Search Results (2,221)

Objectives: The HOSTILE score was developed to assess femoral access challenges in transcatheter valve therapy. Similar vascular issues arise in femoral cannulation for minimally invasive valve surgery, making CT-based planning essential. We adapted the score for surgical use (MOD-HOSTILE) and evaluated its association with neurological and adverse outcomes. Methods: In this single-center retrospective study, the MOD-HOSTILE score (0–11 points) was calculated for 364 patients undergoing minimally invasive heart valve surgery from 2019 to 2023. Patients were stratified into low (0–2), mild (>2–5), and high (>5–11) score categories. Outcomes included 30-day stroke, other neurological events, and perioperative complications. Results: High MOD-HOSTILE patients were significantly older (70 [64.7–73.0] vs. 61 [60.0–63.0] years; p < 0.01) and had higher surgical risk (EuroSCORE II 1.79 [1.26–2.16] vs. 0.83 [0.75–0.94]; p < 0.01). Neurological complications were more frequent in the high MOD-HOSTILE group, including stroke (8.7% vs. 0.9%; p = 0.02) and hemiplegia (13.0% vs. 0.9%; p < 0.01). Axillary cannulated patients had higher MOD-HOSTILE scores than femoral cannulated patients. Stroke risk was highest in patients with high MOD-HOSTILE score undergoing axillary cannulation (high vs. low MOD-HOSTILE, 18.2% vs. 0%; p = 0.04). Thirty-day mortality was comparable between groups (p = 0.09). MOD-HOSTILE predicted stroke with an AUC of 0.78 (95% CI 0.73–0.82) and OR 1.4 per point (95% CI 1.1–2.0). Conclusions: The MOD-HOSTILE score identifies vascular and neurological risk in minimally invasive valve surgery, with scores ≥5 indicating elevated risk of stroke and delirium. Patients with high scores may benefit from alternative surgical strategies. Full article

Actinium-225 (²²⁵Ac) has emerged as a pivotal alpha-emitter in modern radiopharmaceutical therapy, offering potent cytotoxicity with the potential for precise tumour targeting. Accurate, patient-specific image-based dosimetry for ²²⁵Ac is essential to optimize therapeutic efficacy while minimizing radiation-induced toxicity. Establishing a robust dosimetry workflow is particularly challenging due to the complex decay chain, low administered activity, limited count statistics, and the indirect measurement of daughter gamma emissions. Clinical single-photon emission computed tomography/computed tomography protocols with harmonized acquisition parameters, combined with robust volume-of-interest segmentation, artificial intelligence (AI)-driven image processing, and voxel-level analysis, enable reliable time-activity curve generation and absorbed-dose calculation, while reduced mixed-model approaches improve workflow efficiency, reproducibility, and patient-centred implementation. Cadmium zinc telluride-based gamma cameras further enhance quantitative accuracy, enabling rapid whole-body imaging and precise activity measurement, supporting patient-friendly dosimetry. Complementing these advances, the cerium-134/lanthanum-134 positron emission tomography in vivo generator provides a unique theranostic platform to noninvasively monitor ²²⁵Ac progeny redistribution, evaluate alpha-decay recoil, and study tracer internalization, particularly for internalizing vectors. Together, these technological and methodological innovations establish a mechanistically informed framework for individualized ²²⁵Ac dosimetry in targeted alpha therapy, supporting optimized treatment planning and precise response assessment. Continued standardization and validation of imaging, reconstruction, and dosimetry workflows will be critical to translate these approaches into reproducible, patient-specific clinical care. Full article

