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

Artificial Intelligence Increases the Agreement among Physicians Classifying Focal Skeleton/Bone Marrow Uptake in Hodgkin’s Lymphoma Patients Staged with [18F]FDG PET/CT—a Retrospective Study

Purpose

Classification of focal skeleton/bone marrow uptake (BMU) can be challenging. The aim is to investigate whether an artificial intelligence–based method (AI), which highlights suspicious focal BMU, increases interobserver agreement among a group of physicians from different hospitals classifying Hodgkin’s lymphoma (HL) patients staged with [18F]FDG PET/CT.

Methods

Forty-eight patients staged with [18F]FDG PET/CT at Sahlgenska University Hospital between 2017 and 2018 were reviewed twice, 6 months apart, regarding focal BMU. During the second time review, the 10 physicians also had access to AI-based advice regarding focal BMU.

Results

Each physician’s classifications were pairwise compared with the classifications made by all the other physicians, resulting in 45 unique pairs of comparisons both without and with AI advice. The agreement between the physicians increased significantly when AI advice was available, which was measured as an increase in mean Kappa values from 0.51 (range 0.25–0.80) without AI advice to 0.61 (range 0.19–0.94) with AI advice (p = 0.005). The majority of the physicians agreed with the AI-based method in 40 (83%) of the 48 cases.

Conclusion

An AI-based method significantly increases interobserver agreement among physicians working at different hospitals by highlighting suspicious focal BMU in HL patients staged with [18F]FDG PET/CT.

Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin's lymphoma patients staged with FDG-PET/CT

To develop an artificial intelligence (AI)-based method for the detection of focal skeleton/bone marrow uptake (BMU) in patients with Hodgkin’s lymphoma (HL) undergoing staging with FDG-PET/CT. The results of the AI in a separate test group were compared to the interpretations of independent physicians. The skeleton and bone marrow were segmented using a convolutional neural network. The training of AI was based on 153 un-treated patients. Bone uptake significantly higher than the mean BMU was marked as abnormal, and an index, based on the total squared abnormal uptake, was computed to identify the focal uptake. Patients with an index above a predefined threshold were interpreted as having focal uptake. As the test group, 48 un-treated patients who had undergone a staging FDG-PET/CT between 2017–2018 with biopsy-proven HL were retrospectively included. Ten physicians classified the 48 cases regarding focal skeleton/BMU. The majority of the physicians agreed with the AI in 39/48 cases (81%) regarding focal skeleton/bone marrow involvement. Inter-observer agreement between the physicians was moderate, Kappa 0.51 (range 0.25–0.80). An AI-based method can be developed to highlight suspicious focal skeleton/BMU in HL patients staged with FDG-PET/CT. Inter-observer agreement regarding focal BMU is moderate among nuclear medicine physicians.

FDG-PET Imaging for Hodgkin and Diffuse Large B-Cell Lymphoma-An Updated Overview

Since the mid-1990s, ¹⁸F-fluorodeoxglucose (FDG)-positron emission tomography (PET) in combination with computed tomography has come to play a prominent role in the management of malignant lymphomas. One of the first PET applications in oncology was the detection of lymphoma manifestations at staging, where it has shown high sensitivity. Nowadays, this imaging modality is also used during treatment to evaluate the individual chemosensitivity and adapt further therapy accordingly. If the end-of-treatment PET is negative, irradiation in advanced-stage Hodgkin lymphoma patients can be safely omitted after highly effective chemotherapy. Thus far, lymphoma response assessment has mainly been performed using visual criteria, such as the Deauville five-point scale, which became the international standard in 2014. However, novel measures such as metabolic tumor volume or total lesion glycolysis have recently been recognized by several working groups and may further increase the diagnostic and prognostic value of FDG-PET in the future.