Enhancing the diagnostic capacity of [18F]PSMA-1007 PET/MRI in primary prostate cancer staging with artificial intelligence and semi-quantitative DCE: an exploratory study
Abstract Background To investigate the ability of artificial intelligence (AI)-based and semi-quantitative dynamic contrast enhanced (DCE) multiparametric MRI (mpMRI), performed within [18F]-PSMA-1007 PET/MRI, in differentiating benign from malignant prostate tissues in patients with primary prostat...
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Springer
2024-11-01
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| Series: | EJNMMI Reports |
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| Online Access: | https://doi.org/10.1186/s41824-024-00225-5 |
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| author | Philip Alexander Glemser Martin Freitag Balint Kovacs Nils Netzer Antonia Dimitrakopoulou-Strauss Uwe Haberkorn Klaus Maier-Hein Constantin Schwab Stefan Duensing Bettina Beuthien-Baumann Heinz-Peter Schlemmer David Bonekamp Frederik Giesel Christos Sachpekidis |
| author_facet | Philip Alexander Glemser Martin Freitag Balint Kovacs Nils Netzer Antonia Dimitrakopoulou-Strauss Uwe Haberkorn Klaus Maier-Hein Constantin Schwab Stefan Duensing Bettina Beuthien-Baumann Heinz-Peter Schlemmer David Bonekamp Frederik Giesel Christos Sachpekidis |
| author_sort | Philip Alexander Glemser |
| collection | DOAJ |
| description | Abstract Background To investigate the ability of artificial intelligence (AI)-based and semi-quantitative dynamic contrast enhanced (DCE) multiparametric MRI (mpMRI), performed within [18F]-PSMA-1007 PET/MRI, in differentiating benign from malignant prostate tissues in patients with primary prostate cancer (PC). Results A total of seven patients underwent whole-body [18F]-PSMA-1007 PET/MRI examinations including a pelvic mpMRI protocol with T2w, diffusion weighted imaging (DWI) and DCE image series. Conventional analysis included visual reading of PET/MRI images and Prostate Imaging Reporting & Data System (PI-RADS) scoring of the prostate. On the prostate level, we performed manual segmentations for time-intensity curve parameter formation and semi-quantitative analysis based on DCE segmentation data of PC-suspicious lesions. Moreover, we applied a recently introduced deep learning (DL) pipeline previously trained on 1010 independent MRI examinations with systematic biopsy-enhanced histopathological targeted biopsy lesion ground truth in order to perform AI-based lesion detection, prostate segmentation and derivation of a deep learning PI-RADS score. DICE coefficients between manual and automatic DL-acquired segmentations were compared. On patient-based analysis, PET/MRI revealed PC-suspicious lesions in the prostate gland in 6/7 patients (Gleason Score-GS ≥ 7b) that were histologically confirmed. Four of these patients also showed lymph node metastases, while two of them had bone metastases. One patient with GS 6 showed no PC-suspicious lesions. Based on DCE segmentations, a distinction between PC-suspicious and normal appearing tissue was feasible with the parameters fitted maximum contrast ratio (FMCR) and wash-in-slope. DICE coefficients (manual vs. deep learning) were comparable with literature values at a mean of 0.44. Further, the DL pipeline could identify the intraprostatic PC-suspicious lesions in all six patients with clinically significant PC. Conclusion Firstly, semi-quantitative DCE analysis based on manual segmentations of time-intensity curves was able to distinguish benign from malignant tissues. Moreover, DL analysis of the MRI data could detect clinically significant PC in all cases, demonstrating the feasibility of AI-supported approaches in increasing diagnostic certainty of PSMA-radioligand PET/MRI. |
| format | Article |
| id | doaj-art-0f4b87ff27e4410f90a076c331e9e113 |
| institution | Kabale University |
| issn | 3005-074X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
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| series | EJNMMI Reports |
| spelling | doaj-art-0f4b87ff27e4410f90a076c331e9e1132025-08-20T04:01:47ZengSpringerEJNMMI Reports3005-074X2024-11-018111210.1186/s41824-024-00225-5Enhancing the diagnostic capacity of [18F]PSMA-1007 PET/MRI in primary prostate cancer staging with artificial intelligence and semi-quantitative DCE: an exploratory studyPhilip Alexander Glemser0Martin Freitag1Balint Kovacs2Nils Netzer3Antonia Dimitrakopoulou-Strauss4Uwe Haberkorn5Klaus Maier-Hein6Constantin Schwab7Stefan Duensing8Bettina Beuthien-Baumann9Heinz-Peter Schlemmer10David Bonekamp11Frederik Giesel12Christos Sachpekidis13Department of Radiology, German Cancer Research Center (DKFZ)Department of Nuclear Medicine, Faculty of Medicine, Medical Center-University of Freiburg, University of FreiburgDivision of Medical Image Computing, German Cancer Research Center (DKFZ) HeidelbergDepartment of Radiology, German Cancer Research