Habermann, Thomas M.

Abstract Introduction Short diagnosis-to-treatment interval (DTI) is associated with aggressive disease biology and inferior outcomes in diffuse large B-cell lymphoma (DLBCL), and is recognized as a potential source of selection bias in clinical trials if not properly addressed. However, its prognostic significance in follicular lymphoma (FL) remains poorly characterized. We aimed to evaluate the prognostic significance of DTI in newly diagnosed FL patients who received frontline (1L) immunochemotherapy (IC), using a large international dataset and an independent external validation cohort. Methods For the discovery dataset, we evaluated patients diagnosed between 2002 and 2018 from ten observational cohorts as part of the FLIPI24 Consortium. External validation was conducted using data from patients enrolled in the Lymphoma Epidemiology of Outcomes (LEO) Cohort between 2010 and 2015. All patients had FL grade 1–3A and received 1L IC within 100 days of diagnosis. Patients with a concurrent DLBCL were excluded. Missing baseline laboratory values were addressed via multiple imputation. The primary endpoint was event-free survival at 24 months (EFS24); secondary endpoint was 5-year overall survival (OS), both calculated from treatment initiation. DTI was analyzed as continuous (via splines) and dichotomized variable (≤14 vs >14 days; as defined in DLBCL). Results In the discovery cohort (n=3,907), median age was 61 years (IQR 53–69), and 50% were male; 49% of patients were high-risk according to FLIPI, 26% according to PRIMA-PI, and 38% according to FLIPI24. Included patients received R-CHOP (54%), R-CVP (26%), or B-R (20%) as 1L treatment, followed by anti-CD20 maintenance in 60%. The median DTI was 30 days (IQR 17–48). Using the 14-day cut-off, 851 patients (22%) had short DTI. These patients had more adverse baseline characteristics, including B symptoms (42% vs 32%, P<0.01), ECOG performance status ≥2 (15% vs 8%, P<0.01), elevated LDH (51% vs 40%, P<0.01), hemoglobin <12 g/dL (30% vs 17%, P<0.01), and elevated β2-microglobulin (56% vs 44%, P<0.01). Prognostic indices (PIs) were also more frequently high-risk in the short-DTI group: FLIPI (56% vs 47%), PRIMA-PI (36% vs 24%), and FLIPI24 (51% vs 34%, all P<0.01). Spline modeling showed a decreasing EFS24 rate with increasing DTI, reaching a plateau at 30–40 days. In logistic regression model, each additional week of DTI improved EFS24 (OR 0.92, P<0.01). DTI attenuated but remained prognostic after adjusting for PIs (OR 0.93 for FLIPI, 0.95 for PRIMA-PI, 0.96 for FLIPI24, all P<0.01). When analyzed as a dichotomized variable, short DTI was associated with inferior EFS24: OR 1.74 adjusted for FLIPI, 1.63 for PRIMA-PI, and 1.53 for FLIPI24 (all P<0.01). The 2-year EFS of short vs long DTI groups was 71% vs 82% (HR 1.50, 95% CI 1.34–1.67, P<0.01), 5-year OS was 80% vs 86% (HR 1.38, 95% CI 1.21–1.56, P<0.01), and the 5-year cumulative risk of histologic transformation was 7.7% vs. 5.4% (P<0.01). In the validation cohort (n=516), median age was 61 years (IQR 52–69 years), 55% were male; 36% of patients were high-risk according to FLIPI; 29% according to PRIMA-PI, and 33% according to FLIPI24. Patients received B-R (59%) or R-CHOP (41%) as 1L treatment. The median DTI was 31 days (IQR 19–48), 84 patients (16%) had DTI ≤14 days. Similar patterns were observed; patients with short DTI had adverse baseline characteristics, PIs, and shorter 2-year EFS (72% vs 84%, HR 1.69, 95% CI 1.13–2.55, P=0.01). DTI remained associated with EFS24 when analyzed as a continuous variable (per week: OR 0.88 unadjusted, 0.90 with FLIPI, 0.89 with PRIMA-PI, and 0.89 with FLIPI24), and dichotomized (≤14 vs >14 days: OR 1.83 adjusted for FLIPI, 1.88 for PRIMA-PI, and 1.73 for FLIPI24). Conclusion Short DTI, defined as ≤14 days (observed in 22% of FL patients vs 46% in DLBCL), is a strong and independent predictor of inferior outcomes in newly diagnosed FL patients treated with IC. While DTI correlates with adverse baseline features and established PIs, it retains independent prognostic value. Although a 14-day threshold is reasonable for risk modeling, DTI contains greater informational value when modeled as a continuous variable. Among PIs, FLIPI24 most strongly attenuated the DTI effect, underscoring its superior discriminative power over FLIPI and PRIMA-PI. Clinical trials designs should mitigate any potential barriers to enrollment of FL patients requiring urgent therapy.

