Explore the vast range of titles available.

Background We performed a systematic review and meta-analysis to evaluate the prognostic significance of 18 F-FDG PET and PET/CT for evaluation of responses to neoadjuvant chemotherapy (NAC) in breast cancer patients. Methods We searched PubMed, Embase, and the Cochrane Library databases until June 2020 to identify studies that assessed the prognostic value of 18 F-FDG PET scans during or after NAC with regard to overall (OS) and disease-free survival (DFS). Hazard ratios (HRs) and their 95% confidence intervals (CIs) were pooled meta-analytically using a random-effects model. Results Twenty-one studies consisting of 1630 patients were included in the qualitative synthesis. Twelve studies investigated the use of PET scans for interim response evaluation (during NAC) and 10 studies assessed post-treatment PET evaluation (after NAC). The most widely evaluated parameter distinguishing metabolic responders from poor responders on interim or post-treatment PET scans was %ΔSUVmax, defined as the percent reduction of SUVmax compared to baseline PET, followed by SUVmax and complete metabolic response (CMR). For the 17 studies included in the meta-analysis, the pooled HR of metabolic responses on DFS was 0.21 (95% confidence interval [CI], 0.14–0.32) for interim PET scans and 0.31 (95% CI, 0.21–0.46) for post-treatment PET scans. Regarding the influence of metabolic responses on OS, the pooled HRs for interim and post-treatment PET scans were 0.20 (95% CI, 0.09–0.44) and 0.26 (95% CI, 0.14–0.51), respectively. Conclusions The currently available literature suggests that the use of 18 F-FDG PET or PET/CT for evaluation of response to NAC provides significant predictive value for disease recurrence and survival in breast cancer patients and might allow risk stratification and guide rational management.

Purpose We evaluated the performance of deep learning classifiers for bone scans of prostate cancer patients. Methods A total of 9113 consecutive bone scans (5342 prostate cancer patients) were initially evaluated. Bone scans were labeled as positive/negative for bone metastasis using clinical reports and image review for ground truth diagnosis. Two different 2D convolutional neural network (CNN) architectures were proposed: (1) whole body–based (WB) and (2) tandem architectures integrating whole body and local patches, here named as “global–local unified emphasis” (GLUE). Both models were trained using abundant (72%:8%:20% for training:validation:test sets) and limited training data (10%:40%:50%). The allocation of test sets was rotated across all images: therefore, fivefold and twofold cross-validation test results were available for abundant and limited settings, respectively. Results A total of 2991 positive and 6142 negative bone scans were used as input. For the abundant training setting, the receiver operating characteristics curves of both the GLUE and WB models indicated excellent diagnostic ability in terms of the area under the curve (GLUE: 0.936–0.955, WB: 0.933–0.957, P  > 0.05 in four of the fivefold tests). The overall accuracies of the GLUE and WB models were 0.900 and 0.889, respectively. With the limited training setting, the GLUE models showed significantly higher AUCs than the WB models (0.894–0.908 vs. 0.870–0.877, P  < 0.0001). Conclusion Our 2D-CNN models accurately classified bone scans of prostate cancer patients. While both showed excellent performance with the abundant dataset, the GLUE model showed higher performance than the WB model in the limited data setting.

The COVID-19 pandemic highlighted the need for a scientific framework that enables quantitative assessment and control of airborne infection risks in indoor environments. This study identifies limitations in the traditional Wells–Riley model—specifically its assumptions of perfect mixing and steady-state conditions—and addresses these shortcomings by adopting the REHVA (Federation of European Heating, Ventilation and Air Conditioning Associations) infection risk assessment model. We propose a five-tier risk classification system (Monitor, Caution, Alert, High Risk, Critical) based on two key metrics: the probability of infection (Pₙ) and the event reproduction number (R_event). Unlike the classical model, our approach integrates airborne virus removal mechanisms—such as natural decay, gravitational settling, and filtration—with occupant dynamics to reflect realistic contagion scenarios. Simulations were conducted across 10 representative indoor settings—such as classrooms, hospital waiting rooms, public transit, and restaurants—considering ventilation rates and activity-specific viral emission patterns. The results quantify how environmental variables (ventilation, occupancy, time) impact each setting’s infection risk level. Our findings indicate that static mitigation measures such as mask-wearing or physical distancing are insufficient without dynamic, model-based risk evaluation. We emphasize the importance of incorporating real-time crowd density, occupancy duration, and movement trajectories into risk scoring. To support this, we propose integrating computer vision (CCTV-based crowd detection) and entry/exit counting sensors within a live airborne risk assessment framework. This integrated system would enable proactive, science-driven epidemic control strategies, supporting real-time adaptive interventions in indoor spaces. The proposed platform could serve as a practical tool for early warning and management during future airborne disease outbreaks.

