Okcu, Izel

Background Primary mediastinal large B-cell lymphoma (PMBCL) is an uncommon type of non-Hodgkin lymphoma. Clinicopathologic features are distinct from diffuse large B cell lymphoma, but it shares some clinical and biologic features with Nodular sclerosis classic Hodgkin lymphoma. Central nervous system (CNS) relapse appears to be rare in patients with PMBCL, with limited literature reporting a rate of approximately 1.5% to 4%. We examined the incidence of CNS relapse, treatment approaches and survival outcomes in patients with PMBCL. Methods Patients with newly diagnosed PMBCL seen at Mayo Clinic between 2002 and 2022 were identified using the prospective Mayo Clinic Lymphoma Database. Clinical features, treatment details and outcomes were abstracted from the medical record. Cumulative incidence of CNS relapse was analyzed with death as a competing risk. The association of CNS-IPI, systemic treatment, and CNS prophylaxis approach with CNS relapse rate was analyzed using competing risk models and descriptively. Event-free survival (EFS) for the cohort was defined as time from PMBCL diagnosis to first recorded CNS and/or systemic relapse or death from any cause. Overall survival (OS) was defined as time from PMBCL diagnosis (or CNS relapse) to death from any cause. Survival analysis was performed using the Kaplan-Meier method. Results A total of 124 PMBCL cases were identified. The median age at diagnosis was 37 years and 61% were female. The distribution of CNS-IPI was low-risk in 67 (61%), intermediate-risk in 41 (37%), and high-risk in 2 (2%). 64 patients (52%) received R-CHOP and 54 patients (44%) received R-DA-EPOCH as frontline therapy. High-dose methotrexate (HD-MTX) for CNS prophylaxis was administered to 6 patients (5%), 5 with R-CHOP and 1 with R-DA-EPOCH. 16 patients received intrathecal (IT) chemotherapy (methotrexate and/or cytarabine) for CNS prophylaxis, 3 with R-CHOP and 13 with R-DA-EPOCH. The median follow-up was 56.0 months. The 5-year EFS rate was 75.1% and the 5-year OS rate was 90.7% for the entire cohort. 20 patients had systemic relapse only and 3 had CNS relapse. The cumulative incidence of CNS relapse was 1.84% (95% CI 0.46%-7.17%) at 2 years and 2.92% (95% CI 0.50%-8.84%) at 5 years (Figure 1). No statistically significant association of CNS-IPI, systemic therapy or CNS prophylaxis regimen with CNS relapse rate was found in competing risk models; however, this analysis was limited by a small event number (n=3). Among the 3 patients with CNS relapse, 1 had high-risk CNS-IPI and 2 had intermediate risk. All 3 patients were treated initially with R-CHOP. Two patients did not receive any CNS prophylaxis and 1 patient received IT CNS prophylaxis. All three patients who had CNS relapse were female. Patient #1 was diagnosed at age 48 and had simultaneous CNS and systemic relapse 6 months later. The patient was treated with HD-MTX, temozolomide and rituximab for CNS relapse, and subsequently received multiple therapies for systemic disease including R-DHAP, R-ICE, and died 17 months after CNS relapse. Patient #2 was diagnosed at age 22 and had CNS relapse after 8.7 months, followed by systemic relapse 2 months later. The patient received HD-MTX, temozolomide and rituximab for CNS relapse, followed by systemic and whole-brain radiation therapy (RT) for both systemic and CNS relapse. Subsequently the patient received salvage chemotherapy, autologous and allogeneic stem cell transplant for systemic relapse. The patient died 26.8 months after CNS relapse. Patient #3 was diagnosed at age 24 and had systemic relapse after 20.7 months, followed by CNS relapse 5.4 months later. The patient received methotrexate and whole-brain RT following CNS relapse and died 1.2 months after CNS relapse. Besides these 3 patients, we identified 6 PMBCL patients with CNS relapse who were diagnosed at other medical centers and subsequently referred to our institution for management of refractory disease. The survival after CNS relapse was poor, with a median OS of 10.4 months (95% CI 5.6-15.2). Conclusion Our study showed that the CNS relapse rate (1.84% at 2 years and 2.92% at 5 years) was overall low in patients with PMBCL, consistent with previous reports. CNS relapse was associated with poor survival despite aggressive treatment approaches. Novel approaches utilizing biological risk factors for CNS relapse, such as circulating tumor DNA, are needed to identify patients with CNS relapse.

