Kanran Wang

There is considerable inconsistency regarding study results on the association of dietary glycemic index (GI) and glycemic load (GL) with lung cancer risk. We aimed to investigate this relation in the US National Cancer Institute's Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial cohort. Baseline characteristics were collected with the baseline questionnaire, and diet was assessed with the diet history questionnaire. All incident lung cancer cases were verified via pathology. We estimated hazard ratios (HRs) and 95% CIs for lung cancer risk associated with GI and GL by Cox regression modeling. During a median follow-up of 12.2 years (1,213,533 person-years), a total of 1,706 incident lung cancer events occurred including 1,473 (86.3%) cases of non-small cell lung cancer (NSCLC) and 233 (13.7%) of small cell lung cancer (SCLC). After multivariate adjustment, GI was positively associated with lung cancer (4th quartile [Q4] vs 1st quartile [Q1]; HR 1.13; 95% CI, 1.05-1.31), NSCLC (Q4 vs Q1; HR 1.11; 95% CI, 1.05-1.29), and SCLC (Q4 vs Q1; HR 1.34; 95% CI, 1.02-2.27). Conversely, we found an association between dietary GL and a decreased risk of lung cancer (Q4 vs Q1; HR 0.72; 95% CI, 0.57-0.90) and NSCLC (Q4 vs Q1; HR 0.68; 95% CI, 0.53-0.87) but not SCLC (Q4 vs Q1; HR 0.90; 95% CI, 0.51-1.58). These results were consistently observed across subgroup analyses. These findings show that high dietary GI is associated with an increased risk of lung cancer, NSCLC, and SCLC, whereas GL is inversely associated with the risk of lung cancer and NSCLC.

Background: The evidence on associations between ultra-processed foods (UPF) and lung cancer risk is limited and inconsistent. Research question: Are UPF associated with an increased risk of lung cancer, non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC)？ Methods: Data of participants in this study were collected from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. Dietary intakes were assessed through a validated diet history questionnaire. These foods were categorised using the NOVA classification according to the degree of processing in the PLCO Cancer Screening Cohort. All cases of incident lung cancer were pathologically verified. Multivariable Cox regression was used to assess the association between consumption of UPF and lung cancer after adjustment for various potential confounders, including key risk factors related to lung cancer and overall diet quality. Results: A total of 1706 cases of lung cancer cases, including 1473 NSCLC and 233 SCLC, were identified during a mean follow-up of 12.2 years among 101 732 adults (mean age 62.5 years). After multivariable adjustments, individuals in the highest quarters for UPF consumption had a higher risk of lung cancer (HR=1.41, 95% CI 1.22 to 1.60), NSCLC (HR=1.37, 95% CI 1.20 to 1.58) and SCLC (HR=1.44, 95% CI 1.03 to 2.10) compared with those in the lowest quarter. These results remained statistically significant after a large range of subgroup and sensitivity analyses. Conclusions: Higher consumption of UPF is associated with an increased risk of lung cancer, NSCLC and SCLC. Although additional research in other populations and settings is warranted, these findings suggest the healthy benefits of limiting UPF.

Whether the intake of whole grain foods can protect against lung cancer is a long-standing question of considerable public health import, but the epidemiologic evidence has been limited. Therefore we aim to investigate the relationship between whole grain food consumption and lung cancer in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) cohort. Diet was assessed with a self-administered Diet History Questionnaire (DHQ) at baseline. All incident lung cancer cases were pathologically verified. Hazard ratios and 95% confidence intervals for lung cancer risk associated with whole grain food consumption were estimated by Cox proportional hazards regression. During a median follow-up of 12.2 years, a total of 1,706 incident lung cancer events occurred, including 1,473 (86.3%) cases of non-small cell lung cancer (NSCLC) and 233 (13.7%) of small cell lung cancer (SCLC). After multivariate adjustment, comparing the highest quarter of consumption of whole grain foods to the lowest quarter, a 16% lower rate (HR 0.84, 95% CI 0.73–0.98) of lung cancer risks and a 17% lower rate (HR 0.83, 95% CI 0.69–0.98) for NSCLC were found, but no significant difference was shown for SCLC (HR 0.95, 95% CI 0.63–1.44). These results were consistently observed after a large range of subgroup and sensitivity analyses. A linear dose-response pattern was shown for lung cancer, NSCLC, and SCLC (P for non-linearity > 0.05). In this large prospective cohort study, whole grain food consumption was associated with reduced lung cancer and NSCLC. Our findings suggest a potential protective role of whole grain foods against lung cancer.