Abstract Background Neurolymphomatosis (NL) is a rare and frequently underrecognized manifestation of lymphoma, characterized by infiltration of peripheral or cranial nerves by malignant lymphocytes. Limited data exist to guide optimal management. We present the largest single-enterprise (included the 3 Mayo Clinic sites) cohort to date, evaluating disease characteristics, treatment patterns, and outcomes in patients (pts) with NL. Methods We conducted a retrospective analysis of pts ≥18 years (yrs) diagnosed with clinical, radiographic, or biopsy-confirmed NL at Mayo Clinic between January 2002 and December 2024. Baseline characteristics were compared using independent t-tests for continuous variables and Fisher's exact or Pearson's chi-square tests for categorical variables. Primary endpoints were overall survival (OS) and progression-free survival (PFS). Objective response rate (ORR) was a secondary endpoint. Kaplan–Meier methods, log-rank tests, and Cox proportional hazards regression were used to assess survival outcomes and associated factors. Results We included 149 pts. Median age at diagnosis was 64 yrs (range 22–83), and 23% were female. Most presented with stage IV disease (89%), and NL was part of the initial lymphoma presentation in 42% (primary NL). Diagnostic confirmation was based on histopathology and imaging in 55%, imaging alone in 26%, histopathology alone in 16%, and clinical features in 4%. Diffuse large B-cell lymphoma was the most common histology (61%), followed by marginal zone lymphoma (7%), and T-cell lymphoma (7%). Clinical presentation included painful neuropathy (65%), concurrent CNS involvement (37%), systemic disease (43%), elevated LDH (46.9%), and ECOG ≥2 (38%). Pattern of involvement included cranial nerves (29%), peripheral nerves (28%), and spinal nerve roots (27%). Median follow-up was 92 months (95% CI, 63–119). Among 137 treatment-evaluable pts, first-line regimens included methotrexate-based (MTX) (62%), bendamustine-based (B) (11%), cytarabine-based (Ara-C) (4%), and rituximab-based (R) (excluding MTX, B, or Ara-C) (15%) therapies. Radiation was used in 18%, and 21% underwent autologous stem cell transplant (ASCT). Among 103 response-evaluable pts, the ORR was 67% (52% complete response [CR], 16% partial response [PR]). ORRs by regimen were 78% (MTX), 92% (B), and 86% (R). On univariate analysis, primary NL (OR 4.3) and frontline ASCT (OR 8.47) were associated with higher odds of response (p<0.05), while ECOG ≥2 (OR 0.24) and localized disease (OR 0.33) were associated with lower odds. On multivariable analysis (MVA), primary NL (OR 5.0 [1.5–12.1], p=0.02) and ECOG >1 (OR 0.2 [0.06–0.79], p=0.018) remained significant predictors of response. Median PFS was 13 months (95% CI, 9–36), with a 2-yr PFS of 43% (95% CI, 35–53). On MVA, concurrent CNS involvement (HR 2.4, p=0.05), systemic disease (HR 2.7, p=0.02), and ECOG >1 (HR 2.4, p=0.02) were associated with inferior PFS, while primary NL was associated with improved PFS (HR 0.3, p=0.002). Patients undergoing frontline ASCT had a median PFS of 108 months (95% CI, 58–not reached) with a 7-yr PFS of 62% (95% CI, 46–84). Among those receiving second line and beyond (2+) therapy, 24 received MTX-based regimens, 10 received CAR-T, and 8 received tyrosine kinase inhibitors. Post-ASCT median PFS was 30 months (95% CI, 15–NR). Among CAR-T recipients, evaluable responses (n=8) included 4 CR, 1 PR, and 3 PD. Median OS was 56 months (95% CI, 29–133), with a 5-yr OS of 50% (95% CI, 41–60). On MVA, ECOG ≥2 (HR 2.5, p=0.02) predicted worse OS, while receipt of ASCT (HR 0.4, p=0.04) and primary NL (HR 0.5, p=0.04) were associated with improved OS. Conclusion This study represents the largest single-enterprise cohort of pts with NL, providing comprehensive insight into its clinical presentation, treatment patterns, and outcomes. Primary NL and preserved PS were independently associated with improved response rates and survival, while CNS and systemic involvement at diagnosis portended poorer PFS. MTX and bendamustine-based regimens achieved high ORR, and frontline ASCT was associated with significantly prolonged remission duration and OS. These findings highlight the prognostic importance of baseline disease burden and functional status, support the early incorporation of high-intensity regimens in eligible patients, and underscore the urgent need for prospective studies to guide evidence-based management in this rare and understudied population.

