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Wang F, Liu C, Vidal I, Mana-Ay M, Voter AF, Solnes LB, Ross AE, Gafita A, Schaeffer EM, Bivalacqua TJ, Pienta KJ, Pomper MG, Lodge MA, Song DY, Oldan JD, Allaf ME, De Marzo AM, Sheikhbahaei S, Gorin MA, Rowe SP. Comparison of Multiple Segmentation Methods for Volumetric Delineation of Primary Prostate Cancer with Prostate-Specific Membrane Antigen-Targeted 18F-DCFPyL PET/CT. J Nucl Med 2024; 65:87-93. [PMID: 38050147 PMCID: PMC10755517 DOI: 10.2967/jnumed.123.266005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 10/17/2023] [Indexed: 12/06/2023] Open
Abstract
This study aimed to assess the accuracy of intraprostatic tumor volume measurements on prostate-specific membrane antigen-targeted 18F-DCFPyL PET/CT made with various segmentation methods. An accurate understanding of tumor volumes versus segmentation techniques is critical for therapy planning, such as radiation dose volume determination and response assessment. Methods: Twenty-five men with clinically localized, high-risk prostate cancer were imaged with 18F-DCFPyL PET/CT before radical prostatectomy. The tumor volumes and tumor-to-prostate ratios (TPRs) of dominant intraprostatic foci of uptake were determined using semiautomatic segmentation (applying SUVmax percentage [SUV%] thresholds of SUV30%-SUV70%), adaptive segmentation (using adaptive segmentation percentage [A%] thresholds of A30%-A70%), and manual contouring. The histopathologic tumor volume (TV-Histo) served as the reference standard. The significance of differences between TV-Histo and PET-based tumor volume were assessed using the paired-sample Wilcoxon signed-rank test. The Spearman correlation coefficient was used to establish the strength of the association between TV-Histo and PET-derived tumor volume. Results: Median TV-Histo was 2.03 cm3 (interquartile ratio [IQR], 1.16-3.36 cm3), and median TPR was 10.16%. The adaptive method with an A40% threshold most closely determined the tumor volume, with a median difference of +0.19 (IQR, -0.71 to +2.01) and a median relative difference of +7.6%. The paired-sample Wilcoxon test showed no significant difference in PET-derived tumor volume and TV-Histo using A40%, A50%, SUV40%, and SUV50% threshold segmentation algorithms (P > 0.05). For both threshold-based segmentation methods, use of higher thresholds (e.g., SUV60% or SUV70% and A50%-A70%) resulted in underestimation of tumor volumes, and use of lower thresholds (e.g., SUV30% or SUV40% and A30%) resulted in overestimation of tumor volumes relative to TV-Histo and TPR. Manual segmentation overestimated the tumor volume, with a median difference of +2.49 (IQR, 0.42-4.11) and a median relative difference of +130%. Conclusion: Segmentation of intraprostatic tumor volume and TPR with an adaptive segmentation approach most closely approximates TV-Histo. This information might be used to guide the primary treatment of men with clinically localized, high-risk prostate cancer.
