A Multi-center Dental Panoramic Radiography Image Dataset for Impacted Teeth, Periodontitis, and Dental Caries: Benchmarking Segmentation and Classification Tasks.

Panoramic radiography imaging plays a crucial role in the diagnostic process of dental diseases. However, current artificial intelligence research datasets for panoramic radiography dental image processing are often limited to single-center and single-task scenarios, making it difficult to generalize their results. To address this, we present a multi-center, multi-task labeled dataset. In this study, our dataset comprises three datasets obtained from different hospitals. The first set has 4940 panoramic radiography images and corresponding labels from the Stemmatological Hospital of the General Hospital of Ningxia Medical University. The second set includes 716 panoramic radiography images and labels from the People's Hospital of Yinchuan City, Ningxia. The third dataset contains 880 panoramic radiography images and labels from a hospital in Shenzhen, Guangdong Province. This comprehensive dataset encompasses three types of dental diseases: impacted teeth, periodontitis, and dental caries. Specifically, it comprises 2555 images related to impacted teeth, 2735 images related to periodontitis, and 1246 images related to dental caries. In order to evaluate the performance of the dataset, we conducted benchmark tests for segmentation and classification tasks on our dataset. The results show that the presented dataset could be effectively used for benchmarking segmentation and classification tasks critical to the diagnosis of dental diseases. To request our multi-center dataset, please visit the address: https://github.com/qinxin99/qinxini .

Test sensitivity in a prospective cancer screening program: A critique of a common proxy measure.

The true sensitivity of a cancer screening test, defined as the frequency with which the test returns a positive result if the cancer is present, is a key indicator of diagnostic performance. Given the challenges of directly assessing test sensitivity in a prospective screening program, proxy measures for true sensitivity are frequently reported. We call one such proxy empirical sensitivity, as it is given by the observed ratio of screen-detected cancers to the sum of screen-detected and interval cancers. In the setting of the canonical three-state Markov model for progression from preclinical onset to clinical diagnosis, we formulate a mathematical relationship for how empirical sensitivity varies with the screening interval and the mean preclinical sojourn time and identify conditions under which empirical sensitivity exceeds or falls short of true sensitivity. In particular, when the inter-screening interval is short relative to the mean sojourn time, empirical sensitivity tends to exceed true sensitivity, unless true sensitivity is high. The Breast Cancer Surveillance Consortium (BCSC) has reported an estimate of 0.87 for the empirical sensitivity of digital mammography. We show that this corresponds to a true sensitivity of 0.82 under a mean sojourn time of 3.6 years estimated based on breast cancer screening trials. However, the BCSC estimate of empirical sensitivity corresponds to even lower true sensitivity under more contemporary, longer estimates of mean sojourn time. Consistently applied nomenclature that distinguishes empirical sensitivity from true sensitivity is needed to ensure that published estimates of sensitivity from prospective screening studies are properly interpreted.

Surrogate Endpoints as Predictors of Overall Survival in Metastatic Urothelial Cancer: A Trial-level Analysis.

Background: Surrogate endpoints (SEs), such as progression-free survival (PFS) and objective response rate (ORR), are frequently used in clinical trials. The relationship between SEs and overall survival (OS) has not been well described in metastatic urothelial cancer (MUC). Objective: We evaluated trial-level data to assess the relationship between SEs and OS. We hypothesize a moderate surrogacy relationship between both PFS and ORR with OS. Design setting and participants: We systematically reviewed phase 2/3 trials in MUC with two or more treatment arms, and report PFS and/or ORR, and OS. Outcome measurements and statistical analysis: Linear regression was performed, and the coefficient of determination (R2) and surrogate threshold effect (STE) estimate were determined between PFS/ORR and OS. Results and limitations: Of 3791 search results, 59 trials and 62 comparisons met the inclusion criteria. Of the 53 trials that reported PFS, 31 (58%) reported proportional hazard regression for PFS and OS. Linear regression across trials demonstrated an R2 of 0.60 between hazard ratio (HR) for PFS (HRPFS) and HR for OS (HROS), and an STE of 0.41. Linear regression of ΔPFS (median PFS in months of the treatment arm - that of the control arm) and ΔOS demonstrated an R2 of 0.12 and an STE of 14.1 mo. Thirty trials reported ORRs. Linear regression for ORRratio and HROS among all trials found an R2 of 0.08; an STE of 95% was not reached at any value and ΔORR and HROS similarly demonstrated a poor correlation with an R2 value of 0.03. Conclusions: PFS provides only a moderate level of surrogacy for OS; An HRPFS of ≤0.41 provides 95% confidence of OS improvement. ORR is weakly correlated with OS and should be de-emphasized in MUC clinical trials. When PFS is discussed, proportional hazard regression should be reported. Patient summary: We examined the relationship between surrogate endpoints, common outcomes in clinical trials, with survival in urothelial cancer trials. Progression-free survival is moderately correlated, while objective response rate had a poor correlation with survival and should be de-emphasized as a primary endpoint.

