Nazha, Aziz

Abstract Approximately 17% of people aged 65 years and older are anemic, and 10% of death certificates report anemia as a secondary cause of death in the United States. Nonetheless, anemia remains unexplained in 30%-50% of older adults. This unexplained anemia of aging (UAA) is a diagnosis of exclusion. The mechanism, impact, and progression of UAA remain unknown. At older ages, anemia adds to pre-existing co-morbidities with significant adverse health consequences, representing a compelling unmet clinical concern. The National Institute on Aging held a workshop in 2024 to discuss current knowledge and research opportunities. Topics included the epidemiology of anemia at older age and its clinical implications; probable mechanism(s) underlying UAA, that is, low-grade inflammation’s effects on erythropoiesis; the role of microbiota in iron regulation in bone marrow; the importance of ruling out a diagnosis of leukemic clonal hematopoiesis (CH), which is more prevalent in older age; the role of senescence and aging governing hematopoiesis; and the effects of sex hormones on hematopoietic stem cell aging. Understanding the roles of these factors could reduce the proportion of the older anemic population whose anemia remains unexplained and offer insights into new potential diagnostic and intervention strategies. Speakers reviewed previous clinical trials in patients with UAA and CH. They discussed lessons learned and future research priorities, including efforts to develop new diagnostic algorithms and potential uses of machine learning.

Abstract To determine the survival benefit of allogeneic hematopoietic cell transplantation (allo-HCT) in chronic myelomonocytic leukemias (CMML), we assembled a retrospective cohort of CMML patients 18-70 years old diagnosed between 2000 and 2014 from an international CMML dataset (n = 730) and the EBMT registry (n = 384). The prognostic impact of allo-HCT was analyzed through univariable and multivariable time-dependent models and with a multistate model, accounting for age, sex, CMML prognostic scoring system (low or intermediate-1 grouped as lower-risk, intermediate-2 or high as higher-risk) at diagnosis, and AML transformation. In univariable analysis, lower-risk CMMLs had a 5-year overall survival (OS) of 20% with allo-HCT vs 42% without allo-HCT (P < .001). In higher-risk patients, 5-year OS was 27% with allo-HCT vs 15% without allo-HCT (P = .13). With multistate models, performing allo-HCT before AML transformation reduced OS in patients with lower-risk CMML, and a survival benefit was predicted for men with higher-risk CMML. In a multivariable analysis of lower-risk patients, performing allo-HCT before transformation to AML significantly increased the risk of death within 2 years of transplantation (hazard ratio [HR], 3.19; P < .001), with no significant change in long-term survival beyond this time point (HR, 0.98; P = .92). In higher-risk patients, allo-HCT significantly increased the risk of death in the first 2 years after transplant (HR 1.46; P = .01) but not beyond (HR, 0.60; P = .09). Performing allo-HCT before AML transformation decreases life expectancy in lower-risk patients but may be considered in higher-risk patients.