Background: Relapsed DLBCL occurs in up to 40% of patients and has a poor prognosis. Current prognostic tools such as the international prognostic index (IPI) and newer forms (NCCN-IPI) only include baseline clinical features. PET/CT features have not yet been incorporated into prognostic risk models despite being essential for staging and response assessment. Deep learning computer algorithms have consistently demonstrated the ability to analyze imaging data and identify features that are not readily apparent to the human eye. Using a deep learning computer vision model that automatically extracted radiomic features from PET/CTs, we examined the association of changes between baseline PET and interim PET (PET2) radiomic features on survival outcomes and compared the prognostic performance with NCCN-IPI. Methods: Adult patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL) between 2005 and 2015 and enrolled in the prospective MER cohort were included. Native DICOM images from FDG PET/CT scans at baseline and interim PET (PET2, after 2-3 cycles of chemotherapy) were preprocessed for regularity and then segmented using the nnU-Net architecture with an external lymphoma-specific model. Feature extraction and standard uptake value (SUV) characterization were accomplished with PyRadiomics and NiBabel. Our computational method included no manual segmentation correction. Individual radiomics features (n=115) and the % change above (Δ High) and below (Δ low) 50 th quartile from baseline to PET2 were evaluated for association with event free survival (EFS) and overall survival (OS) using a univariate Cox regression model and by Kaplan Meier analysis. EFS was defined as time from PET2 to first systemic relapse or death from any cause. OS was defined as time from PET2 to death from any cause. Additionally, Spearman and Pearson correlation matrices were used to guide feature selection while minimizing collinearity. We then assessed the association of the highest scoring radiomics features with EFS in a multivariate cox regression model. Results: A total of 506 patients had baseline and PET2 scans available for radiomic feature extraction. Median age at diagnosis was 61 years (range 18-90), 43% were females. NCCN-IPI composition and outcomes of our cohort was comparable to the previously published NCCN cohort, with patient distribution and 5-year OS as follows: Low (9%, 5-y OS 83%), Low-intermediate (38%, 5-y OS 49%), High-intermediate (41%, 5-y OS 31%), High (11%, 5-y OS 12%). At a median follow up of 81 months,there were 308 events, and272 patients had died.Several radiomics features were associated with EFS including change in tumor surface area (SA Δ) (HR: 1.75, 95% CI 1.36-2.24, p<0.005, median of 26,927 mm 2, Figure 1A) and change in total metabolic tumor volume (TMTV Δ) (HR: 1.64, 95% CI 1.27-2.10, p<0.005, median of 120,473 mm 3, Figure 1B). As a reference, NCCN-IPI univariate Cox regression analysis of EFS produced a Harrell's C-statistic (c) of 0.624. Single radiomics features of SA Δ and TMTV Δ individually produced unadjusted c-statistics of 0.616 and 0.614 respectively. Combinations of radiomics features (SA Δ, TMTV Δ and NCCN-IPI classification) demonstrated cumulative benefit (c=0.652). Conclusion: Automatic PET-CT radiomic feature extraction using deep learning models is feasible among patients with newly diagnosed DLBCL. The change in tumor SA and TMTV appear to be promising radiomic biomarkers. A low decline in either tumor SA or TMTV by PET2 is associated with higher risk of relapse and poor survival outcomes. As individual characteristics, these features are similar prognostic indicators of EFS as the NCCN-IPI. Further investigation into risk stratification when combining radiomics with clinical and genomic variables is warranted. Figure 1. Event free survival for DLBCL patients with A) a change in tumor surface area by PET2 less than 50% percentile (SA_Δ Low) and greater than 50 th percentile (SA_Δ High) and B) a change in TMTV by PET2 less than 50% percentile (TMTV_Δ Low) and greater than 50 th percentile (TMTV_Δ High).