Abstract Introduction There is currently no standard-of-care for patients (pts) who cannot tolerate full dose anthracycline regimens in frontline large B-cell lymphoma (LBCL). R-miniCHOP is a commonly used regimen that provides curative intent treatment with manageable toxicity. Despite this, lymphoma and treatment-related mortality are the leading causes of death in these pts. Additionally, many pts may not be eligible to receive R-miniCHOP due to comorbidity or frailty. There are limited clinical trial and real-world data focused on this population. Methods Pts with LBCL age 18 years (yrs) and older were identified from the Lymphoma Epidemiology of Outcomes (LEO) cohort, a prospective study of newly diagnosed pts with non-Hodgkin lymphoma enrolled from 2015-2020 at 8 academic medical centers in the US. Pts with LBCL were included in this analysis if they had received R-miniCHOP or a regimen that did not include an anthracycline (nonAC). NonAC regimens included R-CVP, BR, rituximab, radiation and lenalidomide and rituximab. Pts completed the Vulnerable Elders Survey (VES-13) and were considered vulnerable if they had a score >3. Event-free survival (EFS) was defined as the time from start of treatment until progression/relapse, retreatment, or death due to any cause; EFS at 24 months (EFS24) was a secondary endpoint. Overall survival (OS) was defined as the time from start of treatment until death due to any cause. EFS and OS for R-miniCHOP and non-IC groups, as well as vulnerable and not vulnerable by VES-13 were estimated using Cox models. Results A total of 190 pts received R-miniCHOP (N=139) or nonAC (N=51). R-miniCHOP-treated pts were older (median 82 vs 72 yrs) and more commonly female (55% vs 35%). The majority in both groups (86% vs 78%) received their treatment at a LEO center and did not receive therapy on a clinical trial (89% vs 100%). Most pts receiving R-miniCHOP had high-intermediate (32%) or high (30%) international prognostic index (IPI) score, while both high-intermediate (22%) and high (20%) IPI were lower in the nonAC-treated pts. Cell of origin was determined by immunohistochemistry as available clinically, with GCB 42% vs 31%, non-GCB 32% vs 29%, and not done/unknown 26% vs 39% in R-miniCHOP vs nonAC pts. Double-hit status by FISH was known in 115 pts and was positive in 13% of R-miniCHOP pts and 10% of nonAC pts. The diagnosis-to-treatment interval (DTI) was ≤14 days in 30% of R-miniCHOP pts and 24% of nonAC pts. Of pts who had a VES-13 score (N=119), vulnerable status trended higher in the R-miniCHOP (66%) vs the non-IC group (50%; p=0.15). With a median follow-up of 50.5 months, the median EFS was 37.1 months and median OS was 50.0 months. R-miniCHOP, compared with non-IC, was associated with improved EFS (unadjusted HR=0.54, 95% CI 0.36-0.81; p=0.003) and OS (unadjusted HR=0.58, 95% CI 0.38-0.88; p=0.011). When adjusted for age, IPI, LBCL subtype (diffuse large B-cell lymphoma, not otherwise specified vs other), DTI and VES-13, R-miniCHOP was strongly associated with improved EFS (HR=0.26, 95% CI 0.14-0.50; p<0.001) and OS (adjusted HR=0.29, 95% CI 0.15-0.57; p<0.001). EFS24 rate was 56% in all pts and was superior in pts treated with R-miniCHOP vs non-IC (62.7% vs 34.1%; p=0.001). In the R-miniCHOP group, vulnerable status (adjusted for IPI) was associated with inferior OS (HR=2.22, 95% CI 1.09-4.50; p=0.028) and trended towards inferior EFS (HR=1.76, 95% CI 0.93-3.32; p=0.083). In a competing-risk cumulative-incidence analysis, lymphoma was the leading cause of death for pts receiving R-miniCHOP at 2 (0.16, 95% CI 0.11-0.24) and 5 (0.20, 95% CI 0.14-0.28) yrs, as well as for those receiving non-IC at 2 (0.35, 95% CI 0.24-0.53) and 5 (0.38, 95% CI 0.26-0.56) yrs. Treatment-related mortality was similar in both groups. In the R-miniCHOP group, the 2-yr (0.20 vs 0.03) and 5-yr (0.20 vs 0.11) lymphoma-related mortality was higher in pts who were vulnerable vs not by VES-13, while there were no differences for mortality due to therapy or other causes by vulnerable status. Conclusions These data support R-miniCHOP as a curative regimen in older and frail pts with the majority achieving EFS24. R-miniCHOP compared to non-IC use is strongly associated with improved EFS and OS when adjusted for disease and patient related variables. Within the R-miniCHOP group, vulnerable status is associated with inferior OS. Additional research efforts are needed to optimize outcomes for this group of pts.

