Search Results (2,194)

Background/Objectives: Ceftolozane/tazobactam (C/T) and ceftazidime/avibactam (CZA) are critical therapeutic options for multidrug-resistant Gram-negative infections; however, their comparative pharmacodynamics against carbapenem-resistant Pseudomonas aeruginosa (CRPA) remain incompletely defined. This study aimed to compare the bactericidal activity of C/T and CZA administered by intermittent infusion (II) or continuous infusion (CI) using a hollow fiber infection model (HFIM) against a clinical isolate of CRPA. Methods: Clinically relevant concentration–time profiles for C/T and CZA based on prescribing information were simulated in the HFIM. The standard P. aeruginosa strain ATCC 27853 and a GES-producing clinical CRPA isolate were utilized. The primary endpoint was bactericidal activity (≥3 log₁₀ CFU/mL reduction from baseline), while secondary endpoints included regrowth prevention and resistance development based on population analysis profiles (PAPs). Results: Against the standard strain, both agents achieved rapid killing without regrowth. However, for the GES-producing clinical isolate, C/T failed to achieve bactericidal activity. In contrast, CZA demonstrated sustained bacterial killing activity with the most pronounced early-phase bactericidal activity with CI of CZA (−4.25 log₁₀ CFU/mL at 24 h). The bactericidal activity was persistent over 7 days without bacterial regrowth after treatment discontinuation. Conversely, bacterial regrowth occurred with II of CZA after drug withdrawal. PAPs showed the lack of resistance development against CZA, whereas resistance to C/T developed within 48 h after initiating therapy. Conclusions: In this HFIM study, CI of CZA demonstrated the most sustained suppression of bacterial growth and prevented resistance emergence against the tested clinical isolate of CRPA producing GES β-lactamases. Future clinical studies are warranted to assess the effectiveness of the CI regimen. Full article

Artificial intelligence (AI) is increasingly used to support tuberculosis (TB) screening and diagnosis, particularly through computer-aided detection (CAD) applied to chest radiography (CXR). However, the programmatic value of AI depends not only on diagnostic accuracy but also on implementation context, threshold calibration, and integration into diagnostic pathways. We conducted a narrative, state-of-the-art review of AI applications across the TB diagnosis pathway. Evidence was synthesized from World Health Organization policy documents, independent validation initiatives, and peer-reviewed studies published between 2010 and 2026, with a structured selection process aligned with PRISMA principles. CAD for CXR is the most mature AI application and is recommended by WHO for TB screening and triage among individuals aged ≥15 years in specific contexts. Across studies, CAD-CXR demonstrates sensitivity comparable to human readers, although performance varies by product, population, and imaging conditions, necessitating local threshold calibration. Evidence from implementation studies suggests improvements in screening efficiency and potential cost-effectiveness in high-burden settings. Other AI modalities, including computed tomography (CT)-based imaging analysis, point-of-care ultrasound interpretation, cough or stethoscope sound analysis, clinical risk models, and genomic resistance prediction show promising but heterogeneous results, with most requiring further independent validation and prospective evaluation. AI has the potential to strengthen TB screening and diagnostic pathways, but its impact depends on integration into health systems and evaluated using patient- and program-level outcomes rather than accuracy alone. A differentiated approach is needed, with responsible scale-up of policy-endorsed tools alongside rigorous evaluation of emerging technologies to support effective and equitable TB care. Full article