The purpose of the study is to investigate long-term changes on lymphoscintigraphy and their association with clinical factors in breast cancer-related lymphedema (BCRL) patients. This single-center cohort study included BCRL patients who underwent baseline and follow-up lymphoscintigraphy. The percentage of excessive circumference (PEC) of the affected upper limb compared with the unaffected side was used as an indicator of the clinical severity of BCRL. Each 99m Tc-phytate lymphoscintigraphy image was categorized according to the Taiwan lymphoscintigraphy staging system. Clinical parameters and the lymphoscintigraphy stage at baseline and follow-up were compared and analyzed. Eighty-seven patients were included. Baseline and follow-up lymphoscintigraphies were performed at median 7 (interquartile range [IQR]: 2‒14) and 78 (IQR: 49‒116) months after surgery, respectively. Both lymphoscintigraphy stage and PEC showed variable change with overall increases in their severity. Stepwise multivariable analysis revealed follow-up lymphoscintigraphy stage ( P  = 0.001) to be independent variables for PEC at follow-up, however, baseline lymphoscintigraphy stage was not. The clinical courses of BCRL and patients’ lymphoscintigraphy patterns showed diverse changes over long-term follow-up. In addition to initial lymphoscintigraphy for diagnosis, lymphatic remapping by follow-up lymphoscintigraphy can be useful to visualize functional changes in the lymphatic system that may guide the optimal management in BCRL.

Herein, a method for detecting microplastics in water using single-entity electrochemistry is presented, with a focus on the interaction between microplastics in aqueous solution and the surface of an ultramicroelectrode (UME). Polystyrene and polypropylene, two commonly used plastics that were ground and dispersed in aqueous solution, served as the detection target materials. The collisional contact of microplastics with the UME was transduced into a discernible signal. To detect microplastics in solution using an UME, redox species (e.g., ferrocyanide) were continuously oxidized at the electrode, and the resulting steady-state current was monitored. Collisional contact followed by adsorption of microplastics on the UME disturbed the diffusional flux of redox species, resulting in an immediate change in the steady-state current. Detection sensitivity was further enhanced by optimizing the electrolyte composition to induce a migration effect. COMSOL Multiphysics simulations were employed to analyze the magnitude of the current changes as a function of microplastic size. The size distribution obtained from the simulations closely matched measurements from dynamic light scattering (DLS).

Background/Objectives: Corneal epithelial thickness (CET) alterations reflect distinct mechanisms in aqueous-deficient and evaporative dry eye disease (DED) subtypes. In this study, we compare the CET profiles between patients with Sjögren’s syndrome (SS) and those with meibomian gland dysfunction (MGD) to elucidate the underlying mechanisms. Methods: We retrospectively analyzed 30 patients with SS and 30 age- and sex-matched with MGD. Assessments included corneal staining, Ocular Surface Disease Index (OSDI), tear meniscus height (TMH), non-invasive breakup time, lipid layer thickness (LLT), and anterior segment optical coherence tomography (AS-OCT) CET mapping. Regional CET and superior–inferior asymmetry were compared. Results: The SS group exhibited higher corneal staining scores (2.18 ± 1.23 vs. 1.03 ± 1.18, p = 0.001) and lower TMHs (0.14 ± 0.06 vs. 0.18 ± 0.07 mm, p = 0.013), while the MGD group reported greater OSDI scores (40.39 ± 22.49 vs. 31.25 ± 22.81, p = 0.029). A significantly thinner central epithelium (p = 0.043) and localized inferior paracentral thinning (2–5 mm zone, p = 0.008) were noted in SS. Corneal staining was identified as the primary independent predictor of central and inferior CET reduction in both groups. In the MGD group, LLT was associated with the preserved inferior CET (p = 0.045) and superior–inferior thickness difference (p = 0.015). Conclusions: Distinct structural signatures are observed between DED subtypes. SS features central/inferior thinning from aqueous deficiency-mediated friction, whereas MGD shows a relatively preserved epithelial thickness influenced by LLT. Regional CET analysis may provide mechanistic insights into DED subtyping.

