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

Sobhan Moazemi, Zain Khurshid, Annette Erle, Susanne Lütje, Markus Essler, Thomas Schultz und Ralph A. Bundschuh
In: Diagnostics (Aug. 2020), 10:9(622)
 

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.

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Bibtex

@ARTICLE{Moazemi:Diag2020,
    author = {Moazemi, Sobhan and Khurshid, Zain and Erle, Annette and L{\"u}tje, Susanne and Essler, Markus and
              Schultz, Thomas and Bundschuh, Ralph A.},
     pages = {622},
     title = {Machine Learning Facilitates Hotspot Classification in PSMA-PET/CT with Nuclear Medicine Specialist
              Accuracy},
   journal = {Diagnostics},
    volume = {10},
    number = {9},
      year = {2020},
     month = aug,
  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.},
       url = {https://www.mdpi.com/2075-4418/10/9/622/htm},
       doi = {10.3390/diagnostics10090622}
}