Search Results (2,939)

This article analyses trading-performance patterns in a stock market simulation conducted with 134 second-year students at the University of Mons (Belgium) on 11 December 2025. Participants had a virtual capital of 100,000 euros and were free to trade CAC 40 securities without any restrictions on the number or volume of transactions. An academic incentive scheme, combining a participation bonus and bonuses for the three best portfolios, created a tournament-style environment with continuous ranking feedback. This feature is considered as part of the experimental context rather than as a separately identified causal mechanism. We estimate a quadratic model linking performance to activity, measured by the number of mean-centered transactions to reduce the collinearity between the first-degree term and its square, and control exposure via the average percentage of cash in the portfolio, portfolio variability (measured as the standard deviation of portfolio value) and the average trade size. Breusch–Pagan and White tests indicate heteroscedasticity, justifying a robust inference. The results highlight a convex relationship between activity and performance: the marginal association is initially negative but becomes positive above a model-implied upper-tail level corresponding to approximately 46 transactions. This value should not be interpreted as a behavioral level or as a trading rule. The percentage of cash in the portfolio and the average trade size are negatively associated with performance, while the portfolio variability does not show a statistically significant association with performance. Overall, the results indicate heterogeneous trading patterns rather than a single activity–performance profile. Full article

Background/Objectives: Clear cell renal cell carcinoma (ccRCC) is a biologically heterogeneous disease. Beyond VHL inactivation, alterations in chromatin remodeling genes BAP1 and PBRM1 define distinct tumor phenotypes with prognostic implications. We sought to characterize the clinicopathological features and oncological outcomes associated with IHC-defined loss of these markers in a contemporary surgical cohort. Methods: We retrospectively analyzed 214 patients undergoing partial or radical nephrectomy for ccRCC (2010–2021). Loss of BAP1 and PBRM1 expression was assessed by automated immunohistochemistry. Tumors with retained expression were classified as wild-type and compared with those showing loss of at least one marker. Survival outcomes were evaluated using Kaplan–Meier analysis, multivariable Cox models, and Restricted Mean Survival Time (RMST). Results: IHC-defined loss was identified in 19 patients (8.9%): BAP1 in 12 (5.6%) and PBRM1 in 7 (3.3%). Tumors with IHC-defined loss showed more aggressive features, including larger size (7.7 vs. 4.7 cm; p = 0.009), higher necrosis (36.8% vs. 18.5%; p = 0.050), and more advanced stage (pT3–pT4: 47.4% vs. 16.4%; p < 0.001). Kaplan–Meier analysis demonstrated significantly worse survival outcomes in the IHC-loss group across all endpoints (p ≤ 0.011). RMST analysis at 60 months confirmed significantly worse outcomes across all endpoints (p ≤ 0.005). Conclusions: Loss of BAP1 or PBRM1 identifies a biologically aggressive ccRCC subset with worse oncological outcomes. IHC-based molecular profiling is a practical and accessible tool for risk stratification in surgically treated ccRCC. Full article

Background: This study aimed to compare a limited set of predefined diet-, lifestyle-, knowledge-, and psychosocial indicators across school youth, students, and adults in Poland, and to examine their associations with three predefined outcomes: BMI ≥ 25 kg/m², poorer mental well-being, and high stress/overload. Diet quality, daily health-related practices, psychosocial well-being, and stress/overload may co-occur across different life stages, but online survey data require a focused analytical framework to avoid overinterpretation. Methods: This cross-sectional anonymous online survey included 360 respondents: 154 school youth aged 15–19 years, 127 students aged 20–29 years, and 79 adults aged 30 years or older. Dietary assessment was based on the KomPAN questionnaire and included the pro-healthy diet index, non-healthy diet index, and Diet Quality Index. Study-specific scores were used for knowledge, healthy practices, psychosocial well-being, and stress/overload. Analyses were restricted to predefined group comparisons, selected correlations, and three whole-sample adjusted logistic regression models. Results: Adults had the highest BMI and waist/hip circumference, whereas school youth showed the highest non-healthy diet index and more frequent high processed-food intake. Among the knowledge and psychosocial indicators, only obesity knowledge differed significantly between groups, with the highest mean value among students. Stress/overload was inversely associated with psychosocial well-being, and DQI was positively associated with psychosocial well-being after adjustment for age, sex, and group. In adjusted whole-sample models, BMI ≥ 25 kg/m² was positively associated with age and DQI and inversely associated with physical activity frequency and regular meals; the positive DQI–BMI association was interpreted cautiously as potentially reflecting reverse causality, reporting bias, or compensatory dietary modification among respondents with excess body weight. Poorer mental well-being was associated with higher stress/overload and inversely associated with DQI, physical activity frequency, and family meals. High stress/overload was positively associated with highly processed food intake and inversely associated with regular meals. Conclusions: The findings suggest that diet quality, behavioral regularity, and psychosocial burden may be more informative than knowledge alone when describing health-related profiles across age-defined groups. Because the study was cross-sectional, self-reported, anonymous, and based on a modest sample, the results should be interpreted as preliminary and hypothesis-generating rather than causal. Full article

