Citations

Abstract Purpose Lattice radiation therapy (LRT) is a form of spatially fractionated radiation therapy that allows increased total dose delivery aiming for improved treatment response without an increase in toxicities, commonly utilized for palliation of bulky tumors. The LRT treatment planning process is complex, while eligible patients often have an urgent need for expedited treatment start. In this study, we aimed to develop a simulation‐free workflow for volumetric modulated arc therapy (VMAT)‐based LRT planning via deep learning‐predicted synthetic CT (sCT) to expedite treatment initiation. Methods Two deep learning models were initially trained using 3D U‐Net architecture to generate sCT from diagnostic CTs (dCT) of the thoracic and abdomen regions using a training dataset of 50 patients. The models were then tested on an independent dataset of 15 patients using image similarity analysis assessing mean absolute error (MAE) and structural similarity index measure (SSIM) as metrics. VMAT‐based LRT plans were generated based on sCT and recalculated on the planning CT (pCT) for dosimetric accuracy comparison. Differences in dose volume histogram (DVH) metrics between pCT and sCT plans were assessed using the Wilcoxon signed‐rank test. Results The final sCT prediction model demonstrated high image similarity to pCT, with a MAE and SSIM of 38.93 ± 14.79 Hounsfield Units (HU) and 0.92 ± 0.05 for the thoracic region, and 73.60 ± 22.90 HU and 0.90 ± 0.03 for the abdominal region, respectively. There were no statistically significant differences between sCT and pCT plans in terms of organ‐at‐risk and target volume DVH parameters, including maximum dose (Dmax), mean dose (Dmean), dose delivered to 90% (D90%) and 50% (D50%) of target volume, except for minimum dose (Dmin) and (D10%). Conclusion With demonstrated high image similarity and adequate dose agreement between sCT and pCT, our study is a proof‐of‐concept for using deep learning predicted sCT for a simulation‐free treatment planning workflow for VMAT‐based LRT.

Background/Objectives: The accurate delineation of primary tumors (GTVp) and metastatic lymph nodes (GTVn) in head and neck (HN) cancers is essential for effective radiation treatment planning, yet remains a challenging and laborious task. This study aims to develop a deep-learning-based auto-segmentation (DLAS) model trained on external datasets with false-positive elimination using clinical diagnosis reports. Methods: The DLAS model was trained on a multi-institutional public dataset with 882 cases. Forty-four institutional cases were randomly selected as the external testing dataset. DLAS-generated GTVp and GTVn were validated against clinical diagnosis reports to identify false-positive and false-negative segmentation errors using two large language models: ChatGPT-4 and Llama-3. False-positive ruling out was conducted by matching the centroids of AI-generated contours with the slice locations or anatomical regions described in the reports. Performance was evaluated using the Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), and tumor detection precision. Results: ChatGPT-4 outperformed Llama-3 in accurately extracting tumor locations from the diagnostic reports. False-positive contours were identified in 15 out of 44 cases. The DSCmean of the DLAS contours for GTVp and GTVn increased from 0.68 to 0.75 and from 0.69 to 0.75, respectively, after the ruling-out process. Notably, the average HD95 value for GTVn decreased from 18.81 mm to 5.2 mm. Post ruling out, the model achieved 100% precision for GTVp and GTVn when compared with the results of physician-determined contours. Conclusions: The false-positive ruling-out approach based on diagnostic reports effectively enhances the precision of DLAS in the HN region. The model accurately identifies the tumor location and detects all false-negative errors.