Glioblastoma (GB) is one of the most aggressive and invasive cancers. Current treatment protocols for GB include surgical resection, radiotherapy, and chemotherapy with temozolomide. However, despite these treatments, physicians still struggle to effectively image, diagnose, and treat GB. As such, patients frequently experience recurrence of GB, demanding innovative strategies for early detection and effective therapy. Bioconjugated quantum dots (QDs) have emerged as powerful nanoplatforms for precision imaging and targeted drug delivery due to their unique optical properties, tunable size, and surface versatility. Due to their extremely small size, QDs can cross the blood–brain barrier and be used for precision imaging of GB. This review explores the integration of QDs with click chemistry for robust bioconjugation, focusing on artificial intelligence (AI) to advance GB therapy, mechanistic insights into cellular uptake and signaling, and strategies for mitigating toxicity. Click chemistry enables site-specific and stable conjugation of targeting ligands, peptides, and therapeutic agents to QDs, enhancing selectivity and functionalization. Algorithms driven by AI may facilitate predictive modeling, image reconstruction, and personalized treatment planning, optimizing QD design and therapeutic outcomes. We discuss molecular mechanisms underlying interactions of QDs with GB, including receptor-mediated endocytosis and intracellular trafficking, which influence biodistribution and therapeutic efficacy. Use of QDs in photodynamic therapy, which uses reactive oxygen species to induce apoptotic cell death in GB cells, is an innovative therapy that is covered in this review. Finally, this review addresses concerns associated with the toxicity of metal-based QDs and highlights how QDs can be coupled with AI to develop new methods for precision imaging for detecting and treating GB for induction of apoptosis. By converging nanotechnology and computational intelligence, bioconjugated QDs represent a transformative platform for paving a safer path to smarter and more effective clinical interventions of GB. Full article

This review explores the transformative role of three-dimensional (3D) printing in radiation therapy for cancer treatment, emphasizing its potential to deliver patient-specific, cost-effective, and sustainable medical devices. The integration of 3D printing enables rapid fabrication of customized boluses, compensators, immobilization devices, and GRID collimators tailored to individual anatomical and clinical requirements. Comparative analysis reveals that additive manufacturing surpasses conventional machining in design flexibility, lead time reduction, and material efficiency, while offering significant cost savings and recyclability benefits. Case studies demonstrate that 3D-printed GRID collimators achieve comparable dosimetric performance to traditional devices, with peak-to-valley dose ratios optimized for spatially fractionated radiation therapy. Furthermore, emerging applications of artificial intelligence (AI) in conjunction with 3D printing promise automated treatment planning, generative device design, and real-time quality assurance, and are paving the way for adaptive and intelligent radiotherapy solutions. Regulatory considerations, including FDA guidelines for additive manufacturing, are discussed to ensure compliance and patient safety. Despite challenges such as material variability, workflow standardization, and large-scale clinical validation, evidence indicates that 3D printing significantly enhances therapeutic precision, reduces toxicity, and improves patient outcomes. This review underscores the synergy between 3D printing and AI-driven innovations as a cornerstone for next-generation radiation oncology, offering a roadmap for clinical adoption and future research. Full article

HPV-positive OPSCC shows a favourable prognosis, prompting evaluation of de-escalated and adaptive strategies. Quantitative imaging may provide scalable biomarkers to individualise care. Quantitative imaging can support baseline risk stratification, early on-treatment decision-making, and posttreatment surveillance in HPV-positive OPSCC. Real-world translation requires standardised reporting, calibration/harmonisation across centres, rigorous model validation, and workflow integration with radiotherapy planning. Quantitative MRI, CT, and PET, augmented by radiomics and AI, show convergent promise as non-invasive biomarkers to enable safe individualisation of therapy in HPV-positive OPSCC, contingent on methodological rigour and prospective, externally validated studies. Despite this promise, clinical translation faces substantial barriers, including limited external validation, heterogeneous methodologies, and the need for standardised, prospectively validated pipelines. Full article