Abstract Background: Rural/nonmetropolitan (nonmetro) counties face known barriers to comprehensive cancer care compared with metropolitan (metro) counties, which may impact the outcomes for lymphoma patients. We evaluated the impact metro vs nonmetro status on outcomes after a diagnosis of non-Hodgkin lymphoma (NHL) in the context of clinical, demographic, and lifestyle factors. Methods: Of 7724 participants enrolled in the prospective LEO cohort study from 2015-2020, 7610 (98.5%) had their county of residence at diagnosis assigned a RUCC (2013) code. We compared outcomes of participants from metro (defined as RUCC 1-3, which are central counties with ≥1 urban areas of ≥50,000) vs nonmetro counties (RUCC 4-9, which are counties with smaller urban areas as well as completely rural). We estimated hazard ratios (HRs) and 95% confidence intervals (CI) from univariable and multivariable Cox models for associations of metro status with overall survival (OS). Cause of death (lymphoma [including treatment related], non-lymphoma, unknown) was assessed using a competing risk approach. Results: Compared with metro counties (N=6096), LEO participants from nonmetro counties (N=1514) were older (median age 64 vs 62 years; p<0.001), more likely to be non-Hispanic white (94% vs 75%; p<0.001) and married (84% vs 75%; p<0.001), and less likely to have completed graduate or professional school education (16% vs 27%; p<0.001). There were no notable differences (>5%) by metro status for sex, employment status, NHL subtype, stage, performance status, abnormal lactate dehydrogenase (LDH), international prognostic index (IPI), number of comorbidities, time from diagnosis to treatment interval (DTI) or participation in a clinical trial. Compared with LEO participants from metro counties, those from nonmetro counties had inferior OS at 5 years (74.5% vs 78.4%; HR=1.23, CI 1.10-1.37; p<0.001). The OS association only slightly attenuated after adjustment for age, sex, race/ethnicity and NHL subtype (HR=1.20, CI 1.07-1.34; p=0.002), and held in models that adjusted for clinical prognostic factors (stage, NHL subtype, IPI, DTI), socioeconomic status (SES; education, marital status), health status (comorbidity, frailty), or lifestyle factors (smoking, body mass index). Using a competing risk model, the cumulative incidence of death at 5 years due to lymphoma (16.9% vs 14.1%; p=0.002) and non-lymphoma (5.9% vs 3.9%; p=0.002) was higher for participants from nonmetro vs metro counties, respectively. For participants <65 years, nonmetro vs metro status had inferior OS (HR=1.40, CI 1.17-1.67; p<0.001). This association remained after adjustment for age, sex, race/ethnicity and NHL subtype (HR=1.36, CI 1.14-1.63; p<0.001), as well as after adjustment for clinical prognostic, SES, health status, or lifestyle factors. The cumulative incidence of death at 5 years due to lymphoma was higher in participants from nonmetro vs metro counties (15.1% vs 11.5%; p=0.002), while there was no difference by metro status for non-lymphoma related deaths (2.1% vs 1.7%; p=0.47). In contrast, for participants age 65+ years, there was no differences by metro status for OS (HR=1.04, CI 0.91-1.20; p=0.56). After adjustment for age, sex, race/ethnicity and NHL subtype, participants from nonmetro areas had inferior OS (HR=1.12, 0.97-1.29) although this was not statistically significant (p=0.13), and was attenuated in models adjusting for clinical, health status, or lifestyle factors. The cumulative incidence of death at 5 years due to lymphoma was no different in participants from nonmetro vs metro counties (18.9% vs 18.0%; p=0.37), while non-lymphoma related deaths were higher in nonmetro counties (9.9% vs 6.8%; p=0.017). Conclusion: LEO participants from nonmetro vs metro counties had inferior OS overall, although this was predominately observed for younger (<65 years) participants. These associations did not appear to be explained by demographic, NHL subtype, clinical, SES, health status, or lifestyle factors. Of particular note, the risk of death due to lymphoma was higher in nonmetro areas for participants <65 years but not those 65+ years, while the risk of death due to other causes was higher in nonmetro areas for participants 65+ years but not those <65 years. These findings require further study, particularly related to barriers to care, and suggest the potential need for age-specific strategies to optimize outcomes for NHL patients from nonmetro/rural areas.

