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Abstract Patients at risk for progression from myeloid precursor states (idiopathic cytopenia of undetermined significance [ICUS] and clonal cytopenia of undetermined significance [CCUS]) to myelodysplastic syndrome (MDS) are not well defined. Epidemiologic risk factors, clonal changes for MDS development, and evolution from one MDS state to another were available in the MDS Natural History Study (NHS), a prospective cohort enrolled at diverse US sites. Extensive clinical data and biospecimens were collected at baseline and follow-up. A total of 1177 (56%) individuals with MDS or MDS-related precursor conditions (ICUS/idiopathic dysplasia of undetermined significance [IDUS], CCUS, and MDS/myeloproliferative neoplasm overlap) were enrolled and diagnosed in the MDS NHS. The median age was 74 years, with 89% aged ≥60 years, and most were male patients. Median follow-up time was 1.6 years and was similar among ICUS/IDUS, CCUS, and MDS. During follow-up period, 68% of participants terminated the study, 35% died, and 38% had disease progression. Using a refined clinical definition of progression, the greatest proportion of progression was observed among higher-risk-MDS (116 [73%]), followed by lower-risk-MDS (170 [52%]); fewer progression events were observed among participants with CCUS (65 [22%]) and ICUS/IDUS (46 [17%]). Predictors of progression are delineated, including female sex, higher quantity of mutations, the presence of TP53 mutation, and poor/very poor cytogenetic score. Based on these prospective data, guidelines for clinical management including monitoring and surveillance are outlined. The MDS NHS provides real-world data to illustrate how clinical and genotypic differences inform the classification, disease course, and approach to therapy, with informed monitoring guidelines for patients. This trial was registered at www.ClinicalTrials.gov as NCT02775383.

837 Background: A recent randomized trial suggested that CR may influence ICI activity in patients with lung cancer. However, the same has not been established in GI cancers, where reports have shown mixed results with variable definitions of 'early' vs 'late' IT. We evaluated multiple approaches to assess the impact of CR on first-line ICI in GI malignancies. Methods: This retrospective study included patients from three Mayo Clinic sites (N=455). Both previously reported IT cutoffs and a new cutoff derived from ROC analysis were evaluated. Patients were categorized by the median of all ITs, the median of the first four ITs, and whether ≥20% of ITs occurred before the identified cutoff. Univariate and multivariate Cox regression (adjusting for age, MSI status, type of cancer, metastasis status, number of cycles, and receipt of chemotherapy with ICI) assessed associations with OS. KM analyses were performed to illustrate survival differences. The primary endpoint was OS. Results: When patients were categorized based on whether ≥20% of infusions occurred before 14:00 (ROC-derived cutoff), a significant association with OS was observed. On univariate analysis, early infusion timing was associated with improved survival (HR 2.30, 95% CI 1.58–3.32, p <0.001). This remained significant after multivariable adjustment (HR 2.11, 95% CI 1.41–3.16, p < 0.001). Median OS was 14.2 months in the early group vs 3.8 months in the late group (log-rank p < 0.001). Using previously defined cutoffs, categorization by the median IT of all infusions or the median of the first four infusions did not demonstrate a significant association with OS in either univariate or multivariate Cox regression analyses. Conclusions: These findings highlight the potential role of infusion timing as a chronotherapy factor in GI cancers. Given the retrospective design and heterogeneous population, prospective studies are warranted to validate this signal and clarify its clinical relevance. Baseline demographics of patients by infusion timing (early vs late). Variables Early N=412 (%) LateN=43(%) Total N=455(%) p-value Age <70 249(60.4) 21(48.8) 270 (59.3) 0.1 >70 163(39.6) 22(51.2) 185 (40.7) Gender F 118(28.6) 16(37.2) 134 (29.5) 0.3 M 294(71.4) 27(62.8) 321 (70.5) Cancer Type Gastroesophageal 109 (26.5) 8 (18.6) 117 (25.7) 0.1 Hepatocellular 159 (38.6) 24 (55.8) 183 (40.2) Biliary 86 (20.9) 4 (9.3) 90 (19.8) MSI-H 58 (14.1) 7 (16.3) 65 (14.3) Metastasis No 150 (36.4) 22 (51.2) 172 (37.8) 0.1 Yes 262 (63.6) 21 (48.8) 283 (62.2) Immunotherapy Cycle Number <6 189 (45.9) 33 (76.7) 222 (48.8) <0.001 >6 223 (54.1) 10 (23.3) 233 (51.2)

