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Duenweg SR, Brehler M, Lowman AK, Bobholz SA, Kyereme F, Winiarz A, Nath B, Iczkowski KA, Jacobsohn KM, LaViolette PS. Quantitative Histomorphometric Features of Prostate Cancer Predict Patients Who Biochemically Recur Following Prostatectomy. J Transl Med 2023; 103:100269. [PMID: 37898290 PMCID: PMC10872376 DOI: 10.1016/j.labinv.2023.100269] [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: 03/06/2023] [Revised: 10/10/2023] [Accepted: 10/19/2023] [Indexed: 10/30/2023] Open
Abstract
Prostate cancer is the most commonly diagnosed cancer in men, accounting for 27% of the new male cancer diagnoses in 2022. If organ-confined, removal of the prostate through radical prostatectomy is considered curative; however, distant metastases may occur, resulting in a poor patient prognosis. This study sought to determine whether quantitative pathomic features of prostate cancer differ in patients who biochemically experience biological recurrence after surgery. Whole-mount prostate histology from 78 patients was analyzed for this study. In total, 614 slides were hematoxylin and eosin stained and digitized to produce whole slide images (WSI). Regions of differing Gleason patterns were digitally annotated by a genitourinary fellowship-trained pathologist, and high-resolution tiles were extracted from each annotated region of interest for further analysis. Individual glands within the prostate were identified using automated image processing algorithms, and histomorphometric features were calculated on a per-tile basis and across WSI and averaged by patients. Tiles were organized into cancer and benign tissues. Logistic regression models were fit to assess the predictive value of the calculated pathomic features across tile groups and WSI; additionally, models using clinical information were used for comparisons. Logistic regression classified each pathomic feature model at accuracies >80% with areas under the curve of 0.82, 0.76, 0.75, and 0.72 for all tiles, cancer only, noncancer only, and across WSI. This was comparable with standard clinical information, Gleason Grade Groups, and CAPRA score, which achieved similar accuracies but areas under the curve of 0.80, 0.77, and 0.70, respectively. This study demonstrates that the use of quantitative pathomic features calculated from digital histology of prostate cancer may provide clinicians with additional information beyond the traditional qualitative pathologist assessment. Further research is warranted to determine possible inclusion in treatment guidance.
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Affiliation(s)
- Savannah R Duenweg
- Departments of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Michael Brehler
- Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | | | | | | | - Aleksandra Winiarz
- Departments of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Biprojit Nath
- Departments of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | | | | | - Peter S LaViolette
- Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin; Biomedical Engineering, Medical College of Wisconsin, Milwaukee, Wisconsin.
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Duenweg SR, Bobholz SA, Barrett MJ, Lowman AK, Winiarz A, Nath B, Stebbins M, Bukowy J, Iczkowski KA, Jacobsohn KM, Vincent-Sheldon S, LaViolette PS. T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence. Cancers (Basel) 2023; 15:4437. [PMID: 37760407 PMCID: PMC10526331 DOI: 10.3390/cancers15184437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/23/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form, resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predicted eventual BCR with an AUC of 0.97 and classified cancer at an accuracy of 89.9%. This study showcases the application of a radiomic feature-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.
