Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with Lymphoma
Amy J. Weisman, Minnie W. Kieler, Scott B. Perlman, Martin Hutchings, Robert Jeraj, Lale Kostakoglu, Tyler J. Bradshaw
An ensemble of three-dimensional convolutional neural networks was implemented to detect lymph nodes with lymphoma involvement in a group of 90 adult patients with lymphoma, which achieved a detection performance nearly comparable to the differences between two physicians’ annotations.
Purpose: To automatically detect lymph nodes involved in lymphoma on fluorine 18 (18F) fluorodeoxyglucose (FDG) PET/CT images using convolutional neural networks (CNNs).
Materials and Methods: In this retrospective study, baseline disease of 90 patients with lymphoma was segmented on 18F-FDG PET/CT images (acquired between 2005 and 2011) by a nuclear medicine physician. An ensemble of three-dimensional patch-based, multiresolution pathway CNNs was trained using fivefold cross-validation. Performance was assessed using the true-positive rate (TPR) and number of false-positive (FP) findings. CNN performance was compared with agreement between physicians by comparing the annotations of a second nuclear medicine physician to the first reader in 20 of the patients. Patient TPR was compared using Wilcoxon signed rank tests.
Results: Across all 90 patients, a range of 0–61 nodes per patient was detected. At an average of four FP findings per patient, the method achieved a TPR of 85% (923 of 1087 nodes). Performance varied widely across patients (TPR range, 33%–100%; FP range, 0–21 findings). In the 20 patients labeled by both physicians, a range of 1–49 nodes per patient was detected and labeled. The second reader identified 96% (210 of 219) of nodes with an additional 3.7 per patient compared with the first reader. In the same 20 patients, the CNN achieved a 90% (197 of 219) TPR at 3.7 FP findings per patient.
Conclusion: An ensemble of three-dimensional CNNs detected lymph nodes at a performance nearly comparable to differences between two physicians’ annotations. This preliminary study is a first step toward automated PET/CT assessment for lymphoma.
Read Full Article Here: https://doi.org/10.1148/ryai.2020200016