Pathological Effects of Apalutamide in Lower-risk Prostate Cancer: Results From a Phase II Clinical Trial.

PURPOSE: Active surveillance is a safe and effective strategy for men with lower-risk prostate cancer who want to avoid local therapy; however, many patients on active surveillance progress to active treatment (eg, prostatectomy or radiation). We hypothesized that apalutamide would decrease active surveillance attrition rates through downstaging low-grade tumors. MATERIALS AND METHODS: This was an open-label, single-arm, phase II study testing 90 days of oral apalutamide 240 mg daily in men with low- to intermediate-risk prostate cancer on active surveillance. The primary objective was to determine the percentage of patients with a negative biopsy immediately following treatment. Secondary objectives were to assess long-term clinical outcomes, quality of life, safety, and biomarkers of response/resistance. RESULTS: Twenty-three patients enrolled and 22 completed 90 days of apalutamide with post-treatment biopsy. Fifteen (65%) had Grade Group 1 disease, and all others had Grade Group 2 disease. Seven (30%) had favorable- to intermediate-risk disease. Of 22 evaluable patients, 13 (59%) had no residual cancer on post-treatment biopsy. The median time to first positive biopsy was 364 days (95% CI: 91-742 days). The impact of apalutamide on quality of life was minimal and transient. Decipher risk classifier revealed a greater number of negative post-treatment biopsies in those with higher baseline genomic risk score (P = .01). CONCLUSIONS: The negative repeat biopsy rate following 90 days of apalutamide was high in men with prostate cancer followed on active surveillance. Apalutamide was safe, well tolerated, and had minimal impact on quality of life. Randomized studies evaluating the effects of apalutamide in men enrolled on active surveillance are warranted.

Metabolomic Profiles Associated With Incident Ischemic Stroke.

BACKGROUND AND OBJECTIVES: Women have higher lifetime risk of stroke than men, and metabolic factors seem more strongly associated with stroke for women than men. However, few studies in either men or women have evaluated metabolomic profiles and incident stroke. METHODS: We applied liquid chromatography-tandem mass spectrometry to measure 519 plasma metabolites in a discovery set of women in the Nurses' Health Study (NHS; 454 incident ischemic stroke cases, 454 controls) with validation in 2 independent, prospective cohorts: Prevención con Dieta Mediterránea (PREDIMED; 118 stroke cases, 791 controls) and Nurses' Health Study 2 (NHS2; 49 ischemic stroke cases, 49 controls). We applied logistic regression models with stroke as the outcome to adjust for multiple risk factors; the false discovery rate was controlled through the q value method. RESULTS: Twenty-three metabolites were significantly associated with incident stroke in NHS after adjustment for traditional risk factors (q < 0.05). Of these, 14 metabolites were available in PREDIMED and 3 were significantly associated with incident stroke: methionine sulfoxide, N6-acetyllysine, and sucrose (q < 0.05). In NHS2, one of the 23 metabolites (glucuronate) was significantly associated with incident stroke (q < 0.05). For all 4 metabolites, higher levels were associated with increased risk. These 4 metabolites were used to create a stroke metabolite score (SMS) in the NHS and tested in PREDIMED. Per unit of standard deviation of SMS, the odds ratio for incident stroke was 4.12 (95% confidence interval [CI] 2.26-7.51) in PREDIMED, after adjustment for risk factors. In PREDIMED, the area under the receiver operating characteristic curve (AUC) for the model including SMS and traditional risk factors was 0.70 (95% CI 0.75-0.79) vs the AUC for the model including the traditional risk factors only of 0.65 (95% CI 0.70-0.75), corresponding to a 5% improvement in risk prediction with SMS (p < 0.005). DISCUSSION: Metabolites associated with stroke included 2 amino acids, a carboxylic acid, and sucrose. A composite SMS including these metabolites was associated with ischemic stroke and showed improvement in risk prediction beyond traditional risk factors. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that a SMS accurately predicts incident ischemic stroke risk.

Circulating amino acids and amino acid-related metabolites and risk of breast cancer among predominantly premenopausal women.