Cervical cytology is a critical screening strategy for early detection of pre-cancerous and cancerous cervical lesions. The challenge lies in accurately classifying various cervical cytology cell types. Existing automated cervical cytology methods are primarily trained on databases covering a narrow range of coarse-grained cell types, which fail to provide a comprehensive and detailed performance analysis that accurately represents real-world cytopathology conditions. To overcome these limitations, we introduce HiCervix, the most extensive, multi-center cervical cytology dataset currently available to the public. HiCervix includes 40,229 cervical cells from 4,496 whole slide images, categorized into 29 annotated classes. These classes are organized within a three-level hierarchical tree to capture fine-grained subtype information. To exploit the semantic correlation inherent in this hierarchical tree, we propose HierSwin, a hierarchical vision transformer-based classification network. HierSwin serves as a benchmark for detailed feature learning in both coarse-level and fine-level cervical cancer classification tasks. In our comprehensive experiments, HierSwin demonstrated remarkable performance, achieving 92.08% accuracy for coarse-level classification and 82.93% accuracy averaged across all three levels. When compared to board-certified cytopathologists, HierSwin achieved high classification performance (0.8293 versus 0.7359 averaged accuracy), highlighting its potential for clinical applications. This newly released HiCervix dataset, along with our benchmark HierSwin method, is poised to make a substantial impact on the advancement of deep learning algorithms for rapid cervical cancer screening and greatly improve cancer prevention and patient outcomes in real-world clinical settings.

Cervical cancer is the fourth most common cancer in women and its subtyping requires examining histopathological slides or digital images, such as whole slide images (WSIs). However, manually inspecting WSIs with gigapixel sizes can be laborious and prone to errors for pathologists. To address this issue, computer-aided approaches based on weakly-supervised learning techniques have been proposed. These methods can predict disease types directly from WSIs and highlight diagnosis-relevant regions, which can help pathologists achieve faster and more accurate diagnoses. WSIs are divided into overlapping patches using a sliding window approach, and these patches are subsequently screened in a sequential zig-zag pattern to identify spatiotemporal dependencies. These dependencies are further analyzed to generate predictions at the WSI level. Therefore, effective patch feature learning and spatiotemporal aggregation are two key issues in the weakly-supervised WSI classification (WSWC) task. In this paper, we present a label-efficient WSWC method called spatiotemporal aggregation for cervical WSIs (SAC-Net), which jointly performs online feature extraction and feature aggregation to infer the WSI-level prediction in an end-to-end manner. The online feature extractor helps to learn cervical-cancer-specific features and obtain more accurate patch representations. The feature aggregator uses an online instance clustering method to learn proper weight parameters for each cluster, which generates the WSI embedding with enhanced spatiotemporal aggregation. SAC-Net is developed and evaluated on a public cervical WSI dataset (TissueNet) containing 1015 WSIs, which are also externally tested on three independent cervical WSI datasets. Our results demonstrate that SAC-Net achieves state-of-the-art classification performance and is robust. SAC-Net has the potential to be a useful tool for clinical cervical cancer detection.

Retraction: Wang, K, Tang, W, Hao, X, Zhao, J. Ultra‐processed food consumption and risk of dementia and Alzheimer's disease: Long‐term results from the Framingham Offspring Study. Alzheimer's Dement. 2023; 1–11. https://doi.org/10.1002/alz.13351.The above article, published online on 03 July 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal's Editor‐in‐Chief Dr. Donna M. Wilcock, the Alzheimer's Association and Wiley Periodicals LLC. The retraction has been agreed as the authors did not have the appropriate approvals in place from the National Heart, Lung and Blood Institute (NHLBI) for use of the data in this article. This contravenes the journal's policy on data use and the journal is issuing this retraction as a result.