Quantitative phenotyping of pepper seedlings is important for greenhouse plug tray seedling cultivation, but it remains constrained by inefficient manual monitoring, complex greenhouse backgrounds, and growth-stage-dependent discrepancies between two-dimensional image traits and actual leaf biomass. In this study, a cascaded vision framework with stage-specific morphological correction was developed for nondestructive seedling phenotyping. The framework integrated Visual Dynamic Momentum YOLO (VDM-YOLO) for individual seedling localization and growth-stage recognition, Variance Guided Strip Ghost Gated UNet (VSG-UNet) for lightweight, high-resolution leaf segmentation, and a stage-aware correction model for leaf dry biomass estimation. In performance evaluation, VDM-YOLO achieved a mean average precision at an intersection over union threshold of 0.5 (mAP_0.5) of 89.27%, improving mAP_0.5 by 1.82 percentage points over YOLOv12. VSG-UNet achieved a mean intersection over union (mIoU) of 83.9% and a Dice coefficient of 81.8%, while reducing floating point operations (FLOPs) and parameters by 44.2% and 61.2%, respectively, compared with U-Net. After stage-aware calibration, the coefficient of determination (R²) between segmented area and leaf dry weight increased from 0.764 to 0.813, and the root mean square error (RMSE) decreased from 0.0210 g to 0.0190 g. These results demonstrated that the proposed framework provided a proof of concept approach based on RGB images for the nondestructive assessment of leaf area and leaf dry biomass in pepper seedlings under restricted experimental conditions. Full article

Background: Tongue diagnosis is a fundamental diagnostic method in traditional medicine. Studies restricted to single ethnic groups may introduce bias and limit the clinical applicability of digital tongue diagnosis across diverse populations. Objectives: This study examined differences in tongue image features between Japanese and Brazilian (Caucasian ancestry) participants using digital tongue diagnosis technology and explored potential influencing factors. Methods: Tongue images were collected from 143 Japanese and 116 Brazilian participants attending traditional medicine clinics in Japan and Brazil. An independently developed tongue image analysis system (TIAS) was employed to extract shape, texture (gray level co-occurrence matrix), color (L*a*b color space), and deep-learning derived features (crack, prickle, tooth-mark, peel, greasy coating, stasis). Statistical analyses and machine learning models with SHAP explainability were used to compare features and identify key classification parameters. Results: Significant inter-group differences were observed in tongue shape, texture parameters (particularly at the root and tip), color parameters (especially middle-a-mean, middle-b-mean, tip-a-mean, and tip-b-mean), and deep features. The Japanese group showed a markedly higher prevalence of greasy coating (72.03% vs. 41.38%, p < 0.001) and stasis. Machine learning analysis revealed that the b value in the middle region of the tongue (middle-b-mean) contributed most strongly to the classification of greasy coating. Conclusions: The digital tongue image analysis system enables accurate and objective quantification of tongue features. Pronounced ethnic differences exist, particularly in the distribution of greasy coating. The middle-b-mean has the greatest impact on greasy coating classification. These findings underscore the importance of considering ethnic background when developing digital tongue diagnosis systems. Full article

An empirical model is developed to predict significant wave height and significant wave period using only wind speed at 10 m height, fetch, and water depth. The model distinguishes between fetch-limited and duration-limited sea states within a conditional empirical framework that incorporates modified empirical exponents and corrections into classical wave formulations. Validation was performed using wind and wave data from the Global Forecast System at 26 coastal and offshore stations distributed across eleven different pilot seas and oceans worldwide, encompassing a broad spectrum of marine environments and climatic conditions. The proposed model was benchmarked against established empirical approaches. Results indicate a mean prediction error of 6.6% for the significant wave height and 9.6% for the significant wave period, substantially outperforming conventional formulations whose errors exceed 50% under comparable conditions. Unlike existing empirical models that are restricted to specific regions or sea-state conditions, the proposed model demonstrated strong predictive performance across diverse seas, oceans, and climatic conditions, enabling more reliable wave predictions in data-scarce and dynamically complex marine environments. The developed model was further applied to the Red Sea, where it successfully reproduced the spatial variability of significant wave height and wave period. From the results, it has been found that the developed model provides a practical and transferable tool for wave forecasting, coastal engineering, and offshore renewable energy applications. Full article