Known modifiable risk factors account for a small fraction of premenopausal breast cancers. We investigated associations between pre-diagnostic circulating amino acid and amino acid-related metabolites (N = 207) and risk of breast cancer among predominantly premenopausal women of the Nurses' Health Study II using conditional logistic regression (1057 cases, 1057 controls) and multivariable analyses evaluating all metabolites jointly. Eleven metabolites were associated with breast cancer risk (q-value < 0.2). Seven metabolites remained associated after adjustment for established risk factors (p-value < 0.05) and were selected by at least one multivariable modeling approach: higher levels of 2-aminohippuric acid, kynurenic acid, piperine (all three with q-value < 0.2), DMGV and phenylacetylglutamine were associated with lower breast cancer risk (e.g., piperine: ORadjusted (95%CI) = 0.84 (0.77-0.92)) while higher levels of creatine and C40:7 phosphatidylethanolamine (PE) plasmalogen were associated with increased breast cancer risk (e.g., C40:7 PE plasmalogen: ORadjusted (95%CI) = 1.11 (1.01-1.22)). Five amino acids and amino acid-related metabolites (2-aminohippuric acid, DMGV, kynurenic acid, phenylacetylglutamine, and piperine) were inversely associated, while one amino acid and a phospholipid (creatine and C40:7 PE plasmalogen) were positively associated with breast cancer risk among predominately premenopausal women, independent of established breast cancer risk factors.

Identifying metabolomic profiles of inflammatory diets in postmenopausal women.

BACKGROUND: We previously showed that a food-based empirical dietary inflammatory pattern (EDIP) score is associated with circulating inflammatory biomarkers. Metabolomic profiling of inflammatory diets may therefore provide insights on mechanisms contributing to disease etiology and prognosis. We aimed to elucidate metabolites associated with inflammatory diets among postmenopausal women, utilizing a robust study design that incorporates independent discovery and validation datasets. METHODS: This baseline cross-sectional investigation evaluated associations between continuous EDIP scores calculated from food frequency questionnaires and 448 log-transformed plasma metabolites as outcomes in multivariable-adjusted linear regression analyses. Metabolites were measured with liquid chromatography tandem mass spectroscopy. Metabolite discovery was conducted among 1109 Women's Health Initiative (WHI) Hormone Therapy trial participants and results were replicated in an independent dataset of 810 WHI Observational Study participants. Secondary analyses were stratified by standard body mass index (BMI, kg/m2) categories. In discovery and replication datasets statistical significance was based on false-discovery rate adjusted P < 0.05. RESULTS: After adjusting for energy intake, BMI, physical activity, and other confounding variables, 23 metabolites were significantly associated with EDIP score in the discovery dataset. Of these, the following ten were replicated: trigonelline, caffeine, acethylamino-6-amino-3-methyluracil, 7-methylxanthine, 1,7-dimethyluric acid, 3-methylxanthine, C18:3CE, glycine, associated with lower dietary inflammatory potential; whereas C52:3 triacylglycerol and linoleate associated with higher dietary inflammatory potential. Four of the ten were associated [glycine (inversely), caffeine, 1,7-dimethyluric acid, C52:3 triacylglycerol, (positively)], with C-reactive protein levels. In secondary analyses, associations showed differences by BMI category. Four metabolites, related to coffee/caffeine metabolism were inversely associated among normal weight women, and 83 metabolites associated with EDIP among overweight/obese women, including 40 (48%) that were also associated with C-reactive protein. CONCLUSION: Metabolites associated with coffee/caffeine and lipid metabolism may reflect the inflammatory potential of diet. Potential differences by BMI and the linkage to disease outcomes, require further study.

Metabolomic profiles associated with all-cause mortality in the Women's Health Initiative.

BACKGROUND: Metabolomics profiling has shown promise in elucidating the biological pathways underpinning mortality, but there are limited data in female populations. METHODS: We applied a liquid chromatography-tandem mass spectrometry metabolomics platform to EDTA-plasma to measure 470 metabolites at baseline in a discovery set of 943 postmenopausal women (including 417 incident deaths, median time to death of 10.6 years) with validation in an independent set of 1355 postmenopausal women (including 685 deaths, median time to death of 9.1 years) in the Women's Health Initiative. RESULTS: Eight new metabolites were discovered to be associated with all-cause mortality. Findings included protective effects of increased levels of three amino acids (asparagine, homoarginine and tryptophan) and docosatrienoic acid; and detrimental effects of increased levels of C4-OH-carnitine, hexadecanedioate and two purine/pyrimidines (N2, N2-dimethylguanosine and N4-acetylcytidine). In addition, a set of nine previously published metabolite associations were replicated. A metabolite score comprising 17 metabolites was associated with mortality (P < 10-8) after adjustment for risk factors, with a hazard ratio of 1.95 (95% CI: 1.46-2.62) for women in the highest quartile compared with the lowest quartile of metabolite score. The score was robust among younger women and older women, for both cardiovascular and non-cardiovascular mortality, and associated with both early deaths (within the first 10 years of baseline) and later deaths. CONCLUSIONS: Our study fills a gap in the literature by identifying eight novel metabolite associations with all-cause mortality in women, using a robust study design involving independent discovery and validation datasets.