Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on 68Ga-Pentixafor PETCT Imaging Using Deep Learning Methods

الملخص EN

The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM).

68Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes.

However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone.

It is even more difficult to identify lesions with a large heterogeneity.

This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner.

Two convolutional neural networks (CNNs), V-Net and W-Net, were adopted to segment and detect the lesions.

The feasibility of deep learning for lesion detection on 68Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods.

Then the proposed methods were evaluated on real 68Ga-Pentixafor PET/CT scans of MM patients.

The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, and W-Net obtained the best result for segmentation and lesion detection.

It also outperformed traditional machine learning methods such as random forest classifier (RF), k-Nearest Neighbors (k-NN), and support vector machine (SVM).

The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study.