Machine Learning Facilitates Hotspot Classification in PSMA-PET/CT with Nuclear Medicine Specialist Accuracy

Gallium-68 prostate-specific membrane antigen positron emission tomography (68Ga-PSMA-PET) is a highly sensitive method to detect prostate cancer (PC) metastases. Visual discrimination between malignant and physiologic/unspecific tracer accumulation by a nuclear medicine (NM) specialist is essential...

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Bibliographic Details
Published in:Diagnostics (Basel) 2020-08, Vol.10 (9), p.622
Main Authors: Moazemi, Sobhan, Khurshid, Zain, Erle, Annette, Lütje, Susanne, Essler, Markus, Schultz, Thomas, Bundschuh, Ralph A.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Classification
computed tomography (CT)
Machine learning
machine learning (ML)
Metastasis
Nuclear medicine
Physicians
Physiology
positron emission tomography (PET)
Prostate cancer
prostate cancer (PC)
prostate-specific membrane antigen (PSMA)
Radiation therapy
Software
Support vector machines
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Summary:Gallium-68 prostate-specific membrane antigen positron emission tomography (68Ga-PSMA-PET) is a highly sensitive method to detect prostate cancer (PC) metastases. Visual discrimination between malignant and physiologic/unspecific tracer accumulation by a nuclear medicine (NM) specialist is essential for image interpretation. In the future, automated machine learning (ML)-based tools will assist physicians in image analysis. The aim of this work was to develop a tool for analysis of 68Ga-PSMA-PET images and to compare its efficacy to that of human readers. Five different ML methods were compared and tested on multiple positron emission tomography/computed tomography (PET/CT) data-sets. Forty textural features extracted from both PET- and low-dose CT data were analyzed. In total, 2419 hotspots from 72 patients were included. Comparing results from human readers to those of ML-based analyses, up to 98% area under the curve (AUC), 94% sensitivity (SE), and 89% specificity (SP) were achieved. Interestingly, textural features assessed in native low-dose CT increased the accuracy significantly. Thus, ML based on 68Ga-PSMA-PET/CT radiomics features can classify hotspots with high precision, comparable to that of experienced NM physicians. Additionally, the superiority of multimodal ML-based analysis considering all PET and low-dose CT features was shown. Morphological features seemed to be of special additional importance even though they were extracted from native low-dose CTs.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics10090622