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Kuanar S, Cai J, Nakai H, Nagayama H, Takahashi H, LeGout J, Kawashima A, Froemming A, Mynderse L, Dora C, Humphreys M, Klug J, Korfiatis P, Erickson B, Takahashi N. Transition-zone PSA-density calculated from MRI deep learning prostate zonal segmentation model for prediction of clinically significant prostate cancer. Abdom Radiol (NY) 2024:10.1007/s00261-024-04301-z. [PMID: 38896250 DOI: 10.1007/s00261-024-04301-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 06/21/2024]
Abstract
PURPOSE To develop a deep learning (DL) zonal segmentation model of prostate MR from T2-weighted images and evaluate TZ-PSAD for prediction of the presence of csPCa (Gleason score of 7 or higher) compared to PSAD. METHODS 1020 patients with a prostate MRI were randomly selected to develop a DL zonal segmentation model. Test dataset included 20 cases in which 2 radiologists manually segmented both the peripheral zone (PZ) and TZ. Pair-wise Dice index was calculated for each zone. For the prediction of csPCa using PSAD and TZ-PSAD, we used 3461 consecutive MRI exams performed in patients without a history of prostate cancer, with pathological confirmation and available PSA values, but not used in the development of the segmentation model as internal test set and 1460 MRI exams from PI-CAI challenge as external test set. PSAD and TZ-PSAD were calculated from the segmentation model output. The area under the receiver operating curve (AUC) was compared between PSAD and TZ-PSAD using univariate and multivariate analysis (adjusts age) with the DeLong test. RESULTS Dice scores of the model against two radiologists were 0.87/0.87 and 0.74/0.72 for TZ and PZ, while those between the two radiologists were 0.88 for TZ and 0.75 for PZ. For the prediction of csPCa, the AUCs of TZPSAD were significantly higher than those of PSAD in both internal test set (univariate analysis, 0.75 vs. 0.73, p < 0.001; multivariate analysis, 0.80 vs. 0.78, p < 0.001) and external test set (univariate analysis, 0.76 vs. 0.74, p < 0.001; multivariate analysis, 0.77 vs. 0.75, p < 0.001 in external test set). CONCLUSION DL model-derived zonal segmentation facilitates the practical measurement of TZ-PSAD and shows it to be a slightly better predictor of csPCa compared to the conventional PSAD. Use of TZ-PSAD may increase the sensitivity of detecting csPCa by 2-5% for a commonly used specificity level.
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Affiliation(s)
- Shiba Kuanar
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Jason Cai
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Hirotsugu Nakai
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Hiroki Nagayama
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Radiology, Nagasaki University, Nagasaki, Japan
| | | | - Jordan LeGout
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Adam Froemming
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | | | - Chandler Dora
- Department of Urology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Jason Klug
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | | | | | - Naoki Takahashi
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.
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Molière S, Hamzaoui D, Granger B, Montagne S, Allera A, Ezziane M, Luzurier A, Quint R, Kalai M, Ayache N, Delingette H, Renard-Penna R. Reference standard for the evaluation of automatic segmentation algorithms: Quantification of inter observer variability of manual delineation of prostate contour on MRI. Diagn Interv Imaging 2024; 105:65-73. [PMID: 37822196 DOI: 10.1016/j.diii.2023.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 07/28/2023] [Accepted: 08/01/2023] [Indexed: 10/13/2023]
Abstract
PURPOSE The purpose of this study was to investigate the relationship between inter-reader variability in manual prostate contour segmentation on magnetic resonance imaging (MRI) examinations and determine the optimal number of readers required to establish a reliable reference standard. MATERIALS AND METHODS Seven radiologists with various experiences independently performed manual segmentation of the prostate contour (whole-gland [WG] and transition zone [TZ]) on 40 prostate MRI examinations obtained in 40 patients. Inter-reader variability in prostate contour delineations was estimated using standard metrics (Dice similarity coefficient [DSC], Hausdorff distance and volume-based metrics). The impact of the number of readers (from two to seven) on segmentation variability was assessed using pairwise metrics (consistency) and metrics with respect to a reference segmentation (conformity), obtained either with majority voting or simultaneous truth and performance level estimation (STAPLE) algorithm. RESULTS The average segmentation DSC for two readers in pairwise comparison was 0.919 for WG and 0.876 for TZ. Variability decreased with the number of readers: the interquartile ranges of the DSC were 0.076 (WG) / 0.021 (TZ) for configurations with two readers, 0.005 (WG) / 0.012 (TZ) for configurations with three readers, and 0.002 (WG) / 0.0037 (TZ) for configurations with six readers. The interquartile range decreased slightly faster between two and three readers than between three and six readers. When using consensus methods, variability often reached its minimum with three readers (with STAPLE, DSC = 0.96 [range: 0.945-0.971] for WG and DSC = 0.94 [range: 0.912-0.957] for TZ, and interquartile range was minimal for configurations with three readers. CONCLUSION The number of readers affects the inter-reader variability, in terms of inter-reader consistency and conformity to a reference. Variability is minimal for three readers, or three readers represent a tipping point in the variability evolution, with both pairwise-based metrics or metrics with respect to a reference. Accordingly, three readers may represent an optimal number to determine references for artificial intelligence applications.
