Building a specialized lexicon for breast cancer clinical trial subject eligibility analysis

Mental health at different stages of cancer survival: a natural language processing study of Reddit posts

Introduction

The purpose of this study was to use text-based social media content analysis from cancer-specific subreddits to evaluate depression and anxiety-loaded content. Natural language processing, automatic, and lexicon-based methods were employed to perform sentiment analysis and identify depression and anxiety-loaded content.

Methods

Data was collected from 187 Reddit users who had received a cancer diagnosis, were currently undergoing treatment, or had completed treatment. Participants were split according to survivorship status into short-term, transition, and long-term cancer survivors. A total of 72524 posts were analyzed across the three cancer survivor groups.

Results

The results showed that short-term cancer survivors had significantly more depression-loaded posts and more anxiety-loaded words than long-term survivors, with no significant differences relative to the transition period. The topic analysis showed that long-term survivors, more than other stages of survivorship, have resources to share their experiences with suicidal ideation and mental health issues while providing support to their survivor community.

Discussion

The results indicate that Reddit texts seem to be an indicator of when the stressor is active and mental health issues are triggered. This sets the stage for Reddit to become a platform for screening and first-hand intervention delivery. Special attention should be dedicated to short-term survivors.

Use of artificial intelligence for cancer clinical trial enrollment: a systematic review and meta-analysis

BACKGROUND

The aim of this study is to provide a comprehensive understanding of the current landscape of artificial intelligence (AI) for cancer clinical trial enrollment and its predictive accuracy in identifying eligible patients for inclusion in such trials.

METHODS

Databases of PubMed, Embase, and Cochrane CENTRAL were searched until June 2022. Articles were included if they reported on AI actively being used in the clinical trial enrollment process. Narrative synthesis was conducted among all extracted data: accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. For studies where the 2x2 contingency table could be calculated or supplied by authors, a meta-analysis to calculate summary statistics was conducted using the hierarchical summary receiver operating characteristics curve model.

RESULTS

Ten articles reporting on more than 50 000 patients in 19 datasets were included. Accuracy, sensitivity, and specificity exceeded 80% in all but 1 dataset. Positive predictive value exceeded 80% in 5 of 17 datasets. Negative predictive value exceeded 80% in all datasets. Summary sensitivity was 90.5% (95% confidence interval [CI] = 70.9% to 97.4%); summary specificity was 99.3% (95% CI = 81.8% to 99.9%).

CONCLUSIONS

AI demonstrated comparable, if not superior, performance to manual screening for patient enrollment into cancer clinical trials. As well, AI is highly efficient, requiring less time and human resources to screen patients. AI should be further investigated and implemented for patient recruitment into cancer clinical trials. Future research should validate the use of AI for clinical trials enrollment in less resource-rich regions and ensure broad inclusion for generalizability to all sexes, ages, and ethnicities.