Citations

Background & Aims: Multi-target stool DNA (mt-sDNA) is an established stool-based colorectal cancer screening test, yet long-term performance is not well-described. We evaluated follow-up colonoscopy rates & quality, neoplasia yield, and repeat-testing adherence over 10 years with an integrated screening program. Methods: We assembled a retrospective cohort of mt-sDNA tests (September 2014–July 2025) within a multi-site health system. A validated large-language-model pipeline extracted colonoscopy, pathology, and quality endpoints from free-text reports. Detection outcomes (adenoma detection rate, advanced neoplasia, modified advanced adenoma, sessile serrated lesion (SSL), and negative exams) were calculated among high-quality exams; adenocarcinoma, bowel preparation adequacy, and cecal intubation rate (CIR) used all screening exams. We assessed colonoscopy completion ≤1 year after positive mt-sDNA, follow-up colonoscopy findings, findings by time-to-colonoscopy, and adherence to repeat mt-sDNA after negative result. Results: Among 148,624 tests in 110,410 individuals, positivity was 13.6%. Of 19,506 positives, 65.1% completed colonoscopy within 12 months (median 82 days). Colonoscopy after a positive mt-sDNA had high yield: advanced adenoma diagnosis was 30.7%; adenocarcinoma was 1.3%. Of patients who had colonoscopy within 3 years of a negative mt-sDNA test, 45.2% had adenoma/serrated lesion, 9.3% had advanced adenoma and 0.5% had colon cancer. Adenoma detection rate after positive mt-sDNA (>50%) exceeded specialty society quality benchmarks and increased over time; Longer intervals (>6 months) to colonoscopy after a positive test were associated with higher advanced adenoma (trend P=0.001).After an initial negative mt-sDNA, only 35.3% were screened again by mt-sDNA testing within 3 years. Conclusions: Over a decade,mt-sDNA screening achieved high colonoscopy qualitybut revealed critical gaps in the care cascade. One-third of positive tests lacked timely follow-up and nearly two-thirds of negative tests were not repeated on schedule, suggesting opportunities for system-level interventions.

Introduction: Multitarget stool DNA (mt-sDNA) is a sensitive stool-based test for colorectal cancer (CRC) screening. Limited referral-center data suggest low rates of incident aerodigestive cancers after a positive mt-sDNA and negative colonoscopy; community-based outcomes are unknown. Methods: Retrospective cohort study of adults referred for colonoscopy after positive mt-sDNA in a large community practice (2017–2021). Among patients with negative colonoscopy, we manually recorded post-colonoscopy recommendations, findings if additional colonoscopies had been performed, aerodigestive cancer diagnosis, and last clinical follow-up. Observed cancer incidence was compared with Surveillance, Epidemiology, and End Results (SEER) data. Results: Of 1,176 patients completing colonoscopy after positive mt-sDNA, 328 (28%) had negative findings. Median cohort age was 67 (IQR 59-72) years. Colonoscopy was performed at a median of 53 days after mt-sDNA sample collection. Post-colonoscopy recommendations aligned with multi-society task force (MSTF) guidelines in 59% of patients. Thirty-two patients underwent repeat colonoscopy with a median of 3.5 (1.9-4.4) years later; 2 (6.3%) had advanced serrated lesions, and no CRC was detected. Median clinical follow-up was 4.4 (3.4-5.6) years. Seven patients developed aerodigestive cancers (2 pancreatic, 5 lung), consistent with expected age- and sex-adjusted SEER incidence (n=7.62) (p=0.823). Discussion: In this community cohort, when a positive mt-sDNA was followed by negative colonoscopy, rates of subsequent advanced colorectal neoplasia and aerodigestive cancer were low. Most patients received recommendations for subsequent colonoscopy that were consistent with MSTF guidelines. Findings further support the current MSTF recommendation of no additional evaluation for extra-colonic malignancy in asymptomatic patients after high quality follow-up colonoscopy.