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Affiliation(s)
- Sébastien Molière
- Department of Radiology, Hôpitaux Universitaire de Strasbourg, Hôpital de Hautepierre, 67200, Strasbourg, France; Breast and Thyroid Imaging Unit, Institut de Cancérologie Strasbourg Europe, 67200, Strasbourg, France; IGBMC, Institut de Génétique et de Biologie Moléculaire et Cellulaire, 67400, Illkirch, France.
| | - Dimitri Hamzaoui
- Inria, Epione Team, Sophia Antipolis, Université Côte d'Azur, 06902, Nice, France
| | - Benjamin Granger
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, IPLESP, AP-HP, Hôpital Pitié Salpêtrière, Département de Santé Publique, 75013, Paris, France
| | - Sarah Montagne
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France; Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique-Hôpitaux de Paris, 75013, Paris, France; GRC N° 5, Oncotype-Uro, Sorbonne Université, 75020, Paris, France
| | - Alexandre Allera
- Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique-Hôpitaux de Paris, 75013, Paris, France
| | - Malek Ezziane
- Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique-Hôpitaux de Paris, 75013, Paris, France
| | - Anna Luzurier
- Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique-Hôpitaux de Paris, 75013, Paris, France
| | - Raphaelle Quint
- Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique-Hôpitaux de Paris, 75013, Paris, France
| | - Mehdi Kalai
- Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique-Hôpitaux de Paris, 75013, Paris, France
| | - Nicholas Ayache
- Department of Radiology, Hôpitaux Universitaire de Strasbourg, Hôpital de Hautepierre, 67200, Strasbourg, France
| | - Hervé Delingette
- Department of Radiology, Hôpitaux Universitaire de Strasbourg, Hôpital de Hautepierre, 67200, Strasbourg, France
| | - Raphaële Renard-Penna
- Department of Radiology, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, 75020, Paris, France; Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique-Hôpitaux de Paris, 75013, Paris, France; GRC N° 5, Oncotype-Uro, Sorbonne Université, 75020, Paris, France
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3
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Wu C, Montagne S, Hamzaoui D, Ayache N, Delingette H, Renard-Penna R. Automatic segmentation of prostate zonal anatomy on MRI: a systematic review of the literature. Insights Imaging 2022; 13:202. [PMID: 36543901 PMCID: PMC9772373 DOI: 10.1186/s13244-022-01340-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 11/27/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVES Accurate zonal segmentation of prostate boundaries on MRI is a critical prerequisite for automated prostate cancer detection based on PI-RADS. Many articles have been published describing deep learning methods offering great promise for fast and accurate segmentation of prostate zonal anatomy. The objective of this review was to provide a detailed analysis and comparison of applicability and efficiency of the published methods for automatic segmentation of prostate zonal anatomy by systematically reviewing the current literature. METHODS A Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was conducted until June 30, 2021, using PubMed, ScienceDirect, Web of Science and EMBase databases. Risk of bias and applicability based on Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria adjusted with Checklist for Artificial Intelligence in Medical Imaging (CLAIM) were assessed. RESULTS A total of 458 articles were identified, and 33 were included and reviewed. Only 2 articles had a low risk of bias for all four QUADAS-2 domains. In the remaining, insufficient details about database constitution and segmentation protocol provided sources of bias (inclusion criteria, MRI acquisition, ground truth). Eighteen different types of terminology for prostate zone segmentation were found, while 4 anatomic zones are described on MRI. Only 2 authors used a blinded reading, and 4 assessed inter-observer variability. CONCLUSIONS Our review identified numerous methodological flaws and underlined biases precluding us from performing quantitative analysis for this review. This implies low robustness and low applicability in clinical practice of the evaluated methods. Actually, there is not yet consensus on quality criteria for database constitution and zonal segmentation methodology.
