Fully Convolutional Neural Network to Assess Skeleton Tumor Burden in Prostate Cancer Using 68Ga-PSMA-11 PET/CT: Preliminary Results

Tetteh, G.; Gafita, A.; Xu, L.; Zhao, Y.; Dong, C.; Rominger, Axel Oliver; Shi, Kuangyu; Zimmer, C.; Menze, B.H.; Eiber, M. (2018). Fully Convolutional Neural Network to Assess Skeleton Tumor Burden in Prostate Cancer Using 68Ga-PSMA-11 PET/CT: Preliminary Results. European journal of nuclear medicine and molecular imaging, 45(S1), S41-S41. Springer-Verlag

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Aim: Treatment of bone metastases plays an important role for
patients with metastatic prostate cancer (mPC). PSMA-based
PET imaging is increasingly used to delineate bone tumor burden
before therapy. Bone scan Index (BSI) and Bone- PET-Index
(BPI) are promising in assessing treatment outcome in patients
with mPC. However, the semiautomatic segmentation using
conventional methods in skeleton tumor burden assessment
can be time-consuming and lengthy. The emerging of deep
learning methods have provided great potential to extend the
limit of conventional methods. We aimed to assess the feasibility
of deep learning network in bone lesion delineation and tumor
burden quantification. Methods and Materials: A fully convoconvolutional
neural network (FCNN) concept was proposed to automatically
detect bone lesions and characterize osseous tumor
burden from 68Ga-PSMA-11 PET/CT imaging. A pipeline of two
FCNNs was employed in a cascaded form. The first part of the
cascaded network generates bone mask from CT images as anatomical
regions of interest (ROI), while the second part detects
and segments bone lesions based on PET imaging restricted to
the anatomical regions within the generated bone mask. For
proof-of-concept test, 50 68Ga-PSMA-11 PET/CT from patients
with mPC were included. SUV of PET images were calculated
and the bone lesions were semi-automatically annotated using
an in-house developed software. Forty 68Ga-PSMA-11 PET/
CT scans were used as training data set for the FCNN and the
remaining 10 scans were used as test dataset for performance
assessment. The performance of the developed method was
evaluated by considering the overall segmentation result in the
form of a slice-wise lesion detection accuracy and Dice score, including
the Recall and Precision scores. Results: The developed
deep learning method has achieved a slice-wise detection accuracy
of 91% with a positive predictive value (PPV) of 78%. The
average segmentation Dice score was 76%, with a Recall and
Precision scores of 86% and 66%, respectively. Conclusion: Our
results highlight that even in a small size training dataset, deep
learning can successfully detect bone lesions in a 68Ga-PSMA-11
PET/CT setting. A higher accuracy for lesion segmentation
should be obtained by increasing the number of training dataset
and providing physiological lesion contouring to guide the
training process. Accurate bone lesions detection and segmentation
could be further implemented in the treatment setting.

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