Abstract Background: De novo secondary central nervous system (CNS) lymphoma (SCNSL), characterized by concurrent systemic and CNS aggressive B-cell lymphoma at diagnosis, is a rare entity with a dismal prognosis. The prognostic significance of the initial anatomical compartment of CNS involvement (parenchymal vs. leptomeningeal) is not well-defined. We sought to analyze outcomes based on CNS localization in a single-institution cohort. Methods We retrospectively reviewed 34 patients with de novo SCNSL (including diffuse large B-cell lymphoma [DLBCL], high-grade B-cell lymphoma, not otherwise specified (HGBCL NOS), and double-/triple-hit lymphoma) treated at the Mayo Clinic between 2002 and 2025. Clinicopathological features, radiologic findings, treatment details, and survival outcomes were abstracted from the medical records. Patients were categorized by the primary site of CNS involvement. Overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan-Meier method, and groups were compared with the log-rank test. Results The study cohort had a median age of 61.5 years (range 19-79) and a median International Prognostic Index score of 3 (range 2-4). Pathological subtypes included double-/triple-hit lymphoma (n=14, 41.2%), non-germinal center B-cell like (non-GCB) DLBCL (n=11, 32.4%), germinal center B-cell like (GCB) DLBCL (n=8, 23.5%), and HGBCL NOS (n=1, 2.9%). Among patients with available data, 14 (43.8%) were positive for MYC rearrangement, 13 (54.2%) for BCL2 rearrangement, and 10 (45.5%) for BCL6 rearrangement. First-line regimens received by patients included MR-CHOP (n=18, 52.9%), DA-R-EPOCH (n=5, 14.7%), combined CODOX-M/IVAC (n=4, 11.7%), R-CHOP with intrathecal methotrexate (n=3, 8.8%), miscellaneous therapies (n=3, 8.8%), and MRT (n=1, 2.9%). The complete response rate was 58.8% (n=20). Intrathecal chemotherapy was administered to 61.8% of patients, and consolidation with autologous stem cell transplant was performed in 35.3%. Based on the CNS compartment involved, de novo SCNSL (DN-SCNSL) were classified into two subtypes: the leptomeningeal subtype, defined as leptomeningeal involvement with or without parenchymal CNS disease (LM-DN-SCNSL; n=22, 64.7%), and the parenchymal subtype, defined as parenchymal-only CNS involvement (P-DN-SCNSL; n=12, 35.3%). Parenchymal disease was multifocal in 75% of cases, supratentorial in 58.3%, and associated with a midline shift in 30%. Although no tested factors (including pathological subtype, age, sex, and bone marrow involvement) were significantly associated with the anatomical compartment, a non-significant trend was noted for BCL6 rearrangement to be more common in the leptomeningeal group (8/13, 61.5%) than the parenchymal group (2/8, 25.0%) (P = 0.18). Survival outcomes were analyzed for both anatomical and pathological classifications. For the parenchymal versus leptomeningeal cohorts, 2- and 5-year Overall Survival (OS) rates were 75.0% vs. 45.1% and 41.0% vs. 38.7%, respectively (P = 0.34). For Progression-Free Survival (PFS), the 2- and 5-year rates were 48.6% vs. 32.5% and 29.2% vs. 26.0%, respectively (P = 0.53). The 2-year OS rates by pathological subtype were 81.8% for non-GCB DLBCL, 45.8% for DHL/THL, and 37.5% for GCB DLBCL (P = 0.27). Survival outcomes (OS/PFS) were not significantly associated with patient age, sex, pathological subtype, or anatomical compartment. Conclusion DN-SCNSL can be anatomically classified into two subtypes: LM-DN-SCNSL and P-DN-SCNSL. While a prognostic trend favoring the parenchymal subtype was observed, the difference did not achieve statistical significance, a finding likely attributable to the small cohort size. This classification may hold clinical utility for guiding risk stratification, and larger studies are needed to validate these findings and identify the most optimal treatments for each subtype of DN-SCNSL.

