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European Journal of Nuclear Medicine and Molecular Imaging

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Open Access 01-06-2021 | Prostate Cancer | Original Article

Uncovering the invisible—prevalence, characteristics, and radiomics feature–based detection of visually undetectable intraprostatic tumor lesions in ⁶⁸GaPSMA-11 PET images of patients with primary prostate cancer

Authors: Constantinos Zamboglou, Alisa S. Bettermann, Christian Gratzke, Michael Mix, Juri Ruf, Selina Kiefer, Cordula A. Jilg, Matthias Benndorf, Simon Spohn, Thomas F. Fassbender, Peter Bronsert, Mengxia Chen, Hongqian Guo, Feng Wang, Xuefeng Qiu, Anca-Ligia Grosu

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 6/2021

Abstract

Introduction

Primary prostate cancer (PCa) can be visualized on prostate-specific membrane antigen positron emission tomography (PSMA-PET) with high accuracy. However, intraprostatic lesions may be missed by visual PSMA-PET interpretation. In this work, we quantified and characterized the intraprostatic lesions which have been missed by visual PSMA-PET image interpretation. In addition, we investigated whether PSMA-PET-derived radiomics features (RFs) could detect these lesions.

Methodology

This study consists of two cohorts of primary PCa patients: a prospective training cohort (n = 20) and an external validation cohort (n = 52). All patients underwent ⁶⁸Ga-PSMA-11 PET/CT and histology sections were obtained after surgery. PCa lesions missed by visual PET image interpretation were counted and their International Society of Urological Pathology score (ISUP) was obtained. Finally, 154 RFs were derived from the PET images and the discriminative power to differentiate between prostates with or without visually undetectable lesions was assessed and areas under the receiver-operating curve (ROC-AUC) as well as sensitivities/specificities were calculated.

Results

In the training cohort, visual PET image interpretation missed 134 tumor lesions in 60% (12/20) of the patients, and of these patients, 75% had clinically significant (ISUP > 1) PCa. The median diameter of the missed lesions was 2.2 mm (range: 1–6). Standard clinical parameters like the NCCN risk group were equally distributed between patients with and without visually missed lesions (p < 0.05). Two RFs (local binary pattern (LBP) size-zone non-uniformality normalized and LBP small-area emphasis) were found to perform excellently in visually unknown PCa detection (Mann-Whitney U: p < 0.01, ROC-AUC: ≥ 0.93). In the validation cohort, PCa was missed in 50% (26/52) of the patients and 77% of these patients possessed clinically significant PCa. The sensitivities of both RFs in the validation cohort were ≥ 0.8.

Conclusion

Visual PSMA-PET image interpretation may miss small but clinically significant PCa in a relevant number of patients and RFs can be implemented to uncover them. This could be used for guiding personalized treatments.

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Title: Uncovering the invisible—prevalence, characteristics, and radiomics feature–based detection of visually undetectable intraprostatic tumor lesions in 68GaPSMA-11 PET images of patients with primary prostate cancer
Authors: Constantinos Zamboglou
Alisa S. Bettermann
Christian Gratzke
Michael Mix
Juri Ruf
Selina Kiefer
Cordula A. Jilg
Matthias Benndorf
Simon Spohn
Thomas F. Fassbender
Peter Bronsert
Mengxia Chen
Hongqian Guo
Feng Wang
Xuefeng Qiu
Anca-Ligia Grosu
Publication date: 01-06-2021
Publisher: Springer Berlin Heidelberg
Keywords: Prostate Cancer
Prostate Cancer
Positron Emission Tomography
Published in: European Journal of Nuclear Medicine and Molecular Imaging / Issue 6/2021
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-020-05111-3

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Uncovering the invisible—prevalence, characteristics, and radiomics feature–based detection of visually undetectable intraprostatic tumor lesions in ⁶⁸GaPSMA-11 PET images of patients with primary prostate cancer

Abstract

Introduction

Methodology

Results

Conclusion

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Introduction

Methodology

Results

Conclusion

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