Floods remain among the most devastating natural disasters globally, disproportionately impacting low-resource regions where real-time flood forecasting is constrained by limited computational infrastructure and the scarcity of fine-resolution predictive models. Although state-of-the-art global climate models achieve high predictive accuracy, their scale and computational complexity restrict their applicability in localized and resource-constrained settings. This study proposes a deep climate model distillation framework that transfers knowledge from a high-capacity Fourier Neural Operator (FNO)-based global climate model inspired by FourCastNet into lightweight, regionally adaptive student networks suitable for edge deployment. The framework combines climate variables, satellite observations, and hydrological measurements to improve localized flood prediction. Knowledge transfer is achieved through a multi-objective distillation strategy that combines supervised learning, soft-target alignment, and intermediate feature matching. Experimental evaluation across multiple flood-prone regions in Sub-Saharan Africa and South Asia shows that the distilled student model achieves an average classification accuracy of 0.89, an AUC of 0.91, and an F1-score of 0.88, retaining approximately 96.7% of the teacher model’s predictive performance. In continuous discharge estimation, the model attains a mean absolute error of 0.17, RMSE of 0.24, and an R² score of 0.85. The proposed distillation approach yields an 8× reduction in inference latency and over a 20× reduction in model size, enabling real-time execution on low-power edge devices such as the Raspberry Pi 4 and NVIDIA Jetson Nano. The student model further demonstrates robust regional and temporal generalization, with limited performance degradation in unseen geographic areas and during extreme flood years. Full article

Liquid storage tanks are widely used in sectors such as water treatment, oil and gas, food processing, and chemical manufacturing. Knowing the exact amount of liquid in a tank is essential for ensuring safety, preventing spills, and optimizing process control; therefore, the liquid level in a tank must be maintained at a precise reference point. This is where liquid level control for tanks becomes crucial and constitutes a fundamental problem in the industrial sector due to nonlinearities, multivariable coupling, and stochastic disturbances. Given the drawbacks of available control methods, such as classical Model Predictive Control (MPC), which are highly dependent on model accuracy and struggle to reject complex stochastic noise, predicting random disturbances represents a major technological challenge. A new approach is proposed to specifically address the problem and challenge of the four-tank system, where water levels in two lower tanks must be controlled by two pumps, often with varying delays and significant parameter disturbances. To establish a relationship between expected performance and MPC parameters, this approach uses a novel hybrid nonlinear MPC, Extended State Observer, and Physics-Informed Neural State Estimation (NMPC-ESO-PINSE) architecture. A Physics-Informed Neural State Estimation (PINSE) layer, chosen for its learning capacity, is designed to filter sensor noise by applying Bernoulli’s physical laws, while an Extended State Observer (ESO) is integrated to capture and compensate for unmodeled uncertainties in the process. Finally, a proposed hybrid (NMPC-ESO-PINSE) strategy leverages these clean, physically consistent state estimations to solve a non-convex optimization problem via Sequential Quadratic Programming (SQP), computing optimal pump voltages. Extensive numerical simulations demonstrate the superior resilience of this decoupled framework against parametric drifts and continuous noise sequences, yielding a +27.36% reduction in global Root Mean Square Error (RMSE) compared to standard NMPC, accelerating the closed-loop settling time to 15.2 s, and restricting transient overshoot to just 0.18%. Full article