In Coiled Tubing (CT) acoustic telemetry, the reliability of surface signal reception is severely challenged by the “contact dead zone” of traditional probes and complex nonstationary environmental noise. To address these issues, this paper proposes a hardware-software integrated solution for high-fidelity signal extraction. In terms of hardware, a novel pickup probe based on the micro-lever principle is developed. By utilizing a pivoted lever structure with an optimized arm ratio of 2.6 to 1 and a full pressure-balanced mechanism, the design physically overcomes the contact dead zone inherent in traditional pressure-compensating probes and effectively isolates low frequency common-mode interference through a lateral floating architecture. In terms of software, a joint denoising model combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and wavelet thresholding is proposed. A cross-correlation coefficient criterion is introduced to adaptively screen intrinsic mode functions and eliminate residual fluid turbulence noise. Field experiments on a 1500 ft full-scale circulation loop demonstrate that the proposed probe improves the detection sensitivity of the radial breathing mode by approximately 20.6 dB compared to the baseline, while effectively eliminating stick-slip friction noise during dynamic tripping. Furthermore, the joint algorithm increases the Signal to noise Ratio by an additional 16.9 dB under typical pumping conditions of 0.5 bpm, with a normalized cross-correlation exceeding 0.96. These results verify that the proposed method effectively solves the bottleneck of weak signal detection in deep wells, providing robust technical support for CT telemetry operations. Full article

Background/Objectives: Peptide receptor radionuclide therapy (PRRT) is an established treatment for metastatic neuroendocrine tumors (NETs), yet long-term disease control occurs only in a subset of patients. Predicting progression-free survival (PFS) could support individualized treatment planning. This study evaluates laboratory, imaging, and multimodal deep learning models for PFS prediction in PRRT-treated patients. Methods: In this retrospective, single-center study 116 patients with metastatic NETs undergoing [177Lu]Lu-DOTATOC were included. Clinical characteristics, laboratory values, and pretherapeutic somatostatin receptor positron emission tomography/computed tomographies (SR-PET/CTs) were collected. Seven models were trained to classify low- vs. high-PFS groups, including unimodal (laboratory, SR-PET, or CT) and multimodal fusion approaches. Performance was assessed via repeated 3-fold cross-validation with area under the receiver operating characteristic curve (AUROC) and area under the precision–recall curve (AUPRC). Explainability was evaluated by feature importance analysis and gradient based saliency maps. Results: Forty-two patients (36%) displayed short PFS (≤1 year) and 74 patients displayed long PFS (>1 year). Groups were similar in most characteristics, except for higher baseline chromogranin A (p = 0.003), elevated $\gamma$-GT (p = 0.002), and fewer PRRT cycles (p < 0.001) in short-PFS patients. The Random Forest model trained only on laboratory biomarkers reached an AUROC of 0.59 ± 0.02. Unimodal three-dimensional convolutional neural networks using SR-PET or CT performed worse (AUROC 0.42 ± 0.03 and 0.54 ± 0.01, respectively). A multimodal fusion model integrating laboratory values, SR-PET, and CT—augmented with a pretrained CT branch—achieved the best results (AUROC 0.72 ± 0.01, AUPRC 0.80 ± 0.01). Explainability analyses provided insights into model predictions, with explainability patterns in the fusion model appearing physiologically plausible and predominantly tumor-focused. Conclusions: Multimodal deep learning combining SR-PET, CT, and laboratory biomarkers outperformed unimodal approaches for PFS prediction after PRRT. Upon external validation, such models may support risk-adapted follow-up strategies. Full article

Radiation pneumonitis (RP), one of the most common and severe complications in locally advanced non-small cell lung cancer (LA-NSCLC) patients following thoracic radiotherapy, presents significant challenges in prediction due to the complexity of clinical risk factors, incomplete multimodal data, and unavailable slice-level annotations in pre-radiotherapy CT images. To address these challenges, we propose a multimodal fusion framework based on Dual-Layer Attention-Based Adaptive Bag Embedding Multiple-Instance Learning (DAAE-MIL) for accurate RP prediction. This study retrospectively collected data from 995 LA-NSCLC patients who received thoracic radiotherapy between November 2018 and April 2025. After screening, Subject datasets (n = 670) were allocated for training (n = 535), and the remaining samples (n = 135) were reserved for an independent test set. The proposed framework first extracts pre-radiotherapy CT image features using a fine-tuned C3D network, followed by the DAAE-MIL module to screen critical instances and generate bag-level representations, thereby enhancing the accuracy of deep feature extraction. Subsequently, clinical data, radiomics features, and CT-derived deep features are integrated to construct a multimodal prediction model. The proposed model demonstrates promising RP prediction performance across multiple evaluation metrics, outperforming both state-of-the-art and unimodal RP prediction approaches. On the test set, it achieves an accuracy (ACC) of 0.93 and an area under the curve (AUC) of 0.97. This study validates that the proposed method effectively addresses the limitations of single-modal prediction and the unknown key features in pre-radiotherapy CT images while providing significant clinical value for RP risk assessment. Full article