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Affiliation(s)
- Carine Wu
- grid.462844.80000 0001 2308 1657Sorbonne Université, Paris, France ,grid.50550.350000 0001 2175 4109Academic Department of Radiology, Hôpital Tenon, Assistance Publique des Hôpitaux de Paris, 4 Rue de La Chine, 75020 Paris, France
| | - Sarah Montagne
- grid.462844.80000 0001 2308 1657Sorbonne Université, Paris, France ,grid.50550.350000 0001 2175 4109Academic Department of Radiology, Hôpital Tenon, Assistance Publique des Hôpitaux de Paris, 4 Rue de La Chine, 75020 Paris, France ,grid.50550.350000 0001 2175 4109Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France ,grid.462844.80000 0001 2308 1657GRC N° 5, Oncotype-Uro, Sorbonne Université, Paris, France
| | - Dimitri Hamzaoui
- grid.460782.f0000 0004 4910 6551Inria, Epione Team, Sophia Antipolis, Université Côte d’Azur, Nice, France
| | - Nicholas Ayache
- grid.460782.f0000 0004 4910 6551Inria, Epione Team, Sophia Antipolis, Université Côte d’Azur, Nice, France
| | - Hervé Delingette
- grid.460782.f0000 0004 4910 6551Inria, Epione Team, Sophia Antipolis, Université Côte d’Azur, Nice, France
| | - Raphaële Renard-Penna
- grid.462844.80000 0001 2308 1657Sorbonne Université, Paris, France ,grid.50550.350000 0001 2175 4109Academic Department of Radiology, Hôpital Tenon, Assistance Publique des Hôpitaux de Paris, 4 Rue de La Chine, 75020 Paris, France ,grid.50550.350000 0001 2175 4109Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France ,grid.462844.80000 0001 2308 1657GRC N° 5, Oncotype-Uro, Sorbonne Université, Paris, France
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Breto AL, Spieler B, Zavala-Romero O, Alhusseini M, Patel NV, Asher DA, Xu IR, Baikovitz JB, Mellon EA, Ford JC, Stoyanova R, Portelance L. Deep Learning for Per-Fraction Automatic Segmentation of Gross Tumor Volume (GTV) and Organs at Risk (OARs) in Adaptive Radiotherapy of Cervical Cancer. Front Oncol 2022; 12:854349. [PMID: 35664789 PMCID: PMC9159296 DOI: 10.3389/fonc.2022.854349] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 03/29/2022] [Indexed: 11/13/2022] Open
Abstract
Background/Hypothesis MRI-guided online adaptive radiotherapy (MRI-g-OART) improves target coverage and organs-at-risk (OARs) sparing in radiation therapy (RT). For patients with locally advanced cervical cancer (LACC) undergoing RT, changes in bladder and rectal filling contribute to large inter-fraction target volume motion. We hypothesized that deep learning (DL) convolutional neural networks (CNN) can be trained to accurately segment gross tumor volume (GTV) and OARs both in planning and daily fractions' MRI scans. Materials/Methods We utilized planning and daily treatment fraction setup (RT-Fr) MRIs from LACC patients, treated with stereotactic body RT to a dose of 45-54 Gy in 25 fractions. Nine structures were manually contoured. MASK R-CNN network was trained and tested under three scenarios: (i) Leave-one-out (LOO), using the planning images of N- 1 patients for training; (ii) the same network, tested on the RT-Fr MRIs of the "left-out" patient, (iii) including the planning MRI of the "left-out" patient as an additional training sample, and tested on RT-Fr MRIs. The network performance was evaluated using the Dice Similarity Coefficient (DSC) and Hausdorff distances. The association between the structures' volume and corresponding DSCs was investigated using Pearson's Correlation Coefficient, r. Results MRIs from fifteen LACC patients were analyzed. In the LOO scenario the DSC for Rectum, Femur, and Bladder was >0.8, followed by the GTV, Uterus, Mesorectum and Parametrium (0.6-0.7). The results for Vagina and Sigmoid were suboptimal. The performance of the network was similar for most organs when tested on RT-Fr MRI. Including the planning MRI in the training did not improve