Background: CMML is a heterogeneous disease with overlapping features of MDS and MPN. Several risk scores have been proposed in CMML, including CPSS (Such Blood 2013) that identifies 4 risk groups (low, intermediate-1 [int-1], int-2 and high risk) with median overall survival (OS) ranging from 12 months to > 5 years. HSCT is the only curative treatment in CMML, with an expected 20-40% post-transplantation OS. Current guidelines, common to MDS and CMML recommend rapidly transplanting higher-risk CMML and delaying transplant in lower-risk disease (de Witte Blood 2017). Retrospective transplant studies have shown better outcome in lower-risk CMML (Liu et al BBMT 2017). Whether this translates into a survival benefit compared to a delayed transplant strategy requires prospective studies. Multistate models based on retrospective data from transplant and non-transplant patients (pts) have addressed similar issues in MDS (Koreth JCO 2013, Della Porta Leukemia 2017). Through a large collaborative study, we address the question of optimal timing for HSCT in CMML patients through a similar large international collaborative study. Method: We retrospectively selected pts from 2 registries: International MDS/MPN Working Group (IWG cohort, Padron Blood Cancer J 2015) and EBMT with the following criteria: WHO-defined CMML, age ≤ 70y, ECOG 0-2, diagnosis after 2000, available CPSS at the time of diagnosis. Different states were considered in higher- (CPSS int-2 and high) and lower- risk (CPSS low/int-1) pts: diagnosis, AML transformation, transplantation and death. IWG data was used to estimate transition probabilities from diagnosis to AML, transplantation and death and from AML to transplantation and death. EBMT data was used for transplantation to death transitions (Figure 1). Results: 719 and 403 pts were identified in the IWG and EMBT registry, respectively (resp). Median age was 64 (range 16-70) in IWG and 58 (19-70) years in EBMT cohort. Patients were male in 69% and 67% of IWG and EBMT cohorts, respectively (resp). CPSS was low in 22% and 13%, int-1 in 31% and 31%, int-2 in 40% and 45% and high in 8% and 11% of the IWG and EBMT pts, resp. In the IWG cohort, the 1 year cumulative incidence of transformation into AML were 7.3% and 18.2% in lower and higher risk patients, resp. Among the 719 pts from IWG, 102 received HSCT. At the time of diagnosis the expected life time of higher risk (int-2 and high) patients was 25.2 months while it was 44.1 months in lower risk (int-1 and low) patients. Table 1 reports gain or loss in expected life time (until 60 months after diagnosis) for transplantation at different intervals from diagnosis (within 6, 12, 18 and 24 months) for patients who have survived and not transformed to AML until the prediction time point, taking into account CPSS at diagnosis. Expected life time is also given separately for higher risk pts after transformation into AML. There was a modest gain of life expectancy with transplantation in higher risk patients (CPSS int-2 and high) while it was not the case in lower risk patients. The survival gain in higher risk pts with HSCT rose from 1.11 at 6 months to 3.4 months at 24 months. There was also a survival benefit of HSCT in higher risk pts who were transformed to AML during their follow-up which was even higher in terms of gain of months: from 9.89 month if HSCT performed within 6 months to 14.25 months if performed within 18 months. Of note, the loss of survival in lower risk patients who underwent HSCT decreased over time: 11.1 months if HSCT is performed within 6 months and 3.7 months if HSCT is performed within 24 months, probably owing to transition to higher-risk CMML at this stage in some pts, a transition that could not be captured in the present dataset. Conclusion: For the first time, using a multistate model, we could provide evidence that the transplant benefit in CMML is restricted to higher-risk pts, in line with findings in MDS and current guidelines. More analyses will be performed to analyze other subgroups of patients, especially regarding their risk (other classification than CPSS, the role of somatic mutation…). More studies are needed to analyze the role of pre HSCT treatment and its potential impact on post-transplant outcome. Disclosures Robin: Novartis Neovii: Research Funding. Beelen:Medac GmbH Wedel Germany: Consultancy, Honoraria. Fenaux:Celgene Corporation: Honoraria, Research Funding; Astex: Honoraria, Research Funding; Jazz: Honoraria, Research Funding; Aprea: Research Funding. Kroeger:Riemser: Research Funding; Novartis: Honoraria, Research Funding; Medac: Honoraria; DKMS: Research Funding; Neovii: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; JAZZ: Honoraria; Sanofi-Aventis: Honoraria. Nazha:Tolero, Karyopharma: Honoraria; MEI: Other: Data monitoring Committee; Novartis: Speakers Bureau; Jazz Pharmacutical: Research Funding; Incyte: Speakers Bureau; Daiichi Sankyo: Consultancy; Abbvie: Consultancy. Rampal:Agios, Apexx, Blueprint Medicines, Celgene, Constellation, and Jazz: Consultancy; Constellation, Incyte, and Stemline Therapeutics: Research Funding. Finke:Riemser: Honoraria, Other: research support, Speakers Bureau; Neovii: Honoraria, Other: research support, Speakers Bureau; Medac: Honoraria, Other: research support, Speakers Bureau. Komrokji:Agios: Consultancy; DSI: Consultancy; pfizer: Consultancy; celgene: Consultancy; JAZZ: Consultancy; Novartis: Speakers Bureau; JAZZ: Speakers Bureau; Incyte: Consultancy. Killick:JAZZ: Honoraria; Celgene: Honoraria; NOVARTIS: Honoraria, Membership on an entity's Board of Directors or advisory committees; ALEXION: Honoraria. Blaise:Molmed: Consultancy, Honoraria; Sanofi: Honoraria; Pierre Fabre medicaments: Honoraria; Jazz Pharmaceuticals: Honoraria. Garcia-Manero:Amphivena: Consultancy, Research Funding; Helsinn: Research Funding; Novartis: Research Funding; AbbVie: Research Funding; Celgene: Consultancy, Research Funding; Astex: Consultancy, Research Funding; Onconova: Research Funding; H3 Biomedicine: Research Funding; Merck: Research Funding. Patnaik:Stem Line Pharmaceuticals.: Membership on an entity's Board of Directors or advisory committees.