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Affiliation(s)
- Savannah R. Duenweg
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - Samuel A. Bobholz
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Michael J. Barrett
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Allison K. Lowman
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Aleksandra Winiarz
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - Biprojit Nath
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - Margaret Stebbins
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - John Bukowy
- Department of Electrical Engineering and Computer Science, Milwaukee School of Engineering, 1025 N Broadway, Milwaukee, WI 53202, USA
| | - Kenneth A. Iczkowski
- Department of Pathology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA;
| | - Kenneth M. Jacobsohn
- Department of Urology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Stephanie Vincent-Sheldon
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Peter S. LaViolette
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
- Department of Biomedical Engineering, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
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Duenweg SR, Bobholz SA, Lowman AK, Stebbins MA, Winiarz A, Nath B, Kyereme F, Iczkowski KA, LaViolette PS. Whole slide imaging (WSI) scanner differences influence optical and computed properties of digitized prostate cancer histology. J Pathol Inform 2023; 14:100321. [PMID: 37496560 PMCID: PMC10365953 DOI: 10.1016/j.jpi.2023.100321] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 06/13/2023] [Accepted: 06/28/2023] [Indexed: 07/28/2023] Open
Abstract
Purpose Digital pathology is becoming an increasingly popular area of advancement in both research and clinically. Pathologists are now able to manage and interpret slides digitally, as well as collaborate with external pathologists with digital copies of slides. Differences in slide scanners include variation in resolution, image contrast, and optical properties, which may influence downstream image processing. This study tested the hypothesis that varying slide scanners would result in differences in computed pathomic features on prostate cancer whole mount slides. Design This study collected 192 unique tissue slides from 30 patients following prostatectomy. Tissue samples were paraffin-embedded, stained for hematoxylin and eosin (H&E), and digitized using 3 different scanning microscopes at the highest available magnification rate, for a total of 3 digitized slides per tissue slide. These scanners included a (S1) Nikon microscope equipped with an automated sliding stage, an (S2) Olympus VS120 slide scanner, and a (S3) Huron TissueScope LE scanner. A color deconvolution algorithm was then used to optimize contrast by projecting the RGB image into color channels representing optical stain density. The resulting intensity standardized images were then computationally processed to segment tissue and calculate pathomic features including lumen, stroma, epithelium, and epithelial cell density, as well as second-order features including lumen area and roundness; epithelial area, roundness, and wall thickness; and cell fraction. For each tested feature, mean values of that feature per digitized slide were collected and compared across slide scanners using mixed effect models, fit to compare differences in the tested feature associated with all slide scanners for each slide, including a random effect of subject with a nested random effect of slide to account for repeated measures. Similar models were also computed for tissue densities to examine how differences in scanner impact downstream processing. Results Each mean color channel intensity (i.e., Red, Green, Blue) differed between slide scanners (all P<.001). Of the color deconvolved images, only the hematoxylin channel was similar in all 3 scanners (all P>.05). Lumen and stroma densities between S3 and S1 slides, and epithelial cell density between S3 and S2 (P>.05) were comparable but all other comparisons were significantly different (P<.05). The second-order features were found to be comparable for all scanner comparisons, except for lumen area and epithelium area. Conclusion This study demonstrates that both optical and computed properties of digitized histological samples are impacted by slide scanner differences. Future research is warranted to better understand which scanner properties influence the tissue segmentation process and to develop harmonization techniques for comparing data across multiple slide scanners.
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Affiliation(s)
- Savannah R. Duenweg
- Departments of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Samuel A. Bobholz
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Allison K. Lowman
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Margaret A. Stebbins
- Departments of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Aleksandra Winiarz
- Departments of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Biprojit Nath
- Departments of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Fitzgerald Kyereme
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Kenneth A. Iczkowski
- Department of Pathology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Peter S. LaViolette
- Departments of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
- Department of Biomedical Engineering, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
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Delgado-Ortet M, Reinius MAV, McCague C, Bura V, Woitek R, Rundo L, Gill AB, Gehrung M, Ursprung S, Bolton H, Haldar K, Pathiraja P, Brenton JD, Crispin-Ortuzar M, Jimenez-Linan M, Escudero Sanchez L, Sala E. Lesion-specific 3D-printed moulds for image-guided tissue multi-sampling of ovarian tumours: A prospective pilot study. Front Oncol 2023; 13:1085874. [PMID: 36860310 PMCID: PMC9969130 DOI: 10.3389/fonc.2023.1085874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/24/2023] [Indexed: 02/16/2023] Open
Abstract
Background High-Grade Serous Ovarian Carcinoma (HGSOC) is the most prevalent and lethal subtype of ovarian cancer, but has a paucity of clinically-actionable biomarkers due to high degrees of multi-level heterogeneity. Radiogenomics markers have the potential to improve prediction of patient outcome and treatment response, but require accurate multimodal spatial registration between radiological imaging and histopathological tissue samples. Previously published co-registration work has not taken into account the anatomical, biological and clinical diversity of ovarian tumours. Methods In this work, we developed a research pathway and an automated computational pipeline to produce lesion-specific three-dimensional (3D) printed moulds based on preoperative cross-sectional CT or MRI of pelvic lesions. Moulds were designed to allow tumour slicing in the anatomical axial plane to facilitate detailed spatial correlation of imaging and tissue-derived data. Code and design adaptations were made following each pilot case through an iterative refinement process. Results Five patients with confirmed or suspected HGSOC who underwent debulking surgery between April and December 2021 were included in this prospective study. Tumour moulds were designed and 3D-printed for seven pelvic lesions, covering a range of tumour volumes (7 to 133 cm3) and compositions (cystic and solid proportions). The pilot cases informed innovations to improve specimen and subsequent slice orientation, through the use of 3D-printed tumour replicas and incorporation of a slice orientation slit in the mould design, respectively. The overall research pathway was compatible with implementation within the clinically determined timeframe and treatment pathway for each case, involving multidisciplinary clinical professionals from Radiology, Surgery, Oncology and Histopathology Departments. Conclusions We developed and refined a computational pipeline that can model lesion-specific 3D-printed moulds from preoperative imaging for a variety of pelvic tumours. This framework can be used to guide comprehensive multi-sampling of tumour resection specimens.
