We investigated the role of [¹⁸F]FDG positron emission tomography/computed tomography (PET/CT) in evaluating ground-glass nodules (GGNs) by visual analysis and tissue fraction correction. A total of 40 pathologically confirmed ≥1 cm GGNs were evaluated visually and semiquantitatively. [¹⁸F]FDG uptake of GGN distinct from background lung activity was considered positive in visual analysis. In semiquantitative analysis, we performed tissue fraction correction for the maximum standardized uptake value (SUV_max) of GGN. Of the 40 GGNs, 25 (63%) were adenocarcinomas, 9 (23%) were minimally invasive adenocarcinomas (MIAs), and 6 (15%) were adenocarcinomas in situ (AIS). On visual analysis, adenocarcinoma showed the highest positivity rate among the three pathological groups (88%, 44%, and 17%, respectively). Both SUV_max and tissue-fraction–corrected SUV_max (SUV_maxTF) were in the order of adenocarcinoma > MIA > AIS (p = 0.033 and 0.018, respectively). SUV_maxTF was significantly higher than SUV_max before correction (2.4 [1.9–3.0] vs. 1.3 [0.8–1.8], p < 0.001). When using a cutoff value of 2.5, the positivity rate of GGNs was significantly higher in SUV_maxTF than in SUV_max (50% vs. 5%, p < 0.001). The diagnostic sensitivity of [¹⁸F]FDG PET/CT in predicting the malignancy of lung GGN was improved by tissue fraction correction and visual analysis.

This study leverages the innovative imaging capabilities of macrolaser light-sheet microscopy to elucidate the 3D spatial visualization of AD-associated neuropathologic networks in the transparent brains of 44-week-old 5xFAD mice. Brain samples from ten AD and seven control mice were prepared through a hydrophilic tissue-clearing pipeline and immunostained with thioflavin S (β-amyloid), anti-CD11b antibody (microglia), and anti-ACSA-2 antibody (astrocytes). The 5xFAD group exhibited significantly higher average total surface volumes of β-amyloid accumulation than the control group (AD, 898,634,368 µm³ [383,355,488–1,324,986,752]; control, 33,320,178 µm³ [11,156,785–65,390,988], p = 0.0006). Within the AD group, there was significant interindividual and interindividual variability concerning the number and surface volume of individual amyloid particles throughout the entire brain. In the context of neuroinflammation, the 5xFAD group showed significantly higher average total surface volumes of anti-ACSA-2-labeled astrocytes (AD, 59,064,360 µm³ [27,815,500–222,619,280]; control, 20,272,722 µm³ [9,317,288–27,223,352], p = 0.0047) and anti-CD11b labeled microglia (AD, 51,210,100 µm³ [15,309,118–135,532,144]; control, 23,461,593 µm³ [14,499,170–27,924,110], p = 0.0162) than the control group. Contrary to the long-standing finding that early-stage neuroinflammation precedes the subsequent later-stage of neurodegeneration, our data reveal that the second wave, late-stage active neuroinflammation persists in the aged AD brains, even as they continue to show signs of ongoing neurodegeneration and significant amyloid accumulation.

Recent studies have highlighted the significance of peripheral β-amyloid (Aβ) deposition, identifying the eye as a potential early detection site for Alzheimer's disease (AD). However, previous two-dimensional AD ocular studies have been unable to establish a clear correlation between the three-dimensional Aβ accumulation in the entire eyeball and brain while preserving structural integrity. This study employed a combined brain amyloid positron emission tomography/magnetic resonance (PET/MR) and light-sheet fluorescence microscopy (LSFM) platform to assess whether the three-dimensionally measured Aβ burden in the eyeball correlates with that in the brain. Thirteen eyeballs (6 AD, 7 control) and 17 brains (10 AD, 7 control) were collected from ten 44-week-old 5xFAD and seven control mice. The samples underwent tissue clearing and staining with thioflavin S (Aβ), anti-CD11b (microglia), and anti-ACSA-2 (astrocytes) for LSFM imaging and quantified via 3D surface volume. Standardized uptake value ratios from [18F]Flutemetamol PET/MR were also calculated. AD eyeballs presented significantly greater plaque-like surface volumes (median, 51,091,002 µm³ [interquartile range, 38,488,272-64,869,828]) than controls (229,293 µm³ [115,863-311,5320]; P = 0.001). AD brains exhibited higher [18F]Flutemetamol uptake and greater plaque-like surface volumes (898,634,368 µm³ [556,263,488-1,105,326,720]) than controls (33,320,178 µm³ [26,842,538-62,716,956]; P < 0.001). A strong positive correlation was observed between the plaque-like surface volumes in the brain and that in the eyeball (r = 0.810, P = 0.001). No significant correlations were found in other morphologic parameters. Our observation of a strong correlation between the three-dimensional Aβ burden in the whole eyeball and brain advances our understanding of the systemic nature of Aβ pathology and suggests ocular Aβ as a potential independent predictor of brain Aβ burden.