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Affiliation(s)
- Felicia Wang
- School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Chen Liu
- Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Beijing, China
- Department of Nuclear Medicine, Peking University Cancer Hospital and Institute, Beijing, China
| | - Igor Vidal
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | | | - Andrew F Voter
- Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Lilja B Solnes
- Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, Maryland
- Brady Urological Institute, School of Medicine, Johns Hopkins University, Baltimore, Maryland
- Department of Urology, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Ashley E Ross
- Department of Urology, Feinberg School of Medicine, Northwestern Medicine, Chicago, Illinois
| | - Andrei Gafita
- Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Edward M Schaeffer
- Department of Urology, Feinberg School of Medicine, Northwestern Medicine, Chicago, Illinois
| | - Trinity J Bivalacqua
- Division of Urology, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Kenneth J Pienta
- Brady Urological Institute, School of Medicine, Johns Hopkins University, Baltimore, Maryland
- Department of Urology, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Martin G Pomper
- Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, Maryland
- Brady Urological Institute, School of Medicine, Johns Hopkins University, Baltimore, Maryland
- Department of Urology, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Martin A Lodge
- Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Daniel Y Song
- Brady Urological Institute, School of Medicine, Johns Hopkins University, Baltimore, Maryland
- Department of Urology, School of Medicine, Johns Hopkins University, Baltimore, Maryland
- Department of Radiation Oncology and Molecular Radiation Science, Sidney Kimmel Comprehensive Center, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Jorge D Oldan
- Molecular Imaging and Therapeutics, University of North Carolina, Chapel Hill, North Carolina; and
| | - Mohamad E Allaf
- Brady Urological Institute, School of Medicine, Johns Hopkins University, Baltimore, Maryland
- Department of Urology, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Angelo M De Marzo
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland
- Brady Urological Institute, School of Medicine, Johns Hopkins University, Baltimore, Maryland
- Department of Urology, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Sara Sheikhbahaei
- Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Michael A Gorin
- Milton and Carroll Petrie Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Steven P Rowe
- Molecular Imaging and Therapeutics, University of North Carolina, Chapel Hill, North Carolina; and
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Wang YF, Lo CY, Chen LY, Chang CW, Huang YT, Huang YY, Huang YH. Comparing the Detection Performance Between Multiparametric Magnetic Resonance Imaging and Prostate-Specific Membrane Antigen PET/CT in Patients With Localized Prostate Cancer: A Systematic Review and Meta-analysis. Clin Nucl Med 2023; 48:e321-e331. [PMID: 37145456 DOI: 10.1097/rlu.0000000000004646] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
PURPOSE Multiparametric MRI (mpMRI) has been promoted as an auxiliary diagnostic tool for prostate biopsy. However, prostate-specific membrane antigen (PSMA) including 68 Ga-PSMA-11, 18 F-DCFPyL, and 18 F-PSMA-1007 applied PET/CT imaging was an emerging diagnostic tool in prostate cancer patients for staging or posttreatment follow-up, even early detecting. Many studies have used PSMA PET for comparison with mpMRI to test the diagnostic ability for early prostate cancer. Unfortunately, these studies have shown conflicting results. This meta-analysis aimed to compare the differences in diagnostic performance between PSMA PET and mpMRI for detecting and T staging localized prostatic tumors. METHODS This meta-analysis involved a systematic literature search of PubMed/MEDLINE and Cochrane Library databases. The pooling sensitivity and specificity of PSMA and mpMRI verified by pathological analysis were calculated and used to compare the differences between the 2 imaging tools. RESULTS Overall, 39 studies were included (3630 patients in total) from 2016 to 2022 in the current meta-analysis and found that the pooling sensitivity values for localized prostatic tumors and T staging T3a and T3b of PSMA PET were 0.84 (95% confidence interval [CI], 0.83-0.86), 0.61 (95% CI, 0.39-0.79), and 0.62 (95% CI, 0.46-0.76), respectively, whereas those of mpMRI were found to be 0.84 (95% 0.78-0.89), 0.67 (95% CI, 0.52-0.80), and 0.60 (95% CI, 0.45-0.73), respectively, without significant differences ( P > 0.05). However, in a subgroup analysis of radiotracer, the pooling sensitivity of 18 F-DCFPyL PET was higher than mpMRI (relative risk, 1.10; 95% CI, 1.03-1.17; P < 0.01). CONCLUSIONS This meta-analysis found that whereas 18 F-DCFPyL PET was superior to mpMRI at detecting localized prostatic tumors, the detection performance of PSMA PET for localized prostatic tumors and T staging was comparable to that of mpMRI.