the segmentation of the RT-Fr MRI. There was a significant correlation between the average organ volume and the corresponding DSC (r = 0.759, p = 0.018). Conclusion We have established a robust workflow for training MASK R-CNN to automatically segment GTV and OARs in MRI-g-OART of LACC. Albeit the small number of patients in this pilot project, the network was trained to successfully identify several structures while challenges remain, especially in relatively small organs. With the increase of the LACC cases, the performance of the network will improve. A robust auto-contouring tool would improve workflow efficiency and patient tolerance of the OART process.
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Affiliation(s)
- Adrian L Breto
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Benjamin Spieler
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Olmo Zavala-Romero
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Mohammad Alhusseini
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Nirav V Patel
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - David A Asher
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Isaac R Xu
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Jacqueline B Baikovitz
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Eric A Mellon
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - John C Ford
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Radka Stoyanova
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Lorraine Portelance
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States
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Montagne S, Hamzaoui D, Allera A, Ezziane M, Luzurier A, Quint R, Kalai M, Ayache N, Delingette H, Renard-Penna R. Challenge of prostate MRI segmentation on T2-weighted images: inter-observer variability and impact of prostate morphology. Insights Imaging 2021; 12:71. [PMID: 34089410 PMCID: PMC8179870 DOI: 10.1186/s13244-021-01010-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 05/05/2021] [Indexed: 12/29/2022] Open
Abstract
Background Accurate prostate zonal segmentation on magnetic resonance images (MRI) is a critical prerequisite for automated prostate cancer detection. We aimed to assess the variability of manual prostate zonal segmentation by radiologists on T2-weighted (T2W) images, and to study factors that may influence it. Methods Seven radiologists of varying levels of experience segmented the whole prostate gland (WG) and the transition zone (TZ) on 40 axial T2W prostate MRI images (3D T2W images for all patients, and both 3D and 2D images for a subgroup of 12 patients). Segmentation variabilities were evaluated based on: anatomical and morphological variation of the prostate (volume, retro-urethral lobe, intensity contrast between zones, presence of a PI-RADS ≥ 3 lesion), variation in image acquisition (3D vs 2D T2W images), and reader’s experience. Several metrics including Dice Score (DSC) and Hausdorff Distance were used to evaluate differences, with both a pairwise and a consensus (STAPLE reference) comparison. Results DSC was 0.92 (± 0.02) and 0.94 (± 0.03) for WG, 0.88 (± 0.05) and 0.91 (± 0.05) for TZ respectively with pairwise comparison and consensus reference. Variability was significantly (p < 0.05) lower for the mid-gland (DSC 0.95 (± 0.02)), higher for the apex (0.90 (± 0.06)) and the base (0.87 (± 0.06)), and higher for smaller prostates (p < 0.001) and when contrast between zones was low (p < 0.05). Impact of the other studied factors was non-significant. Conclusions Variability is higher in the extreme parts of the gland, is influenced by changes in prostate morphology (volume, zone intensity ratio), and is relatively unaffected by the radiologist’s level of expertise. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-021-01010-9.