Abstract Background Myelodysplastic syndromes (MDS) and chronic myelomonocytic leukemia (CMML) are mainly diagnosed based on morphological changes in the bone marrow. The diagnosis can be challenging in patients (pts) with pancytopenia with minimal dysplasia, and is subject to inter-observer variability. Somatic mutations can be identified in either disease but no genes, in isolation or in combination, are specific for disease phenotype. We developed a geno-clinical model that uses mutational data, peripheral blood values, and clinical variables to predict an MDS vs. CMML diagnosis in pts who presented with cytopenias, in the absence of bone marrow biopsy results. Method We combined genomic and clinical data from 1897 pts treated at our institution (593) and the Munich Leukemia Laboratory (1304). Pts were diagnosed with MDS or CMML according to 2008 WHO criteria. Diagnosis of MDS and CMML was confirmed by independent hematopathologists that were not associated with the study. A genomic panel of 40 genes commonly mutated in myeloid malignancies was included. The initial cohort was randomly (computer generated) divided into learner (80%) and validation (20%) cohorts. Multiple machine learning algorithms were applied to predict the phenotype. Feature extraction algorithms were used to extract genomic/clinical variables that impacted the algorithm decision and to visualize the impact of each variable on phenotype. Prediction performance was evaluated according to the area under the curve of the receiver operator characteristic (ROC-AUC) and confusion/accuracy matrices. Results Of 1897 pts included, 1368 pts had MDS and 529 had CMML. The median age for the entire cohort was 72 years (range, 11-102); 37% were female. The median white blood cell count (WBC) was 5.1x109/L (range, 0.60-176), absolute monocyte count (AMC) 0.19 x109/L (range, 0-96), absolute lymphocyte count (ALC) 0.77x109/L (range, 0-62), absolute neutrophil count (ANC) 2.44x109/L (range, 0-170), hemoglobin (Hgb) 10.2 (range, 3.9-19.6), and platelet (Plt) count 111x103/mL (range 2-1491). The most commonly mutated genes in all pts were: TET2 (33%), ASXL1 (26%), SF3B1 (21%), SRSF2 (16%), RUNX1 (12%), DNMT3A (10%), CBL (7%), U2AF1 (7%), STAG2 (6%), EZH2 (6%), ZRSR2 (6%), NRAS (6%). In CMML, they were: TET2 (51%), ASXL1 (43%), SRSF2 (25%), RUNX1 (18%), CBL (16%), KRAS (12%), NRAS (11%), EZH2 (9%), JAK2 (6%), U2AF1 (5%), SF3B1 (4%), and DNMT3A (3%). In MDS, they were: TET2 (27%), SF3B1 (24%), ASXL1 (21%), SRSF2 (13%), DNMT3A (12%), RUNX1 (10%), STAG2 (8%), U2AF1 (8%), ZRSR2 (7%), TP53 (7%), BCOR (5%), and EZH2 (5%).The median total number of mutations/sample was 2 (range 0-27) for all pts, 2 (range 0-8) for CMML, and 2 (range 0-27) for MDS. A set of 83 genomic/clinical variables were evaluated and several feature extraction algorithms were used to identify the least number of variables that have the most significant impact on the algorithm's decision. These variables included: AMC, ALC, TET2, ANC, ASXL1, SF3B1, Hgb, number of mutations/sample, AEC, age, Plt, splenomegaly, RUNX1, NRAS, CBL, U2AF1, STAG2, DNMT3A, TP53, EZH2, SRSF2, and ZRSR2, Figure 1. When applying the model to the validation cohort, the ROC-AUC was .98 with an accuracy of 94%, with other statistical values as follows: specificities CMML 93%, MDS 96%; sensitivities CMML 96%, MDS 93%; positive predictive values CMML 84%, MDS 98%; negative predictive values CMML 98%, MDS 84%. Individual pt data can also be entered into the model, with a probability of whether the diagnosis is MDS vs. CMML provided along with the impact of each variable on the decision, as shown in Figure 1. When the analysis was restricted to mutations only, the accuracy of the model dropped dramatically (77%, ROC-AUC .85). Conclusions We propose a novel approach using interpretable, individualized modeling to predict MDS vs. CMML phenotypes based on genomic and clinical data without the need for bone marrow biopsy data. This approach can aid clinicians and hematopathologists when