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Singh D, Das CJ, Kumar V, Singh A, Mehndiratta A. Quantification of prostate tumour diameter and volume from MR images using 3D ellipsoid model and its impact on PI-RADS v2.1 assessment. Sci Rep 2022; 12:21501. [PMID: 36513800 PMCID: PMC9748032 DOI: 10.1038/s41598-022-26065-6] [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: 05/15/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
Maximum diameter and volume of the tumour provide important clinical information and are decision-making parameters for patients suspected with prostate cancer (PCa). The objectives of this study were to develop an automated method for 3D tumour measurement and compare it with the radiologist's manual assessment, as well as to investigate the impact of 3D tumour measurement on Prostate Imaging-Reporting and Data System version-2.1 (PI-RADS v2.1) scoring of prostate cancer. Tumour maximum diameter and volume were calculated using automated ellipsoid-fit method. For all PI-RADS scores, mean ± standard deviation range of tumour maximum diameter and volume measured using ellipsoid-fit method were 1.36 ± 0.28 to 1.97 ± 0.67 cm and 0.49 ± 0.31 to 1.05 ± 0.78 cc and manual assessment were in range of 0.73 ± 0.12 to 1.14 ± 0.25 cm and 0.36 ± 0.21 to 0.93 ± 0.39 cc, respectively. Ellipsoid-fit method showed significantly (p < 0.05) higher values for maximum diameter and volume than manual assessment. 3D measurement of tumour using ellipsoid-fit method was found to have higher maximum diameter and volume values (in 40-61% patients) compared to conventional assessment by radiologist, which may have an impact on PI-RADS v2.1 scoring system.
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Affiliation(s)
- Dharmesh Singh
- grid.417967.a0000 0004 0558 8755Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Chandan J. Das
- grid.413618.90000 0004 1767 6103Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India
| | - Virendra Kumar
- grid.413618.90000 0004 1767 6103Department of NMR, All India Institute of Medical Sciences, New Delhi, India
| | - Anup Singh
- grid.417967.a0000 0004 0558 8755Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India ,grid.413618.90000 0004 1767 6103Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India
| | - Amit Mehndiratta
- grid.417967.a0000 0004 0558 8755Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India ,grid.413618.90000 0004 1767 6103Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India ,grid.417967.a0000 0004 0558 8755Centre for Biomedical Engineering, IIT Delhi Hauz-Khas, Room No-298, Block III, New Delhi, 110016 India
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A combinatorial neural network analysis reveals a synergistic behaviour of multiparametric magnetic resonance and prostate health index in the identification of clinically significant prostate cancer. Clin Genitourin Cancer 2022; 20:e406-e410. [DOI: 10.1016/j.clgc.2022.04.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 04/19/2022] [Accepted: 04/23/2022] [Indexed: 11/21/2022]
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Yue Y, Li N, Shahid H, Bi D, Liu X, Song S, Ta D. Gross Tumor Volume Definition and Comparative Assessment for Esophageal Squamous Cell Carcinoma From 3D 18F-FDG PET/CT by Deep Learning-Based Method. Front Oncol 2022; 12:799207. [PMID: 35372054 PMCID: PMC8967962 DOI: 10.3389/fonc.2022.799207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 02/17/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundThe accurate definition of gross tumor volume (GTV) of esophageal squamous cell carcinoma (ESCC) can promote precise irradiation field determination, and further achieve the radiotherapy curative effect. This retrospective study is intended to assess the applicability of leveraging deep learning-based method to automatically define the GTV from 3D 18F-FDG PET/CT images of patients diagnosed with ESCC.MethodsWe perform experiments on a clinical cohort with 164 18F-FDG PET/CT scans. The state-of-the-art esophageal GTV segmentation deep neural net is first employed to delineate the lesion area on PET/CT images. Afterwards, we propose a novel equivalent truncated elliptical cone integral method (ETECIM) to estimate the GTV value. Indexes of Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean surface distance (MSD) are used to evaluate the segmentation performance. Conformity index (CI), degree of inclusion (DI), and motion vector (MV) are used to assess the differences between predicted and ground truth tumors. Statistical differences in the GTV, DI, and position are also determined.ResultsWe perform 4-fold cross-validation for evaluation, reporting the values of DSC, HD, and MSD as 0.72 ± 0.02, 11.87 ± 4.20 mm, and 2.43 ± 0.60 mm (mean ± standard deviation), respectively. Pearson correlations (R2) achieve 0.8434, 0.8004, 0.9239, and 0.7119 for each fold cross-validation, and there is no significant difference (t = 1.193, p = 0.235) between the predicted and ground truth GTVs. For DI, a significant difference is found (t = −2.263, p = 0.009). For position assessment, there is no significant difference (left-right in x direction: t = 0.102, p = 0.919, anterior–posterior in y direction: t = 0.221, p = 0.826, and cranial–caudal in z direction: t = 0.569, p = 0.570) between the predicted and ground truth GTVs. The median of CI is 0.63, and the gotten MV is small.ConclusionsThe predicted tumors correspond well with the manual ground truth. The proposed GTV estimation approach ETECIM is more precise than the most commonly used voxel volume summation method. The ground truth GTVs can be solved out due to the good linear correlation with the predicted results. Deep learning-based method shows its promising in GTV definition and clinical radiotherapy application.
