6 research outputs found
The impact of responding to patient messages with large language model assistance
Documentation burden is a major contributor to clinician burnout, which is
rising nationally and is an urgent threat to our ability to care for patients.
Artificial intelligence (AI) chatbots, such as ChatGPT, could reduce clinician
burden by assisting with documentation. Although many hospitals are actively
integrating such systems into electronic medical record systems, AI chatbots
utility and impact on clinical decision-making have not been studied for this
intended use. We are the first to examine the utility of large language models
in assisting clinicians draft responses to patient questions. In our two-stage
cross-sectional study, 6 oncologists responded to 100 realistic synthetic
cancer patient scenarios and portal messages developed to reflect common
medical situations, first manually, then with AI assistance.
We find AI-assisted responses were longer, less readable, but provided
acceptable drafts without edits 58% of time. AI assistance improved efficiency
77% of time, with low harm risk (82% safe). However, 7.7% unedited AI responses
could severely harm. In 31% cases, physicians thought AI drafts were
human-written. AI assistance led to more patient education recommendations,
fewer clinical actions than manual responses. Results show promise for AI to
improve clinician efficiency and patient care through assisting documentation,
if used judiciously. Monitoring model outputs and human-AI interaction remains
crucial for safe implementation.Comment: 4 figures and tables in main, submitted for revie
Genomic and clinical characterization of stromal infiltration markers in prostate cancer
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154519/1/cncr32688_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154519/2/cncr32688.pd
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Development and Validation of a Novel TP53 Mutation Signature That Predicts Risk of Metastasis in Primary Prostate Cancer
Prostate tumors with TP53 gene mutations are molecularly heterogenous, and the presence of TP53 gene mutations has been linked to inferior outcomes. We developed an RNA-based gene signature that detects underlying TP53 gene mutations and identifies wild-type prostate tumors that are analogous to TP53-mutant tumors.
Using genomic expression profiles from The Cancer Genome Atlas, we developed a mutation signature score to predict prostatic tumors with a molecular fingerprint similar to tumors with TP53 mutations. Area under the receiver operating characteristic curve assessed model accuracy in predicting TP53 mutations, and Cox regression models measured association between the signature and progression-free survival and metastasis-free survival (MFS).
The TP53 signature score achieved an area under the receiver operating characteristic curve of 0.84 in the training and 0.82 in the validation cohorts for predicting an underlying mutation. In three retrospective cohorts, a high score was prognostic for poor 5-year MFS: 46% versus 81% (hazard ratio [HR], 3.05; P < .0001; Johns Hopkins University cohort), 64% versus 83% (HR, 2.77; P < .0001; Mayo Clinic cohort), and 71% versus 97% (HR, 6.8; P = .0001; Brigham and Women’s Hospital cohort). The signature also identified TP53 wild-type tumors molecularly analogous to TP53 mutant tumors, wherein high signature score correlated with worse 5-year MFS (50% vs. 82%; HR, 3.05; P < .0001).
This novel mutational signature predicted tumors with TP53 mutations, identified TP53 wild-type tumors analogous to mutant tumors, and was independently associated with poor MFS. This signature can therefore be used to strengthen existing clinical risk-stratification tools.
TP53 is the most frequently mutated gene in cancer. We developed a molecular signature detecting underlying TP53 alterations. This signature identified aggressive tumors, and after adjusting for known predictors of worse outcomes, the signature identified patients with prostate cancer likely to develop metastases. These findings may help doctors identify patients with prostate cancer with aggressive tumors and hence, choose the optimal treatment
Prostate cancer‐specific mortality burden by risk group among men with localized disease: Implications for research and clinical trial priorities
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Novel genomic signature predictive of response to immune checkpoint blockade: A pan-cancer analysis from project Genomics Evidence Neo-plasia Information Exchange (GENIE)
•High tumor mutation burden can be predictive of better response to immune checkpoint blockade but varies across cancers and has inconsistent definitions.•Genomic alterations in 16 genes that clustered into neuronal development/differentiation (PTPRD, NTRK3, ZFHX3, NOTCH3, EPHA5, EPHA7), receptor tyrosine kinase/phosphatase signaling (EPHA5, PTPRD, NTRK3, LATS1, PPM1D, EPHA7), and epigenetic regulation (ARID1A, TET1, SETD2, CREBBP, CIC, POLE) were associated with survival in patients treated with immune checkpoint blockade.•The ImmGA signature is predictive of response to immune checkpoint blockade.
Background: High tumor mutation burden (TMB) and total mutation count (TMC) can be predictive of better response to immune checkpoint blockade (ICB). Nevertheless, TMB and TMC are limited by variation across cancers and inconsistent definitions due to different profiling methods (targeted vs whole genome sequencing). Our objective was to identify genomic alterations (GAs) associated with ICB response and builds a novel genomic signature predictive of ICB response, independent of TMB/TMC.
Methods: This was a pan-cancer next generation sequencing (NGS)-association study using January 2014-May 2016 data from AACR Project Genomics Evidence Neo-plasia Information Exchange (GENIE). Participants included 6619 patients with metastatic or un-resectable cancer across 9 cancer types (including 1572 ICB-treated patients). GA data was collected using next-generation sequencing (NGS) assays and downloaded from cbioportal.org. Predictive analyses for ICB response were performed to develop the signature (ImmGA).
Results: GAs in 16 genes were associated with improved OS in ICB-treated patients (p < 0.005). 13 GAs were associated with an OS benefit in ICB-treated patients (Pinteraction < 0.05); these genes composed the ImmGA signature. High ImmGA score (≥2 alterations out of 13 predictive GAs) was associated with better OS in ICB-treated patients (AHR:0.67, 95%CI [0.6–0.75], p = 1.4e−12), even after accounting for TMC (Pinteraction = 8e−16). High ImmGA was associated with better OS in ICB-treated patients across most cancers and across different ICB treatment modalities.
Conclusion: A novel signature predictive of ICB response (ImmGA) was developed from 13 GAs. Further investigation of the utility of ImmGA for treatment and trial selection is warranted