Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin’s lymphoma patients staged with FDG-PET/CT
Artikel i vetenskaplig tidskrift, 2021

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.

Författare

M. Sadik

Sahlgrenska universitetssjukhuset

Jesús López-Urdaneta

Sahlgrenska universitetssjukhuset

Johannes Ulén

Eigenvision AB

Olof Enqvist

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Armin Krupic

Sahlgrenska universitetssjukhuset

Rajender Kumar

Post Graduate Institute of Medical Education and Research

Per Ola Andersson

Södra Älvsborgs Sjukhus (SÄS)

E. Tragardh

Skånes universitetssjukhus (SUS)

Scientific Reports

2045-2322 (ISSN)

Vol. 11 1 10382

Ämneskategorier

Hematologi

Reumatologi och inflammation

Radiologi och bildbehandling

DOI

10.1038/s41598-021-89656-9

Mer information

Senast uppdaterat

2021-06-04