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Affiliation(s)
- Maria Delgado-Ortet
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
| | - Marika A. V. Reinius
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Cathal McCague
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Vlad Bura
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Department of Radiology, Clinical Emergency Children’s Hospital, Cluj-Napoca, Romania
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Research Center for Medical Image Analysis & Artificial Intelligence (MIAAI), Danube Private University, Krems, Austria
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, SA, Italy
| | - Andrew B. Gill
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Marcel Gehrung
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Stephan Ursprung
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Helen Bolton
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Krishnayan Haldar
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Pubudu Pathiraja
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - James D. Brenton
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Mercedes Jimenez-Linan
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Lorena Escudero Sanchez
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
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Mayer R, Turkbey B, Choyke P, Simone CB. Pilot study for generating and assessing nomograms and decision curves analysis to predict clinically significant prostate cancer using only spatially registered multi-parametric MRI. Front Oncol 2023; 13:1066498. [PMID: 36761948 PMCID: PMC9902912 DOI: 10.3389/fonc.2023.1066498] [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: 10/10/2022] [Accepted: 01/04/2023] [Indexed: 01/25/2023] Open
Abstract
Background Current prostate cancer evaluation can be inaccurate and burdensome. To help non-invasive prostate tumor assessment, recent algorithms applied to spatially registered multi-parametric (SRMP) MRI extracted novel clinically relevant metrics, namely the tumor's eccentricity (shape), signal-to-clutter ratio (SCR), and volume. Purpose Conduct a pilot study to predict the risk of developing clinically significant prostate cancer using nomograms and employing Decision Curves Analysis (DCA) from the SRMP MRI-based features to help clinicians non-invasively manage prostate cancer. Methods This study retrospectively analyzed 25 prostate cancer patients. MP-MRI (T1, T2, diffusion, dynamic contrast-enhanced) were resized, translated, and stitched to form SRMP MRI. Target detection algorithm [adaptive cosine estimator (ACE)] applied to SRMP MRI determines tumor's eccentricity, noise reduced SCR (by regularizing or eliminating principal components (PC) from the covariance matrix), and volume. Pathology assessed wholemount prostatectomy for Gleason score (GS). Tumors with GS >=4+3 (<=3+4) were judged as "Clinically Significant" ("Insignificant"). Logistic regression combined eccentricity, SCR, volume to generate probability distribution. Nomograms, DCA used all patients plus training (13 patients) and test (12 patients) sets. Area Under the Curves for (AUC) for Receiver Operator Curves (ROC) and p-values evaluated the performance. Results Combining eccentricity (0.45 ACE threshold), SCR (3, 4 PCs), SCR (regularized, modified regularization) with tumor volume (0.65 ACE threshold) improved AUC (>0.70) for ROC curves and p-values (<0.05) for logistic fit. DCA showed greater net benefit from model fit than univariate analysis, treating "all," or "none." Training/test sets achieved comparable AUC but with higher p-values. Conclusions Performance of nomograms and DCA based on metrics derived from SRMP-MRI in this pilot study were comparable to those using prostate serum antigen, age, and PI-RADS.
