Alagöz, Oğuzhan

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.

Objectives Machine learning (ML)–based emulators improve the calibration of decision-analytical models, but their performance in complex microsimulation models is yet to be determined. Methods We demonstrated the use of an ML-based emulator with the Colorectal Cancer (CRC)-Adenoma Incidence and Mortality (CRC-AIM) model, which includes 23 unknown natural history input parameters to replicate the CRC epidemiology in the United States. We first generated 15,000 input combinations and ran the CRC-AIM model to evaluate CRC incidence, adenoma size distribution, and the percentage of small adenoma detected by colonoscopy. We then used this data set to train several ML algorithms, including deep neural network (DNN), random forest, and several gradient boosting variants (i.e., XGBoost, LightGBM, CatBoost) and compared their performance. We evaluated 10 million potential input combinations using the selected emulator and examined input combinations that best estimated observed calibration targets. Furthermore, we cross-validated outcomes generated by the CRC-AIM model with those made by CISNET models. The calibrated CRC-AIM model was externally validated using the United Kingdom Flexible Sigmoidoscopy Screening Trial (UKFSST). Results The DNN with proper preprocessing outperformed other tested ML algorithms and successfully predicted all 8 outcomes for different input combinations. It took 473 s for the trained DNN to predict outcomes for 10 million inputs, which would have required 190 CPU-years without our DNN. The overall calibration process took 104 CPU-days, which included building the data set, training, selecting, and hyperparameter tuning of the ML algorithms. While 7 input combinations had acceptable fit to the targets, a combination that best fits all outcomes was selected as the best vector. Almost all of the predictions made by the best vector laid within those from the CISNET models, demonstrating CRC-AIM’s cross-model validity. Similarly, CRC-AIM accurately predicted the hazard ratios of CRC incidence and mortality as reported by UKFSST, demonstrating its external validity. Examination of the impact of calibration targets suggested that the selection of the calibration target had a substantial impact on model outcomes in terms of life-year gains with screening. Conclusions Emulators such as a DNN that is meticulously selected and trained can substantially reduce the computational burden of calibrating complex microsimulation models. Highlights Calibrating a microsimulation model, a process to find unobservable parameters so that the model fits observed data, is computationally complex. We used a deep neural network model, a popular machine learning algorithm, to calibrate the Colorectal Cancer Adenoma Incidence and Mortality (CRC-AIM) model. We demonstrated that our approach provides an efficient and accurate method to significantly speed up calibration in microsimulation models. The calibration process successfully provided cross-model validation of CRC-AIM against 3 established CISNET models and also externally validated against a randomized controlled trial.

10500 Background: Inherited pathogenic variants in ATM, CHEK2, and PALB2 confer moderate to high risks of breast cancer. The optimal approach to screening in these women has not been established. Methods: We used two simulation models from the Cancer Intervention and Surveillance Modeling Network (CISNET) and data from the Cancer Risk Estimates Related to Susceptibility consortium (CARRIERS) to project lifetime breast cancer incidence and mortality in ATM, CHEK2, and PALB2 carriers. We simulated screening with annual mammography from ages 40-74 alone and with annual magnetic resonance imaging (MRI) starting at ages 40, 35, 30, and 25. Joint and separate mammography and MRI screening performance was based on published literature. Lifetime outcomes per 1,000 women were reported as means and ranges across both models. Results: Estimated risk of breast cancer by age 80 was 22% (21-23%) for ATM, 28% (26-30%) for CHEK2, and 40% (38-42%) for PALB2. Screening with MRI and mammography reduced breast cancer mortality by 52-60% across variants (Table). Compared to no screening, starting MRI at age 30 increased life years (LY)/1000 women by 501 (478-523) in ATM, 620 (587-652) in CHEK2, and 1,025 (998-1,051) in PALB2. Starting MRI at age 25 versus 30 gained 9-12 LY/1000 women with 517-518 additional false positive screens and 197-198 benign biopsies. Conclusions: For women with ATM, CHEK2, and PALB2 pathogenic variants, breast cancer screening with MRI and mammography halves breast cancer mortality. These mortality benefits are similar to those for MRI screening for BRCA1/2 mutation carriers and should inform practice guidelines.[Table: see text]

BACKGROUNDThe incidence of metachronous colorectal cancer (MCRC) among colorectal cancer (CRC) survivors varies significantly, and the optimal colonoscopy surveillance practice for mitigating MCRC incidence is unknown.METHODSA cost‐effectiveness analysis was used to compare the performances of the US Multi‐Society Task Force guideline and all clinically reasonable colonoscopy surveillance strategies for 50‐ to 79‐year‐old posttreatment CRC patients with a computer simulation model.RESULTSThe US guideline [(1,3,5)] recommends the first colonoscopy 1 year after treatment, whereas the second and third colonoscopies are to be repeated at 3‐ and 5‐year intervals. Some promising alternative cost‐effective strategies were identified. In comparison with the US guideline, under various scenarios for a 20‐year period, 1) reducing the surveillance interval of the guideline after the first colonoscopy by 1 year [(1,2,5)] would save up to 78 discounted life‐years (LYs) and prevent 23 MCRCs per 1000 patients (incremental cost‐effectiveness ratio [ICER] ≤ $23,270/LY), 2) reducing the intervals after the first and second negative colonoscopies by 1 year [(1,2,4)] would save/prevent up to 109 discounted LYs and 36 MCRCs (ICER ≤ $52,155/LY), and 3) reducing the surveillance intervals after the first and second negative colonoscopy by 1 and 2 years [(1,2,3)] would save/prevent up to 141 discounted LYs and 50 MCRCs (ICER ≤ $63,822/LY). These strategies would require up to 1100 additional colonoscopies per 1000 patients. Although the US guideline might not be cost‐effective in comparison with a less intensive oncology guideline [(3,3,5); the ICER could be as high as $140,000/LY], the promising strategies would be cost‐effective in comparison with such less intensive guidelines unless the cumulative MCRC incidence were very low.CONCLUSIONSThe US guideline might be improved by a slight increase in the surveillance intensity at the expense of moderately increased cost. More research is warranted to explore the benefits/harms of such practices. Cancer 2016;122:2560–70. © 2016 American Cancer Society.

Objectives. Some aspects of the natural history of metachronous colorectal cancer (MCRC), such as the rate of progression from adenomatous polyp to MCRC, are unknown. The objective of this study is to estimate a set of parameters revealing some of these unknown characteristics of MCRC. Methods. The authors developed a computer simulation model that mimics the progression of MCRC for a 5-year period following the treatment of primary colorectal cancer (CRC). They obtained the inputs of the simulation model using longitudinal data for 284 CRC patients from the Mayo Clinic, Rochester. Results. Five-year MCRC incidence and all-cause mortality were 7.4% and 12.7% in the patient cohort, respectively. Statistical analysis showed that 5-year MCRC incidence was associated with gender ( P = 0.05), whereas both all-cause and CRC-related mortalities were associated with age ( P < 0.001 and P = 0.01). Estimated annual probabilities of progression from adenomatous polyp to MCRC and from MCRC to metastatic MCRC were 0.14 and 0.28, respectively. Annual probabilities of mortality after MCRC and metastatic MCRC treatments were estimated to be 0.06 and 0.26, respectively. The estimated annual probability of mortality due to undetected MCRC was 0.16. Conclusions. The results imply that MCRC, especially in women, may be more common than suggested by previous studies. In addition, statistics derived from the clinical data and results of the simulation model indicate that gender and age affect the progression of MCRC.