Objectives We aimed to evaluate the diagnostic ability for the prediction of histologic grades and prognostic values on recurrence and death of pretreatment 2-[ 18 F]FDG PET/CT in patients with resectable thymic epithelial tumours (TETs). Methods One hundred and fourteen patients with TETs who underwent pretreatment 2-[ 18 F]FDG PET/CT between 2012 and 2018 were retrospectively evaluated. TETs were classified into three histologic subtypes: low-risk thymoma (LRT, WHO classification A/AB/B1), high-risk thymoma (HRT, B2/B3), and thymic carcinoma (TC). Area under the receiver operating characteristics curve (AUC) was used to assess the diagnostic performance of PET/CT variables (maximum standardised uptake value [SUVmax], metabolic tumour volume [MTV], total lesion glycolysis [TLG], maximum diameter). Cox proportional hazards models were built using PET/CT and clinical variables. Results The tumours included 52 LRT, 33 HRT, and 29 TC. SUVmax showed good diagnostic ability for differentiating HRT/TC from LRT (AUC 0.84, 95% confidence interval [CI] 0.76 − 0.92) and excellent ability for differentiating TC from LRT/HRT (AUC 0.94, 95% CI 0.90 − 0.98), with significantly higher values than MTV, TLG, and maximum diameter. With an optimal cut-off value of 6.4, the sensitivity, specificity, and accuracy for differentiating TC from LRT/HRT were 69%, 96%, and 89%, respectively. In the multivariable Cox proportional hazards analyses for freedom-from-recurrence, SUVmax was an independent prognostic factor ( p  < 0.001), whereas MTV and TLG were not. SUVmax was a significant predictor for overall survival in conjunction with clinical stage and resection margin. Conclusion SUVmax showed excellent diagnostic performance for prediction of TC and significant prognostic value in terms of recurrence and survival. Key Points • Maximum standardised uptake value (SUVmax) shows excellent performance in the differentiation of thymic carcinoma from low- and high-risk thymoma. • SUVmax is an independent prognostic factor for freedom-from-recurrence in the multivariable Cox proportional hazard model and a significant predictor for overall survival. • 2-[ 18 F]FDG PET/CT can provide a useful diagnostic and prognostic imaging biomarker in conjunction with histologic classification and stage and help choose appropriate management for thymic epithelial tumours.

Purpose Left ventricular mechanical dyssynchrony (LVMD) is an important prognostic factor in coronary artery disease. A growing body of evidence indicates that LVMD parameters derived from phase analysis of gated myocardial SPECT may allow risk stratification for future cardiac events. We performed a systematic review and meta-analysis on the prognostic value of LVMD on gated SPECT in patients with coronary artery disease. Methods PubMed, Embase, and the Cochrane library were searched until August 25, 2022, for studies reporting the prognostic value of LVMD on gated SPECT for outcomes of all-cause death, cardiac death, or major adverse cardiovascular event (MACE) in patients with coronary artery disease. Hazard ratios (HRs) and their 95% confidence intervals (CIs) were meta-analytically pooled using a random-effects model. Results Nine studies (26,750 patients) were included in a qualitative synthesis. Among the SPECT LVMD parameters used in various studies, high phase standard deviation, phase bandwidth, and phase entropy were widely evaluated and reported to be associated with high rates of all-cause death, cardiac death, or MACE. For five studies (23,973 patients) in the quantitative synthesis, the pooled HR of LVMD for predicting MACE was 2.81 (95% CI 2.03–3.88). Studies using combined phase parameters to define LVMD showed higher HRs than a study using phase entropy ( p  = 0.0180). Conclusion LVMD from gated myocardial SPECT is a significant prognostic factor for coronary artery disease. Phase analysis of gated SPECT may be useful for accurate risk stratification and could be applied for clinical decision-making in such patients.