Cross-material continuous tool wear prediction is difficult because a model must preserve the physical wear scale, not only align high-dimensional sensor features. This limitation is critical in milling, where the target variable is the continuous flank wear width ($\mathrm{VB}$) and material shift can distort the mapping from sensor response to wear magnitude. We address this problem by recasting cross-domain tool wear prediction as monotone wear-scale adaptation. We propose Multi-Physics Monotone Score Transport (MPMST), a monotone score transport framework that constructs a tool-wear-oriented score from sensor-derived candidate cues, transports the target-domain score onto the source-domain wear scale, and then predicts wear through isotonic regression. We also evaluate One-Physics Monotone Score Transport (OPMST), a force-only variant that uses the same score-transport pipeline with a restricted cue family. On Mondragon Unibertsitatea–Tool Condition Monitoring (MU-TCM) with two cross-material transfer tasks, the validation-driven MPMST configuration reduces mean absolute error by approximately 63% relative to Correlation Alignment (CORAL) and by approximately 31% relative to a physics-informed Gaussian process baseline. The results support monotone score construction and score transport as practical mechanisms for continuous tool wear prediction under domain shift, while also showing that MU-TCM is strongly force dominated. Full article

Diabetes mellitus (DM) fundamentally disrupts the oral microbiome, initiating a dysbiotic shift that drives progressive periodontal tissue breakdown. This transition is mediated by complex, bidirectional immune crosstalk, primarily centering on the upregulation of the Th17/Interleukin-17 (IL-17) inflammatory pathway. This systematic review and meta-analysis quantified the specific impact of this diabetic microbiota on immune activation and periodontal destruction. A comprehensive search of PubMed/MEDLINE, Scopus, Web of Science, and the Cochrane Library was conducted for studies published up to 2026. Eligible studies included assessing oral/salivary microbiome shifts and their localized or systemic immunological consequences in diabetic periodontitis. A random-effects meta-analysis synthesized standardized mean differences (Hedges’ g) to evaluate the magnitude of these effects. Quantitative synthesis of preclinical data (four studies yielding eight discrete comparisons) revealed that exposure to a diabetic/dysbiotic microbiota significantly increased overall immune activation and periodontal inflammation relative to eubiotic controls (pooled Hedges’ g = 3.73, 95% CI 2.96–4.51). Subgroup analyses confirmed profound, statistically significant effects specifically on the Th17/IL-17 axis (g = 4.03) and periodontal bone destruction pathways (g = 3.37). Preclinical murine data suggests diabetes-associated oral dysbiosis may contribute to periodontal destruction by upregulating the Th17/IL-17 immune axis. However, direct extrapolation to humans is restricted, necessitating further clinical studies to validate these findings. Full article

The natural mineral sinhalite, MgAlBO${}_4$, was investigated by means of single-crystal NMR spectroscopy. In its orthorhombic space group, the aluminium and boron atoms occupy Wyckoff positions $4b$ and $4c$, but the specific site symmetry of the boron atoms situated on a mirror plane leads two only two instead of four magnetically inequivalent atoms per site. This structural feature also restricts the corresponding NMR interaction tensors and reduces known assignment ambiguities regarding the NMR resonances of ${}^{27}$Al and ${}^{11}$B to distinct atomic positions. Comparing the experimentally determined eigenvectors of the quadrupolar coupling tensors with the results of DFT calculations for the electric field gradients further condensed the remaining options and delivered the full quadrupolar coupling tensors for both nuclides in the crystal structure of sinhalite. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) affects 25–30% of adults globally. Dietary sugar reduction is one of the key therapeutic targets, but elimination of sugar-sweetened foods may compromise adherence to calorie-restricted diets. Brazzein, a natural sweet protein that is 500–2000 times sweeter than sucrose, offers a promising substitute, yet clinical data in patients with MASLD are lacking. In a double-blind, randomized, two-period crossover trial, 103 adults with MASLD tasted iso-sweet vanilla ice cream sweetened with either brazzein or sucrose on two consecutive days. Overall impression and sensory attributes (appearance, color, aroma, taste, and texture) were rated on 5-point hedonic scales, and the percentage of the 100 g portion consumed was recorded. Brazzein-sweetened ice cream met the prespecified criteria for both non-inferiority and equivalence versus sucrose for overall impression. Top-2 box acceptance (ratings ≥ 4) was extremely high and nearly identical (96.1% for brazzein and 98.1% for sucrose). Mean consumption exceeded 98% of the portion for both products, with no significant difference between sweeteners. Secondary sensory ratings were closely similar, and multivariate analyses indicated highly overlapping sensory profiles. Exploratory subgroup analyses suggested consistent findings across most demographic and clinical characteristics, although participants with advanced liver fibrosis (LSM ≥ 9.6 kPa) showed numerically higher ratings for sucrose. In exploratory analyses, liver stiffness was associated with slightly lower intake at higher stiffness values. This study provides the first evidence that brazzein-sweetened ice cream maintains short-term sensory acceptability comparable to a conventional sucrose-sweetened product in adults with MASLD. These findings support further development and evaluation of brazzein-containing sugar-reduced foods, including repeated-exposure sensory studies and separate metabolic investigations. Full article

