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

May Sadik; Jesús López-Urdaneta; Johannes Ulén; Olof Enqvist; Per-Ola Andersson; Rajender Kumar; Elin Trägårdh

doi:10.1007/s13139-022-00765-3

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

Nucl Med Mol Imaging. 2023 Apr;57(2):110-116. doi: 10.1007/s13139-022-00765-3. Epub 2022 Aug 19.

Authors

May Sadik¹, Jesús López-Urdaneta¹, Johannes Ulén², Olof Enqvist^{2

3}, Per-Ola Andersson^{4

5}, Rajender Kumar⁶, Elin Trägårdh⁷

Affiliations

¹ Department of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden.
² Eigenvision AB, Malmö, Sweden.
³ Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.
⁴ Department of Haematology, Södra Älvsborg Hospital, Borås, Sweden.
⁵ Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.
⁶ Department of Nuclear Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, India.
⁷ Clinical Physiology and Nuclear Medicine, Lund University and Skåne University Hospital, Malmö, Sweden.

Abstract

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.

Keywords: Artificial intelligence; Bone marrow; Fluorodeoxyglucose F18; Hodgkin disease; Observer variation.