830 Background: Immune checkpoint inhibitors (ICIs) are standard therapy for MSI-H GI cancers. Lynch syndrome, caused by germline mutations in mismatch repair (MMR) genes, leads to MSI-H tumors and robust ICI responses, but its effect on irAEs remains poorly defined. Clarifying this risk may inform treatment selection and toxicity monitoring. Methods: We retrospectively reviewed MSI-H or MMR-deficient (dMMR) GI cancer patients (pts) treated with ICIs across Mayo Clinic (AZ, MN, FL) from January 2017 to June 2025. Eligible pts were identified using Mayo Data Explorer. Demographics, treatment, and irAEs (graded per CTCAE v5.0) were collected. Lynch status was determined by germline testing when available. Logistic regression estimated odds ratios (ORs) for any grade and grade ≥3 irAEs, adjusting for age, cancer type, prior chemotherapy, and treatment regimen. Results: A total of 209 pts with dMMR/MSI-H GI cancer were included: 61 with Lynch syndrome and 148 without. Lynch pts were younger (median 58 vs 76 years, p < 0.001) and more likely to have received prior chemotherapy (62% vs 43%, p = 0.009). Colorectal cancer was the predominant cancer type (74%), but Lynch pts had proportionally more non-colorectal GI cancers (15% vs 2.7%, p = 0.007). Overall, 29% of pts experienced an irAE, with rates significantly higher in Lynch vs non-Lynch pts (43% vs 24%, p = 0.005). The most frequent irAEs were gastrointestinal, endocrine, and dermatologic, followed by hepatic and pulmonary events. Grade ≥3 irAEs occurred in 16% overall, with no significant difference between groups (p = 0.124). In adjusted models, Lynch syndrome was independently associated with a higher risk of any grade irAE (OR 2.14, 95% CI 1.09 - 4.19, p = 0.026), while risk of severe irAEs was not increased (OR 1.55, 95% CI 0.64 - 3.59, p = 0.316). Lynch syndrome pts were more likely to have combination therapy (17% vs 6.8%, p = 0.029). However, there were no differences observed in any grade irAE between monotherapy and combination immunotherapy (OR 1.36, 95% CI 0.49 - 3.54, p = 0.535). Conclusions: Lynch syndrome is associated with a higher prevalence of any-grade irAEs among dMMR/MSI-H GI cancer pts treated with ICIs, though severe events were not increased. Incorporating Lynch status into immunotherapy planning may improve risk stratification and guide regimen selection.

103 Background: Clinicians spend significant time extracting and synthesizing information to risk-stratify and make adjuvant chemotherapy (AC) recommendations for CRC. By automating parts of this process, LLMs could help reduce documentation burden and variability in decision-making. Methods: We selected 144 stage III, pMMR CRC patients treated at Mayo Clinic (2017–2021). HIPAA-compliant Gemini-2.0-Flash-001 and GPT-4o extracted clinical variables (age, neuropathy grade, pathological stage) from EHRs and generated AC recommendations. Two approaches were compared: (1) a rule-based algorithm with nested if/else statements translating variables into NCCN-based recommendations, and (2) a dynamic LLM agent generating recommendations using a structured prompt informed by NCCN guidelines. Concordance with treating physicians was assessed, with disagreements reviewed by a blinded GI oncologist. Results: The dataset included 144 patients with median age 67 (IQR 66–68), 48.6% female. The LLM pipeline extracted neuropathy grade with 100% accuracy and pathological T/N stages with 99.3% accuracy. Concordance with physician recommendations was 93.1% for the rule-based algorithm and 62.5% for the LLM agent. In discordant cases, blinded adjudication by a GI oncologist favored the algorithm and LLM agent over physicians in most cases (Table 1). Conclusions: This exploratory work shows LLM agents can reliably extract variables and generate evidence-based treatment recommendations for stage III CRC. The high rate of expert preference for LLM recommendations in discordant cases is intriguing but limited by small sample size and possible adjudicator bias. Discordant physician recommendations reflect the complexity of decision-making, including patient preferences, performance status, comorbidities, and subtle findings not fully captured in EHRs. Rather than replacing judgment, we envision LLMs as decision support tools—analogous to an advanced trainee reviewing charts and presenting evidence-based recommendations to the attending physician with ultimate authority. Larger multi-institutional studies with multiple adjudicators are needed to validate these findings and clarify AI’s role in oncology. Patient characteristics, LLM extraction accuracy, and concordance with physician recommendations. Metric Result Total patients 144 Median age (IQR) 67 (66-68) Female (%) 48.6% LLM extraction accuracy – Neuropathy grade 100% LLM extraction accuracy – Pathological T and N stages 99.3% Primary Concordance – Rule-based algorithm vs. physician 93.1% Adjudicator preference of rule-based algorithm over treating physician for discordant cases 100% Concordance – LLM agent vs. physician 62.5% Adjudicator preference of LLM agent over treating physician for discordant cases 82.0%