Background: Fecal immunochemical tests (FITs), fecal occult blood tests (FOBTs), and multitarget stool DNA (mt-sDNA) tests are convenient, noninvasive colorectal cancer (CRC) screening strategies. However, delays in or failure to complete follow-up colonoscopy (FU-CY) after a positive noninvasive test are associated with higher CRC incidence, complications, and mortality. We sought to evaluate FU-CY rates, time to colonoscopy, and compliance predictors. Patients and Methods: This retrospective cohort study linked 2 data sources: Komodo Health Data with Exact Sciences Laboratories. Data were collected from January 2016 to June 2023 and analyzed in 2024. Study participants (ages 45–75 years) were individuals with positive stool-based CRC screening tests (FIT, guaiac-based FOBT, or mt-sDNA tests). FU-CY compliance was assessed at 30-day intervals up to 365 days, and logistic regression was used to identify predictors of FU-CY. Results: Among 362,646 participants, colonoscopy compliance was 77.1% for mt-sDNA and 45.1% for FIT/FOBT ( P <.001). Overall FU-CY compliance was lowest among Asian and Black populations, but was significantly higher in these subgroups for mt-sDNA versus FIT (Asian: 74.2% vs 41.1%; Black: 71.3% vs 44.6%). The strongest predictor of compliance was use of mt-sDNA (odds ratio, 3.98; P <.001). More than half of participants across all race groups who completed FU-CY after mt-sDNA did so within the first 120 days, whereas none of the FIT/FOBT racial subgroups reached 50% completion within 365 days. Conclusions: Timely completion of colonoscopy after a noninvasive test is critical, as failure to undergo colonoscopy or delays in completion are associated with increased CRC incidence and more advanced-stage disease. These findings demonstrate that mt-sDNA use may help close CRC screening disparity gaps compared with FIT testing. FU-CY adherence is higher, and time to colonoscopy is shorter following a positive mt-sDNA test versus FIT across all racial and ethnic subgroups.

Abstract Large Language Models (LLMs) demonstrate strong capabilities in healthcare applications. However, recruiting human evaluators to assess usability and effectiveness becomes challenging in the large-scale LLM-generated outcomes. Existing metrics are often outdated for evaluating LLM-based healthcare technologies. This paper proposes a Research through Evaluation (RtE) approach that can refine metrics and improve assessments of LLM-generated outputs. We deploy RtE in a retrospective comparative study of prostate cancer patient inquiries, comparing responses from human clinical care teams with those generated by a GPT-4-based in-basket bot. Through three rounds of co-evaluations with clinical professionals and an LLM-as-graders study with four LLMs, the RtE method shows that starting with small, iterative evaluations helps human graders adjust metrics to suit their daily practices better. Additionally, in the LLM-as-graders study, LLMs exhibit similar behavior, preferring the in-basket bot's responses over the clinical care team. Our methodology and findings demonstrate that the RtE method can enhance the transparency and robustness of evaluation metrics for LLM-generated content, with the potential to generalize to evaluations in other clinical practices.

e15647 Background: Colorectal cancer (CRC) screening offers an opportunity to prevent invasive disease by detecting and removing precancerous lesions. Colonoscopy-based detection of adenomas and serrated polyps is strongly associated with reduced risk of interval CRC. While the performance of noninvasive CRC screening tests is well described, comparative evidence on adenoma detection across screening colonoscopy and stool-based strategies with follow-up colonoscopy remains limited. We conducted a systematic review to compare adenoma detection across CRC screening modalities. Methods: MEDLINE (PubMed) and Embase were searched from January 1, 2000, through July 10, 2025, for peer-reviewed, U.S.-based studies reporting colonoscopy among average-risk adults aged ≥45 years, who underwent screening colonoscopy or follow-up colonoscopy after a positive stool-based screening test [fecal immunochemical test (FIT) or multi-target stool DNA (mt-sDNA) test]. The primary outcome was the detection of one or more adenomas at colonoscopy. High-risk or surveillance cohorts, AI-assisted colonoscopy, diagnostic accuracy trials, and non-U.S. studies were excluded. Results: Among 7,710 de-duplicated records, 147 studies underwent full-text review, and 47 studies were included in quantitative synthesis, with reported adenoma detection for screening colonoscopy alone (n = 31), follow-up colonoscopy after a positive FIT alone (n = 4), follow-up colonoscopy after a positive mt-sDNA test alone (n = 4), and for more than one of the screening strategies of interest (n = 8). Across included studies, overall adenoma detection differed by screening pathway. Follow-up colonoscopy after a positive mt-sDNA test demonstrated the highest adenoma detection (66.3%; 95% CI, 61.2-71.1), with lower adenoma detection observed for colonoscopy after a positive FIT (50.6%; 95% CI, 43.4-57.8) and screening colonoscopy alone (33.8%; 95% CI, 30.9-36.8) (Table 1). Conclusions: Detection of adenomas was highest following colonoscopy performed after a positive mt-sDNA test compared with FIT-positive follow-up or screening colonoscopy alone. These findings highlight differences in downstream detection across CRC screening tests and suggest that, in settings where colonoscopy resources are constrained, initial mt-sDNA screening with timely follow-up colonoscopy may help prioritize patients most likely to benefit. CRC screening modality No. of Studies Adenoma detected Study population Proportion, % (95% CI) Heterogeneity, I² (%) Between-study variance (τ 2 ) P-value* mt-sDNA 8 17,436 29,098 66.3 (61.2-71.1) 94.6 0.0924 <0.0001 FIT 11 17,588 32,630 50.6 (43.4-57.8) 94.0 0.2187 <0.0001 Screening colonoscopy 38 2,351,801 6,378,891 33.8 (30.9-36.8) 99.8 0.1710 NA *P-value represents adenoma detection vs. screening colonoscopy adenoma detection.