encountering pts with cytopenias and a diagnosis suspicious for MDS vs. CMML. The model also provides feature attributions that allow for quantitative understanding of the complex interplay among genotype, clinical variables, and phenotype. Disclosures Sekeres: Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Savona:Boehringer Ingelheim: Consultancy; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding. Gerds:CTI Biopharma: Consultancy; Celgene: Consultancy; Apexx Oncology: Consultancy; Incyte: Consultancy. Sallman:Celgene: Research Funding, Speakers Bureau. Komrokji:Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Maciejewski:Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Nazha:MEI: Consultancy.

Abstract Introduction: CMML is a clinically heterogeneous myeloid neoplasm hallmarked by the coexistence of dysplastic and proliferative patho-clinical features, which can include cytopenias, constitutional symptoms, splenomegaly, and leukocytosis. However, according to the FAB classification schema, CMML may be differentiated in dysplastic and proliferative subgroups only by the presence of leukocytosis in the latter (WBC ≥13 x 109/L). We hypothesize that incorporation of other clinically discriminating features may yield a more informative CMML stratification system. To address this, we propose three distinct CMML categories and explore their clinical relevance leveraging our existing international CMML database (Padron E et al. Blood Cancer J. 20151). Method: 1622 WHO-defined CMML cases diagnosed between 1973 and 2014 were collected from eight large cancer centres that include up to 80 discrete data elements as previously described1. Cases were placed into three clinically distinct groups and the Pearson Chi-Square test and the Kruskal-Wallis test were applied respectively to compare categorical and continuous characteristics. The Kaplan-Meier (KM) method was used to estimate median OS and the log rank test was used to compare survival curves.Cox models whereapplied to obtain univariate and adjusted hazard ratios. Identification of optimal cut-off values for continuous variable was supported by graphical inspection of martingale residuals from the null Cox model. Statistical analyses were done in SPSS v23 and R v.3.3.0. Results: We propose three categories to delineate clinically distinct CMML subtypes: (1) Myelodysplastic (MD)-CMML: WBC≤10 x 109/L, PB-immature myeloid cells (IMC) = 0%, no splenomegaly (2) MD/MP-CMML: WBC 10-20 x 109/L or WBC ≤10 x 109/L but PB-IMC>0% and/or splenomegaly (3) Myeloproliferative (MP)-CMML: WBC >20 x 109/L. A recursive partitioning approach was used to identify the WBC cut points, with splenomegaly and IMC added to more accurately depict the MPN aspect of CMML. Numbers of patients included in the MD-, MD/MP-, and MP-CMML subcategories were 319 (19.7%), 789 (48.6%) and 514 (31.7%), respectively (Table 1). According to the FAB criteria, the MD/MP group included 521 (66%) MD- (WBC ≤13 x 109/L) and 268 (34%) MP-CMML (WBC>13 x 109/L) patients suggesting that the proposed classification clinically reclassifies FAB-defined CMML. Within the MD/MP group, 344 patients (21.2%) had a WBC <10 x 109/L but with IMC>0% and/or splenomegaly. Comparison of overall survival (OS) among proposed groups demonstrated that this classification schema was capable of discriminating the CMML natural history (Figure 1). In comparison to MD/MP-CMML, the unadjusted OS Hazard Ratio (HR) was 0.60 (95% CI 0.49-0.73) for MD-CMML and 1.57 (95% CI 1.36-1.81) for MP-CMML (p<0.001 for both). This difference was retained after adjusting for BM-Blasts (< vs ≥5%), IPSS or CPSS cytogenetics (High-risk vsInterm/low-risk), and elevated levels of LDH (HR was 0.73 with 95% CI 0.57-0.93, p=0.011 for MD-CMML, and 1.33 with 95% CI 1.12-1.58, p=0.001 for MP-CMML, respectively). We next explored whether each proposed group may have distinct variables that uniquely govern its prognosis. Peripheral blood blasts >5%, RBC- and PLT-transfusion-dependence predicted poor OS only in the MD