Background/Objectives: Sex estimation from craniofacial morphology is a fundamental component of biological profile construction in forensic anthropology. Population-specific reference data for Thai individuals derived from computed tomography (CT) remain limited, and direct comparisons between discriminant function analysis (DFA) and machine learning classifiers are frequently complicated by inconsistent validation protocols. This study aimed to characterize sexual dimorphism in CT-derived craniofacial measurements, compare the classification performance of DFA, support vector machine (SVM), and random forest (RF) under a unified validation protocol, and demonstrate their practical application in a forensic context. Methods: CT images from 300 Thai adults (150 males, 150 females; age range 20–90 years) were obtained from Srinagarind Hospital, Khon Kaen University. Eight linear craniofacial measurements spanning the cranial vault, facial skeleton, nasal aperture, and orbital region were obtained from each case. DFA, SVM, and RF were developed and compared under a unified leave-one-out cross-validation protocol. Classification performance was assessed using accuracy, AUC, and Matthews correlation coefficient (MCC). Results: Seven of eight measurements exhibited statistically significant sexual dimorphism, with facial breadth and nasal height demonstrating the greatest dimorphism. DFA achieved the highest classification accuracy of 85.7%, AUC of 0.924, and MCC of 0.713, incorporating five measurements into the canonical function. SVM and RF achieved comparable accuracy of 84.7% and 84.0%, respectively. All three classifiers correctly classified both forensic application cases with high confidence. Conclusions: CT-derived craniofacial measurements provide a reliable basis for sex estimation in Thai adults. The convergence of performance across all three classifiers under a unified internal validation protocol strengthens confidence in the internally validated performance estimates. The derived discriminant function equation and saved machine learning models constitute a complementary and immediately applicable toolkit for CT-based forensic sex estimation in the Thai population. Full article

The growing digitalization of supply chains and increasing sustainability requirements create the need for structured tools that assess organizational readiness for Cognitive Twin (CT) adoption. However, existing digital twin and sustainability maturity models rarely integrate technological architecture, governance, and circularity within a unified framework. To address this gap, the study proposes the Supply Chain Twin Sustainability–Cognitive Maturity Model (SCT-SCMM), a novel framework that explicitly integrates governance structures, sustainability objectives, and a hierarchical system architecture into the assessment of Cognitive Twin readiness. Unlike existing models, the proposed framework captures the interdependencies between technological capabilities, decision intelligence, and governance mechanisms across multiple system layers, providing a systemic perspective on sustainable digital transformation. The framework structures organizational readiness through five interdependent layers: Physical, Control, Communication, Decision-making, and Governance, and defines staged maturity levels reflecting progression toward sustainable cognitive autonomy. This layered architecture enables the simultaneous evaluation of operational automation, digital intelligence, and institutional governance as co-evolving dimensions of Cognitive Twin adoption. The model was developed through a structured literature review and operationalized using a hybrid multi-criteria and fuzzy-based evaluation approach, enabling the evaluation of complex socio-technical systems under uncertainty. The framework was applied in an automated product-to-human warehouse case study to evaluate technological, sustainability, and governance readiness. The results demonstrate the model’s ability to identify maturity gaps, reveal inter-layer dependencies, and prioritize transformation pathways toward more resilient and circular logistics systems. By integrating governance, sustainability, and system architecture into a single maturity model, SCT-SCMM extends existing digital twin maturity approaches and provides a transparent decision-support tool for guiding staged Cognitive Twin adoption in next-generation sustainable supply chains. Full article

