Automated Lugano Metabolic Response Assessment in F-Fluorodeoxyglucose-Avid Non-Hodgkin Lymphoma With Deep Learning on F-Fluorodeoxyglucose-Positron Emission Tomography.

Purpose: Artificial intelligence can reduce the time used by physicians on radiological assessments. For F-fluorodeoxyglucose-avid lymphomas, obtaining complete metabolic response (CMR) by end of treatment is prognostic.

Methods: Here, we present a deep learning-based algorithm for fully automated treatment response assessments according to the Lugano 2014 classification.

A comparison of the prognostic performance of the Lugano 2014 and RECIL 2017 response criteria in patients with NHL from the phase III GOYA and GALLIUM trials.

The Lugano 2014 criteria are the standard for response assessment in lymphoma. We compared the prognostic performance of Lugano 2014 and the more recently developed response evaluation criteria in lymphoma (RECIL 2017), which relies primarily on computed tomography and uses unidimensional measurements, in patients with previously untreated diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) from the phase III GOYA and GALLIUM trials, respectively. Concordance between responses according to the Lugano 2014 and RECIL 2017 criteria was analyzed.

Positron Emission Tomography-Adapted Therapy in Bulky Stage I/II Classic Hodgkin Lymphoma: CALGB 50801 (Alliance).

Purpose: Patients with bulky stage I/II classic Hodgkin lymphoma (cHL) are typically treated with chemotherapy followed by radiation. Late effects associated with radiotherapy include increased risk of second cancer and cardiovascular disease. We tested a positron emission tomography (PET)-adapted approach in patients with bulky, early-stage cHL, omitting radiotherapy in patients with interim PET-negative (PET-) disease and intensifying treatment in patients with PET-positive (PET+) disease.