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Affiliation(s)
- Rulon Mayer
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States,OncoScore, Garrett Park, MD, United States,*Correspondence: Rulon Mayer,
| | - Baris Turkbey
- Molecular Imaging Branch, National Institutes of Health (NIH), Bethesda, MD, United States
| | - Peter Choyke
- Molecular Imaging Branch, National Institutes of Health (NIH), Bethesda, MD, United States
| | - Charles B. Simone
- Department of Radiation Oncology, New York Proton Center, New York, NY, United States
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Mayer R, Turkbey B, Choyke P, Simone CB. Assessing and testing anomaly detection for finding prostate cancer in spatially registered multi-parametric MRI. Front Oncol 2023; 12:1033323. [PMID: 36698418 PMCID: PMC9869917 DOI: 10.3389/fonc.2022.1033323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/30/2022] [Indexed: 01/07/2023] Open
Abstract
Background Evaluating and displaying prostate cancer through non-invasive imagery such as Multi-Parametric MRI (MP-MRI) bolsters management of patients. Recent research quantitatively applied supervised target algorithms using vectoral tumor signatures to spatially registered T1, T2, Diffusion, and Dynamic Contrast Enhancement images. This is the first study to apply the Reed-Xiaoli (RX) multi-spectral anomaly detector (unsupervised target detector) to prostate cancer, which searches for voxels that depart from the background normal tissue, and detects aberrant voxels, presumably tumors. Methods MP-MRI (T1, T2, diffusion, dynamic contrast-enhanced images, or seven components) were prospectively collected from 26 patients and then resized, translated, and stitched to form spatially registered multi-parametric cubes. The covariance matrix (CM) and mean μ were computed from background normal tissue. For RX, noise was reduced for the CM by filtering out principal components (PC), regularization, and elliptical envelope minimization. The RX images were compared to images derived from the threshold Adaptive Cosine Estimator (ACE) and quantitative color analysis. Receiver Operator Characteristic (ROC) curves were used for RX and reference images. To quantitatively assess algorithm performance, the Area Under the Curve (AUC) and the Youden Index (YI) points for the ROC curves were computed. Results The patient average for the AUC and [YI] from ROC curves for RX from filtering 3 and 4 PC was 0.734[0.706] and 0.727[0.703], respectively, relative to the ACE images. The AUC[YI] for RX from modified Regularization was 0.638[0.639], Regularization 0.716[0.690], elliptical envelope minimization 0.544[0.597], and unprocessed CM 0.581[0.608] using the ACE images as Reference Image. The AUC[YI] for RX from filtering 3 and 4 PC was 0.742[0.711] and 0.740[0.708], respectively, relative to the quantitative color images. The AUC[YI] for RX from modified Regularization was 0.643[0.648], Regularization 0.722[0.695], elliptical envelope minimization 0.508[0.605], and unprocessed CM 0.569[0.615] using the color images as Reference Image. All standard errors were less than 0.020. Conclusions This first study of spatially registered MP-MRI applied anomaly detection using RX, an unsupervised target detection algorithm for prostate cancer. For RX, filtering out PC and applying Regularization achieved higher AUC and YI using ACE and color images as references than unprocessed CM, modified Regularization, and elliptical envelope minimization.
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Affiliation(s)
- Rulon Mayer
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States,OncoScore, Garrett Park, MD, United States,*Correspondence: Rulon Mayer,
| | - Baris Turkbey
- Molecular Imaging Branch, National Institutes of Health (NIH), Bethesda, MD, United States
| | - Peter Choyke
- Molecular Imaging Branch, National Institutes of Health (NIH), Bethesda, MD, United States
| | - Charles B. Simone
- Department of Radiation Oncology, New York Proton Center, New York, NY, United States,Memorial Sloan Kettering Cancer Center, New York, NY, United States
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Duenweg SR, Brehler M, Bobholz SA, Lowman AK, Winiarz A, Kyereme F, Nencka A, Iczkowski KA, LaViolette PS. Comparison of a machine and deep learning model for automated tumor annotation on digitized whole slide prostate cancer histology. PLoS One 2023; 18:e0278084. [PMID: 36928230 PMCID: PMC10019669 DOI: 10.1371/journal.pone.0278084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 03/04/2023] [Indexed: 03/18/2023] Open
Abstract
One in eight men will be affected by prostate cancer (PCa) in their lives. While the current clinical standard prognostic marker for PCa is the Gleason score, it is subject to inter-reviewer variability. This study compares two machine learning methods for discriminating between cancerous regions on digitized histology from 47 PCa patients. Whole-slide images were annotated by a GU fellowship-trained pathologist for each Gleason pattern. High-resolution tiles were extracted from annotated and unlabeled tissue. Patients were separated into a training set of 31 patients (Cohort A, n = 9345 tiles) and a testing cohort of 16 patients (Cohort B, n = 4375 tiles). Tiles from Cohort A were used to train a ResNet model, and glands from these tiles were segmented to calculate pathomic features to train a bagged ensemble model to discriminate tumors as (1) cancer and noncancer, (2) high- and low-grade cancer from noncancer, and (3) all Gleason patterns. The outputs of these models were compared to ground-truth pathologist annotations. The ensemble and ResNet models had overall accuracies of 89% and 88%, respectively, at predicting cancer from noncancer. The ResNet model was additionally able to differentiate Gleason patterns on data from Cohort B while the ensemble model was not. Our results suggest that quantitative pathomic features calculated from PCa histology can distinguish regions of cancer; however, texture features captured by deep learning frameworks better differentiate unique Gleason patterns.