and MD/MP-subcategories (p<0.005), but had no impact in the MP-CMML. Male gender and elevated LDH were only significantly associated with prognosis in the MD/MP group (p<0.05 and p<0.005, respectively) while absolute lymphocyte count >2.5 x 109/L and absolute monocyte count >10 x109/L were uniquely predictive for shorter survival in the MP-CMML subcategory (p<0.005). Notably, we additionally identified gene mutations that uniquely predicted prognosis in each proposed group (Table 2). Conclusions: We demonstrate that our proposed 3-group clinical classification schema is capable of independently stratifying prognosis. Further, our analysis identified clinical and genetic variables that uniquely govern each group's prognosis, suggesting independent clinical behaviour. Further investigations are warranted to validate these groups. Disclosures Jabbour: ARIAD: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Research Funding; BMS: Consultancy. Fenaux:Celgene, Janssen,Novartis, Astex, Teva: Honoraria, Research Funding. Kantarjian:Bristol-Myers Squibb: Research Funding; ARIAD: Research Funding; Amgen: Research Funding; Pfizer Inc: Research Funding; Delta-Fly Pharma: Research Funding; Novartis: Research Funding. Komrokji:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Speakers Bureau.

Abstract Introduction: CMML is a heterogeneous malignancy characterized by peripheral monocytosis and a propensity for AML transformation. Given the low incidence of CMML and its broad range complexity, a large disease-specific data set is necessary to discern independent co-variates predictive of disease behavior. Several prognostic models derived from smaller data sets have been developed to stratify CMML patients into distinct groups that are predictive for OS. However, the validity of these models in a large international data set has never been investigated. Here, we present the largest International CMML data base established to date and report key baseline characteristics predictive for disease outcome. We externally validate and perform a detailed statistical comparison between the International Prognostic Scoring System (IPSS), Revised (R)-IPSS, Global MD Anderson Scoring System (MDASC), MD Anderson Prognostic Score (MDAPS), Dusseldorf Score (DS), Mayo, and Spanish Scoring Systems (CPSS). Methods: Data were abstracted by each contributing institution and deposited for central data review at the Moffitt Cancer Center. Manual review of cases was performed to ensure data quality prior to analysis. Only WHO defined CMML was included. The primary objective of this study was to establish an international CMML data set and validate the above models calculated at the time of presentation to each center. All prognostic models were calculated as previously described. The Kaplan–Meier (KM) method was used to estimate median OS and the log rank test was used to compare KM survival estimates with SPSS version 21.0. Random Forest Survival (RSF) and ROC analysis was done with R. Results: Between July 1981 and June 2014, 1832 CMML patients were captured in the International CMML database. Median age at diagnosis was 70 (16-93) years with a male (67%) predominance. By the WHO classification, the majority of patients had CMML-1 (79.9% vs. 20.1%) and most patients were evenly subcategorized as MPN-CMML (49.8%) versus MDS-CMML (50.2%) by FAB criteria. Splenomegaly was demonstrable in 25%. Most patients had favorable cytogenetics by both IPSS (70%) and CPSS (67.6%) classification schemas. Mean BM blast % was 5.6 and mean monocyte count was 4.85x103/dL. Median OS of the entire data set was 31.6 months. All tested prognostic models were valid and able to predict OS (p<0.0001). To compare relative model performance, 1013 complete cases with sufficient data to calculate all risk models were compared using time dependent receiver operator characteristic (ROC) curves and their area under the curves (AUC). ROC curves were calculated for OS at 36 months. The R-IPSS model had the highest AUC (0.694) while the