Design: Retrospective observational cohort study conducted at university and private practice setting. Objective: To evaluate whether operator experience affects the predictability of orthodontic tooth movements and the overall treatment duration in clear aligner therapy. Materials and Methods: This retrospective observational study was conducted at the Dental School of the University of Turin and in private orthodontic settings. Seventy-two patients (50 females, 22 males; median age: 24.6 years; IQR = 5.9) with mild to moderate malocclusions were included and equally distributed between two groups: 36 patients treated by postgraduate orthodontic students (Group B) and 36 patients treated by experienced orthodontists (Group E). Post-treatment digital models were analyzed to assess discrepancies between the predicted and achieved tooth positions. The accuracy of specific movements—rotation, vertical displacement, and treatment duration—was statistically evaluated using the Mann–Whitney U test. Most of these differences, although statistically significant, remained below established thresholds for clinical relevance (0.5 mm/2°). Results: Expert operators achieved significantly greater accuracy in controlling the vertical movements of the upper central incisors (p = 0.01) and the rotational movements of the upper first molars (p = 0.03), upper lateral incisors (p = 0.03), lower incisors (p = 0.001), and lower premolars (p = 0.001). In contrast, non-expert operators demonstrated superior outcomes in the control of vertical movements of the upper premolars (p = 0.01) and in the rotational movement of the lower canines (p = 0.03). Treatment duration was significantly shorter in the expert group, with a median difference of 4.1 months (p = 0.0037). Conclusions: These findings confirm the importance of clinical experience in enhancing the predictability and efficiency of clear aligner therapy, particularly in complex movements. However, the improved performance of non-expert operators in selected areas—such as vertical control of upper premolars and rotation of lower canines—suggests that conservative movement planning may also play a role in improving clinical outcomes. Overall, expert clinicians achieved more predictable tooth movements and shorter treatment durations, underscoring the value of structured training and accumulated clinical expertise in optimizing clear aligner therapy. Full article

Background and Objectives: This study aimed to comparatively evaluate the association of commonly used comorbidity scores with survival, mortality, and recurrence in ovarian cancer patients aged 50 years and above. Materials and Methods: In this single-center, retrospective study, 130 female patients diagnosed between 2017 and 2024 who had received systemic therapy and had complete medical records were included. Comorbidity scores—including the Charlson Comorbidity Index (CCI), Cumulative Illness Rating Scale-Geriatric (CIRS-G), Adult Comorbidity Evaluation-27 (ACE-27), Elixhauser Comorbidity Index, Index of Coexistent Disease (ICED), and Functional Comorbidity Index (FCI)—were calculated for each patient. Survival analyses were conducted using the Kaplan–Meier method and Cox regression modeling. The prognostic accuracy of comorbidity scores was assessed via receiver operating characteristic (ROC) curve analysis. Results: Patients with higher CCI scores had significantly shorter survival, and CCI was identified as an independent prognostic factor in multivariate analysis. While other comorbidity scores were associated with overall survival in univariate analyses, they lost statistical significance in multivariate models. Patients with a higher comorbidity burden experienced more frequent disease recurrence and shorter time to recurrence. Conclusions: Comorbidity burden is a key clinical determinant of survival and disease trajectory in older patients with ovarian cancer. The CCI demonstrated the highest prognostic accuracy in this population and may serve as a valuable tool in individualized treatment planning. Integration of comorbidity-based assessments into standard decision-making processes is recommended in geriatric oncology practice. Full article