Purpose: Convolutional neural networks (CNNs) have been studied for detecting bone metastases on bone scans; however, the application of ConvNeXt and transformer models has not yet been explored. This study aims to evaluate the performance of various deep learning models, including the ConvNeXt and transformer models, in diagnosing metastatic lesions from bone scans. Materials and Methods: We retrospectively analyzed bone scans from patients with cancer obtained at 2 institutions: the training and validation sets (n=4626) were from Hospital 1 and the test set (n=1428) was from Hospital 2. The deep learning models evaluated included ResNet18, the Data-Efficient Image Transformer (DeiT), the Vision Transformer (ViT Large 16), the Swin Transformer (Swin Base), and ConvNeXt Large. Gradient-weighted class activation mapping (Grad-CAM) was used for visualization. Results: Both the validation set and the test set demonstrated that the ConvNeXt large model (0.969 and 0.885, respectively) exhibited the best performance, followed by the Swin Base model (0.965 and 0.840, respectively), both of which significantly outperformed ResNet (0.892 and 0.725, respectively). Subgroup analyses revealed that all the models demonstrated greater diagnostic accuracy for patients with polymetastasis compared with those with oligometastasis. Grad-CAM visualization revealed that the ConvNeXt Large model focused more on identifying local lesions, whereas the Swin Base model focused on global areas such as the axial skeleton and pelvis. Conclusions: Compared with traditional CNN and transformer models, the ConvNeXt model demonstrated superior diagnostic performance in detecting bone metastases from bone scans, especially in cases of polymetastasis, suggesting its potential in medical image analysis.

We integrated visual and quantitative methods for analyzing the stability of respiration using four methods: phase space diagrams, Fourier spectra, Poincaré maps, and Lyapunov exponents. Respiratory patterns of 139 patients were grouped based on the combination of the regularity of amplitude, period, and baseline positions. Visual grading was done by inspecting the shape of diagram and classified into two states: regular and irregular. Quantitation was done by measuring standard deviation ofxandvcoordinates of Poincaré map (SD_x, SD_v) or the height of the fundamental peak (A₁) in Fourier spectrum or calculating the difference between maximal upward and downward drift. Each group showed characteristic pattern on visual analysis. There was difference of quantitative parameters (SD_x, SD_v,A₁, and MUD-MDD) among four groups (one way ANOVA,p=0.0001for MUD-MDD, SD_x, and SD_v,p=0.0002forA₁). In ROC analysis, the cutoff values were 0.11 for SD_x(AUC: 0.982,p<0.0001), 0.062 for SD_v(AUC: 0.847,p<0.0001), 0.117 forA₁(AUC: 0.876,p<0.0001), and 0.349 for MUD-MDD (AUC: 0.948,p<0.0001). This is the first study to analyze multiple aspects of respiration using various mathematical constructs and provides quantitative indices of respiratory stability and determining quantitative cutoff value for differentiating regular and irregular respiration.

Background: Visual assessment of coronary artery calcium (CAC) on ungated chest CT has been described previously. However, its reliability and clinical utility remain uncertain, particularly in PET/CT studies that use low-dose, low-slice CT and are susceptible to respiratory artifacts. Methods: We retrospectively analyzed 106 patients (median age, 66 years [interquartile range, 60–75 years]; 67 men [63.2%]) who underwent PET/CT and electrocardiogram (ECG)-gated chest CT within a 90-day interval. Six readers (three radiologists and three nuclear medicine physicians) independently assessed CAC on PET/CT using a standard four-point visual scale and a 0–12 ordinal scale based solely on written instructions. Agatston scoring was also performed. Interobserver agreement and concordance with ECG-gated chest CT Agatston score categories were calculated. Major adverse cardiovascular events (MACE) were recorded over a median follow-up of 3.5 years. Results: Interobserver agreement was good for both the standard visual (κ = 0.761) and ordinal (κ = 0.779) scales. Concordance with ECG-gated CT Agatston categories was higher for standard visual (κ = 0.849) and ordinal (κ = 0.750) scoring than for PET/CT Agatston categories (κ = 0.464). Both qualitative scales tended to underestimate CAC categories compared with ECG-gated CT; however, severe CAC on PET/CT predicted MACE (hazard ratios: 4.41 standard visual; 6.59 ordinal), and the ordinal scale significantly stratified MACE-free survival (p = 0.047). Conclusions: Standard visual and ordinal CAC scoring on the ungated CT portion of PET/CT is quick, reproducible, closely mirrors ECG-gated-CT Agatston grading, and offers prognostic value for future MACE in cancer patients.