6579 Background: Treatment strategies in chronic myelomonocytic leukemia (CMML) are not standardized and include hydroxyurea (HU) and hypomethylating agents (HMA). In a phase 3 study comparing HU and decitabine, in proliferative CMML, response rates were higher for decitabine (56% vs 31%; p<0.01) but overall survival was similar (p=0.67) between the two groups (Itzykson, R. JCO, 2023. 41:1888). In the current retrospective study, we examined the survival impact of different treatment strategies among 457 Mayo Clinic patients with CMML. Methods: The current study was conducted under institutional review board approved minimum risk protocols allowing retrospective patient data collection and analysis. Diagnostic criteria were according to the International Consensus Classification (Arber et al. Blood 2022. 140:1200). All statistical analyses were conducted using JMP 17 software. For survival analysis, patients were censored at time of allogeneic stem cell transplant (ASCT). Results: A total of 457 patients were considered (median age 72 years; 68% males). 209 patients received CMML-directed therapy: HU (N=102), HMA (N=78; azacitidine 32 and decitabine 46). Responses were adjudicated separately for leukocytosis and anemia; normalization of leukocyte count was achieved in 21% vs. 16 % (p<0.01) for HU vs. HMA and overall response in anemia in 1% vs. 9% (p=0.02), respectively. Overall survival did not appear to be impacted by different treatment strategies at any stage of CMML; (i) treated vs. untreated (p=0.3), (ii) HU vs. HMA for first-line therapy (p=0.3), and (iii) HU/HMAs vs. other drugs as first-line therapy (p=0.1). Blast transformation-free survival (BTFS) was also similar between HU vs. HMA as well as HU/HMA vs. other drugs. In univariate analysis, BTFS was inferior in patients receiving CMML-directed therapy (p<0.01); the particular association (HR 2; 95% CI 1.2-3.3) retained its significance in multivariable analysis that also included other risk factors for BTFS: bone marrow blast ≥10% (HR 4.3), circulating blast ≥2% (HR 2.7), WBC ≥13 x 10 9 /L (HR 1.8), and ASXL1 mutation (HR 1.7). Conclusions: In the current retrospective study that included a large number of patients with CMML, chemotherapy with HU or HMA did not appear to affect overall survival but might have increased the risk of blast transformation. The study also suggests superiority of HU for the treatment of leukocytosis and HMA for anemia. Predictors of blast transformation-free survival (BTFS) in 457 patients diagnosed with chronic myelomonocytic leukemia (CMML). BTFS Univariable analysis p-value (HR) Multivariable analysis p-value (HR) CMML-directed therapy <0.01 (2.9; 1.8-4.7) <0.01 (2; 1.2-3.3) Bone marrow blasts ≥10% <0.01 (12.4) 0.01 (4.3) Circulating blasts ≥2% <0.01 (5.2) <0.01 (2.7) WBC ≥13 x 10 9 /L <0.01 (2.9) 0.02 (1.8) ASXL1 mut <0.01 (2.1) 0.02 (1.7)

e18590 Background: TP53 mutations ( TP53 MUT ) are known to be associated with adverse prognosis in acute myeloid leukemia (AML) and multihit TP53 MUT in myelodysplastic syndromes (MDS). The prognostic relevance of multihit TP53 MUT in myeloproliferative neoplasms (MPN) was recently highlighted. The current study illustrates survival comparisons in multihit TP53- mutated MPN vs. AML and analysis of associated risk factors. Methods: A Mayo Clinic enterprise-wide database search identified 61 MPN and 81 AML cases with multihit TP53 MUT . Conventional statistical methods were used for analyses. Results: 142 patients with multihit TP53 MUT were included: i) chronic phase MPN (MPN-CP; N=19), ii) accelerated phase MPN (MPN-AP; N=14), iii) blast phase MPN (MPN-BP; N=28) and iv) AML, not including MPN-BP (N=81). Concomitant ASXL1 MUT , EZH2 MUT , IDH1 MUT and IDH2 MUT were more common in MPN-BP, compared to AML. At a median follow up of 0.6 years, 124 (87%) deaths and 19 (13%) allogeneic stem cell transplantations (ASCT) were documented. Overall survival (OS), calculated from time of TP53 MUT detection was similar between MPN-BP (median 4.6 months) and MPN-AP (5.6 months; p=0.5). OS in MPN-BP/AP (median 4.8 months) was inferior to that of AML (median 7.4 months; p=0.04) while that of MPN-CP (median 11.6 months) was similar to AML (0.07) but superior to that of MPN-BP/AP (p<0.01). Age-adjusted multivariable analysis (MVA) confirmed the independent prognostic significance of undergoing ASCT (HR 0.4, p=0.03), MPN-CP or achieving response to pre-transplant therapy (HR 0.2, p<0.01), and concurrent TET2 MUT or DNMT3A MUT (HR 2.7, p<0.01) on OS. Based on these risk factors, a 3-tiered risk model was constructed: low (no risk factors; N=18; median OS 23.8 months); intermediate (1 risk factor; N=44; median 11.1 months); and high (2 or more risk factors; N=80; median 4 months; p<0.01). Comparative analysis of risk factors among distinct diseases confirmed the prognostic significance of ASCT (HR 0.3, p=0.03), achieving response to pre-transplant therapy (0.4, p<0.01) and concurrent TET2 MUT or DNMT3A MUT (HR 5.0, p<0.01) in AML. In MPN AP/BP, OS was influenced by having received chemotherapy vs. supportive care (HR 0.1; p<0.01) and achieving response to pre-transplant therapy (0.2, p<0.01). No significant risk factors for OS were identified in MPN-CP. In MVA, ASCT did not appear to influence OS in MPN AP/BP (p=0.7) and MPN-CP (p=0.4) but the number of informative cases was too low to make definitive conclusions. Conclusions: The current study highlights the equally detrimental impact of multihit TP53 MUT in securing long-term survival in MPN and AML. Short-term survival was positively influenced by ASCT, at least in AML, and disease stage or remission status at time of mutation detection and ASCT, respectively.