Landslides constitute a globally pervasive and highly destructive natural hazard. Although artificial intelligence (AI)-driven landslide susceptibility mapping has emerged as an effective tool for delineating high-risk zones, its predictive performance is frequently constrained by inherent data noise and insufficient characterization of landslide triggering factors, restricting the credibility of the mapping results. In this study, to remedy this limitation, we adopt fractal analysis to extract latent inherent information from topographic features. Specifically, the box-counting method and multifractal analysis are applied to excavate the intrinsic nonlinear characteristics embedded in eight topographic factors, and an improved K-means algorithm is utilized to perform feature selection and construct a dedicated fractal feature dataset, which is fed to advanced AI models. Our results indicate that the information dimension ($D_1$) of the slope gradient, the correlation dimension ($D_2$) of aspect, land relief, the $D_2$ of roughness, the $D_2$ of plan curvature, the multifractal spectrum width ($∆\mathsf{\alpha }$) of profile curvature, the $D_2$ of elevation, and the surface cutting depth were the most effective features, demonstrating superior performance in capturing landslide targets. Comparative performance evaluations reveal that AI models trained on fractal features demonstrate substantially superior predictive capabilities compared to AI models trained on raw features. This superiority is consistently evidenced across key evaluation metrics, including overall accuracy, kappa coefficient, F1-score, and predictive efficiency, demonstrating that the integration of fractal characteristics significantly augments model robustness and predictive efficacy. To mitigate the ‘black-box’ problem of AI modeling, Shapley additive explanations were employed to quantify individual feature contributions and elucidate the underlying predictive mechanisms. Our findings indicate that the integration of fractal analysis yields highly discriminative and robust feature representations, thereby expanding the representational capacity of the models and improving predictive accuracy. Furthermore, a joint assessment of spatial uncertainty and susceptibility maps demonstrates that these models exhibit low predictive variance and high spatial stability when delineating high-susceptibility zones. Notably, models utilizing fractal-derived features achieve superior spatial capture efficiency. The resultant topographic features characterized by fractal representation and selected via the improved K-means algorithm can significantly improve the predictive performance of trained AI models in landslide susceptibility mapping tasks, offering a scientific and viable technical approach for future landslide prediction and prevention. Full article

Background: Selenium (Sel) supplementation has been proposed as an antioxidant adjunct in Graves’ orbitopathy (GO), with early randomized evidence suggesting benefits in quality of life (QoL), ocular involvement, and disease progression in mild GO. However, subsequent trials across populations with different Sel status and disease severity have yielded inconsistent findings. This systematic review and meta-analysis of randomized controlled trials (RCTs) reassessed the efficacy of Sel supplementation in GO. Methods: PubMed, Scopus, and the Cochrane Library were searched from inception to 1 May 2026 for RCTs, comparing Sel supplementation with placebo or no Sel supplementation in patients with GO (PROSPERO “CRD420261395074”). Heterogeneity was assessed using I² statistics and Cochran’s Q test. Risk ratios (RRs) were calculated using the Mantel–Haenszel method, and mean differences (MDs) using the Inverse-Variance method. Random-effects models with restricted maximum-likelihood estimation were applied. Results: Five RCTs including 303 patients were analyzed, of whom 165 (56%) received Sel. Sel supplementation was associated with a significant reduction in clinical activity score (MD −1.05; 95% CI −1.61 to −0.48; I² = 52%; p < 0.01). No significant differences were observed in palpebral aperture (MD −0.12; 95% CI −1.22 to 0.98; I² = 58%; p = 0.83), although this anatomical parameter should be interpreted cautiously because it may be influenced by thyroid functional status and hyperthyroidism-related Müller muscle hyperfunction. No significant differences were observed in QoL improvement (RR 1.72; 95% CI 0.43 to 6.92; I² = 86%; p = 0.24) or visual function (MD 6.31; 95% CI −1.40 to 14.03; I² = 45%; p = 0.11). Conclusions: Sel supplementation may improve clinical activity score in patients with Graves’ orbitopathy, but this finding should be interpreted cautiously given the small number of trials, limited sample size, and clinically relevant heterogeneity. Current evidence does not show consistent benefits for palpebral aperture, quality of life, or visual function. Larger RCTs stratified by baseline Sel status and disease severity are needed before firm conclusions can be drawn. Full article

