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To evaluate the association between radiomic features (RFs) extracted from 18F‐FDG PET/CT (18F‐FDG‐PET) with progression‐free survival (PFS) and overall survival (OS) in diffuse large‐B‐cell lymphoma (DLBCL) patients eligible to first‐line chemotherapy. DLBCL patients who underwent 18F‐FDG‐PET prior to first‐line chemotherapy were retrospectively analyzed. RFs were extracted from the lesion showing the highest uptake. A radiomic score to predict PFS and OS was obtained by multivariable Elastic Net Cox model. Radiomic univariate model, clinical and combined clinical‐radiomic multivariable models to predict PFS and OS were obtained. 112 patients were analyzed. Median follow‐up was 34.7 months (Inter‐Quartile Range (IQR) 11.3–66.3 months) for PFS and 41.1 (IQR 18.4–68.9) for OS. Radiomic score resulted associated with PFS and OS (p < 0.001), outperforming conventional PET parameters. C‐index (95% CI) for PFS prediction were 0.67 (0.58–0.76), 0.81 (0.75–0.88) and 0.84 (0.77–0.91) for clinical, radiomic and combined clinical‐radiomic model, respectively. C‐index for OS were 0.77 (0.66–0.89), 0.84 (0.76–0.91) and 0.90 (0.81–0.98). In the Kaplan‐Meier analysis (low‐IPI vs. high‐IPI), the radiomic score was significant predictor of PFS (p < 0.001). The radiomic score was an independent prognostic biomarker of survival in DLBCL patients. The extraction of RFs from baseline 18F‐FDG‐PET might be proposed in DLBCL to stratify high‐risk versus low‐risk patients of relapse after first‐line therapy, especially in low‐IPI patients. [ABSTRACT FROM AUTHOR]

Featured Application: The present work contributes to the investigation on the robustness and reproducibility of radiomic studies, a fundamental step for bringing radiomics towards clinical implementation. Radiomic analysis of 18F[FDG] PET/CT images might identify predictive imaging biomarkers, however, the reproducibility of this quantitative approach might depend on the methodology adopted for image analysis. This retrospective study investigates the impact of PET segmentation method and the selection of different target lesions on the radiomic analysis of baseline 18F[FDG] PET/CT images in a population of newly diagnosed diffuse large B-cell lymphoma (DLBCL) patients. The whole tumor burden was segmented on PET images applying six methods: (1) 2.5 standardized uptake value (SUV) threshold; (2) 25% maximum SUV (SUVmax) threshold; (3) 42% SUVmax threshold; (4) 1.3∙liver uptake threshold; (5) intersection among 1, 2, 4; and (6) intersection among 1, 3, 4. For each method, total metabolic tumor volume (TMTV) and whole-body total lesion glycolysis (WTLG) were assessed, and their association with survival outcomes (progression-free survival PFS and overall survival OS) was investigated. Methods 1 and 2 provided stronger associations and were selected for the next steps. Radiomic analysis was then performed on two target lesions for each patient: the one with the highest SUV and the largest one. Fifty-three radiomic features were extracted, and radiomic scores to predict PFS and OS were obtained. Two proportional-hazard regression Cox models for PFS and OS were developed: (1) univariate radiomic models based on radiomic score; and (2) multivariable clinical–radiomic model including radiomic score and clinical/diagnostic parameters (IPI score, SUVmax, TMTV, WTLG, lesion volume). The models were created in the four scenarios obtained by varying the segmentation method and/or the target lesion; the models' performances were compared (C-index). In all scenarios, the radiomic score was significantly associated with PFS and OS both at univariate and multivariable analysis (p < 0.001), in the latter case in association with the IPI score. When comparing the models' performances in the four scenarios, the C-indexes agreed within the confidence interval. C-index ranges were 0.79–0.81 and 0.80–0.83 for PFS radiomic and clinical–radiomic models; 0.82–0.87 and 0.83–0.90 for OS radiomic and clinical–radiomic models. In conclusion, the selection of either between two PET segmentation methods and two target lesions for radiomic analysis did not significantly affect the performance of the prognostic models built on radiomic and clinical data of DLBCL patients. These results prompt further investigation of the proposed methodology on a validation dataset. [ABSTRACT FROM AUTHOR]