Abstract Introduction The International Prognostic Index (IPI, NEJM, 1993) has guided risk stratification in large B-cell lymphoma (LBCL) for over three decades. Its simplicity and clinical utility have driven its widespread adoption, even in the era of precision medicine. However, the prognostic value of the IPI and its components may not be uniform across the age spectrum of LBCL patients. We evaluated the contemporary prognostic performance of the IPI in a large international cohort, focusing on age-stratified performance and the relative contributions of its individual components, grouped as patient- and disease-related factors. Methods We harmonized and pooled data from 6,941 patients with newly diagnosed systemic LBCL treated with R-CHOP-like regimens (R-CHOP, R-miniCHOP, DA-EPOCH-R, R-CHOEP, R-CHOP+X) with complete IPI data from three prospective cohorts: NiHiL (Czech Republic, n=4,590; 2010–2023), LEO (USA, n=1,789; 2015–2020), and MER (USA, n=562; 2010–2015). IPI components included age, ECOG performance status (PS) as patient-related, and Ann Arbor clinical stage, serum lactate dehydrogenase (LDH), and extranodal (EN) involvement as disease-related factors. Prognostic performance was evaluated using multivariable Cox regression models and C-statistics. Age-stratified analysis was performed for: ≤40 years (7%; n=492), 41–60 years (28%; n=1,909), 61–80 years (59%; n=4,080), and >80 years (7%; n=460). The primary endpoint was overall survival (OS). Results In the pooled cohort, the median age was 66 years (range 18–95). Compared to the original IPI cohort (n=3,273), our cohort was older (age >60 years: 66% vs 41%) but otherwise comparable: ECOG PS 2–4 (25% vs 24%), clinical stage III–IV (63% vs 66%), elevated LDH (60% vs 52%), and >1 EN involvement (32% vs 30%). In multivariable analysis, age (≤60 vs >60 years) was the strongest predictor of OS (HR=2.78; vs HR=1.96 in original IPI report), followed by ECOG PS (HR=2.07; vs 1.80), clinical stage (HR=1.50; vs 1.47), LDH (HR=1.41; vs 1.85, all P<0.01); EN involvement was not significant (P=0.50, HR=1.03 vs 1.48). Risk group distribution shifted from the original IPI: 28% were low-risk (0–1 factor; vs 35%), 23% low-intermediate (2; vs 27%), 24% high-intermediate (3; vs 22%), and 25% high-risk (4–5; vs 16%). Corresponding 2-year OS rates were 95%, 86%, 78%, and 63%, superior to those of the original IPI cohort (87%, 67%, 55%, 44%; pre-rituximab era). Age was associated with an increasing risk of mortality, with a more pronounced rise beyond 60 years. In a piecewise Cox model, the HR per year was 1.04 for ages ≤60 (linear), 1.05 for 61–69 (accelerated), and 1.06 for ≥70 years (exponential, all P<0.01). ECOG PS retained prognostic significance across age groups: HRs were 1.87 (P=0.06) for ≤40 years, 1.63 (P<0.01) for 41–60, 2.22 (P<0.01) for 61–80, and 1.43 (P<0.01) for >80 years. The strength of other IPI components declined with age. Clinical stage showed decreasing impact: HR 2.08 (P=0.09), 1.97 (P<0.01), 1.49 (P<0.01), and 1.31 (P=0.05). LDH was significant only up to age 80 years: HRs were 2.07 (P=0.04), 1.96 (P<0.01), 1.32 (P<0.01), and 1.21 (P=0.13). EN involvement was non-significant across all groups: HRs were 1.37 (P=0.34), 1.26 (P=0.05), 0.98 (P=0.74), and 1.06 (P=0.67). The IPI C-statistics was 0.676 overall and declined with age: ≤40 years (0.708), 41–60 years (0.683), 61–80 years (0.641), and >80 years (0.598). We next stratified the IPI factors into patient- (age, ECOG PS) and disease-related (clinical stage, LDH, EN involvement). In patients ≤40 years, disease-related factors outperformed patient-related (C-index 0.693 vs 0.602), as in the 41–60 group (C-index 0.677 vs 0.589). In patients aged 61–80, predictive values were comparable (0.613 vs 0.622). In >80-year-olds, both declined substantially (0.580 vs 0.568). Conclusion The IPI remains a valuable prognostic tool in LBCL, particularly among younger patients. However, its overall predictive accuracy declines with age. In patients ≤60 years old, prognosis is primarily driven by disease-related factors, suggesting a role for molecular classifiers to enhance risk stratification. In older patients, incorporation of additional patient-level factors, such as comorbidities and nutritional status could be considered. These findings underscore the need for more individualized prognostic tools for LBCL patients. Funding: NU21-03-00411, P50 CA97274, U01 CA195568, Charles University Haematology-Oncology Cooperatio Program.

