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Marketing authorizations for oncology drugs are often based on single‐arm trials (SATs), and real‐world data‐based external comparator cohorts (ECCs) are increasingly explored to contextualize results. In retrospective studies, comparators may be identified by time of diagnosis but included based on time of relapse; such sampling strategies can introduce bias, potentially leading to erroneous efficacy conclusions. Simulated diffuse large B‐cell lymphoma (DLBCL) cohorts were generated using published time‐to‐relapse and post‐relapse survival distributions. Three ECC sampling strategies were evaluated: naïve (screening and inclusion periods coincide), delayed (inclusion period extends beyond screening), and displacement (screening and inclusion periods shifted relative to each other). Different sampling strategies caused significant changes in the time‐to‐relapse distribution in the ECC and, by proxy, the estimated 2‐year post‐relapse overall survival (22.5–52.2%). Naïve sampling consistently resulted in oversampling of early relapses, though bias was reduced with extended inclusion periods; in hypothetical trials, it produced inflated Type I errors (10.5–84.9%) despite α = 0.025. Delayed sampling reduced bias when the inclusion period was sufficiently extended to capture late relapses, yielding unbiased estimates under long delays (Type I error of 2.5% at a 6‐year delay). Displacement sampling depended strongly on the degree of shift: moderate shifts minimized bias, whereas long displacements introduced substantial distortion (Type I error of 2.7% and 0.0% at displacements of 2 and 5, respectively). Real‐world data‐based ECCs in relapsed/refractory oncology are susceptible to selection bias from sampling strategies, particularly through distortions in time‐to‐relapse, leading to erroneous efficacy conclusions; similar bias may occur in non‐oncology settings where time‐to‐treatment initiation is prognostic.

ABSTRACT One quarter of patients with diffuse large B‐cell lymphoma (DLBCL) who achieve a complete response following front‐line therapy will eventually experience disease relapse. Existing prognostic models use baseline factors to predict outcomes from the start of initial therapy and are not intended for use following completion of treatment. We developed a conditional event‐free survival (cEFS) tool that predicts a patient's risk of lymphoma recurrence after completing front‐line therapy taking into consideration baseline risk characteristics plus the time the patient has been in remission. Model development used pooled and harmonized data from two observational cohorts and four randomized trials of patients with newly diagnosed DLBCL treated with immunochemotherapy and either achieving a complete response or not experiencing an event within 12 weeks of end of therapy. Cox proportional‐hazards regression modeling with backwards stepwise selection was utilized for model development. External validation was performed using a large independent data registry. In total, data from 2757 patients were used for model development. The risk of experiencing an event at a time point after having survived a period of time could be computed parsimoniously using four basic predictors: age, ECOG performance status grade, Ann Arbor stage, and lactate dehydrogenase level. C‐index in the model development cohort was 0.65 (95% CI, 0.63–0.68), compared to 0.64 (95% CI, 0.62–0.66) in the external validation cohort. We believe that this dynamic risk prediction tool will not only aid clinicians in providing data‐driven recommendations for surveillance, but also assist patients themselves with key decisions, such as family planning.

Abstract A short diagnosis-to-treatment interval independently predicts inferior outcomes in follicular lymphoma. Using international discovery and validation cohorts, we demonstrate its prognostic value beyond established prognostic indices, with implications for risk stratification, clinical trial design, and interpretation.

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.

ABSTRACTThe development of new first‐line treatments for patients with follicular lymphoma (FL) is becoming increasingly challenging due to already excellent survival outcomes. The present study investigated the outcomes of patients with FL who underwent contemporary first‐line therapies but would not have been eligible for inclusion in recent trials and explored how commonly used in/exclusion criteria impacted their survival outcomes. This study included adult patients diagnosed with FL in the period 2000–2018 registered in the Danish Lymphoma Registry. Through searches on ClinicalTrials.gov, four recent 1st line phase 3 randomized controlled trials with R‐Bendamustine, R‐CVP, and/or R‐CHOP as control or experimental arms were included. Inclusion and exclusion criteria for each trial were retrieved and categorized. Patients were then divided into trial‐eligible and ineligible groups according to blood test results correlated to organ function and ECOG performance score (PS). Survival outcomes were significantly worse among trial‐ineligible patients, with adjusted differences between trial‐eligible and ineligible patients of 12%–20% in five‐year overall survival (OS) overall. Inclusion criteria based on PS and renal function were the main drivers of OS differences. More inclusive trials will lead to faster recruitment and secure focus on developing medicines for the group of patients with the worst outcomes.