Abstract Background Ten years after the original publication, the Centiloid framework is now broadly used to harmonize amyloid‐PET quantification, facilitate data sharing and comparison across cohorts, and even assist visual interpretation in clinical settings. We evaluated the global implementation of Centiloids by comparing their distribution and corresponding positivity thresholds across cohorts. Methods We gathered data from publicly available cohorts and reached out to investigators across the world to collect cross‐sectional Centiloids, demographic and clinical information, and visual read data. Gaussian mixture models (GMM, k=2) were fitted to Centiloid values for each cohort and cutoffs were calculated as mean + 2SD of the lower Gaussian. When visual reads were available, we determined Centiloid cutoffs that maximized correspondence with visual reads (Cohen's kappa). Data was combined across cohorts using random effects meta‐analyses. Results As of January 2025, we included 37 cohorts ( n = 41,678 participants) with heterogeneous pipelines, radiotracers, and clinical and demographic characteristics (Table‐1). The low Gaussian peaks ranged from ‐9 to 10CL; the meta‐analysis identified a common peak at 1CL. The second peaks were more heterogeneous (range=38‐102CL; meta‐analysis outcome=64CL). Across cohorts, the proportion of cognitively unimpaired versus impaired participants impacted the position of both peaks, with better separation in cohorts enriched in impaired individuals (Figure 1C). The meta‐analysis indicated a GMM‐based cutoff of 19CL (95%CI: 16‐21CL, Figure 1B); subgroup analyses showed no evidence of significant effect between single versus multicenter settings (17 versus 20CL, p = 0.30), MRI‐based or PET‐only processing (18 versus 19CL, p = 0.88), and no evidence of difference across radiotracers (Flutemetamol: 16CL; PIB: 17CL, Flutafuranol: 18CL, Florbetaben: 19CL, Florbetapir: 20CL, p = 0.78). In a subset of 29,496 participants with visual reads available, binary visual reads corresponded well to Centiloids (common kappa=0.86, Figure 2A). The visual read‐based cutoff of 24CL (95%CI: 21‐27CL, Figure 2B) maximized correspondence between visual read and quantification and was slightly higher than the GMM‐based cutoff. All meta‐analysis models showed high non‐random heterogeneity (I 2 >80%) across studies, suggesting non‐random differences in peaks and cutoffs. Conclusions Meta‐analysis‐based cutoffs align well with thresholds from the existing literature. High heterogeneity among studies underscores the need to investigate contributing factors, raising concerns about applying common cutoffs.

Abstract Background Sex, education and race/ethnicity are all associated with risk of Alzheimer's disease dementia. Here, we assess the effects of self‐reported sex, educational attainment and race/ethnicity on amyloid‐positivity, and tau‐PET‐positivity in 12,048 (7,394 cognitively unimpaired [CU], 2,177 MCI, and 2,477 dementia) individuals from 42 cohorts worldwide. Method Logistic generalized estimating equations were used to estimate frequency of amyloid‐positivity (using cohort‐specific thresholds for amyloid‐PET [84%] or CSF) and tau‐PET‐positivity (cohort‐specific thresholds of 2SD above mean temporal uptake in amyloid‐negative controls). We assessed: i) sex and APOEε4 ( N = 10,098) associations, to complement earlier findings of a higher frequency of tau‐positivity in females, ii) effects of lower/higher education ( N = 10,970; cohort‐specific median‐split), and iii) effects of race/ethnicity (non‐Hispanic White [hereafter: White], N = 4880; Asian, N = 116; Black or African‐American [hereafter: Black], N = 353; Hispanic, N = 356, only from Northern‐American cohorts). Outcomes were frequency of amyloid‐positivity in CU individuals only, and tau‐PET‐positivity in both amyloid‐positive (AB+) CU and cognitively impaired (CI, i.e. MCI and dementia) individuals. Interaction effects on the relationship between age and amyloid/tau‐positivity were assessed and only retained in the models when significant. Result Female sex was associated with an APOEε4 ‐independent increased frequency of amyloid‐positivity (β=0.51[0.22], p = 0.02) in CU and increase of tau‐positivity in both AB+CU (β=0.27[0.08]) and AB+CI (β=0.37[0.08], both p <0.01). Remarkably, tau‐positivity frequencies of female APOEε4 non‐carriers were equivalent to male APOEε4 carriers in AB+CI (Figure 1). No significant sex* APOE interactions were observed. In CU, higher education was associated with lower amyloid‐positivity frequency (β=‐0.12[0.05], p = 0.02). In contrast, among AB+CU, there was an age*education interaction effect that indicated more pronounced age effects on tau‐positivity in individuals with higher education (age*education:β interaction =0.03[0.01], p <0.01). There were no education effects in AB+CI (Figure 2). In CU, an age*race/ethnicity interaction effect was observed across all non‐White groups compared to White (Hispanic:β interaction =‐0.05[0.01], p <0.01; Black:‐0.04[0.01], p <0.01; Asian:‐0.02[0.01], p = 0.04). This suggests that the impact of age on amyloid‐positivity was less pronounced in non‐White groups. Furthermore, in AB+CI, Hispanic ethnicity was related to higher tau‐positivity frequency than White (β=0.51[0.22], p = 0.02; Figure 3). Conclusion In this multi‐center initiative comprised of clinical and community‐based cohorts, we observed that self‐reported sex, educational attainment and race/ethnicity were related to positivity‐frequencies of Alzheimer's disease pathology.

