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Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with Lymphoma
Amy J Weisman1, Minnie W Kieler1, Scott B Perlman1
1Department of Medical Physics, University of Wisconsin-Madison, Madison Wis (A.J.W., R.J.); Department of Radiology, Wisconsin Institutes for Medical Research, University of Wisconsin-Madison, 1111 Highland Ave, Room 1005, Madison, WI 53705 (M.W.K., S.B.P., T.J.B.); Department of Hematology, Rigshospitalet, Copenhagen, Denmark (M.H.); Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia (R.J.); and Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Va (L.K.).
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.© RSNA, 2020.