Aims: To examine the longitudinal associations between total and individual whole grain (WG) food intake and the risk of all‐cause dementia and Alzheimer's disease (AD) dementia.Methods: This study included 2958 subjects (mean age at baseline was 61 ± 9 years) from the Framingham Offspring Cohort. Standardized interviews, physician examinations, and laboratory tests were collected approximately every 4 years, and the Food Frequency Questionnaire (FFQ) was conducted in cycle 5. Proportional hazards models and cubic spline regression examined associations between WG foods and all‐cause dementia and AD dementia.Results: Over an average of 12.6 years of follow‐up, there were 322 dementia cases, of which 247 were AD dementia. After multivariate and dietary adjustments, individuals with the highest category for total WG food consumption had a lower risk of all‐cause dementia [HR 0.66, 95% confidence interval (CI) 0.51–0.81] and AD dementia (HR 0.60, 95% CI 0.46–0.78) than individuals with the lowest category. The results remained comparable in different subgroups stratifying for age, sex, education, body mass index, and smoking status without significant interaction. Moreover, these inverse associations were seen for most individual WG foods except popcorn. A nonlinear dose–response association was shown between total WG intake and all‐cause dementia and AD dementia, where the rate reduction slightly plateaued at more than one and two servings/day, respectively.Conclusions: Higher consumption of total and several common individual WG foods was strongly associated with a lower risk of all‐cause dementia and AD dementia.

Whole grains (WG) have been widely recognized as healthy foods but few prospective studies have examined WG foods consumption and all-cause dementia and Alzheimer's disease (AD) dementia. This pilot study aimed to investigate the relationship between WG and dementia. 2958 subjects from the Framingham Offspring cohort were included with the Food Frequency Questionnaire (FFQ) to assess their diet intake. And multivariate Cox proportional regression was conducted to examine the relations. After an average follow-up of 12.6 years, 322 all-cause dementia were documented, including 247 AD dementia. In the fully adjusted model, participants in the highest vs. the lowest quintiles of WG consumption had lower risks of all-cause dementia (HR, 0.72; 95% CI, 0.53-0.84; P for trend <0.001) and AD dementia (HR, 0.64; 95% CI, 0.47-0.80; P for trend <0.001). High consumption of WG foods is associated with decreased risks of all-cause dementia and AD dementia.t disease mortality. Our findings are from a preliminary study and need to be confirmed in comprehensive settings and integrated statistical methods.

BACKGROUND Whole grains (WG) have been widely recognized as healthy foods which were associated with reduced risk of several diseases and mortality, but few prospective studies have examined WG foods consumption and adverse cognitive outcomes. OBJECTIVE We aim to examine the longitudinal associations between total and individual whole grain (WG) foods intake and the risk of all dementia and Alzheimer’s disease (AD) dementia. METHODS This study included 2958 subjects (mean age at baseline was 61±9 years) from the Framingham Offspring cohort. Standardized interviews, physician examinations, and laboratory tests were collected approximately every 4 y and the Food Frequency Questionnaire (FFQ) was conducted in cycle 5. Proportional hazards models and cubic spline regression examined associations between WG foods and all dementia and AD dementia. RESULTS Over an average of 12.6 years of follow-up, there were 322 dementia cases of which 247 were AD dementia. After multivariate and dietary adjustments, individuals with the highest category for total WG foods consumption had a lower risk of all dementia (HR: 0.66; 95%CI: 0.51-0.82) and AD dementia (HR: 0.60; 95%CI: 0.46-0.79) relative to individuals with the lowest category. And the results remained comparable in different subgroups stratifying for age, sex, education, BMI, and smoking status without significant interaction. Moreover, these inverse associations were seen for most individual WG foods except popcorn. A non-linear dose-response association was shown between total WG intake and all dementia and AD dementia where the rate reduction slightly plateaued at more than one and two servings/day respectively. CONCLUSIONS Higher consumption of total and several common individual WG foods was significantly associated with a lower risk of all dementia and AD dementia. CLINICALTRIAL ClinicalTrials.gov NCT00005121, https://clinicaltrials.gov/ct2/show/NCT00005121.

This study aimed to investigate the association between preeclampsia and all-cause dementia and Alzheimer’s Disease (AD). This population-based cohor