Cone-beam computed tomography (CBCT) offers advantages of low radiation dose and rapid acquisition but suffers from scatter-induced shading artifacts that limit diagnostic value compared to multi-detector CT (MDCT). While CycleGAN enables unpaired image translation, its uniform loss application struggles with localized artifact removal. We propose a two-stage learning framework with YOLO-based region correction loss. Stage 1 trains a standard CycleGAN to establish stable CBCT-MDCT domain mapping. Stage 2 fine-tunes the model by applying gradient magnitude minimization loss selectively to artifact regions detected by a pretrained YOLO detector, enabling focused correction while preserving anatomical structures. Using 11,000 2D CBCT slices from 17 patients (14 training, 3 testing) and 23,500 2D MDCT slices from 50 patients, our method achieves a 14.0% reduction in artifact score compared to baseline CycleGAN while maintaining high structural similarity (SSIM > 0.96). Independent evaluation using integral nonuniformity (INU) and shading index (SI) confirms consistent improvement across physics-based metrics. The self-regulating mechanism, where YOLO detection confidence naturally decreases as artifacts diminish, provides automatic adjustment without manual intervention. This work demonstrates that combining staged learning with object detection offers an effective solution for localized artifact removal in medical image translation, potentially improving diagnostic accuracy while preserving the low-dose benefits of CBCT. Full article

Background/Objectives: Malignant salivary gland tumors (MSGTs) are rare, biologically heterogeneous neoplasms in which histopathologic diagnosis and classification are challenging and subject to interobserver variability. Artificial intelligence (AI) approaches using radiologic, histopathologic, and molecular data, including radiomics, deep learning, and biomarker-based models, have been proposed as adjunctive diagnostic tools. This systematic review aimed to identify and critically appraise AI/ML models across radiologic, histopathologic, and molecular domains for distinct diagnostic tasks in MSGTs, and to integrate their diagnostic roles through a functional meta-synthesis. Methods: We conducted a PRISMA 2020-compliant systematic review. Embase, PubMed/MEDLINE, and Scopus were searched from inception to February 2026. Eligible studies developed or validated AI/ML diagnostic or classification models in human salivary gland tumor cohorts and reported extractable performance metrics. Results: From 1265 records, eight studies (1922 participants) met the inclusion criteria, spanning CT/MRI radiomics or deep learning (n = 4), whole-slide histopathology deep learning (n = 3), and DNA methylation-based classification (n = 1). External validation was reported in two CT-based benign–malignant discrimination studies, with AUCs of 0.890 (95% CI 0.844–0.937) and 0.745 (95% CI 0.699–0.791). Heterogeneity in model construction, outcome definitions, and validation strategies precluded meta-analysis. Risk of bias was frequently high in QUADAS-2/PROBAST assessments, driven by retrospective sampling, limited blinding, and analysis-related concerns, while calibration and utility were rarely assessed. Conclusions: AI/ML models for MSGTs demonstrate promising diagnostic performance, particularly for preoperative benign–malignant discrimination, but the current evidence base is limited by heterogeneity, predominantly internal validation, and high risk of bias. The functional meta-synthesis identified three convergent diagnostic domains: malignancy discrimination, histopathologic subtype classification, and molecular/epigenetic taxonomy refinement. Full article

