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Deep-learning-based cardiac amyloidosis classification from early acquired pet images

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Abstract

The objective of the present work was to evaluate the potential of deep learning tools for characterizing the presence of cardiac amyloidosis from early acquired PET images, i.e. 15 min after [18F]-Florbetaben tracer injection. 47 subjects were included in the study: 13 patients with transthyretin-related amyloidosis cardiac amyloidosis (ATTR-CA), 15 patients with immunoglobulin light-chain amyloidosis (AL-CA), and 19 control-patients (CTRL). [18F]-Florbetaben PET/CT images were acquired in list mode and data was sorted into a sinogram, covering a time interval of 5 min starting 15 min after the injection. The resulting sinogram was reconstructed using OSEM iterative algorithm. A deep convolutional neural network (CAclassNet) was designed and implemented, consisting of five 2D convolutional layers, three fully connected layers and a final classifier returning AL, ATTR and CTRL scores. A total of 1107 2D images (375 from AL-subtype patients, 312 from ATTR-subtype, and 420 from Controls) have been considered in the study and used to train, validate and test the proposed network. CAclassNet cross-validation resulted with train error mean ± sd of 2.001% ± 0.96%, validation error of 4.5% ± 2.26%, and net accuracy of 95.49% ± 2.26%. Network test error resulted in a mean ± sd values of 10.73% ± 0.76%. Sensitivity, specificity, and accuracy evaluated on the test dataset were respectively for AL-CA sub-type: 1, 0.912, 0.936; for ATTR-CA: 0.935, 0.897, 0.972; for control subjects: 0.809, 0.971, 0.909. In conclusion, the proposed CAclassNet model seems very promising as an aid for the clinician in the diagnosis of CA from cardiac [18F]-Florbetaben PET images acquired a few minutes after the injection.

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Data availability

Acquired PET images, duly anonymized, are made available to anyone interested. Custom code is available to anyone interested.

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Authors and Affiliations

  1. CNR Institute of Clinical Physiology, CNR Research Area—Via Moruzzi, 1, 56124, Pisa, Italy

    Maria Filomena Santarelli

  2. Fondazione Toscana “G. Monasterio”, Pisa, Italy

    Dario Genovesi, Vincenzo Positano, Brunella Favilli, Assuero Giorgetti & Paolo Marzullo

  3. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Michele Scipioni

  4. Scuola Universitaria Superiore ‘S. Anna”, Pisa, Italy

    Giuseppe Vergaro & Michele Emdin

  5. Dipartimento di Ingegneria Dell’Informazione: DII, Pisa University, Pisa, Italy

    Luigi Landini

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Correspondence to Maria Filomena Santarelli.

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The study was approved by the institutional ethics committee and by the AIFA (Agenzia Italiana del Farmaco) committee. The study complied with the Declaration of Helsinki.

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Santarelli, M.F., Genovesi, D., Positano, V. et al. Deep-learning-based cardiac amyloidosis classification from early acquired pet images. Int J Cardiovasc Imaging 37, 2327–2335 (2021). https://doi.org/10.1007/s10554-021-02190-7

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