Background/Objectives: Dementia represents a growing public health challenge. The WHO Global Action Plan on the Public Health Response to Dementia emphasizes early detection, risk reduction, and innovation as key priorities. Mild Behavioral Impairment (MBI), defined as the emergence of persistent neuropsychiatric symptoms in older individuals, represents a potential marker of early neurodegeneration and possible window for early intervention. This review explores the role of MBI in dementia prevention, mapping current evidence within the WHO Global Action Plan framework. Methods: A comprehensive search was performed in PubMed, Scopus, and the official WHO website, during 1 September 2025–10 November 2025, without time restrictions. Eligible sources included original clinical studies, reviews, and policy documents addressing MBI, dementia prevention, and public health. Data were thematically synthesized according to the seven objectives of WHO: (1) dementia as a public health priority, (2) dementia awareness and friendliness, (3) dementia risk reduction, (4) dementia diagnosis, treatment, care and support, (5) support for dementia carers, (6) information systems for dementia, and (7) dementia research and innovation. Results: Accumulating evidence indicates that MBI assessment can capture early behavioral manifestations of neurodegenerative and other forms of dementia, correlating with fluid, neuroimaging and genetic biomarkers. Integrating MBI screening through the easy-to-administer MBI Checklist (MBI-C) into clinical and community-based care, including telemedicine pathways and research, may enhance early identification and personalized interventions, enrich the pool for clinical trials, and facilitate research in biomarker and therapy. MBI-related research further supports its integration in remote digital monitoring and population-based prevention. Conclusions: Embedding MBI-informed screening and interventions into national dementia strategies aligns with WHO objectives for early, equitable and scalable prevention and brain health. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder marked by decreased amyloid-beta (Aβ) clearance, enhanced Aβ aggregation, an increased risk of amyloid-related imaging abnormalities (ARIA), and blood–brain barrier (BBB) dysfunction. The APOE4 allele, being the leading genetic risk factor for AD, contributes strongly to these symptoms. This review covers the relationship between APOE4 status and the efficacy of FDA-approved monoclonal antibody (mAb) therapies, namely aducanumab, lecanemab, and donanemab. Across several clinical trials, APOE4 carriers exhibited higher rates of ARIA-E and ARIA-H compared to non-carriers. While the therapies did often meet biomarker endpoints (i.e., reduced amyloid), benefits were only observed in early and mild AD, and cognitive benefits were often marginal. Going forward, experimental apoE4-targeted immunotherapies may ease the burden of APOE4-related pathology. The field is shifting towards a more integrated approach, focusing on earlier interventions, biomarker-driven precision treatment, and improved drug delivery systems, such as subcutaneous injections, receptor-mediated transport, and antibodies with enhanced BBB penetration. As it stands, high treatment costs, limited accessibility, and strict eligibility criteria all stand as barriers to treatment. By integrating the APOE4 genotype into treatment planning and focusing on disease-stage-specific approaches, a safer and more effective means of treating AD could be achieved. Full article

Hair loss disorders, particularly androgenetic alopecia (AGA), are common conditions that carry significant psychosocial impact. Current standard therapies, including minoxidil, finasteride, and hair transplantation, primarily slow progression or re-distribute existing follicles and do not regenerate lost follicular structures. In recent years, regenerative medicine has been associated with a gradual shift toward approaches that aim to restore follicular function and architecture. Stem cell-derived conditioned media and exosomes have shown the ability to activate Wnt/β-catenin signaling, enhance angiogenesis, modulate inflammation, and promote dermal papilla cell survival, resulting in improved hair density and shaft thickness with favorable safety profiles. Autologous cell-based therapies, including adipose-derived stem cells and dermal sheath cup cells, have demonstrated the potential to rescue miniaturized follicles, although durability and standardization remain challenges. Adjunctive interventions such as microneedling and platelet-rich plasma (PRP) further augment follicular regeneration by inducing controlled micro-injury and releasing growth and neurotrophic factors. In parallel, machine learning-based diagnostic tools and deep hair phenotyping offer improved severity scoring, treatment monitoring, and personalized therapeutic planning, while robotic Follicular Unit Excision (FUE) platforms enhance surgical precision and graft preservation. Advances in tissue engineering and 3D follicle organoid culture suggest progress toward producing transplantable follicle units, though large-scale clinical translation is still in early development. Collectively, these emerging biological and technological strategies indicate movement beyond symptomatic management toward more targeted, multimodal approaches. Future progress will depend on standardized protocols, regulatory clarity, and long-term clinical trials to define which regenerative approaches can reliably achieve sustainable follicle renewal in routine cosmetic dermatology practice. Full article