Background: Drug-resistant tuberculosis remains a major challenge in resource-limited settings, particularly in rural regions of the Eastern Cape Province, where limited laboratory infrastructure, constrained access to advanced molecular diagnostics, shortages of specialized healthcare personnel, and prolonged diagnostic turnaround times can delay appropriate treatment initiation. This study examined whether routinely detectable genomic resistance markers could be integrated with parsimonious machine learning approaches to support early risk stratification for isoniazid (INH) and/or rifampicin (RIF) resistance and multidrug-resistant tuberculosis (MDR-TB). Methods: We conducted a retrospective analysis of clinical, demographic, and genomic data from 207 Mycobacterium tuberculosis isolates representing 207 unique patients. Resistance was classified as INH and/or RIF resistance or MDR-TB (concurrent resistance to both drugs). Predictors included age, sex, and canonical resistance-associated mutations (katG S315T, inhA −15C>T, and rpoB codon substitutions). Logistic regression was used to estimate adjusted odds ratios (aORs), while Random Forest models were applied to assess non-linear feature importance. Internal validation was performed using 10-fold cross-validation. A systems network analysis mapped the integration of model-derived risk bands into Clinical Governance structures and Community-Engaged Education pathways, including interventions delivered by Community Health Workers (CHWs). Results: INH and/or RIF resistance was identified in 58.9% of isolates, with 21.7% classified as MDR-TB. The most frequently detected mutations were katG S315T (29.0%) and rpoB S450L (26.6%). Logistic regression identified rpoB S450L (aOR 4.20; 95% CI: 2.10–8.45) and katG S315T (aOR 2.85; 95% CI: 1.40–5.80) as the strongest independent predictors, while age and sex were not statistically significant. Models demonstrated strong internal discrimination (AUCs of 0.96 for INH and/or RIF resistance and 0.99 for MDR-TB). Risk stratification categorized 18% of patients as high risk. Scenario-based modelling suggested that prioritizing high-risk patients for reflex Line Probe Assay testing could reduce the median time to appropriate treatment from 14 to 3 days and may reduce progression from isoniazid-resistant TB to MDR-TB under specified operational assumptions. Conclusions: Mutation-informed predictive modelling demonstrates strong internally validated discrimination and provides a structured framework for risk-stratified intervention. Integrating probability-based risk thresholds within Clinical Governance systems and community-level support structures, including CHW-led adherence and education strategies, may support earlier treatment optimization in high-burden rural settings. External validation and prospective implementation studies are required before broader programmatic adoption. Full article

Soaking bovine tooth blocks in demineralization solution is a widely used method to simulate caries-like demineralization for further experimental studies. The objective of this study was to evaluate the degree and depth of mineral loss in bovine enamel and dentin blocks under various controlled conditions and to investigate the relationships between these factors and mineral loss, providing guidance for researchers to achieve targeted demineralization outcomes. A total of 54 enamel blocks and 54 dentin blocks were divided into 18 groups according to the exposed area and solution volume and then immersed in demineralization solution. Micro-CT scans were performed before immersion, as well as after 1, 2, 3, 7, and 10 days of immersion. The results were analyzed using data analysis software and subsequently summarized into graphical representations. The analysis revealed that soaking time and solution volume showed positive correlations with mineral loss, whereas the exposed area was negatively correlated with mineral loss. Mean mineral loss increased significantly with immersion time in all groups (e.g., from 6314 to 25,670 vol%·μm in the dentin 3 × 3 mm², 50 mL group, p < 0.05). After 7 days, specimens immersed in larger solution volumes showed significantly greater mineral loss than those immersed in smaller volumes (p < 0.05). In addition, larger exposed areas resulted in greater mineral loss after 3 days of immersion. Mean mineral loss followed a power function relationship with time when the solution volume was sufficiently high relative to the exposed surface area. In contrast, when the solution volume was limited, a logarithmic relationship between time and mineral loss was observed. Given its superior stability, the mean mineral loss appears to be a more reliable indicator for assessing tooth demineralization. Based on our results, more controlled and reproducible demineralization conditions can be achieved, which may contribute to improving the reliability of in vitro caries models and facilitating the evaluation of preventive and therapeutic strategies. Full article

