paralleling reductions in cutaneous disease activity and cardiometabolic risk markers. Two NB-UVB cohorts demonstrated significant hs-CRP reductions of roughly 20–30% and modulation of vitamin D-related inflammatory proteins, suggesting systemic anti-inflammatory effects, although these changes appeared less pronounced than with biologic therapy and were not accompanied by vascular imaging. Conclusions: Contemporary systemic psoriasis therapies, particularly biologic agents targeting the IL-23/Th17 axis and TNF-α, are associated with consistent reductions in aortic vascular inflammation and broad improvements in systemic inflammatory biomarkers, whereas NB-UVB phototherapy confers more modest but measurable systemic anti-inflammatory effects, although the current evidence does not allow differentiation between individual biologic classes in terms of magnitude of effect. Although reductions in vascular and systemic inflammatory markers were observed across therapies targeting TNF-α, IL-12/23, IL-17, and IL-23, the small number of mechanistic imaging studies and absence of head-to-head comparisons do not allow robust differentiation between biologic classes or support a uniform class effect. The convergence of imaging and biomarker data reinforces psoriasis as a clinically relevant model of inflammation-driven atherosclerosis and supports the concept that effective control of psoriatic inflammation may contribute to cardiovascular risk modification, highlighting the need for integrated cardiovascular risk assessment in routine care. However, the imaging evidence base remains limited to four small mechanistic PET/CT studies with relatively short follow-up, which constrains the strength and generalizability of conclusions regarding vascular inflammation. Larger, adequately powered, event-driven prospective trials with standardized imaging and biomarker endpoints are needed to determine whether these vascular and systemic anti-inflammatory effects translate into reduced cardiovascular events in psoriatic disease; because of methodological and reporting heterogeneity across the 13 included studies, these conclusions are based on a structured narrative synthesis rather than a formal quantitative meta-analysis. PROSPERO registration number: CRD420261296646. Full article

Low-count positron emission tomography (PET) is inherently affected by Poisson-dominated noise, which degrades image contrast, structural delineation, and quantitative reliability. This study systematically evaluated residual learning-based deep neural networks to investigate the influence of residual block depth on PET image restoration performance under low-count conditions. We employed a physically controlled striatum phantom, fabricated using 3D printing technology, to ensure reproducible acquisition conditions and controlled physical variability. PET images were acquired using a clinical PET/computed tomography (CT) system with list-mode acquisition. Low-count images reconstructed from short-duration acquisition were paired with high-count reference images reconstructed from extended acquisitions. We compared conventional filtering techniques, including median, Wiener, and modified median Wiener filters, with residual network (ResNet)-based models incorporating 8, 16, and 32 residual blocks. Image quality was quantitatively assessed using contrast-to-noise ratio (CNR), coefficient of variation (COV), line profile analysis, universal quality index (UQI), and perceptual image patch similarity (LPIPS). The results demonstrated that ResNet-based restorations substantially outperformed conventional filtering techniques in contrast recovery, signal stability, and structural preservation. The ResNet-16 model achieved the most balanced performance, yielding the highest CNR (9.02) and lowest COV (0.105), while also demonstrating superior structural and perceptual similarity, as indicated by UQI (0.9224) and LPIPS (0.0174), relative to the high-count reference images. Deeper network configurations exhibited diminishing returns and reduced structural consistencies. These findings indicate that an intermediate residual block depth is optimal for low-count PET image restoration and highlight the importance of architectural optimization in deep learning-based PET image enhancement with phantom-based evaluation frameworks. Full article