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Affiliation(s)
- Yaoting Yue
- Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, China
| | - Nan Li
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Husnain Shahid
- Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, China
| | - Dongsheng Bi
- Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, China
| | - Xin Liu
- Academy for Engineering and Technology, Fudan University, Shanghai, China
- *Correspondence: Xin Liu, ; Shaoli Song,
| | - Shaoli Song
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China
- *Correspondence: Xin Liu, ; Shaoli Song,
| | - Dean Ta
- Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
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MR Imaging in Real Time Guiding of Therapies in Prostate Cancer. Life (Basel) 2022; 12:life12020302. [PMID: 35207589 PMCID: PMC8878909 DOI: 10.3390/life12020302] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 01/31/2022] [Accepted: 02/11/2022] [Indexed: 11/22/2022] Open
Abstract
Magnetic resonance imaging (MRI)-guided therapy for prostate cancer (PCa) aims to reduce the treatment-associated comorbidity of existing radical treatment, including radical prostatectomy and radiotherapy. Although active surveillance has been used as a conservative method to reduce overtreatment, there is a growing demand for less morbidity and personalized (focal) treatment. The development of multiparametric MRI was of real importance in improving the detection, localization and staging of PCa. Moreover, MRI has been useful for lesion targeting within the prostate, as it is used in the guidance of prostate biopsies, by means of cognitive registration, MRI-ultrasound fusion guidance or direct in-bore MRI-guidance. With regard to PCa therapies, MRI is used for precise probe placement into the lesion and to accurately monitor the treatment in real-time. Moreover, advances in MR-compatible thermal ablation allow for noninvasive real-time temperature mapping during treatment. In this review, we present an overview of the current status of MRI-guided therapies in PCa, focusing on cryoablation, focal laser ablation, high intensity focused ultrasound and transurethral ultrasound ablation. We explain the important role of MRI in the evaluation of the completeness of the ablation and during follow-up. Finally, we will discuss the challenges and future development inherent to these new technologies.
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Hsieh PF, Li TR, Lin WC, Chang H, Huang CP, Chang CH, Yang CR, Yeh CC, Huang WC, Wu HC. Combining prostate health index and multiparametric magnetic resonance imaging in estimating the histological diameter of prostate cancer. BMC Urol 2021; 21:161. [PMID: 34801024 PMCID: PMC8606059 DOI: 10.1186/s12894-021-00928-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 11/08/2021] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Although multiparametric magnetic resonance imaging (mpMRI) is widely used to assess the volume of prostate cancer, it often underestimates the histological tumor boundary. The aim of this study was to evaluate the feasibility of combining prostate health index (PHI) and mpMRI to estimate the histological tumor diameter and determine the safety margin during treatment of prostate cancer. METHODS We retrospectively enrolled 72 prostate cancer patients who underwent radical prostatectomy and had received PHI tests and mpMRI before surgery. We compared the discrepancy between histological and radiological tumor diameter stratified by Prostate Imaging-Reporting and Data System (PI-RADS) score, and then assessed the influence of PHI on the discrepancy between low PI-RADS (2 or 3) and high PI-RADS (4 or 5) groups. RESULTS The mean radiological and histological tumor diameters were 1.60 cm and 2.13 cm, respectively. The median discrepancy between radiological and histological tumor diameter of PI-RADS 4 or 5 lesions was significantly greater than that of PI-RADS 2 or 3 lesions (0.50 cm, IQR (0.00-0.90) vs. 0.00 cm, IQR (-0.10-0.20), p = 0.02). In the low PI-RADS group, the upper limit of the discrepancy was 0.2 cm; so the safety margin could be set at 0.1 cm. In the high PI-RADS group, the upper limits of the discrepancy were 1.2, 1.6, and 2.2 cm in men with PHI < 30, 30-60, and > 60; so the safety margin could be set at 0.6, 0.8, and 1.1 cm, respectively. CONCLUSIONS Radiological tumor diameter on mpMRI often underestimated the histological tumor diameter, especially for PI-RADS 4 or 5 lesions. Combining mpMRI and PHI may help to better estimate the histological tumor diameter.