Abstract Introduction The LEO Comorbidity Index (LCI), comprising ten equally weighted comorbidity categories (respiratory, cardiovascular, digestive, hepatic, renal, autoimmune, diabetes, cancer history, HIV, and stroke), has demonstrated prognostic utility in patients with non-Hodgkin lymphomas. However, its predictive accuracy may vary with increasing age, due to age-related rises in comorbidity burden and physiological vulnerability. To investigate these dynamics, we evaluated the age-stratified predictive performance of LCI in patients with large B-cell lymphoma (LBCL), with a focused analysis on individuals aged 80+ years. We also examined which specific comorbidities most strongly influenced overall survival (OS) and developed a simplified, clinically applicable index tailored to this population. Methods We first included adults ≥40 years with newly diagnosed LBCL and recorded comorbidities from the LEO registry (USA, 2015–2020; n=1,652) to assess age-stratified predictive performance of LCI using the C-index. Next, we focused on patients 80+ years (n=263), pooled from the LEO cohort (n=147) and NiHiL registry (restricted to General Hospital, Prague, Czech Republic, 2010–2023; n=116). Comorbidities were collected from patient self-reports (LEO) and/or medical records (LEO/NiHiL). Univariate and multivariate Cox models were used. The primary endpoint was OS. Results Among the total LBCL population (n=1,652), LCI predictive performance improved by age category with C-index for patients <65 of 0.584, 65–79 years 0.579, and ≥80 years 0.647, indicating stronger discrimination in the 80+ group. Subsequent analysis focused on patients aged 80+ years (n=263; median age 83). Within this cohort, 34% had ECOG performance status (PS) 2–4, 66% stage III–IV disease, 59% elevated lactate dehydrogenase, 35% >1 extranodal site involved, and 58% International Prognostic Index (IPI) score of 3–5. A total of 377 comorbidities were reported, averaging 1.43 per patient (1.22 in LEO; 1.61 in NiHiL). LCI distribution was: 0 points in 24% (n=62), 1 point in 33% (n=88), and 2–5 points in 43% (n=113). Higher LCI scores had worse ECOG PS 2–4: 19% (LCI=0), 33% (LCI=1), and 43% (LCI ≥2; P=0.01), and inferior OS (median 6.9 years LCI=0, 3.0 years LCI=1, and 2.8 years LCI≥2, resp.; P=0.02). Presence of ≥1 comorbidity was associated with shorter OS compared to no comorbidities (HR=1.67, P=0.01). The LCI was an independent predictor of OS after adjusting for age (HR=1.18, P=0.01, C-index 0.623), IPI (HR=1.16, P=0.03, C-index 0.628) as well as for recently proposed SENIOR-IPI (HR=1.14, P=0.049, C-index 0.665). When evaluating individual comorbidities, heart disease (HR=1.54, P=0.01), stroke (HR=1.89, P=0.03), and recent malignancy (HR=1.99, P=0.02) were independent predictors of OS. Based on these findings, we developed a simplified LCI (sLCI) for use in 80+ patients incorporating only the three comorbidities (1 point each). Patients were categorized as sLCI: 0 (49%, n=129), 1 (44%, n=115), and 2–3 points (7%, n=19). sLCI scores 0, 1, 2–3 were associated with ECOG PS 2–4 27%, 37%, and 63%, respectively (P=0.01) and inferior OS: median 5.3 years (0), 2.5 years (1), and 1.4 years (2–3; P<0.01). sLCI remained an independent and strong predictor of OS after adjusting for age (HR=1.71, P<0.01, C-index 0.636), IPI (HR=1.66, P<0.01, C-index 0.635), and SENIOR-IPI (HR=1.60, P<0.04, C-index 0.659). The results were consistent in both NiHiL and LEO cohorts and remained significant among patients treated with anthracyclines (sLCI adjusted for age: HR=1.58, P<0.01, C-index 0.607; for IPI: HR=1.53, P=0.01, C-index 0.623; for SENIOR-IPI: HR=1.51, P=0.01, C-index 0.633). Conclusion Comorbidity burden becomes increasingly prognostic with higher age in LBCL patients. In the 80+ individuals, comorbidities – particularly heart disease, recent cancer, and stroke – have an independent impact on OS. While the original LCI captures the impact of broadly defined comorbidities, its prognostic utility attenuates in the 80+ patients, likely reflecting limited relevance of indolent chronic conditions in this population's constrained survival horizon. The sLCI, focused on the most prognostically relevant comorbidities, offers practical risk stratification. Incorporation of sLCI into clinical decision-making – alongside other prognostic indices – could better identify high-risk individuals and guide treatment strategies. Funding: NU21-03-00411, U01 CA195568.