Abstract In epilepsy patients lacking magnetic resonance imaging‐visible lesions such as focal cortical dysplasia (FCD), surgical resections target regions with abnormal electroencephalographic (EEG) activity. However, histopathological identification of subtle cortical architectural abnormalities in these specimens remains challenging. We investigated whether artificial intelligence (AI)‐based morphometric and spatial analysis of NeuN‐stained cortical sections could detect neuronal architectural disorganization in epilepsy resections, including regions without definitive histologic dysplasia. Whole slide images were generated from 83 FCD regions and 19 neurologically normal autopsy controls. Regions of interest were annotated in QuPath as FCD, FCD‐adjacent, FCD‐distant, apparently normal (abnormal EEG without histologic dysplasia), and true normal (autopsy controls). NeuN‐positive neurons were detected using QuPath (90% sensitivity, 12% false positive rate). Spatial and morphometric features—including neuronal clustering, distribution inhomogeneity, and nuclear morphology—were extracted and used to train a multinomial, Least Absolute Shrinkage and Selection Operator‐regularized regression classifier. The classifier achieved 70.6% accuracy in subregion classification and 88.2% accuracy in overall specimen diagnosis. Notably, regions with abnormal EEG but lacking histologic dysplasia exhibited quantifiable architectural disorganization similar to those seen in areas adjacent to FCD and distinct from control tissue. AI‐driven analysis of neuronal morphology and spatial distribution reveals subtle cortical disorganization in epilepsy resections, including in histologically ambiguous regions. Further investigation is warranted to determine if this methodology can enhance the diagnostic yield of neuropathological evaluation and support more precise surgical targeting in epilepsy.

PURPOSE Cancer recurrence is a critical outcome for patients and physicians. Retrospective cancer recurrence data can evaluate recurrence-directed treatment and generate novel interventions targeting recurrent cancers. However, large cancer databases do not provide recurrence-related information, stymying study at scale, and consequently require significant manual record review. Automated evaluation of records may allow for the rapid generation of easily analyzed data sets, accelerating the evaluation of recurrence altogether. METHODS Patients treated with radiation therapy at one tertiary referral center from 2010 to 2018 with a verified cancer status (cancer recurrence v no cancer recurrence) were identified. Patients with recurrent disease were initially identified through manual record review, and the associated pathology report was collected. Google Automated Machine Learning with Natural Language Processing (AutoNLP) and Google Gemini 1.5 Pro were used to generate a model for binary classification, with comparison to the gold-standard manually developed data set. RESULTS A total of 7,054 patients were identified. 3,431 (48.6%) were female, with a median age of 64 years. Head and neck (1,482, 21%), breast (1,480, 21%), upper GI (1,307, 18.5%), and lung/thorax (1,107, 15.7%) were the most common disease sites. Recurrence was verified for 1,546 patients (21.9%) using pathology reports, of which 1,249 positive cases were paired with 651 negative pathology reports for model development. Google Gemini 1.5 Pro consistently outperformed AutoNLP across all measurements of accuracy, generating a greater absolute difference in precision, recall, negative predictive value, and specificity, and a higher likelihood of correct classification at the individual level, rendering Gemini superior in recurrence status extraction. CONCLUSION AutoNLP and Google Gemini 1.5 Pro are promising tools for identifying recurrence from pathology reports, with the latter demonstrating superior overall performance, making it particularly suitable for clinical translation.