Center (DKFZ)Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ)Department of Nuclear Medicine, University Hospital HeidelbergDivision of Medical Image Computing, German Cancer Research Center (DKFZ) HeidelbergInstitute of Pathology, Heidelberg University HospitalDepartment of Urology, University Hospital HeidelbergDepartment of Radiology, German Cancer Research Center (DKFZ)Department of Radiology, German Cancer Research Center (DKFZ)Department of Radiology, German Cancer Research Center (DKFZ)Department of Nuclear Medicine, Medical Faculty, University Hospital DuesseldorfClinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ)Abstract Background To investigate the ability of artificial intelligence (AI)-based and semi-quantitative dynamic contrast enhanced (DCE) multiparametric MRI (mpMRI), performed within [18F]-PSMA-1007 PET/MRI, in differentiating benign from malignant prostate tissues in patients with primary prostate cancer (PC). Results A total of seven patients underwent whole-body [18F]-PSMA-1007 PET/MRI examinations including a pelvic mpMRI protocol with T2w, diffusion weighted imaging (DWI) and DCE image series. Conventional analysis included visual reading of PET/MRI images and Prostate Imaging Reporting & Data System (PI-RADS) scoring of the prostate. On the prostate level, we performed manual segmentations for time-intensity curve parameter formation and semi-quantitative analysis based on DCE segmentation data of PC-suspicious lesions. Moreover, we applied a recently introduced deep learning (DL) pipeline previously trained on 1010 independent MRI examinations with systematic biopsy-enhanced histopathological targeted biopsy lesion ground truth in order to perform AI-based lesion detection, prostate segmentation and derivation of a deep learning PI-RADS score. DICE coefficients between manual and automatic DL-acquired segmentations were compared. On patient-based analysis, PET/MRI revealed PC-suspicious lesions in the prostate gland in 6/7 patients (Gleason Score-GS ≥ 7b) that were histologically confirmed. Four of these patients also showed lymph node metastases, while two of them had bone metastases. One patient with GS 6 showed no PC-suspicious lesions. Based on DCE segmentations, a distinction between PC-suspicious and normal appearing tissue was feasible with the parameters fitted maximum contrast ratio (FMCR) and wash-in-slope. DICE coefficients (manual vs. deep learning) were comparable with literature values at a mean of 0.44. Further, the DL pipeline could identify the intraprostatic PC-suspicious lesions in all six patients with clinically significant PC. Conclusion Firstly, semi-quantitative DCE analysis based on manual segmentations of time-intensity curves was able to distinguish benign from malignant tissues. Moreover, DL analysis of the MRI data could detect clinically significant PC in all cases, demonstrating the feasibility of AI-supported approaches in increasing diagnostic certainty of PSMA-radioligand PET/MRI.https://doi.org/10.1186/s41824-024-00225-518F-PSMA-1007PET/MRIAIDCEPrimary stagingProstate cancer |
| spellingShingle | Philip Alexander Glemser Martin Freitag Balint Kovacs Nils Netzer Antonia Dimitrakopoulou-Strauss Uwe Haberkorn Klaus Maier-Hein Constantin Schwab Stefan Duensing Bettina Beuthien-Baumann Heinz-Peter Schlemmer David Bonekamp Frederik Giesel Christos Sachpekidis Enhancing the diagnostic capacity of [18F]PSMA-1007 PET/MRI in primary prostate cancer staging with artificial intelligence and semi-quantitative DCE: an exploratory study EJNMMI Reports 18F-PSMA-1007 PET/MRI AI DCE Primary staging Prostate cancer |
| title | Enhancing the diagnostic capacity of [18F]PSMA-1007 PET/MRI in primary prostate cancer staging with artificial intelligence and semi-quantitative DCE: an exploratory study |
| title_full | Enhancing the diagnostic capacity of [18F]PSMA-1007 PET/MRI in primary prostate cancer staging with artificial intelligence and semi-quantitative DCE: an exploratory study |
| title_fullStr | Enhancing the diagnostic capacity of [18F]PSMA-1007 PET/MRI in primary prostate cancer staging with artificial intelligence and semi-quantitative DCE: an exploratory study |
| title_full_unstemmed | Enhancing the diagnostic capacity of [18F]PSMA-1007 PET/MRI in primary prostate cancer staging with artificial intelligence and semi-quantitative DCE: an exploratory study |
| title_short | Enhancing the diagnostic capacity of [18F]PSMA-1007 PET/MRI in primary prostate cancer staging with artificial intelligence and semi-quantitative DCE: an exploratory study |
| title_sort | enhancing the diagnostic capacity of 18f psma 1007 pet mri in primary prostate cancer staging with artificial intelligence and semi quantitative dce an exploratory study |
| topic | 18F-PSMA-1007 PET/MRI AI DCE Primary staging Prostate cancer |
| url | https://doi.org/10.1186/s41824-024-00225-5 |
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