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