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Affiliation(s)
- Po-Fan Hsieh
- Department of Urology, China Medical University Hospital, No. 2, Yu-Der Rd, Taichung, 40447, Taiwan
- School of Medicine, China Medical University, Taichung, 40402, Taiwan
- Graduate Institute of Biomedical Sciences, School of Medicine, China Medical University, Taichung, 40402, Taiwan
| | - Tzung-Ruei Li
- Department of Urology, China Medical University Hospital, No. 2, Yu-Der Rd, Taichung, 40447, Taiwan
| | - Wei-Ching Lin
- School of Medicine, China Medical University, Taichung, 40402, Taiwan
- Department of Radiology, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Han Chang
- Department of Pathology, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Chi-Ping Huang
- Department of Urology, China Medical University Hospital, No. 2, Yu-Der Rd, Taichung, 40447, Taiwan
- School of Medicine, China Medical University, Taichung, 40402, Taiwan
| | - Chao-Hsiang Chang
- Department of Urology, China Medical University Hospital, No. 2, Yu-Der Rd, Taichung, 40447, Taiwan
| | - Chi-Rei Yang
- Department of Urology, China Medical University Hospital, No. 2, Yu-Der Rd, Taichung, 40447, Taiwan
| | - Chin-Chung Yeh
- Department of Urology, China Medical University Hospital, No. 2, Yu-Der Rd, Taichung, 40447, Taiwan
| | - Wen-Chin Huang
- Graduate Institute of Biomedical Sciences, School of Medicine, China Medical University, Taichung, 40402, Taiwan
| | - Hsi-Chin Wu
- Department of Urology, China Medical University Hospital, No. 2, Yu-Der Rd, Taichung, 40447, Taiwan.
- School of Medicine, China Medical University, Taichung, 40402, Taiwan.
- Department of Urology, China Medical University Beigang Hospital, Beigang, Yunlin, 651012, Taiwan.
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Kalantar R, Lin G, Winfield JM, Messiou C, Lalondrelle S, Blackledge MD, Koh DM. Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges. Diagnostics (Basel) 2021; 11:1964. [PMID: 34829310 PMCID: PMC8625809 DOI: 10.3390/diagnostics11111964] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/14/2021] [Accepted: 10/19/2021] [Indexed: 12/18/2022] Open
Abstract
The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive, non-systematic and clinically-oriented overview of 74 DL-based segmentation studies, published between January 2016 and December 2020, for bladder, prostate, cervical and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations.
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Affiliation(s)
- Reza Kalantar
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK; (R.K.); (J.M.W.); (C.M.); (S.L.); (D.-M.K.)
| | - Gigin Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan;
| | - Jessica M. Winfield
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK; (R.K.); (J.M.W.); (C.M.); (S.L.); (D.-M.K.)
- Department of Radiology, The Royal Marsden Hospital, London SW3 6JJ, UK
| | - Christina Messiou
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK; (R.K.); (J.M.W.); (C.M.); (S.L.); (D.-M.K.)
- Department of Radiology, The Royal Marsden Hospital, London SW3 6JJ, UK
| | - Susan Lalondrelle
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK; (R.K.); (J.M.W.); (C.M.); (S.L.); (D.-M.K.)