Background/Objectives: Non-muscle invasive bladder cancer (NMIBC) management is increasingly complex due to conflicting guideline-based risk classifications, ongoing Bacillus Calmette–Guérin (BCG) shortages, and emerging alternative therapies. Computational Histology Artificial Intelligence (CHAI) tests are clinically available, providing insights from tumor specimens including predicting BCG responsiveness and individualized recurrence and progression risks, which may support precision medicine. This technology features biomarkers purpose-built for clinically unmet needs and has practical advantages including a fast turnaround time and no need for consumption of tissue or other specimens. We assessed the impact of such tests on physicians’ decision-making in routine, real-world NMIBC management. Methods: Physicians at six centers ordered CHAI tests (Vesta Bladder) at their discretion during routine NMIBC care. Tumor specimens were processed by a CLIA/CAP-accredited laboratory (Valar Labs, Houston, TX, USA) where H&E-stained slides were analyzed with the CHAI assay to extract histomorphic features of the tumor and microenvironment, which were algorithmically assessed to generate biomarker test results. For each case from 24 June 2024 to 18 July 2025, ordering physicians were surveyed to assess pre- and post-test management plans and post-test result usefulness. Results: Among 105 high-grade NMIBC cases with complete survey results available, primary management changed in 67% (70/105). Changes included modality shifts (n = 7; three to radical cystectomy with high prognostic risk scores; four avoiding cystectomy with low scores) and intravesical agent change (n = 63). Surveillance was intensified in 7%, predominantly among those with ≥90th percentile risk scores. The therapeutic agent changed in 80% (40/50) of predictive biomarker-present (indicative of poor response to BCG) tumors vs. 48% (23/48) of biomarker-absent tumors. Conclusions: In two thirds of cases, CHAI biomarker results influenced clinical decision-making during routine care. BCG predictive biomarker results frequently guided intravesical agent selection. These results have implications for optimizing clinical outcomes, especially in the setting of ongoing BCG shortages. Prognostic risk stratification results guided treatment escalation vs. de-escalation, including surveillance intensification and surgical vs. bladder-sparing decisions. CHAI biomarkers are currently utilized in routine clinical care and informing precision NMIBC management. Full article

Background/Objectives: Non-small-cell lung cancer (NSCLC) is a common disease with a high mortality rate and is often treated with immunotherapies; however, prognostic markers are required to identify patients who are most likely to benefit from these treatments. Therefore, we designed this study to assess the prognostic significance of the C-PLAN index, which includes performance status (PS) and C-reactive protein (CRP). Methods: A total of 560 patients were included in this multicenter study. Patients had been diagnosed with NSCLC and had received nivolumab therapy. The C-PLAN index, defined in 2022, is a score derived from the combination of PS, CRP, lactate dehydrogenase (LDH), albumin, and neutrophil–lymphocyte ratio (NLR). Patients were classified into good-, moderate-, and poor-prognosis groups according to the C-PLAN score. Results: The median metastatic overall survival was 25 months in the group with a C-PLAN score < 2 and 6 months in the group with a C-PLAN score ≥ 2 (p < 0.001). The median metastatic progression-free survival was 11 months in the group with a C-PLAN score < 2 and 3 months in the group with a C-PLAN score ≥ 2. Conclusion: This is the first comprehensive study demonstrating that the C-PLAN index can be used for prognostic purposes in immunotherapy. This score, which can be easily, economically, and practically calculated in outpatient clinics, can predict patient prognosis and determine who should receive longer durations of immunotherapy. Full article

Artificial intelligence (AI) is rapidly reshaping thoracic surgery, advancing from decision support to the threshold of autonomous intervention. AI-driven technologies—including machine learning (ML), deep learning (DL), and computer vision—have demonstrated significant improvements in diagnostic accuracy, surgical planning, intraoperative navigation, and postoperative outcome prediction. In lung cancer and thoracic oncology, AI enhances imaging analysis, histopathological classification, and risk stratification, supporting multidisciplinary decision-making and personalized therapy. Robotic-assisted and AI-guided systems are optimizing surgical precision and workflow efficiency, while real-time decision-support tools and augmented reality are improving intraoperative safety. Despite these advances, widespread adoption is limited by challenges in algorithmic bias, data integration, regulatory approval, and ethical transparency. The literature emphasizes the need for multicenter validation, explainable AI, and robust governance frameworks to ensure safe and effective clinical integration. Future research should focus on digital twin technology, federated learning, and transparent AI outputs to further enhance reliability and accessibility. AI is poised to transform thoracic surgery, but responsible implementation and ongoing evaluation are essential for realizing its full potential. The aim of this review is to evaluate and synthesize the current landscape of artificial intelligence (AI) applications across the thoracic surgical pathway, from preoperative decision-support to intraoperative guidance and emerging autonomous interventions. Full article

