ORCID Profile
0000-0003-3623-8136
Current Organisation
IQVIA Canada
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Publisher: Oxford University Press (OUP)
Date: 31-08-2016
DOI: 10.1093/NTR/NTW218
Abstract: Electronic cigarettes (e-cigarettes) are being used as cessation aids by many smokers despite a lack of empirical evidence regarding their safety and efficacy. We analyzed the association of e-cigarette use and smoking abstinence in a population of smokers accessing standard smoking cessation treatment (nicotine replacement therapy [NRT] plus behavioral counseling) through primary care clinics in Ontario, Canada. Participants were recruited through 187 primary care clinics across Ontario, Canada and were eligible for up to 26 weeks of brief behavioral counseling and in idualized dosing of NRT at no cost. Adjusted logistic regression models were used to examine the association between concurrent e-cigarette use and smoking abstinence at 3- and 6-month follow-ups. Of the 6526 participants who completed a 3-month follow-up, 18.1% reported using an e-cigarette while in treatment. The majority of e-cigarette users (78.2%) reported using an e-cigarette for smoking cessation. At 3-month follow-up, e-cigarette use was negatively associated with abstinence after controlling for confounders (adjusted odds ratio [AOR] = 0.706, p < .001, 95% confidence interval [CI] = 0.607-0.820). E-cigarette use was also negatively associated with abstinence at 6-month follow-up (AOR = 0.502, p < .001, 95% CI = 0.393-0.640). E-cigarette use was negatively associated with successful quitting in this large community s le of smokers accessing standard evidence-based smoking cessation treatment through primary care clinics, even after adjusting for covariates such as severity of tobacco dependence, gender, and age. The findings suggest that concurrent use of e-cigarettes with NRT may harm cessation attempts. This study confirms previous findings from observational studies regarding the negative association between e-cigarette use and smoking cessation, but in a large cohort of smokers enrolled in an evidence-based treatment program. The implications of these findings are that concurrent use of e-cigarettes during a quit attempt utilizing cost-free evidence-based treatment (NRT plus behavioral counseling) does not confer any added benefit and may h er successful quitting.
Publisher: Wiley
Date: 08-2023
DOI: 10.1002/PATH.6155
Abstract: The clinical significance of the tumor‐immune interaction in breast cancer is now established, and tumor‐infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple‐negative (estrogen receptor, progesterone receptor, and HER2‐negative) breast cancer and HER2‐positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state‐of‐the‐art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false‐positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in‐depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple‐negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Publisher: Wiley
Date: 08-2023
DOI: 10.1002/PATH.6165
Abstract: Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector‐based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well‐described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.
No related grants have been discovered for Ginnie Ng.