Background and Clinical Significance: Modern neuro-oncologists encounter a major challenge when dealing with glioblastomas located in the dominant hemisphere’s temporo-basal area, because their invasive nature disrupts the proximity to eloquent cortical areas (language and speech), as well as skull base venous structures, which can lead to a quick decline in function from the disruptions in these networks and the disconnection of corridor-level pathways. This manuscript illustrates the application of metric-based phenotyping, anatomically defined imaging, and venous-anchored microsurgical techniques that can aid in preserving the remaining functional reserve in patients with dominant hemisphere glioblastomas and demonstrate measurable outcomes through longitudinal follow-up data. Case Presentation: A 48-year-old right-handed male patient presented with a four-week history of progressively worsening symptoms consistent with a dominant hemisphere syndrome, resulting in a significant decrease in his independence (mRS 0 → 4; BI 55/100; IADL 2/8). His symptoms included non-fluent expressive aphasia with a marked inability to generate words and respond to verbal cues (BNT 8/30; SF 4 WPM). Additionally, he experienced prolonged lateralizing hemisensory decompensation and corticospinal tract dysfunction. Imaging studies revealed a large multiloculated cystic lesion located in the left temporo-basal region. The lesion displayed a thick irregular peripheral enhancement pattern with mural nodules and septa, and surrounding T2 hyperintensity extending into the temporal associative white matter, indicating disruption of the lexical–semantic networks and corridor-level tracts. Utilizing continuous SSEPS/MEPs during surgery, a skull base parallel ventral temporal corridor was developed to allow decompression of the cyst first, followed by cyst evacuation, inside-out cytoreduction, subpial dissection, and specific preservation of both superficial and deep temporal veins using selective capsular preservation at venous interface locations where necessary. Postoperative CT scans performed on POD #3 and POD #7 indicated stable decompression without hemorrhage or hydrocephalus complications, followed by rapid quantitative improvement in NIHSS (8 → 2), MoCA (18 → 26), BNT (8 → 26), SF (4 → 12), mRS (2 at discharge, 1 at follow-up), BI (85 at discharge, 95 at follow-up), and IADL (6/8 at discharge, 8/8 at follow-up). Histopathological examination confirmed a diagnosis of glioblastoma. Conclusions: This case study supports a model of a network- and vein-constrained glioblastoma of the dominant hemisphere in the temporo-basal region that can result in substantial restoration of language capabilities and preservation of functional reserves for additional therapies using venous-anchored subpial microsurgical approaches. The use of objective and quantifiable measures of phenotyping and longitudinal follow-up tracking could provide a reproducible method for measuring the degree of recovery of the affected network(s) and establishing safe boundaries for temporal glioma surgery. Full article

In marine renewable energy applications, offshore steel pipelines are subjected to complex combined loads during installation and operation, leading to significant plastic deformation and potential catastrophic fracture. To accurately characterize pipeline fracture failure, this study develops an enhanced failure assessment framework based on the Modified Mohr–Coulomb (MMC) criterion, integrating experimental parameter evaluation with numerical simulation for in-service offshore pipelines. The key parameters of the MMC model were determined directly from in-service pipeline samples to account for operational degradation. First, the plastic parameters were obtained by fitting the Swift hardening law to uniaxial tensile tests. Fracture parameters were then calibrated using a suite of five notched tensile specimens. Mesh sensitivity was analyzed using CT experiments to establish a suitable mesh size for the MMC-based damage model, enabling precise characterization of crack evolution from initiation to final tearing. Unlike prior applications, this framework is employed to investigate the response of X80 pipelines under combined tension, bending, and external pressure loading. Three-dimensional finite element models were developed to systematically analyze the stress–strain response, moment–curvature behavior, and evolution of hoop stress distribution. Results show that while the failure stress remains relatively stable under varying external pressure, both the critical strain and critical curvature increase markedly with pressure, by up to 20.9%. They also reveal a pronounced hierarchy in the influence of crack geometry on the failure behavior. Crack depth dominates failure sensitivity, affecting critical strain and pressure response far more than crack width or length. The reduction in failure stress for deep cracks under 12 MPa external pressure is over three times greater than for shallow cracks. In contrast, variations in crack length exert the most negligible influence on failure characteristics, with observed discrepancies of less than 6%. Overall, this research provides a high-precision failure prediction framework for in-service pipelines by quantitatively analyzing failure behavior under combined loads. It effectively characterizes failure evolution paths that differ from design conditions and dynamically tracks the residual fracture resistance after time-dependent degradation, offering a fundamental reference for the reliability assessment of pipelines in complex marine environments. Full article