DS model had the lowest (0.635). The difference in AUC between the R-IPSS and DS models was statistically significant (p = 0.003) whereas there was no significant difference from other models tested. To determine which models were most vulnerable to reclassification from low-risk to higher risk, we calculated a vulnerability score defined by the number of models able to up-stage low-risk disease in more than 15% of cases. Using this metric, the Mayo and MDASC scores were least vulnerable to up-staging by other models. Lastly, we used an orthogonal method for determination of variable importance (RSF for OS at 36 months). RSF is an ensemble method of classification and regression tree methodology. Ranking of variables by their weighted importance determined by this procedure identified the top five variables were hemoglobin, circulating blasts, platelet count, number of cytopenias, and karyotype. A new risk model was developed using the results of the RSF with an AUC of 0.720, which was comparable but not significantly different than other modern scoring systems. Conclusions: This represents the largest international CMML-specific data set derived from eight unique centers of excellence. All modern prognostic models were valid and performed comparably. Despite novel strategies for variable importance discovery, an improved model could not be constructed. This data suggest that the power of clinical variables has reached an asymptote and highlights the need for incorporation of molecular and other novel biological features. This data set is currently being populated with molecular data and is being leveraged to test published CMML molecular prognostic models which will be disclosed at the time of presentation. Disclosures Garcia-Manero: Epizyme, Inc: Research Funding. Fenaux:Celgene, Janssenm, Novartis: Research Funding. Kantarjian:ARIAD, Pfizer, Amgen: Research Funding. Sekeres:Celgene: Membership on an entity's Board of Directors or advisory committees; Amgen Corp: Membership on an entity's Board of Directors or advisory committees; Boehringer-Ingelheim Corp: Membership on an entity's Board of Directors or advisory committees.

Purpose To investigate the risk and outcomes of second hematologic malignancies (SHMs) in a population-based cohort of patients with well-differentiated thyroid cancer (WDTC) treated or not with radioactive iodine (RAI). Methods Patients with WDTC were identified from SEER registries. Competing risk regression analysis was performed to calculate the risks of SHMs that occurred after WDTC treatment and outcomes after SHM development were assessed. Results Of 148,215 patients with WDTC, 53% received surgery alone and 47% received RAI. In total, 783 patients developed an SHM after a median interval of 6.5 years (interquartile range, 3.3 to 11.2 years) from WDTC diagnosis. In multivariable analysis, compared with those undergoing thyroidectomy alone, RAI treatment was associated with an increased early risk of developing acute myeloid leukemia (AML; hazard ratio, 1.79; 95% CI, 1.13 to 2.82; P = .01) and chronic myeloid leukemia (CML; hazard ratio, 3.44; 95% CI, 1.87 to 6.36; P < .001). This increased risk of AML and CML after RAI treatment was seen even in low-risk and intermediate-risk WDTC tumors. Occurrence of AML but not CML in patients with WDTC was associated with shorter median overall survival compared with matched controls (8.0 years v 31.0 years; P = .001). In addition, AML developing after RAI trended toward inferior survival compared with matched controls with de novo AML (median overall survival, 1.2 years v 2.9 years; P = .06). Conclusion Patients with WDTC treated with RAI had an increased early risk of developing AML and CML but no other hematologic malignancies. AML that arises after RAI treatment has a poor prognosis. RAI use in patients with WDTC should be limited to patients with high-risk disease features, and patients with WDTC treated with adjuvant RAI should be monitored for myeloid malignancies as part of cancer surveillance.