Diabetes mellitus type 2 is associated with increased tumor expression of programmed death-ligand 1 (PD-L1) in surgically resected non-small cell lung cancer - A matched case-control study

OBJECTIVES: Programmed death-ligand 1 (PD-L1) expression is a biomarker for cancer immunotherapy. Diabetes mellitus type-2 is a comorbid disease associated with adverse outcomes in Non-Small Cell Lung Cancer (NSCLC). We aimed to investigate the differences in PD-L1 expression in diabetics. METHODS: A matched case-control cohort of surgically-resected NSCLC was assembled from an early multicenter study (PMID: 19152440). PD-L1 immunohistochemistry (Clone 22C3) was graded by a tumor positive score (TPS) system (TPS0: no staining; TPS1: \u3c 1%; TPS2: 1-49%; TPS3: \u3e /=50%). Variables showing significance at univariate survival analysis were fit in a Cox regression survival model. RESULTS: Diabetics (n=40) and nondiabetics (n=39) showed no differences in age, gender, cancer stage, and follow-up. NSCLCs were more likely PD-L1 positive in diabetics but with tumor positivity \u3c 50% (TPS0: 7.5 vs. 20.5%, TPS1: 35 vs. 25.6%, TPS2: 45 vs.23.1%, TPS3: 12.5 vs. 30.8%, respectively; P \u3c 0.05). In diabetics, squamous cell carcinomas (SCC) and adenocarcinomas were mainly TPS2 (65% vs. 20%) and TPS1 (50% vs. 26%), respectively. Peritumoral inflammation correlated with TPS (r=0.228), a relationship accentuated in diabetics (r=0.377, P \u3c 0.05) but diminished and non-significant in nondiabetics (r=0.136, P \u3e /=0.05). This association was stronger in SCC (r=0.424). Diabetes was associated with increased tumor recurrence (HR: 3.08; 95%CI: 1.027-9.23). CONCLUSION: Diabetes is associated with an increase in peritumoral inflammation, PD-L1 positivity, and recurrence in NSCLC, more pronounced in SCC, suggesting the possibility of metabolic reprogramming and upregulation of PD-L1 by inducible pathways. reserved

Strong versus Weak Data Labeling for Artificial Intelligence Algorithms in the Measurement of Geographic Atrophy

Purpose: To gain an understanding of data labeling requirements to train deep learning models for measurement of geographic atrophy (GA) with fundus autofluorescence (FAF) images. Design: Evaluation of artificial intelligence (AI) algorithms. Subjects: The Age-Related Eye Disease Study 2 (AREDS2) images were used for training and cross-validation, and GA clinical trial images were used for testing. Methods: Training data consisted of 2 sets of FAF images; 1 with area measurements only and no indication of GA location (Weakly labeled) and the second with GA segmentation masks (Strongly labeled). Main Outcome Measures: Bland–Altman plots and scatter plots were used to compare GA area measurement between ground truth and AI measurements. The Dice coefficient was used to compare accuracy of segmentation of the Strong model. Results: In the cross-validation AREDS2 data set (n = 601), the mean (standard deviation [SD]) area of GA measured by human grader, Weakly labeled AI model, and Strongly labeled AI model was 6.65 (6.3) mm2, 6.83 (6.29) mm2, and 6.58 (6.24) mm2, respectively. The mean difference between ground truth and AI was 0.18 mm2 (95% confidence interval, [CI], −7.57 to 7.92) for the Weakly labeled model and −0.07 mm2 (95% CI, −1.61 to 1.47) for the Strongly labeled model. With GlaxoSmithKline testing data (n = 156), the mean (SD) GA area was 9.79 (5.6) mm2, 8.82 (4.61) mm2, and 9.55 (5.66) mm2 for human grader, Strongly labeled AI model, and Weakly labeled AI model, respectively. The mean difference between ground truth and AI for the 2 models was −0.97 mm2 (95% CI, −4.36 to 2.41) and −0.24 mm2 (95% CI, −4.98 to 4.49), respectively. The Dice coefficient was 0.99 for intergrader agreement, 0.89 for the cross-validation data, and 0.92 for the testing data. Conclusions: Deep learning models can achieve reasonable accuracy even with Weakly labeled data. Training methods that integrate large volumes of Weakly labeled images with small number of Strongly labeled images offer a promising solution to overcome the burden of cost and time for data labeling. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article

Women's Health Initiative Diet Intervention Did Not Increase Macular Pigment Optical Density in an Ancillary Study of a Subsample of the Women's Health Initiative12

In this study, we examined the impact of long-term (>8 y), low-fat, high-fruit and -vegetable diets on levels of lutein and zeaxanthin in the macula of the retina, as indicated by the OD of macular pigment. Macular pigment OD, measured by heterochromatic flicker photometry, was compared in women aged 60–87 y, who, 7–18 mo earlier (median 12 mo), had been in the dietary modification intervention (n = 158) or comparison (n = 236) groups of the Women's Health Initiative (WHI) at the Madison, WI site for a mean of 8.5 y. Women in the intervention group ate more fruits and vegetables (mean ± SEM) (6.1 ± 0.2 vs. 4.6 ± 0.2 servings/d; P < 0.0001) and had higher intakes of lutein and zeaxanthin from foods and supplements (2.7 ± 0.2 vs. 2.1 ± 0.1 mg/d; P = 0.0003) than the comparison group. However, macular pigment density did not differ between the intervention (0.36 ± 0.02 OD units) and comparison (0.35 ± 0.01 OD units) groups. It tended to be higher (11%; P = 0.11) in women consuming lutein and zeaxanthin in the highest compared with the lowest quintile (median 6.4 vs. 1.1 mg/d). The increase in fruit and vegetable intake among dietary modification participants of this WHI subsample was not of sufficient magnitude to alter the mean density of retinal carotenoids, given other existing dietary conditions in this sample