Abstract Background The Joint Propagation Model (JPM)‐based CenTauR scale was recently introduced to harmonize tau‐PET quantification across different radiotracers. This study examined how CenTauR harmonization enhances the comparability of tau‐PET quantification across matched cohorts using different tracers. The evaluation focused on three key aspects: (1) comparing tau‐PET positivity rates as defined by CenTauR, (2) evaluating its diagnostic accuracy for symptomatic AD, and (3) analyzing longitudinal changes in tau‐PET rates over time. Method The JPM, developed by the CPAD‐led Tau PET Harmonization Working Group (Leuzy et al., Alzheimers Dement. 2024; Figure 1A), models relationships between anchor point subjects and head‐to‐head tau‐PET SUVR data onto the CenTauR scale, providing conversion equations for multiple tracers. JPM equations were applied to [ 18 F]flortaucipir SUVR (Meta‐temporal ROI) data from 561 cognitively impaired participants in the ADNI and OASIS‐3 studies. Using ROC analysis, we determined the CenTauR cut‐off for positivity (T+) that maximized the Youden index for discriminating between FDA‐approved positive/negative visual reads. Three separate cohorts, scanned using [ 18 F]flortaucipir (ADNI), [ 18 F]MK‐6240 (CPAS), and [ 18 F]RO‐948 (BioFINDER‐2), were matched 1:1 by age, MMSE, amyloid‐β, and clinical diagnosis. CenTauR‐based metrics were compared across these cohorts. Result A cut‐off of 18 CenTauRs was found to optimally classify [ 18 F]flortaucipir PET visual reads (Figure 1B). The matching procedure identified 1089 participants (363 per cohort). The frequency of T+ using the 18 CenTauRs cut‐off for the Meta‐Temporal ROI was highly comparable across tracers across groups, except for the Aβ‐positive cognitively unimpaired group, where variability was more pronounced due to a smaller sample size (Figure 2A). Similarly, tau‐PET discriminative accuracy for symptomatic AD vs controls remained similar across [ 18 F]flortaucipir and [ 18 F]MK‐6240 (Figure 2B). In a separate sample of 212 matched participants from ADNI ([ 18 F]flortaucipir) and AIBL ([ 18 F]MK‐6240) with baseline and 1‐year follow‐up tau‐PET scans, 1‐year change in CenTauRs was comparable (Figure 2C). An exploratory voxelwise transformation, utilizing the Meta‐Temporal conversion equation in a representative case scanned with both [ 18 F]flortaucipir and [ 18 F]MK‐6240, demonstrates the potential for voxelwise CenTauR harmonization (Figure 3). Conclusion These analyses suggest that CenTauR harmonization increases the comparability of tau‐PET data acquired with different tracers. Additional validation analyses with larger cohorts including other radiotracers ([ 18 F]PI‐2620) are underway.