Background: Peri-implantitis, a condition characterized by inflammation and progressive bone loss around dental implants, presents a significant challenge in contemporary dentistry. Conventional non-surgical treatments often fail to fully eliminate bacterial biofilms, particularly on complex implant surfaces. Laser therapies have emerged as potential adjuncts due to their antimicrobial and bio-modulatory properties. However, their microbiological effectiveness and suitability for individualized patient treatment planning remain unclear. Objective: Τhis study aims to systematically assess and synthesize the microbiological effects of various laser-assisted non-surgical treatments for peri-implantitis compared to conventional mechanical debridement. Methods: This systematic review and meta-analysis followed PRISMA guidelines and was registered in PROSPERO (CRD420251035354). Randomized controlled trials (RCTs) evaluating microbiological changes following laser-assisted non-surgical treatment of peri-implantitis, with a minimum follow-up of one month, were identified through searches in multiple databases and registries up to February 2025. The ncluded studies used lasers such as diode, Er: YAG, and photodynamic therapy (PDT) either alone or as adjuncts to mechanical debridement. Outcomes of interest included bacterial counts. Risk of bias was assessed using the RoB2 tool, and certainty of evidence was evaluated via GRADE. Quantitative synthesis used random-effects meta-analysis, with standardized mean differences (SMDs) calculated. Results: Eight RCTs involving 266 patients and 335 implants were included in the systematic review. Quantitative synthesis of three pathogens (counts of Fusobacterium nucleatum, P. gingivalis, T. denticola) across three studies displayed no statistically significant differences between laser and control groups at 3 and 6 months (p > 0.05 for all comparisons). When examining individual study findings, PDT, particularly in patients with diabetes or acute abscess, showed short-term reductions in red complex bacteria (e.g., Porphyromonas gingivalis and Treponema denticola). In contrast, diode and Er: YAG lasers demonstrated inconsistent or transient effects. The quality of evidence was rated as very low according to GRADE. Conclusions: Laser-assisted therapies, especially PDT, may provide targeted microbiological benefit in selected patient groups, supporting their adjunctive use within personalized treatment planning rather than as replacements for mechanical debridement, which remains the gold standard. Further high-quality RCTs incorporating well-defined patient risk profiles, such as systemic conditions and behavioral factors, and precision treatment algorithms are needed. Full article

Prostate cancer remains one of the most common malignancies in men worldwide, with incidence and mortality steadily increasing across diverse populations. While early detection and radical prostatectomy can achieve durable control in a subset of patients, approximately 40% of men will ultimately experience biochemical recurrence often in the absence of clinically detectable disease. Conventional imaging approaches—CT, MRI, and bone scintigraphy—have limited sensitivity for early relapses, frequently leading to delayed diagnosis and suboptimal treatment planning. The discovery of prostate-specific membrane antigen (PSMA) in 1987 and its subsequent clinical translation into positron emission tomography (PET) imaging with [⁶⁸Ga]Ga-PSMA-11 in 2012, followed by U.S. FDA approval in 2020, has transformed the landscape of prostate cancer imaging. PSMA PET has demonstrated superior accuracy over conventional imaging, as highlighted in the landmark proPSMA trial and now serves as the foundation for theranostic approaches that integrate diagnostic imaging with targeted radioligand therapy. The clinical approval of [¹⁷⁷Lu]Lu-PSMA-617 (Pluvicto^®: (lutetium Lu 177 vipivotide tetraxetan, Advanced Accelerator Applications USA, Inc., a Novartis company) has established targeted radioligand therapy as a viable option for men with metastatic castration-resistant prostate cancer, extending survival in patients with limited alternatives. Emerging strategies, including next-generation ligands with improved tumor uptake and altered clearance pathways, as well as the integration of artificial intelligence for imaging quantification, are poised to further refine patient selection, dosimetry, and treatment outcomes. This review highlights the evolution of PSMA-based imaging and therapy, discusses current clinical applications and limitations, and outlines future directions for optimizing theranostic strategies in prostate cancer care. Full article