A Deep Learning-Aided Automated Method for Calculating Metabolic Tumor Volume in Diffuse Large B-Cell Lymphoma
by
, , , and
Russ A. Kuker
1,
David Lehmkuhl
1,
Deukwoo Kwon
2,
Weizhao Zhao
3,
Izidore S. Lossos
4,
Craig H. Moskowitz
4,
Juan Pablo Alderuccio
4,*,† and
Fei Yang
5,*,† 1
Department of Radiology, Division of Nuclear Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA
2
Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA
3
Department of Biomedical Engineering, University of Miami, Coral Gables, FL 33146, USA
4
Sylvester Comprehensive Cancer Center, Department of Medicine, Division of Hematology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
5
Sylvester Comprehensive Cancer Center, Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33136, USA
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cancers 2022, 14(21), 5221; https://doi.org/10.3390/cancers14215221
Submission received: 23 August 2022
/
Revised: 18 October 2022
/
Accepted: 20 October 2022
/
Published: 25 October 2022
(This article belongs to the Special Issue Recent Advances in Deep Learning and Medical Imaging for Cancer Treatment)
Simple Summary
In recent years metabolic tumor volume (MTV) has been shown to predict outcomes in lymphoma. However, the current methods used to measure MTV are time-consuming and require manual input from the nuclear medicine reader. Therefore, we aimed to develop a deep-learning-aided automated method to calculate MTV. We tested this approach in 100 patients with diffuse large B-cell lymphoma enrolled in a clinical trial cohort. We observed a high correlation between nuclear medicine readers and the automated method, underscoring the potential of this approach to integrate PET-based biomarkers in clinical research.
Abstract
Metabolic tumor volume (MTV) is a robust prognostic biomarker in diffuse large B-cell lymphoma (DLBCL). The available semiautomatic software for calculating MTV requires manual input limiting its routine application in clinical research. Our objective was to develop a fully automated method (AM) for calculating MTV and to validate the method by comparing its results with those from two nuclear medicine (NM) readers. The automated method designed for this study employed a deep convolutional neural network to segment normal physiologic structures from the computed tomography (CT) scans that demonstrate intense avidity on positron emission tomography (PET) scans. The study cohort consisted of 100 patients with newly diagnosed DLBCL who were randomly selected from the Alliance/CALGB 50,303 (NCT00118209) trial. We observed high concordance in MTV calculations between the AM and readers with Pearson’s correlation coefficients and interclass correlations comparing reader 1 to AM of 0.9814 (p < 0.0001) and 0.98 (p < 0.001; 95%CI = 0.96 to 0.99), respectively; and comparing reader 2 to AM of 0.9818 (p < 0.0001) and 0.98 (p < 0.0001; 95%CI = 0.96 to 0.99), respectively. The Bland–Altman plots showed only relatively small systematic errors between the proposed method and readers for both MTV and maximum standardized uptake value (SUVmax). This approach may possess the potential to integrate PET-based biomarkers in clinical trials.
