Computer-aided detection of mantle cell lymphoma on 18F-FDG PET/CT using a deep learning convolutional neural network.

Abstract

¹⁸F-FDG PET/CT can provide quantitative characterization with prognostic value for mantle cell lymphoma (MCL). However, detection of MCL is performed manually, which is labor intensive and not a part of the routine clinical practice. This study investigates a deep learning convolutional neural network (DLCNN) for computer-aided detection of MCL on ¹⁸F-FDG PET/CT. We retrospectively analyzed 142 baseline ¹⁸F-FDG PET/CT scans of biopsy-confirmed MCL acquired between May 2007 and October 2018. Of the 142 scans, 110 were from our institution and 32 were from outside institutions. An Xception-based U-Net was constructed to classify each pixel of the PET/CT images as MCL or not. The network was first trained and tested on the within-institution scans by applying five-fold cross-validation. Sensitivity and false positives (FPs) per patient were calculated for network evaluation. The network was then tested on the outside-institution scans, which were excluded from network training. For the 110 within-institution patients (85 male; median age, 58 [range: 39-84] years), the network achieved an overall median sensitivity of 88% (interquartile range [IQR]: 25%) with 15 (IQR: 12) FPs/patient. Sensitivity was dependent on lesion size and SUV_max but not on lesion location. For the 32 outside-institution patients (24 male; median age, 59 [range: 40-67] years), the network achieved a median sensitivity of 84% (IQR: 24%) with 14 (IQR: 10) FPs/patient. No significant performance difference was found between the within and outside institution scans. Therefore, DLCNN can potentially help with MCL detection on ¹⁸F-FDG PET/CT with high sensitivity and limited FPs.