Journal: Science Advances

Article Title: Intraoperative visualization of the tumor microenvironment and quantification of extracellular vesicles by label-free nonlinear imaging

doi: 10.1126/sciadv.aau5603

Figure Lengend Snippet: ( A ) Flowchart of EV segmentation and quantification algorithm. ( B ) Representative THG-contrast image acquired from the tumor microenvironment and processed binary image, highlighting the presence of EVs within the tumor microenvironment. ( C ) Comparison of EV density from breast cancer cases versus healthy breast reduction cases. The average EV density is 142 ± 55 nl −1 for the cancer cases, while it is only 23 ± 8 nl −1 for the healthy breast reduction cases. **** P < 0.0001 (one-sided Student’s t test). ( D ) EV density data from each case are registered by the distance from tumor to closest surgical margin and the cancer invasiveness grade. An overall decreasing trend of EV density is identified with increasing tumor-to-margin distance. Data points are divided into three groups (shaded areas) representing different histologic grades of IDC. ( E ) Relationship between EV density and IDC histologic grade. To minimize the effect of spatial heterogeneity, EV data were chosen from cases within a small range of margin distances (0 to 8 mm). Sample size of each IDC grade is indicated above each bar. *** P < 0.001, ** P < 0.01, * P < 0.1 (multiway ANOVA test, multiple comparison test).

Article Snippet: Multiway ANOVA (anovan; MATLAB) was performed to examine the relationship between EV density and the corresponding grade of IDC/DCIS.

Techniques: Comparison

Journal: Neoplasia (New York, N.Y.)

Article Title: Lung cancer lesion detection in histopathology images using graph‐based sparse PCA network ☆

doi: 10.1016/j.neo.2023.100911

Figure Lengend Snippet: Comparison of the proposed GS-PCANet method and other state-of-the-art alternatives by a two-way ANOVA. Values reported by ANOVA (using MATLAB function anova2 ) across the methods are S S = 0.2103 , d f = 7 , M S = 0.0300 , F = 36.27 , p ≪ 1 e − 5 , indicating that the improved accuracy of the proposed GS-PCANet method is statistically significant. The intervals shown represent 95% confidence intervals of the detection accuracies for the proposed method (blue) and the competing methods (red). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Article Snippet: Values reported by ANOVA (using MATLAB function anova2 ) across the methods are S S = 0.2103 , d f = 7 , M S = 0.0300 , F = 36.27 , p ≪ 1 e − 5 , indicating that the improved accuracy of the proposed GS-PCANet method is statistically significant.

Techniques: Comparison