PURPOSE Patients diagnosed with advanced-stage Hodgkin lymphoma (aHL) have historically been risk-stratified using the International Prognostic Score (IPS). This study investigated if a machine learning (ML) approach could outperform existing models when it comes to predicting overall survival (OS) and progression-free survival (PFS). PATIENTS AND METHODS This study used patient data from the Danish National Lymphoma Register for model development (development cohort). The ML model was developed using stacking, which combines several predictive survival models (Cox proportional hazard, flexible parametric model, IPS, principal component, penalized regression) into a single model, and was compared with two versions of IPS (IPS-3 and IPS-7) and the newly developed aHL international prognostic index (A-HIPI). Internal model validation was performed using nested cross-validation, and external validation was performed using patient data from the Swedish Lymphoma Register and Cancer Registry of Norway (validation cohort). RESULTS In total, 707 and 760 patients with aHL were included in the development and validation cohorts, respectively. Examining model performance for OS in the development cohort, the concordance index (C-index) for the ML model, IPS-7, IPS-3, and A-HIPI was found to be 0.789, 0.608, 0.650, and 0.768, respectively. The corresponding estimates in the validation cohort were 0.749, 0.700, 0.663, and 0.741. For PFS, the ML model achieved the highest C-index in both cohorts (0.665 in the development cohort and 0.691 in the validation cohort). The time-varying AUCs for both the ML model and the A-HIPI were consistently higher in both cohorts compared with the IPS models within the first 5 years after diagnosis. CONCLUSION The new prognostic model for aHL on the basis of ML techniques demonstrated a substantial improvement compared with the IPS models, but yielded a limited improvement in predictive performance compared with the A-HIPI.