Testicular metabolism can be non-invasively assessed using FDG-PET/CT, which provides insights into physiological and age-related changes. Understanding normal testicular FDG uptake is essential to distinguish between benign variation and pathological findings. In this retrospective study, 80 men (mean age: 54.7 years, range: 26–79) who underwent PET/CT for lung nodule evaluation were analyzed, excluding individuals with testicular disease, prior surgery, or elevated blood glucose (>180 mg/dL). FDG uptake (SUV_mean) and testicular volume were measured for each testis, and correlations with age, blood glucose, and volume were assessed using standard statistical methods. The mean testicular SUV_mean was 2.62 ± 0.50, showing a significant negative correlation with age and a weak positive correlation with testicular volume. After adjusting for volume, the negative association with age persisted, while no significant relationship with blood glucose was observed. These findings indicate that physiological testicular FDG uptake gradually declines with age, reflecting both metabolic and structural alterations. Recognizing these normal patterns is critical for accurate PET/CT interpretation and reducing the risk of false-positive findings. Full article

Objectives: Lesion-level dynamics may reveal pulmonary tuberculosis (PTB) heterogeneity and help identify factors associated with treatment outcomes. Methods: A total of 288 serial Computed Tomography (CT) scans from 125 PTB patients were obtained from the National Institute of Allergy and Infectious Diseases (NIAID) TB Portals database (2008–2023). Lesions were segmented and annotated to obtain volume and imaging features, and a conservative longitudinal volume quantification method was used to characterize dynamic volume patterns. The proportion of lesions with different patterns was analyzed at the patient level to assess trajectory diversity. Firth’s penalized logistic regression was used to identify factors associated with treatment outcomes. Results: Among 435 lesions in 125 patients, five patterns emerged: Stable, Decrease, Increase, Mix-I-D (increase then decrease), and Mix-D-I (decrease then increase). Multiple patterns coexisted in 66.7% of treatment success patients and all treatment failure patients. Mix-D-I lesions were identified more frequently in treatment failure patients (25.0% vs. 1.4%, p = 0.027), and in multivariable analysis, the presence of Mix-D-I lesions was statistically associated with treatment failure (p = 0.024). Conclusions: PTB lesions showed high trajectory heterogeneity. The presence of Mix-D-I lesions may point to an unfavorable treatment course, suggesting lesion dynamics could serve as a potential indicator for poor outcomes. By quantifying lesion-level trajectories on serial CT scans, we extend PET/CT-based evidence and support the value of routine monitoring in clinical management of tuberculosis. Full article

Foundation models, known as the large-scale, pretrained models capable of generalizing across diverse tasks, have significantly advanced the field of medical image analysis. While most early applications focused on 2D modalities, the unique challenges and opportunities associated with volumetric medical imaging have recently attracted growing interest. This study provides a comprehensive overview of the current landscape of foundation models tailored for volumetric medical image analysis, with a focus on CT, MRI, and PET imaging. We examine key components of these models, including 3D architectures, training strategies, and supported modalities. In addition, we highlight their contribution to major clinical tasks such as classification and prediction, segmentation, image registration, quality enhancement, and visual question answering. Critical challenges of these models, including high computational cost, limited and less diverse 3D datasets, and domain adaptation, are discussed alongside the promising solutions and future research directions. By synthesizing recent advances in volumetric foundation models and outlining key technical and clinical challenges, this review provides a thorough roadmap toward the development of scalable, generalizable, and clinically applicable AI systems for volumetric medical images. Full article

Background and Objectives: Dual-phase amyloid PET imaging has been proposed to provide complementary information regarding amyloid burden and cerebral perfusion. This exploratory pilot study evaluated whether semiquantitative parameters derived from dual-phase PET/CT could differentiate individuals operationally classified as Alzheimer’s disease with mild functional impairment (AD-MFI) from those with mild cognitive impairment (MCI). Materials and Methods: Twenty-four participants (AD-MFI, n = 19; MCI, n = 5) underwent dual-phase amyloid PET/CT and structural MRI. Early phase SUV (eSUV), delayed-phase SUV (dSUV), standardized uptake value ratios (SUVR), and the difference between early and delayed uptake (SUV_diff) were analyzed across predefined cortical regions. Group differences were assessed using nonparametric tests, with false discovery rate (FDR) and Bonferroni corrections applied for multiple comparisons. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. Results: Several regional parameters demonstrated nominally significant group differences in uncorrected analyses; however, none remained statistically significant after correction for multiple comparisons. Among the evaluated metrics, SUV_diff demonstrated the highest diagnostic performance (sensitivity 84.2%, specificity 80.0%), followed by eSUV (68.4%, 100%) and MRI cortical volume (47.4%, 100%). Delayed-phase parameters alone showed limited discriminatory robustness despite observed group-level differences. Conclusions: In this exploratory cohort, SUV_diff showed moderate discriminatory potential between AD-MFI and MCI. However, given the small sample size and multiplicity of comparisons, the results should be interpreted as hypothesis-generating. Larger, prospective studies are required to determine the reproducibility and clinical utility of dual-phase semiquantitative parameters. Full article