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Affiliation(s)
- Sarah Montagne
- Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France. .,Academic Department of Radiology, Hôpital Tenon, Assistance Publique des Hôpitaux de Paris, Paris, France. .,Sorbonne Universités, GRC n° 5, Oncotype-Uro, Paris, France.
| | - Dimitri Hamzaoui
- Inria, Epione Team, Université Côte D'Azur, Sophia Antipolis, Nice, France
| | - Alexandre Allera
- Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France
| | - Malek Ezziane
- Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France
| | - Anna Luzurier
- Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France
| | - Raphaelle Quint
- Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France
| | - Mehdi Kalai
- Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France
| | - Nicholas Ayache
- Inria, Epione Team, Université Côte D'Azur, Sophia Antipolis, Nice, France
| | - Hervé Delingette
- Inria, Epione Team, Université Côte D'Azur, Sophia Antipolis, Nice, France
| | - Raphaële Renard-Penna
- Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France.,Academic Department of Radiology, Hôpital Tenon, Assistance Publique des Hôpitaux de Paris, Paris, France.,Sorbonne Universités, GRC n° 5, Oncotype-Uro, Paris, France
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Scobioala S, Kittel C, Wolters H, Huss S, Elsayad K, Seifert R, Stegger L, Weckesser M, Haverkamp U, Eich HT, Rahbar K. Diagnostic efficiency of hybrid imaging using PSMA ligands, PET/CT, PET/MRI and MRI in identifying malignant prostate lesions. Ann Nucl Med 2021; 35:628-638. [PMID: 33742373 PMCID: PMC8079339 DOI: 10.1007/s12149-021-01606-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 03/10/2021] [Indexed: 12/26/2022]
Abstract
OBJECTIVE The objective of this study was to assess the accuracy of 68Ga-PSMA-11 PET/MRI, 18F-PSMA-1007 PET/CT, 68Ga-PSMA-11 PET/CT, and multiparametric (mp)MRI for the delineating of dominant intraprostatic lesions (IPL). MATERIALS AND METHODS 35 patients with organ-confined prostate cancer who were assigned to definitive radiotherapy (RT) were divided into three groups based on imaging techniques: 68Ga-PSMA-PET/MRI (n = 9), 18F-PSMA-PET/CT (n = 16) and 68Ga-PSMA-PET/CT (n = 10). All patients without PSMA-PET/MRI received an additional mpMRI. PSMA-PET-based automatic isocontours and manual contours of the dominant IPLs were generated for each modality. The biopsy results were then used to validate whether any of the prostate biopsies were positive in the marked lesion using Dice similarity coefficient (DSC), Youden index (YI), sensitivity and specificity. Factors that can predict the accuracy of IPLs contouring were analysed. RESULTS Diagnostic performance was significantly superior both for manual and automatic IPLs contouring using 68Ga-PSMA-PET/MRI (DSC/YI SUV70%-0.62/0.51), 18F-PSMA-PET/CT (DSC/YI SUV70%-0.67/0.53) or 68Ga-PSMA-PET/CT (DSC/YI SUV70%-0.63/0.51) compared to mpMRI (DSC/YI-0.47/0.41; p < 0.001). The accuracy for delineating IPLs was not improved by combination of PET/CT and mpMRI images compared to PET/CT alone. Significantly superior diagnostic accuracy was found for large prostate lesions (at least 15% from the prostate volume) and higher Gleason score (at least 7b) comparing to smaller lesions with lower GS. CONCLUSION IPL localization was significantly improved when using PSMA-imaging procedures compared to mpMRI. No significant difference for delineating IPLs was found between hybrid method PSMA-PET/MRI and PSMA-PET/CT. PSMA-based imaging technique should be considered for the diagnostics of IPLs and focal treatment modality.
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Affiliation(s)
- Sergiu Scobioala
- Department of Radiation Oncology, University Hospital Muenster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany.
- West German Cancer Center, Muenster and Essen, Germany.