PURPOSE Although most patients with follicular lymphoma (FL) can expect an indolent course, progressive lymphoma remains the primary cause of death during the first decade after diagnosis. Progression of disease within 24 months (POD24) of starting first-line (1L) immunochemotherapy defines a high-risk population with poor survival, but better risk stratification at diagnosis is needed. METHODS The FLIPI24 model was developed and internally validated to predict 24-month event rates using individual data from 4,485 patients treated with 1L immunochemotherapy from 10 observational cohorts of FL. Overall and cause-specific survival was further evaluated in FLIPI24 risk groups. External validation in the 1L immunochemotherapy setting was performed using the prospective observational Lymphoma Epidemiology of Outcomes (LEO) cohort (N = 565) and three randomized phase III trials (N = 3,192); extension to all patients with FL (any 1L therapy) was performed in the LEO cohort (N = 1,445) and its Molecular Epidemiology Resource subcohort (N = 1,074). RESULTS The FLIPI24 model uses age and four blood-based variables (hemoglobin, lactate dehydrogenase, beta-2 microglobulin, and WBC count). FLIPI24 showed consistent performance across validation and extension data sets, which was superior to existing prognostic tools. Across the four external immunochemotherapy validation data sets, patients with high-risk FLIPI24 (23%-32% of patients) had significantly higher 24-month event rates (22%-35%) and inferior 5-year overall survival (77%-83%) compared with patients with low-risk FLIPI24 (29%-31% of patients, 24-month event rates: 10%-12%; 5-year OS: 96%-97%). Results were consistent when evaluating lymphoma-related death and when extended to all patients with FL. CONCLUSION The FLIPI24 model robustly stratifies, at diagnosis, patients with FL at increased risk of lymphoma-related death versus patients with very low lymphoma-related mortality during the first decade after diagnosis. FLIPI24 can be used to enrich future clinical trial designs in newly diagnosed FL.

Abstract Glioblastoma, IDH-wildtype (GBM), and primary central nervous system lymphoma (PCNSL) are two aggressive brain tumors with distinct treatment strategies but overlapping imaging features on MRI. Accurate preoperative differentiation remains a clinical challenge, due to their overlapping imaging features, even when advanced MRI techniques are employed. This study explored deep learning approaches leveraging multi-sequence MRI to distinguish GBM from PCNSL. Models were developed utilizing 146 PCNSL cases, which were matched to146 GBM by age, sex and year of diagnosis. A 3D-DenseNet121 convolutional neural network was trained using two analytical strategies: (i) loss-based model selection and (ii) ensemble prediction using five-fold cross-validation. Model performance was evaluated on an independent validation set of 256 GBM and 37 PCNSL from the same institution as well as 36 PCNSL cases from an external institution. All MRIs were preprocessed using the FeTS pipeline. Using the independent validation cohort, area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CI) were computed overall and stratified by demographic characteristics. Using both T1 post-contrast (T1Gd) and T2-weighted images, the loss-based model achieved AUC=0.83 (95% CI: 0.77-0.88) and the ensemble model yielded an AUC=0.82 (95% CI: 0.76-0.89). Model performance was similar across males and females; however, model performance was better for subjects older than 50 years of age versus younger than 50. When using only T1Gd images, the loss-based model dropped to AUC=0.55 (95% CI: 0.48-0.63), whereas the ensemble model had AUC=0.79 (95% CI: 0.72-0.85). Models trained with only T2 images achieved AUCs of 0.67 (95% CI: 0.60-0.73) (loss-based) and 0.75 (95% CI: 0.69-0.82) (ensemble). These results highlight the added value of the multi-sequence input. Our results confirm the feasibility of using automated deep learning models to differentiate GBM from PCNSL. Notably, the model generalized well to external MRI data, emphasizing its potential utility as a diagnostic aid in real-world clinical workflows.