Radiomic Features from PSMA PET for Intraprostatic Tumor Discrimination and Characterization in Patients with Prostate Cancer. a Comparison Study with Histology Reference

Zamboglou, Constantinos; Carles, M.; Fechter, Tobias; Fassbender, T; Ruf, Juri; Bronsert, Peter; Koeber, Goeran; Jilg, Cordula A.; Baltas, Dimos; Mix, Michael; Grosu, Anca L.

doi:10.1016/j.ijrobp.2019.06.1789

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prostate-specific membrane antigen positron emission tomography (PSMA-PET) has recently been established for initial staging in primary PCa patients (3). It is also increasingly used in order to improve intraprostatic lesion detection (4)(5)(6), focal therapy guidance (7) and noninvasive PCa characterization (8). Most of the studies evaluated 68Ga-PSMA-11 as radiopharmaceutical.…”

Section: Introductionmentioning

confidence: 99%

Intraprostatic Tumor Segmentation on PSMA PET Images in Patients with Primary Prostate Cancer with a Convolutional Neural Network

et al. 2020

View full text Add to dashboard Cite

Accurate delineation of the intraprostatic gross tumour volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cancer (PCa). Prostate-specific membrane antigen positron emission tomography (PSMA-PET) may outperform MRI in GTV detection. However, visual GTV delineation underlies interobserver heterogeneity and is time consuming. The aim of this study was to develop a convolutional neural network (CNN) for automated segmentation of intraprostatic tumour (GTV-CNN) in PSMA-PET. Methods:The CNN (3D U-Net) was trained on 68Ga-PSMA-PET images of 152 patients from two different institutions and the training labels were generated manually using a validated technique. The CNN was tested on two independent internal (cohort 1: 68Ga-PSMA-PET, n=18 and cohort 2: 18F-PSMA-PET, n=19) and one external (cohort 3: 68Ga-PSMA-PET, n=20) testdatasets. Accordance between manual contours and GTV-CNN was assessed with Dice-Sørensen coefficient (DSC). Sensitivity and specificity were calculated for the two internal testdatasets (cohort 1: n=18, cohort 2: n=11) by using whole-mount histology.Results: Median DSCs for cohorts 1-3 were 0.84 (range: 0.32-0.95), 0.81 (range: 0.28-0.93) and 0.83 (range: 0.32-0.93), respectively. Sensitivities and specificities for GTV-CNN were comparable with manual expert contours: 0.98 and 0.76 (cohort 1) and 1 and 0.57 (cohort 2), respectively. Computation time was around 6 seconds for a standard dataset. Conclusion:The application of a CNN for automated contouring of intraprostatic GTV in 68Ga-PSMA-and 18F-PSMA-PET images resulted in a high concordance with expert contours and in high sensitivities and specificities in comparison with histology reference. This robust, accurate and fast technique may be implemented for treatment concepts in primary PCa. The trained model and the study's source code are available in an open source repository.

show abstract

Section: Introductionmentioning

confidence: 99%

Intraprostatic Tumor Segmentation on PSMA PET Images in Patients with Primary Prostate Cancer with a Convolutional Neural Network

et al. 2020

View full text Add to dashboard Cite

show abstract

“…PSMA PET-CT is gaining increasing importance for initial prostate cancer staging and has also been employed in radiomics analyses. For example, it was shown that PSMA PET-CT radiomics can distinguish between intermediate and high-risk prostate cancer [174]. Although this may have the potential to better select patients for tumor biopsies and treatment approach, such a methodology is not yet used in routine clinical practice for patient management.…”

Section: Clinical Relevance: Impact On Patients Managementmentioning

confidence: 99%

Joint EANM/SNMMI guideline on radiomics in nuclear medicine

Hatt

Krizsán²,

Rahmim

et al. 2022

Eur J Nucl Med Mol Imaging

View full text Add to dashboard Cite

Purpose The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. Methods In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives. Conclusion Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.

show abstract

Radiomic Features from PSMA PET for Intraprostatic Tumor Discrimination and Characterization in Patients with Prostate Cancer. a Comparison Study with Histology Reference

Cited by 2 publications

References 0 publications

Intraprostatic Tumor Segmentation on PSMA PET Images in Patients with Primary Prostate Cancer with a Convolutional Neural Network

Intraprostatic Tumor Segmentation on PSMA PET Images in Patients with Primary Prostate Cancer with a Convolutional Neural Network

Joint EANM/SNMMI guideline on radiomics in nuclear medicine

Contact Info

Product

Resources

About