| | - Christopher Kittel
- Department of Radiation Oncology, University Hospital Muenster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany
- West German Cancer Center, Muenster and Essen, Germany
| | - Heidi Wolters
- Department of Radiation Oncology, University Hospital Muenster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany
- West German Cancer Center, Muenster and Essen, Germany
| | - Sebastian Huss
- Department of Pathology, University Hospital of Muenster, Muenster, Germany
- West German Cancer Center, Muenster and Essen, Germany
| | - Khaled Elsayad
- Department of Radiation Oncology, University Hospital Muenster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany
- West German Cancer Center, Muenster and Essen, Germany
| | - Robert Seifert
- Department of Nuclear Medicine, University Hospital of Muenster, Muenster, Germany
- West German Cancer Center, Muenster and Essen, Germany
| | - Lars Stegger
- Department of Nuclear Medicine, University Hospital of Muenster, Muenster, Germany
- West German Cancer Center, Muenster and Essen, Germany
| | - Matthias Weckesser
- Department of Nuclear Medicine, University Hospital of Muenster, Muenster, Germany
- West German Cancer Center, Muenster and Essen, Germany
| | - Uwe Haverkamp
- Department of Radiation Oncology, University Hospital Muenster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany
- West German Cancer Center, Muenster and Essen, Germany
| | - Hans Theodor Eich
- Department of Radiation Oncology, University Hospital Muenster, Albert-Schweitzer-Campus 1, 48149, Muenster, Germany
- West German Cancer Center, Muenster and Essen, Germany
| | - Kambiz Rahbar
- Department of Nuclear Medicine, University Hospital of Muenster, Muenster, Germany
- West German Cancer Center, Muenster and Essen, Germany
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Zavala-Romero O, Breto AL, Xu IR, Chang YCC, Gautney N, Dal Pra A, Abramowitz MC, Pollack A, Stoyanova R. Segmentation of prostate and prostate zones using deep learning : A multi-MRI vendor analysis. Strahlenther Onkol 2020; 196:932-942. [PMID: 32221622 PMCID: PMC8418872 DOI: 10.1007/s00066-020-01607-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 03/10/2020] [Indexed: 11/25/2022]
Abstract
PURPOSE Develop a deep-learning-based segmentation algorithm for prostate and its peripheral zone (PZ) that is reliable across multiple MRI vendors. METHODS This is a retrospective study. The dataset consisted of 550 MRIs (Siemens-330, General Electric[GE]-220). A multistream 3D convolutional neural network is used for automatic segmentation of the prostate and its PZ using T2-weighted (T2-w) MRI. Prostate and PZ were manually contoured on axial T2‑w. The network uses axial, coronal, and sagittal T2‑w series as input. The preprocessing of the input data includes bias correction, resampling, and image normalization. A dataset from two MRI vendors (Siemens and GE) is used to test the proposed network. Six different models were trained, three for the prostate and three for the PZ. Of the three, two were trained on data from each vendor separately, and a third (Combined) on the aggregate of the datasets. The Dice coefficient (DSC) is used to compare the manual and predicted segmentation. RESULTS For prostate segmentation, the Combined model obtained DSCs of 0.893 ± 0.036 and 0.825 ± 0.112 (mean ± standard deviation) on Siemens and GE, respectively. For PZ, the best DSCs were from the Combined model: 0.811 ± 0.079 and 0.788 ± 0.093. While the Siemens model underperformed on the GE dataset and vice versa, the Combined model achieved robust performance on both datasets. CONCLUSION The proposed network has a performance comparable to the interexpert variability for segmenting the prostate and its PZ. Combining images from different MRI vendors on the training of the network is of paramount importance for building a universal model for prostate and PZ segmentation.
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Affiliation(s)
- Olmo Zavala-Romero
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Adrian L Breto
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Isaac R Xu
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | | | - Nicole Gautney
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alan Dal Pra
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Matthew C Abramowitz
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alan Pollack
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Radka Stoyanova
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.
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Delgadillo R, Ford JC, Abramowitz MC, Dal Pra A, Pollack A, Stoyanova R. The role of radiomics in prostate cancer radiotherapy. Strahlenther Onkol 2020; 196:900-912. [PMID: 32821953 PMCID: PMC7545508 DOI: 10.1007/s00066-020-01679-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 08/07/2020] [Indexed: 12/24/2022]
Abstract
"Radiomics," as it refers to the extraction and analysis of a large number of advanced quantitative radiological features from medical images using high-throughput methods, is perfectly suited as an engine for effectively sifting through the multiple series of prostate images from before, during, and after radiotherapy (RT). Multiparametric (mp)MRI, planning CT, and cone beam CT (CBCT) routinely acquired throughout RT and the radiomics pipeline are developed for extraction of thousands of variables. Radiomics data are in a format that is appropriate for building descriptive and predictive models relating image features to diagnostic, prognostic, or predictive information. Prediction of Gleason score, the histopathologic cancer grade, has been the mainstay of the radiomic efforts in prostate cancer. While Gleason score (GS) is still the best predictor of treatment outcome, there are other novel applications of quantitative imaging that are tailored to RT. In this review, we summarize the radiomics efforts and discuss several promising concepts such as delta-radiomics and radiogenomics for utilizing image features for assessment of the aggressiveness of prostate cancer and its outcome. We also discuss opportunities for quantitative imaging with the advance of instrumentation in MRI-guided therapies.