- Department of Radiology, The Royal Marsden Hospital, London SW3 6JJ, UK
| | - Matthew D. Blackledge
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK; (R.K.); (J.M.W.); (C.M.); (S.L.); (D.-M.K.)
| | - Dow-Mu Koh
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK; (R.K.); (J.M.W.); (C.M.); (S.L.); (D.-M.K.)
- Department of Radiology, The Royal Marsden Hospital, London SW3 6JJ, UK
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Wake N, Rosenkrantz AB, Sodickson DK, Chandarana H, Wysock JS. MRI guided procedure planning and 3D simulation for partial gland cryoablation of the prostate: a pilot study. 3D Print Med 2020; 6:33. [PMID: 33141272 PMCID: PMC7607830 DOI: 10.1186/s41205-020-00085-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 10/25/2020] [Indexed: 12/15/2022] Open
Abstract
PURPOSE This study reports on the development of a novel 3D procedure planning technique to provide pre-ablation treatment planning for partial gland prostate cryoablation (cPGA). METHODS Twenty men scheduled for partial gland cryoablation (cPGA) underwent pre-operative image segmentation and 3D modeling of the prostatic capsule, index lesion, urethra, rectum, and neurovascular bundles based upon multi-parametric MRI data. Pre-treatment 3D planning models were designed including virtual 3D cryotherapy probes to predict and plan cryotherapy probe configuration needed to achieve confluent treatment volume. Treatment efficacy was measured with 6 month post-operative MRI, serum prostate specific antigen (PSA) at 3 and 6 months, and treatment zone biopsy results at 6 months. Outcomes from 3D planning were compared to outcomes from a series of 20 patients undergoing cPGA using traditional 2D planning techniques. RESULTS Forty men underwent cPGA. The median age of the cohort undergoing 3D treatment planning was 64.8 years with a median pretreatment PSA of 6.97 ng/mL. The Gleason grade group (GGG) of treated index lesions in this cohort included 1 (5%) GGG1, 11 (55%) GGG2, 7 (35%) GGG3, and 1 (5%) GGG4. Two (10%) of these treatments were post-radiation salvage therapies. The 2D treatment cohort included 20 men with a median age of 68.5 yrs., median pretreatment PSA of 6.76 ng/mL. The Gleason grade group (GGG) of treated index lesions in this cohort included 3 (15%) GGG1, 8 (40%) GGG2, 8 (40%) GGG3, 1 (5%) GGG4. Two (10%) of these treatments were post-radiation salvage therapies. 3D planning predicted the same number of cryoprobes for each group, however a greater number of cryoprobes was used in the procedure for the prospective 3D group as compared to that with 2D planning (4.10 ± 1.37 and 3.25 ± 0.44 respectively, p = 0.01). At 6 months post cPGA, the median PSA was 1.68 ng/mL and 2.38 ng/mL in the 3D and 2D cohorts respectively, with a larger decrease noted in the 3D cohort (75.9% reduction noted in 3D cohort and 64.8% reduction 2D cohort, p 0.48). In-field disease detection was 1/14 (7.1%) on surveillance biopsy in the 3D cohort and 3/14 (21.4%) in the 2D cohort, p = 0.056) In the 3D cohort, 6 month biopsy was not performed in 4 patients (20%) due to undetectable PSA, negative MRI, and negative MRI Axumin PET. For the group with traditional 2D planning, treatment zone biopsy was positive in 3/14 (21.4%) of the patients, p = 0.056. CONCLUSIONS 3D prostate cancer models derived from mpMRI data provide novel guidance for planning confluent treatment volumes for cPGA and predicted a greater number of treatment probes than traditional 2D planning methods. This study prompts further investigation into the use of 3D treatment planning techniques as the increase of partial gland ablation treatment protocols develop.
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Affiliation(s)
- Nicole Wake
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 East 210th Street, Bronx, NY, 10467, USA. .,Center for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, USA.
| | - Andrew B Rosenkrantz
- Center for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, USA
| | - Daniel K Sodickson
- Center for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, USA
| | - Hersh Chandarana
- Center for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, USA
| | - James S Wysock
- Division of Urologic Oncology, Department of Urology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, USA
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