ObjectiveTo perform a systematic review and meta-analysis to determine intrahepatic distant recurrence (IDR) risk of hepatobiliary phase (HBP) hypointense nodules without arterial phase hyperenhancement (APHE) on pretreatment gadoxetic acid–enhanced MRI in patients with hepatocellular carcinoma (HCC) treated with either hepatectomy or radiofrequency ablation (RFA).MethodsPubMed and EMBASE databases were searched up to April 6, 2019. We included studies that evaluated HBP hypointense nodules without APHE as risk factors for IDR in HCC patients treated with either hepatectomy or RFA. Hazard ratios (HR) were meta-analytically pooled using random effects model. Subgroup analyses stratified to clinicopathologic variables were performed to explore heterogeneity. Methodological quality of included studies was assessed using Quality in Prognostic Studies (QUIPS) tool.ResultsEight studies with 842 patients were analyzed. The overall pooled HR for IDR was 2.44 (95% CI, 1.99–2.98) and were 2.14 (95% CI, 1.66–2.76) and 3.07 (95% CI, 2.19–4.31) for patients that underwent hepatectomy and RFA, respectively. No significant heterogeneity was present (I2 = 0%). The presence of these nodules was consistently shown to be significant factors for IDR in other subgroups (HR = 1.74–3.07). Study quality was generally moderate.ConclusionsHBP hypointense nodules without APHE are risk factors for IDR in HCC patients treated with either RFA or hepatectomy. Stratification of patient management with regard to performing additional tests or treatment for these nodules and modification of proper follow-up strategies may be required in patients with HCC who have these nodules on pretreatment gadoxetic acid–enhanced MRI.Key Points• HBP hypointense nodules without APHE constitute an entity that is unique in gadoxetic acid–enhanced MRI.• HBP hypointense nodules without APHE are risk factors for IDR in HCC patients treated with either RFA or hepatectomy.• Stratification of management and modification of proper follow-up strategies may be required in HCC patients who have these nodules on pretreatment gadoxetic acid–enhanced MRI.

Objective We aimed to develop deep learning classifiers for assessing therapeutic response on bone scans of patients with prostate cancer. Methods A set of 3791 consecutive bone scans coupled with their last previous scan (1528 patients) was evaluated. Bone scans were labeled as “progression” or “nonprogression” on the basis of clinical reports and image review. A 2D-convolutional neural network architecture was trained with three different preprocessing methods: 1) no preprocessing (Raw), 2) spatial normalization (SN), and 3) spatial and count normalization (SCN). Data were allocated into training, validation, and test sets in the ratio of 72:8:20, with the 20% independent test set rotating all scans over a five-fold testing procedure. A Grad-CAM algorithm was employed to generate class activation maps to visualize the lesions contributing to the decision. Diagnostic performance was compared using area under the receiver operating characteristics curves (AUCs). Results The data consisted of 791 scans labeled as “progression” and 3000 scans labeled as “nonprogression.” The AUCs of the classifiers were 0.632–0.710 on the Raw dataset, were significantly higher with the use of SN at 0.784–0.854 ( p  < 0.001 for Raw versus SN), and higher still with SCN at 0.954–0.979 ( p  < 0.001 for SN versus SCN). Class activation maps of the SCN model visualized lesions contributing to the model’s decision of progression. Conclusion With preprocessing of spatial and count normalization, our deep learning model achieved excellent performance in classifying the therapeutic response of bone scans in patients with prostate cancer.