[¹⁸F]-PSMA PET/CT is a high-impact modality for the staging and restaging of prostate cancer, but its wide anatomic coverage and tracer biology generate frequent incidental findings on both PET and the accompanying low-dose CT (LDCT). This narrative review is restricted in scope to fluorine-18 PSMA tracers because tracer-specific biodistribution and pitfall profiles shape what is perceived as incidentaloma: how confidently lesions can be categorized, and how often borderline findings trigger downstream testing, particularly for skeletal foci with [¹⁸F]-PSMA-1007. Specifically, [¹⁸F]-PSMA-1007 shows substantially higher rates of focal unspecific bone uptake than [⁶⁸Ga]-PSMA-11—reported in multicenter studies as affecting up to 40–50% of patients—which directly inflates the pool of potential incidentalomas and creates a tracer-specific false-positive problem with no parallel in gallium-68 practice. Additionally, [¹⁸F]-DCFPyL has different urinary clearance kinetics that affect bladder and ureteral uptake patterns, altering what qualifies as physiologic versus incidental in the pelvis. These differences mean that the threshold for Category B versus C classification—and the appropriate cascade-resistant language—must be tuned to the specific tracer in use. A framework built on [⁶⁸Ga]-PSMA-11 data would systematically underestimate bone pitfall frequency in [¹⁸F]-PSMA-1007 practice and could therefore paradoxically increase rather than reduce cascades if applied uncritically across tracers. These biodistribution differences have direct and concrete consequences for reporting behaviour and downstream management. In [¹⁸F]-PSMA-1007 practice, a focal bone uptake without a CT correlate in a mechanically plausible location—such as an anterior rib or vertebral endplate—should trigger Category B language in the report conclusion: the finding is documented in the body with explicit safety netting (“most consistent with unspecific uptake; no routine workup unless interval growth, new pain, or aggressive CT morphology”), and no referral to bone scintigraphy or MRI is generated. Without tracer-specific awareness, the same finding would typically prompt a reflex bone scan or whole-body MRI referral, delaying definitive prostate cancer management by weeks and adding imaging costs without diagnostic gain. By contrast, in [⁶⁸Ga]-PSMA-11 practice, an equivalent focal bone uptake without a CT correlate carries a higher prior probability of true metastatic disease given the lower background rate of unspecific uptake and should more often be reported at Category B with a lower threshold for escalation or more cautious language. For [¹⁸F]-DCFPyL, the higher urinary activity in the pelvis means that ureteral segments can mimic lymph node disease; recognizing this as a physiologic variant (Category C) rather than an equivocal nodal finding (Category B) avoids unnecessary pelvic MRI referrals that would otherwise be triggered by an uncontextualized report. In practical terms, the tracer-specific calibration of the overlay therefore changes not only the category assigned but also the specific safety-netting language and the escalation trigger, which directly modifies the downstream management pathway for each affected finding type. The scanned population—predominantly older men with a high prevalence of degenerative, inflammatory, and vascular abnormalities—creates substantial background noise that can drive low-value diagnostic cascades if incidental findings are communicated without actionability context. We integrate society-endorsed frameworks (EANM/SNMMI procedure guideline 2.0; E-PSMA; PSMA-RADS; and PROMISE/miTNM with miPSMA score) and propose a cascade-aware overlay for incidental findings that can be appended to existing PSMA reporting standards rather than replacing them. The A/B/C actionability overlay is a structured expert-consensus framework informed by existing evidence-based guidelines for specific finding types and by tracer-specific cohort data; it has not yet been prospectively validated as a standalone tool, and its current level of evidence is therefore analogous to a structured expert recommendation rather than an evidence-based clinical guideline. We operationalize a three-tier actionability scheme across PET- and CT-dominant findings, provide cascade-resistant language for conclusions, and clarify why SUVmax-only “probability scales” for lymph nodes are not recommended in routine reports. Three practical tables summarize PET incidental findings, lymph node reporting frameworks, and LDCT incidental findings, and two structured report templates are provided (concise and extended), with the extended version explicitly labelling actionability tiers and escalation triggers. Finally, we outline concrete AI use cases for standardization and triage while emphasizing governance to avoid the amplification of false positives and paradoxical growth of cascades. Full article