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.

Keywords: 18F-FDG PET/CT; Mantle cell lymphoma; computer-aided detection; convolutional neural network; deep learning.

Figure 1

Flowchart of patient inclusion and exclusion of the study.

Figure 2

Framework of the constructed DLCNN (left) and a convolutional block of the Xception encoder (right). The inputs are the curated PET and CT images. For each input channel, resolutions of the feature maps after the ReLU activation layers are 128 × 128, 64 × 64, 32 × 32, 16 × 16, and 8 × 8. Starting from the bottom layer, the feature maps are first resized, convoluted, and concatenated with the upper level feature maps from each channel. The concatenated feature maps from the PET and CT channels are then joined to form the PET/CT feature map, which was resized, convoluted, and connected with the next level feature maps.

Figure 3

Mantle cell lymphoma detection of a 45-year-old male patient. The scan was acquired within our institution. From left to right, the columns are ¹⁸F-FDG PET image, PET/CT images, reference standard contours, and prediction maps. White arrows on the reference standard images indicate false negatives, orange arrows on prediction maps indicate false positives, and blue arrows indicate over-prediction covering small lesions. The scale bars on the left represent SUV, and those on the right (ranging between 0 and 1) represent pixel-wise probabilities of being lesions. On the coronal slice, there is a false negative in the abdomen. The false positives are believed to be results of elevated physiologic and inflammatory activities.

Figure 4

Mantle cell lymphoma detection of a 56-year-old male patient. The scan was acquired from an outside institution. From left to right, the columns are ¹⁸F-FDG PET image, PET/CT images, the reference standard contours, and prediction maps. Orange arrows on prediction maps indicate false positives, and blue arrows indicate over-prediction covering small lesions. The scale bars on the left represent SUV, and those on the right (ranging between 0 and 1) represent pixel-wise probabilities of being lesions. All lesions were detected, and a false positive was found near the neck.

Figure 5

Free-response receiver operating characteristic of the DLCNN. The sensitivity was plotted against the false positives per patient as the confidence threshold decreased from 90% to 10% with a step of 10%. As the threshold decreased, the sensitivity increased at a cost of increased false positives per patient. The error bars in both directions are 95% confidence intervals.