Abstract
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.
Highlights
Computer-aided diagnosis (CAD) based on artificial intelligence (AI) and machine learning (ML)will revolutionize the process of image reading in radiology and nuclear medicine [1]
We have shown that ML algorithms are capable of discriminating between malignant or physiological/unspecific tracer accumulations in 68Ga-prostate-specific membrane antigen (PSMA)-positron emission tomography/computed tomography (PET/computed tomography (CT)) with similar accuracy as
We have shown that ML algorithms are capable of discriminating between malignant or physiological/unspecific tracer accumulations in 68 Ga-PSMA-positron emission tomography (PET)/CT with similar accuracy as achieved by experienced nuclear medicine physicians
Summary
Computer-aided diagnosis (CAD) based on artificial intelligence (AI) and machine learning (ML). Will revolutionize the process of image reading in radiology and nuclear medicine [1]. Innovative tools will assist physicians in handling large data-sets of images more efficiently. A central issue in this context will be the development of tools for the automated classification of lesions to pre-define pathological findings following work-up by the physician. CAD was proposed as early as 1998 for lung nodules in computed tomography (CT) examinations [2]. Many other applications have been described—for example, in mammography [3] and positron emission tomography (PET) [4].
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