e18600 Background: Recent studies have suggested that basophilia in myeloproliferative neoplasms (MPN), in general, and in primary myelofibrosis (PMF), in particular, might be associated with poor prognosis. In the current study, we examined for clinical and genotype correlates as well as the prognostic impact of absolute eosinophil (AEC) and basophil (ABC) counts in essential thrombocythemia (ET) and polycythemia vera (PV). Methods: Diagnostic criteria were according to the International Consensus Classification. Study patients were selected based on the availability of information on AEC and ABC. Institutional reference range for AEC was 0.03 to 0.48 x 10 9 /L and for ABC 0.01 to 0.08 x 10 9 /L. "Eosinophilia" was defined as an AEC of ≥0.5 x 10 9 /L. "Basophilia" was evaluated both at an ABC of ≥0.1 x 10 9 /L and ABC of ≥0.3 x 10 9 /L, the latter representing 3 x the upper limit of the normal reference range. Results: Essential thrombocythemia 660 patients were considered. Median (range) values for AEC and ABC were 0.2 x 10 9 /L (0-2.8) and 0.07 x 10 9 /L (0-1.7), respectively. On univariate analysis, AEC and ABC, as continuous variables, were predictive of inferior overall (OS; p<0.01 for both) but not leukemia-free (LFS), myelofibrosis-free (MFFS), venous thrombosis-free (VTFS), or arterial thrombosis-free (ATFS) survival. In univariable analysis, AEC ≥0.5 x 10⁹/L and ABC ≥0.1 x 10⁹/L negatively impacted OS (p<0.01 for both); significance was sustained during multivariate analysis that included IPSET risk variables: AEC ≥0.5 x 10⁹/L (p<0.01; HR 1.9) and ABC ≥0.1 x 10 9 /L (p<0.01; HR 1.7). Polycythemia vera 478 patients were considered. Median (range) values for AEC and ABC were 0.27 x 10 9 /L (0-6.5) and 0.12 x 10 9 /L (0-3.2), respectively. In univariable analysis, neither AEC nor ABC, as continuous variables were predictive of OS, LFS, MFFS, VTFS, or ATFS. AEC ≥0.5 x 10⁹/L also did not have any impact on disease progression or thromboses. However, ABC ≥0.1 x 10 9 /L was associated with a lower risk of post-diagnosis arterial thrombosis (p=0.03; HR 0.6), and ABC ≥0.3 x 10 9 /L was predictive of inferior LFS (p<0.01; HR 6.5) and MFFS (p<0.01; HR 2.5). ABC ≥0.3 x 10 9 /L sustained prognostic relevance in LFS (p<0.01; HR 7.6) during multivariable analysis that included leukocytosis, platelet count, basophil percentage, age, and palpable splenomegaly. Similar findings were noted with regards to MFFS for ABC ≥0.3 x 10 9 /L (p=0.01; HR 2.3) when compared against palpable splenomegaly and age. Conclusions: The current study suggests that AEC ≥0.5 x 10 9 /L and ABC ≥0.1 x 10 9 /L in ET are independently associated with shortened overall survival, while basophilia (ABC ≥0.3 x 10 9 /L) in PV is an independent predictor of leukemic and myelofibrotic disease progression.