Background: Current guidelines recommend lobectomy for tumors > 20 mm on CT, yet systematic CT–pathology size discordance may contribute to size-threshold–driven surgical decisions. We hypothesized that CT-based tumor diameter differs from pathological size near the 20 mm surgical boundary, potentially leading a proportion of patients to undergo more extensive resection than pathology would indicate under a size-only rule. Methods: We retrospectively analyzed 1096 patients undergoing thoracoscopic surgery for clinical stage I non-small cell lung cancer at a single center (2020–2024). CT–pathology agreement was assessed via Bland–Altman analysis. Optimal CT cut-off was identified using restricted cubic spline (RCS) modeling, internally validated with bootstrap resampling (B = 2000), and evaluated by decision curve analysis (DCA). Results: CT showed size-dependent bias: overestimation in small tumors (T1a: +4.21 mm) transitioning to underestimation in larger lesions (≥T2: −7.49 mm). At the 20 mm threshold, 15.8% of patients (n = 173) underwent lobectomy despite pathological size ≤ 20 mm (potential overtreatment). RCS modeling and bootstrap-optimized DCA identified 23 mm as the candidate revised threshold. Adopting CT > 23 mm would reclassify 108 patients from lobectomy to sublobar resection, reducing size-threshold–defined potential overtreatment by 51.4% while maintaining sensitivity for true ≥ T2 tumors. Conclusions: CT demonstrates size-dependent discordance with pathological size; this discordance likely reflects both CT measurement inaccuracy and specimen shrinkage after fixation, and the relative contributions cannot be separated from these data. A candidate 23 mm CT threshold, supported by DCA and internal bootstrap validation, could reduce size-threshold–defined potential overtreatment by 51% in this cohort. Prospective multicenter validation is required before clinical implementation. Full article

Background and objectives: Early recovery after major head-and-neck reconstruction is shaped by nutritional vulnerability and functional decline. We evaluated whether preoperative CT-defined low skeletal muscle mass—considered here as an imaging-derived muscle-depletion phenotype rather than the full consensus syndrome of sarcopenia—predicts swallowing milestones, weight trajectory, and patient-reported outcomes at 12 weeks. Methods: In a prospective longitudinal cohort of 74 adults undergoing oncologic resection with reconstruction, low skeletal muscle mass was derived from preoperative cervical CT-based skeletal muscle measurements and nutritional risk was screened with NRS-2002. Outcomes included FOIS, PEG dependence, percent weight loss, MDADI, and European Organisation for Research and Treatment of Cancer QLQ-C30/QLQ-H&N35 at 12 weeks. A multivariable logistic regression explored a composite poor-recovery endpoint (PEG at 12 weeks and/or FOIS ≤ 3 and/or MDADI < 55). Results: Low skeletal muscle mass (32/74, 43.2%) was associated with longer length of stay (13.4 ± 4.1 vs. 10.3 ± 3.3 days; p < 0.001) and more major complications (31.2% vs. 11.9%; p = 0.04). At 12 weeks, affected patients had greater weight loss (10.9 ± 3.4% vs. 8.6 ± 2.6%; p = 0.003), lower FOIS (3.9 ± 1.1 vs. 4.6 ± 1.1; p = 0.01), lower MDADI (57.1 ± 10.9 vs. 66.6 ± 11.9; p = 0.001), and higher PEG dependence (31.2% vs. 9.5%; p = 0.018). Low skeletal muscle mass remained associated with poor recovery after adjustment (aOR 5.4; 95% CI 1.4–24.0; p = 0.016); adjuvant radiotherapy was also associated (aOR 4.3; p = 0.049). Model discrimination was good (AUC 0.81). Conclusions: Preoperative CT-defined low skeletal muscle mass was associated with impaired early recovery after major head-and-neck reconstruction, particularly when adjuvant radiotherapy was anticipated; however, these findings should be interpreted as exploratory and hypothesis-generating. Full article