Purpose: Improved predictive tools are desired in classic Hodgkin lymphoma (cHL) to improve prognostication and to better risk stratify patients (pts) for clinical trials. In younger pts who tolerate intensive therapies, focus has been on limiting late treatment toxicity through risk-adapted, PET-guided treatment strategy. In older pts, acute treatment toxicities are limiting factors for optimal disease control. Due to these fundamental differences, predictive tools may perform differently in younger and older pts. In particular, tools developed in younger pts may be suboptimal in older populations and vice versa. The primary objective of this study was to assess the performance of the A-HIPI (development was limited to 18-65 years of age) against the international prognostic score (IPS) for modelling overall survival (OS) and progression-free survival (PFS) in younger and older pts with cHL. Methods: This study utilized data from the population-based, nationwide Danish National Lymphoma registry. Pts with advanced-stage cHL (Ann Arbor stage III-IV or IIB with extra nodal disease) were included and categorized by age in two groups: ages ≤65 years vs. >65 years. In the primary analysis, all pts regardless of therapy were included. A sensitivity analysis restricting to pts starting on BEACOPP or ABVD was performed (Only ABVD in the >65 cohort). Concordance index (C-index) and calibration were used to assess model performance and generalizability. Calibration was visualized using calibration plots and quantified by calibration in the large (CITL) and calibration slope (CS) at 5-years post diagnosis. Results: A total of 893 advanced-stage HL pts fulfilled the inclusion criteria (634 in the ≤65 cohort and 259 in the >65 cohort). The 5-year OS and PFS in the >65-year cohort were 44% (Confidence intervals (CI); 37-51%) and 35% (CI; 30-42%), respectively. Fifty-six pts in the >65 cohort were classified as low-intermediate-risk (IPS; 1-2), 128 intermediate-risk (IPS; 3-4), and 75 high-risk (IPS; 5-7). For the ≤65 cohort, the 5-year OS and PFS was 88% (CI; 85-90%) and 76% (CI; 73-80%), respectively. Twenty-three pts were classified as low-risk (IPS; 0), 244 low-intermediate-risk, 286 intermediate-risk, and 81 high-risk. The A-HIPI had a C-index of 0.74 and 0.63 for OS and PFS, respectively in the ≤65 years cohort vs. 0.65 and 0.64 for OS and PFS, respectively, in the >65 cohort. The IPS had a C-index in the ≤65 cohort of 0.66 and 0.60 for OS and PFS, respectively, vs. 0.58 and 0.59 for OS and PFS, respectively, in the >65 cohort (i.e., the IPS performed better in the ≤65 cohort in terms of OS and PFS but was inferior to the A-HIPI). When restricting to pts treated with ABVD or BEACOPP, the A-HIPI C-index in the >65 cohort (116 pts) were reduced to 0.55 for OS and 0.54 for PFS. For the ≤65 cohort (595 pts), the A-HIPI C-index was stable at 0.75 and 0.62 for OS and PFS, respectively. In terms of A-HIPI calibration in the >65 cohort, CITL and CS were -0.78 and 0.39 for OS vs. -0.98 and 0.80 for PFS. In the ≤65 cohort, CITL and CS were -0.06 and 0.99 for OS vs. 0.21 and 1.18 for PFS. Visual inspection of the calibration plot (Figure 1) for the >65 cohort showed that the A-HIPI underestimated the observed proportion for both OS and PFS (i.e., the observed events were higher than the predicted). Calibration plot for the ≤65 cohort (Figure 2) indicated that the A-HIPI accurately modelled the observed proportion for both OS and PFS (i.e., the predicted probability was consistent with the observed proportion). Conclusion: The newly developed A-HIPI was validated in a large population-based cohort of Danish pts with advanced-stage cHL. Performance of the A-HIPI was high in the ≤65 cohort and superior to the IPS for OS prediction. Calibration of A-HIPI was excellent in pts ages ≤65 years. However, A-HIPI performed significantly worse among pts >65 years and attained poor performance in pts receiving ABVD or BEACOPP, which is not surprising since patients older than 65 were not included in the development. This study emphasizes the importance of further efforts to develop or update models that apply specifically in older individuals. The inclusion of additional variables reflecting comorbidities and pts fitness into a prognostic model may be necessary to effectively deliver accurate prognoses and risk assessments for the older pt.

PURPOSE CNS progression or relapse is an uncommon but devastating complication of aggressive B-cell lymphoma. There is no consensus regarding the optimal approach to CNS prophylaxis. This study was designed to determine whether high-dose methotrexate (HD-MTX) is effective at preventing CNS progression in patients at high risk of this complication. PATIENTS AND METHODS Patients age 18-80 years with aggressive B-cell lymphoma and high risk of CNS progression, treated with curative-intent anti–CD20-based chemoimmunotherapy, were included in this international, retrospective, observational study. Cause-specific hazard ratios (HRs) and cumulative risks of CNS progression were calculated according to use of HD-MTX, with time to CNS progression calculated from diagnosis for all patients (all-pts) and from completion of frontline systemic lymphoma induction therapy, for patients in complete response at completion of chemoimmunotherapy (CR-pts). RESULTS Two thousand four hundred eighteen all-pts (HD-MTX; n = 425) and 1,616 CR-pts (HD-MTX; n = 356) were included. CNS International Prognostic Index was 4-6 in 83.4% all-pts. Patients treated with HD-MTX had a lower risk of CNS progression (adjusted HR, 0.59 [95% CI, 0.38 to 0.90]; P = .014), but significance was not retained when confined to CR-pts (adjusted HR, 0.74 [95% CI, 0.42 to 1.30]; P = .29), with 5-year adjusted risk difference of 1.6% (95% CI, –1.5 to 4.4; all-pts) and 1.4% (95% CI, –1.5 to 4.1; CR-pts). Subgroups were underpowered to draw definitive conclusions regarding the efficacy of HD-MTX in individual high-risk clinical scenarios; however, there was no clear reduction in CNS progression risk with HD-MTX in any high-risk subgroup. CONCLUSION In this large study, high-risk patients receiving HD-MTX had a 7.2% 2-year risk of CNS progression, consistent with the progression risk in previously reported high-risk cohorts. Use of HD-MTX was not associated with a clinically meaningful reduction in risk of CNS progression.