Objective: To establish benchmark cutoffs for robotic liver resection (R-LR), encompassing both major and minor resections, and to determine the impact of patient selection on outcomes. Background: R-LR is a key advancement in minimally invasive liver surgery but lacks standardized benchmarks, especially for minor resections. While guidelines endorse R-LR, its role in optimizing outcomes remains unclear. This study establishes the first benchmarks for R-LR, enabling comparisons across surgical modalities and refining patient selection. Methods: This retrospective, multicenter study analyzed consecutive adult patients undergoing R-LR at 30 international centers (2020–2023). Benchmark centers had an annual case volume of ≥15 R-LR. Benchmark criteria included ASA ≤2, no major comorbidities, no prior liver resections, and Child-Pugh A status. Benchmark cutoffs for 14 key outcomes were set at the 50th and 75th percentiles of median values across benchmark centers. Multivariable logistic regression identified predictors of textbook outcomes. Results: Eighteen high-volume centers contributed 4028 cases with 2632 (65.3%) meeting benchmark criteria. Malignancy was the indication in 29.6%, most commonly hepatocellular carcinoma followed by colorectal liver metastases. Major liver resection was performed in 42.6%. The distribution of Iwate difficulty scores was low (25.4%), intermediate (53.9%), advanced (15.9%), and expert (4.9%). Benchmark cutoffs were established for minor and major resection, and stratified by Iwate low (0-3), intermediate (4-6) and high (7-12): Open conversion (≤6.5% minor, ≤10.5% major), major complications within 90 days (≤5.2% minor, ≤16.7% major), R1 resection (≤9.2% minor, ≤6.7% major). Benchmark cases performed at low-volume centers were able to achieve outcomes within the corresponding benchmark cutoffs. In fact, patient selection reflected by the proportion of benchmark patients, rather than case volume, was associated with textbook outcomes. Conclusions: This study defines R-LR benchmarks, emphasizing patient selection over center volume for optimal outcomes. Benchmark cutoffs guide training and support the expansion of R-LR.

ABSTRACTPolycythemia vera (PV) is invariably associated with a JAK2 mutation, with over 50% of patients harboring additional non‐JAK2 mutations. In the current study, 319 patients with PV underwent NGS at diagnosis or in chronic phase PV (Group A: N = 270, 85%) or at the time of fibrotic (Group B; N = 37, 12%) or leukemic (Group C; N = 12, 4%) transformation. Mutational frequencies involving TP53/SRSF2/IDH1/U2AF1 were significantly (p < 0.05) different between patients in the mutually exclusive Groups A (2%/4%/2%/0.4%), B (8%/0%/0%/5%), and C (50%/25%/17%/8%). Analyses on phenotype/genotype associations and prognostic impact on overall (OS), leukemia‐free (LFS), and myelofibrosis‐free (MFFS) survival were limited to Group A patients. ASXL1MUT was associated with younger age (p < 0.01), SRSF2MUT with older age and leukocytosis (p < 0.01), and TP53MUT with leukocytosis (p < 0.01). Mutation co‐segregation was apparent between ASXL1 and IDH2 (p < 0.01) or SRSF2 (p < 0.01), SRSF2 and IDH2 (p < 0.01), and TP53 and NRAS (p = 0.01). Multivariable analysis identified SRSF2MUT (p < 0.01; HR, 4.2, 1.9–9.5), IDH2MUT (p = 0.01; HR, 5.3, 1.8–15.3), ASXL1MUT (p = 0.04; HR, 2.0, 1.1–3.7), leukocyte count ≥ 15 × 109/L (p < 0.01; HR 2.0, 1.3–3.1), and advanced age (p < 0.01) as risk factors for OS. Median OS in the presence (N = 235; 87%) or absence (N = 35; 13%) of any adverse mutation (i.e., SRSF2MUT, ASXL1MUT, or IDH2MUT) was 8.8 versus 17.8 years (p = 0.01; HR 1.8, 1.1–2.9). In addition, ASXL1MUT (p = 0.02; HR, 1.6–24.9), SRSF2MUT (p = 0.06; HR, 11.9, 1.1–126.2), and advanced age (p = 0.04) were associated with inferior LFS, and SRSF2MUT (p < 0.01; HR, 24.0, 5.5–103.8) and abnormal karyotype (p < 0.01; HR 3.8, 1.6–8.9) with inferior MFFS. The number of non‐JAK2 mutations was significant in predicting outcome in univariate but not multivariable analysis. The observations from the current study highlight the prognostic significance of non‐JAK2 mutations in PV and the prospect of their inclusion in future prognostic models.