Predictive Value of PET Response Combined with Baseline Metabolic Tumor Volume in Peripheral T-Cell Lymphoma Patients.

Peripheral T-cell lymphoma (PTCL) is a heterogeneous group of aggressive non-Hodgkin lymphomas with poor outcomes on current therapy. We investigated whether response assessed with PET/CT combined with baseline total metabolic tumor volume (TMTV) could detect early relapse or refractory disease. Methods: From 7 European centers, 140 patients with nodal PTCL who underwent baseline PET/CT were selected. Forty-three had interim PET (iPET) performed after 2 cycles (iPET2), 95 had iPET performed after 3 or 4 cycles (iPET3/4), and 96 had end-of-treatment PET (eotPET). Baseline TMTV was computed with a 41% SUV <sub>max</sub> threshold, and PET response was reported using the Deauville 5-point scale. Results: With a median of 43 mo of follow-up, the 2-y progression-free survival (PFS) and overall survival (OS) were 51% and 67%, respectively. iPET2-positive patients (Deauville score ≥ 4) had a significantly worse outcome than iPET2-negative patients (P < 0.0001, hazard ratio of 6.8 for PFS; P < 0.0001, hazard ratio of 6.6 for OS). The value of iPET3/4 was also confirmed for PFS (P < 0.0001) and OS (P < 0.0001). The 2-y PFS and OS for iPET3/4-positive (n = 28) and iPET3/4-negative (n = 67) patients were 16% and 32% versus 75% and 85%, respectively. The eotPET results also reflected patient outcome. A model combining TMTV and iPET3/4 stratified the population into distinct risk groups (TMTV ≤ 230 cm <sup>3</sup> and iPET3/4-negative [2-y PFS/OS, 79%/85%]; TMTV > 230 cm <sup>3</sup> and iPET3/4-negative [59%/84%]; TMTV ≤ 230 cm <sup>3</sup> and iPET3/4-positive [42%/50%]; TMTV > 230 cm <sup>3</sup> and iPET3/4-positive [0%/18%]). Conclusion: iPET response is predictive of outcome and allows early detection of high-risk PTCL patients. Combining iPET with TMTV improves risk stratification in individual patients

Focal skeletal FDG uptake indicates poor prognosis in cHL regardless of extent and first-line chemotherapy

18F-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (FDG-PET/CT) is used for staging classical Hodgkin lymphoma (cHL) with high sensitivity for skeletal involvement. However, it is unclear whether a single bone lesion carries the same adverse prognosis as multifocal lesions and if this is affected by type of chemotherapy [ABVD (adriamycin, bleomycin, vincristine, dacarbazine) versus BEACOPP (bleomycin, etoposide, adriamycin, cyclophosphamide, vincristine, procarbazine, prednisone)]. We reviewed the clinico-pathological and outcome data from 209 patients with newly diagnosed cHL staged by FDG-PET/CT. Patterns of skeletal/bone marrow uptake (BMU) were divided into ‘low’ and ‘high’ diffuse BMU (i.e. without focal lesions), and unifocal or multifocal lesions. Additional separate survival analysis was performed, taking type of chemotherapy into account. Forty patients (19·2%) had skeletal lesions (20 unifocal, 20 multifocal). The 3-year progression-free-survival (PFS) was 80% for patients with ‘low BMU’, 87% for ‘high BMU’, 69% for ‘unifocal’ and 51% for ‘multifocal’ lesions; median follow-up was 38 months. The presence of bone lesions, both uni- and multifocal, was associated with significantly inferior PFS (log rank P = 0·0001), independent of chemotherapy type. Thus, increased diffuse BMU should not be considered as a risk factor in cHL, whereas unifocal or multifocal bone lesions should be regarded as important predictors of adverse outcome, irrespective of the chemotherapy regimen used.</p

Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments

Abstract Background Current radiological assessments of 18fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging data in diffuse large B-cell lymphoma (DLBCL) can be time consuming, do not yield real-time information regarding disease burden and organ involvement, and hinder the use of FDG-PET to potentially limit the reliance on invasive procedures (e.g. bone marrow biopsy) for risk assessment. Methods Our aim is to enable real-time assessment of imaging-based risk factors at a large scale and we propose a fully automatic artificial intelligence (AI)-based tool to rapidly extract FDG-PET imaging metrics in DLBCL. On availability of a scan, in combination with clinical data, our approach generates clinically informative risk scores with minimal resource requirements. Overall, 1268 patients with previously untreated DLBCL from the phase III GOYA trial (NCT01287741) were included in the analysis (training: n = 846; hold-out: n = 422). Results Our AI-based model comprising imaging and clinical variables yielded a tangible prognostic improvement compared to clinical models without imaging metrics. We observed a risk increase for progression-free survival (PFS) with hazard ratios [HR] of 1.87 (95% CI: 1.31–2.67) vs 1.38 (95% CI: 0.98–1.96) (C-index: 0.59 vs 0.55), and a risk increase for overall survival (OS) (HR: 2.16 (95% CI: 1.37–3.40) vs 1.40 (95% CI: 0.90–2.17); C-index: 0.59 vs 0.55). The combined model defined a high-risk population with 35% and 42% increased odds of a 4-year PFS and OS event, respectively, versus the International Prognostic Index components alone. The method also identified a subpopulation with a 2-year Central Nervous System (CNS)-relapse probability of 17.1%. Conclusion Our tool enables an enhanced risk stratification compared with IPI, and the results indicate that imaging can be used to improve the prediction of central nervous system relapse in DLBCL. These findings support integration of clinically informative AI-generated imaging metrics into clinical workflows to improve identification of high-risk DLBCL patients. Trial Registration Registered clinicaltrials.gov number: NCT01287741. Graphical Abstrac

Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments

Abstract Background Current radiological assessments of 18fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging data in diffuse large B-cell lymphoma (DLBCL) can be time consuming, do not yield real-time information regarding disease burden and organ involvement, and hinder the use of FDG-PET to potentially limit the reliance on invasive procedures (e.g. bone marrow biopsy) for risk assessment. Methods Our aim is to enable real-time assessment of imaging-based risk factors at a large scale and we propose a fully automatic artificial intelligence (AI)-based tool to rapidly extract FDG-PET imaging metrics in DLBCL. On availability of a scan, in combination with clinical data, our approach generates clinically informative risk scores with minimal resource requirements. Overall, 1268 patients with previously untreated DLBCL from the phase III GOYA trial (NCT01287741) were included in the analysis (training: n = 846; hold-out: n = 422). Results Our AI-based model comprising imaging and clinical variables yielded a tangible prognostic improvement compared to clinical models without imaging metrics. We observed a risk increase for progression-free survival (PFS) with hazard ratios [HR] of 1.87 (95% CI: 1.31–2.67) vs 1.38 (95% CI: 0.98–1.96) (C-index: 0.59 vs 0.55), and a risk increase for overall survival (OS) (HR: 2.16 (95% CI: 1.37–3.40) vs 1.40 (95% CI: 0.90–2.17); C-index: 0.59 vs 0.55). The combined model defined a high-risk population with 35% and 42% increased odds of a 4-year PFS and OS event, respectively, versus the International Prognostic Index components alone. The method also identified a subpopulation with a 2-year Central Nervous System (CNS)-relapse probability of 17.1%. Conclusion Our tool enables an enhanced risk stratification compared with IPI, and the results indicate that imaging can be used to improve the prediction of central nervous system relapse in DLBCL. These findings support integration of clinically informative AI-generated imaging metrics into clinical workflows to improve identification of high-risk DLBCL patients. Trial Registration Registered clinicaltrials.gov number: NCT01287741. Graphical Abstrac