Abstract Background Ten years after the original publication, the Centiloid framework is now broadly used to harmonize amyloid‐PET quantification, facilitate data sharing and comparison across cohorts, and even assist visual interpretation in clinical settings. We evaluated the global implementation of Centiloids by comparing their distribution and corresponding positivity thresholds across cohorts. Methods We gathered data from publicly available cohorts and reached out to investigators across the world to collect cross‐sectional Centiloids, demographic and clinical information, and visual read data. Gaussian mixture models (GMM, k=2) were fitted to Centiloid values for each cohort and cutoffs were calculated as mean + 2SD of the lower Gaussian. When visual reads were available, we determined Centiloid cutoffs that maximized correspondence with visual reads (Cohen’s kappa). Data was combined across cohorts using random effects meta‐analyses. Results As of January 2025, we included 37 cohorts ( n = 41,678 participants) with heterogeneous pipelines, radiotracers, and clinical and demographic characteristics (Table‐1). The low Gaussian peaks ranged from ‐9 to 10CL; the meta‐analysis identified a common peak at 1CL. The second peaks were more heterogeneous (range=38‐102CL; meta‐analysis outcome=64CL). Across cohorts, the proportion of cognitively unimpaired versus impaired participants impacted the position of both peaks, with better separation in cohorts enriched in impaired individuals (Figure‐1C). The meta‐analysis indicated a GMM‐based cutoff of 19CL (95%CI: 16‐21CL, Figure‐1B); subgroup analyses showed no evidence of significant effect between single versus multicenter settings (17 versus 20CL, p = 0.30), MRI‐based or PET‐only processing (18 versus 19CL, p = 0.88), and no evidence of difference across radiotracers (Flutemetamol: 16CL; PIB: 17CL, Flutafuranol: 18CL, Florbetaben: 19CL, Florbetapir: 20CL, p = 0.78). In a subset of 29,496 participants with visual reads available, binary visual reads corresponded well to Centiloids (common kappa=0.86, Figure‐2A). The visual read‐based cutoff of 24CL (95%CI: 21‐27CL, Figure‐2B) maximized correspondence between visual read and quantification and was slightly higher than the GMM‐based cutoff. All meta‐analysis models showed high non‐random heterogeneity (I 2 >80%) across studies, suggesting non‐random differences in peaks and cutoffs. Conclusions Meta‐analysis‐based cutoffs align well with thresholds from the existing literature. High heterogeneity among studies underscores the need to investigate contributing factors, raising concerns about applying common cutoffs.

Abstract Background The Joint Propagation Model (JPM)‐based CenTauR scale was recently introduced to harmonize tau‐PET quantification across different radiotracers. This study examined how CenTauR harmonization enhances the comparability of tau‐PET quantification across matched cohorts using different tracers. The evaluation focused on three key aspects: (1) comparing tau‐PET positivity rates as defined by CenTauR, (2) evaluating its diagnostic accuracy for symptomatic AD, and (3) analyzing longitudinal changes in tau‐PET rates over time. Method The JPM, developed by the CPAD‐led Tau PET Harmonization Working Group (Leuzy et al., Alzheimers Dement. 2024; Figure 1A), models relationships between anchor point subjects and head‐to‐head tau‐PET SUVR data onto the CenTauR scale, providing conversion equations for multiple tracers. JPM equations were applied to [¹⁸F]flortaucipir SUVR (Meta‐temporal ROI) data from 561 cognitively impaired participants in the ADNI and OASIS‐3 studies. Using ROC analysis, we determined the CenTauR cut‐off for positivity (T+) that maximized the Youden index for discriminating between FDA‐approved positive/negative visual reads. Three separate cohorts, scanned using [¹⁸F]flortaucipir (ADNI), [¹⁸F]MK‐6240 (CPAS), and [¹⁸F]RO‐948 (BioFINDER‐2), were matched 1:1 by age, MMSE, amyloid‐β, and clinical diagnosis. CenTauR‐based metrics were compared across these cohorts. Result A cut‐off of 18 CenTauRs was found to optimally classify [ 18 F]flortaucipir PET visual reads (Figure 1B). The matching procedure identified 1089 participants (363 per cohort). The frequency of T+ using the 18 CenTauRs cut‐off for the Meta‐Temporal ROI was highly comparable across tracers across groups, except for the Aβ‐positive cognitively unimpaired group, where variability was more pronounced due to a smaller sample size (Figure 2A). Similarly, tau‐PET discriminative accuracy for symptomatic AD vs controls remained similar across [ 18 F]flortaucipir and [ 18 F]MK‐6240 (Figure 2B). In a separate sample of 212 matched participants from ADNI ([¹⁸F]flortaucipir) and AIBL ([¹⁸F]MK‐6240) with baseline and 1‐year follow‐up tau‐PET scans, 1‐year change in CenTauRs was comparable (Figure 2C). An exploratory voxelwise transformation, utilizing the Meta‐Temporal conversion equation in a representative case scanned with both [¹⁸F]flortaucipir and [¹⁸F]MK‐6240, demonstrates the potential for voxelwise CenTauR harmonization (Figure 3). Conclusion These analyses suggest that CenTauR harmonization increases the comparability of tau‐PET data acquired with different tracers. Additional validation analyses with larger cohorts including other radiotracers ([¹⁸F]PI‐2620) are underway.