Background: Phantoms are essential in medical imaging, providing reproducible and quantitative means for system and protocol evaluation, image quality assessment, and dosimetry without patient exposure. Additive manufacturing enables rapid, accurate fabrication of phantoms ranging from simple geometries to complex anthropomorphic models. Ongoing developments in 3D printing technologies and polymer formulations have enhanced mechanical properties and printability, but also affect X-ray attenuation behaviour, necessitating an update with current materials to facilitate the choice of appropriate materials mimicking body tissues in radiographic phantoms. Methods: Attenuation properties of 27 photopolymer resins and 22 thermoplastic filaments (based on PLA, ABS, HIPS, PETG/PCTG, and PVB) were quantified using a clinical CT scanner at 70–140 kV to establish reference data for material selection. Results: At 120 kV, resins exhibited attenuation values between 124 and 384 Hounsfield Units (HU), and filaments ranged from −69 to 308 HU (PLA-based filaments: 160 to 241 HU, ABS: −32 to 43 HU, PETG/PCTG: 151 to 308 HU, and HIPS: −69 to −22 HU). Energy dependence of HU values is presented from 70 to 140 kV tube potential. Compared to the 2021 study, a wider selection of X-ray opacities is available. Regarding SLA/DLP printing, resins with higher attenuation were identified, and flexible resins that had provided a choice of low attenuation printing materials in the range of 60 to 90 HU at 120 kV tended to replicate attenuation properties closer to rigid photopolymers; i.e., HU values were slightly higher. In FDM filaments, a wide variation in different PLA-, ABS-, and HIPS-based filaments is found. In copolymers from the PET/PCTG/PETG family, very inhomogeneous X-ray attenuations are still found, as anticipated. Conclusions: The range of X-ray attenuation observed demonstrates that commercially available 3D printing materials can replicate clinically relevant tissues and tissue-equivalent contrasts. Furthermore, the available range of attenuations has increased, as has the finer gradation of these materials. These findings support the design of patient- and task-specific imaging phantoms for optimization of acquisition protocols, image quality evaluation, and radiation dose studies, as well as facilitate the selection of appropriate phantom materials. Full article

This study investigates the development of advanced protective gloves by applying novel 3D-printed PET-G mesh overlay structures onto three textile substrates—polyamide (PA), polyester (PES), and cotton—using ultrasonic welding and contact welding. The focus was on assessing weld quality, thickness uniformity, and functional durability. Weld morphology and bonding integrity were evaluated using X-ray microtomography (micro-CT), while bending fatigue tests assessed mechanical performance under cyclic loading. The results show that ultrasonic welding produces more uniform welds, enhancing fatigue resistance, particularly on cotton and polyamide substrates. Non-uniform welds with thicker or uneven areas, typical of contact welding, correlated with reduced mechanical durability. These findings highlight the potential of additively manufactured overlay structures for hybrid protective gloves, demonstrating that weld thickness uniformity and substrate compatibility are key factors in optimizing mechanical performance. This work extends our previous research by introducing new 3D-printed overlay architectures and provides valuable insights into the practical implementation of additively manufactured polymeric structures in PPE development. Full article