Host immune homeostasis as an independent prognostic indicator has been inadequately evaluated in aggressive non-Hodgkin's lymphomas (NHL). The present study addresses the prognostic significance in aggressive NHLs of the immunologic profile evaluated by pretreatment serum levels of immunoglobulins (Ig) and lymphocyte-monocyte ratio (LMR). In this series of 90 patients with aggressive lymphoma, the median level for IgG was 1,024mg/dl (range 436-2236), and for LMR was 2.2 (range 0.2-13.8). CR rate was higher with IgG levels =1,024mg/dL (91% vs 77% p=0.059). LMR = 2.2 was associated with lower 1-year PFS (73% vs. 92%, p 0.016). Patients with good/very good R-IPI showed a reduced PFS if IgG or LMR was low, while patients with poor R-IPI did better if LMR or IgG levels were high. We combined both parameters with the RIPI and produced a four-risk prognostic score showing one-year PFS of 95% (95% CI 68%-99%), 100% (95% CI 100%-100%), 73% (95% CI 52%-86%), and 59% (95% CI 31%-79%), in patients with zero, one, two and three risk factors, respectively. The results indicate for the first time the value of baseline serum Ig levels in the prognostic assessment of aggressive lymphoma. [ABSTRACT FROM AUTHOR]

This monocentric retrospective study included 70 consecutive relapsed/refractory Hodgkin lymphoma (RR-HL) patients receiving reduced-intensity allogeneic stem cell transplantation (alloSCT). We evaluated overall and progression-free survival (OS, PFS), graft-versus host disease/relapse-free survival (GFRS), and chronic GVHD-free OS (cGVHD-free OS) defined as OS without moderate-to-severe cGVHD. Patients had a median age of 33 years (range, 18-60 years), 23% had refractory disease (SD/PD). Donors were HLA identical (39%), unrelated (30%), or haploidentical (31%). Median follow-up was 6.2 years. Five-year OS was 59% and PFS was 49%. NRM was 16% at 1 year. 44% of patients had cGVHD, and 14% moderate-to-severe cGVHD at last follow-up. GFRS and cGVHD-free OS were 26 and 48% at 5 years. In multivariate analysis, resistant disease at alloSCT impacted survival and GFRS. In conclusion, disease response before alloSCT impacts survival and GFRS. GVHD outcomes may help comparing the long-term effects of the new salvage treatments that bridge patients to alloSCT. [ABSTRACT FROM AUTHOR]

To evaluate the association between radiomic features (RFs) extracted from 18 F-FDG PET/CT ( 18 F-FDG-PET) with progression-free survival (PFS) and overall survival (OS) in diffuse large-B-cell lymphoma (DLBCL) patients eligible to first-line chemotherapy. DLBCL patients who underwent 18 F-FDG-PET prior to first-line chemotherapy were retrospectively analyzed. RFs were extracted from the lesion showing the highest uptake. A radiomic score to predict PFS and OS was obtained by multivariable Elastic Net Cox model. Radiomic univariate model, clinical and combined clinical-radiomic multivariable models to predict PFS and OS were obtained. 112 patients were analyzed. Median follow-up was 34.7 months (Inter-Quartile Range (IQR) 11.3-66.3 months) for PFS and 41.1 (IQR 18.4-68.9) for OS. Radiomic score resulted associated with PFS and OS (p < 0.001), outperforming conventional PET parameters. C-index (95% CI) for PFS prediction were 0.67 (0.58-0.76), 0.81 (0.75-0.88) and 0.84 (0.77-0.91) for clinical, radiomic and combined clinical-radiomic model, respectively. C-index for OS were 0.77 (0.66-0.89), 0.84 (0.76-0.91) and 0.90 (0.81-0.98). In the Kaplan-Meier analysis (low-IPI vs. high-IPI), the radiomic score was significant predictor of PFS (p < 0.001). The radiomic score was an independent prognostic biomarker of survival in DLBCL patients. The extraction of RFs from baseline 18 F-FDG-PET might be proposed in DLBCL to stratify high-risk versus low-risk patients of relapse after first-line therapy, especially in low-IPI patients., (© 2023 The Authors. Hematological Oncology published by John Wiley & Sons Ltd.)