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Affiliation(s)
- Rodrigo Delgadillo
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, 33136, Miami, FL, USA
| | - John C Ford
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, 33136, Miami, FL, USA
| | - Matthew C Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, 33136, Miami, FL, USA
| | - Alan Dal Pra
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, 33136, Miami, FL, USA
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, 33136, Miami, FL, USA
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, 33136, Miami, FL, USA.
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Wittenstein O, Hiepe P, Sowa LH, Karsten E, Fandrich I, Dunst J. Automatic image segmentation based on synthetic tissue model for delineating organs at risk in spinal metastasis treatment planning. Strahlenther Onkol 2019; 195:1094-1103. [PMID: 31037351 PMCID: PMC6868111 DOI: 10.1007/s00066-019-01463-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 03/25/2019] [Indexed: 12/25/2022]
Abstract
Purpose One of the main goals in software solutions for treatment planning is to automatize delineation of organs at risk (OARs). In this pilot feasibility study a clinical validation was made of computed tomography (CT)-based extracranial auto-segmentation (AS) using the Brainlab Anatomical Mapping tool (AM). Methods The delineation of nine extracranial OARs (lungs, kidneys, trachea, heart, liver, spinal cord, esophagus) from clinical datasets of 24 treated patients was retrospectively evaluated. Manual delineation of OARs was conducted in clinical routine and compared with AS datasets using AM. The Dice similarity coefficient (DSC) and maximum Hausdorff distance (HD) were used as statistical and geometrical measurements, respectively. Additionally, all AS structures were validated using a subjective qualitative scoring system. Results All patient datasets investigated were successfully processed with the evaluated AS software. For the left lung (0.97 ± 0.03), right lung (0.97 ± 0.05), left kidney (0.91 ± 0.07), and trachea (0.93 ± 0.04), the DSC was high with low variability. The DSC scores of other organs (right kidney, heart, liver, spinal cord), except the esophagus, ranged between 0.7 and 0.9. The calculated HD values yielded comparable results. Qualitative assessment showed a general acceptance in more than 85% of AS OARs—except for the esophagus. Conclusions The Brainlab AM software is ready for clinical use in most of the OARs evaluated in the thoracic and abdominal region. The software generates highly conformal structure sets compared to manual contouring. The current study design needs revision for further research.
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Affiliation(s)
- Olaf Wittenstein
- Department of Radiation Oncology, Universitätsklinikum Schleswig-Holstein Campus Kiel, Arnold-Heller-Straße 3, Haus 50, 24105, Kiel, Germany.
| | - Patrick Hiepe
- R&D Anatomical Mapping, Brainlab AG, Olof-Palme-Straße 9, 81829, Munich, Germany
| | - Lars Henrik Sowa
- R&D Anatomical Mapping, Brainlab AG, Olof-Palme-Straße 9, 81829, Munich, Germany
| | - Elias Karsten
- Department of Radiation Oncology, Universitätsklinikum Schleswig-Holstein Campus Kiel, Arnold-Heller-Straße 3, Haus 50, 24105, Kiel, Germany
| | - Iris Fandrich
- Department of Radiation Oncology, Universitätsklinikum Schleswig-Holstein Campus Kiel, Arnold-Heller-Straße 3, Haus 50, 24105, Kiel, Germany
| | - Juergen Dunst
- Department of Radiation Oncology, Universitätsklinikum Schleswig-Holstein Campus Kiel, Arnold-Heller-Straße 3, Haus 50, 24105, Kiel, Germany
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