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Affiliation(s)
- Savannah R Duenweg
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Michael Brehler
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Samuel A Bobholz
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Allison K Lowman
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Aleksandra Winiarz
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Fitzgerald Kyereme
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Andrew Nencka
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Kenneth A Iczkowski
- Department of Pathology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Peter S LaViolette
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
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van der Beek JN, Fitski M, de Krijger RR, Wijnen MHWA, van den Heuvel-Eibrink MM, Vermeulen MA, van der Steeg AFW, Littooij AS. Direct correlation of MRI with histopathology in pediatric renal tumors through the use of a patient-specific 3-D-printed cutting guide: a feasibility study. Pediatr Radiol 2023; 53:235-243. [PMID: 36040524 PMCID: PMC9892092 DOI: 10.1007/s00247-022-05476-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 06/16/2022] [Accepted: 07/31/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND Pediatric renal tumors are often heterogeneous lesions with variable regions of distinct histopathology. Direct comparison between in vivo imaging and ex vivo histopathology might be useful for identification of discriminating imaging features. OBJECTIVE This feasibility study explored the use of a patient-specific three-dimensional (3D)-printed cutting guide to ensure correct alignment (orientation and slice thickness) between magnetic resonance imaging (MRI) and histopathology. MATERIALS AND METHODS Before total nephrectomy, a patient-specific cutting guide based on each patient's preoperative renal MRI was generated and 3-D printed, to enable consistent transverse orientation of the histological specimen slices with MRI slices. This was expected to result in macroscopic slices of 5 mm each. The feasibility of the technique was determined qualitatively, through questionnaires administered to involved experts, and quantitatively, based on structured measurements including overlap calculation using the dice similarity coefficient. RESULTS The cutting guide was used in eight Wilms tumor patients receiving a total nephrectomy, after preoperative chemotherapy. The median age at diagnosis was 50 months (range: 4-100 months). The positioning and slicing of the specimens were rated overall as easy and the median macroscopic slice thickness of each specimen ranged from 5 to 6 mm. Tumor consistency strongly influenced the practical application of the cutting guide. Digital correlation of a total of 32 slices resulted in a median dice similarity coefficient of 0.912 (range: 0.530-0.960). CONCLUSION We report the feasibility of a patient-specific 3-D-printed MRI-based cutting guide for pediatric renal tumors, allowing improvement of the correlation of MRI and histopathology in future studies.
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Affiliation(s)
- Justine N. van der Beek
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands ,Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Matthijs Fitski
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Ronald R. de Krijger
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands ,Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | | | | | | | - Annemieke S. Littooij
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands ,Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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Mayer R, Turkbey B, Choyke P, Simone CB. Combining and analyzing novel multi-parametric magnetic resonance imaging metrics for predicting Gleason score. Quant Imaging Med Surg 2022; 12:3844-3859. [PMID: 35782272 PMCID: PMC9246760 DOI: 10.21037/qims-21-1092] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/08/2022] [Indexed: 08/17/2023]
Abstract
BACKGROUND Radiologists currently subjectively examine multi-parametric magnetic resonance imaging (MP-MRI) to determine prostate tumor aggressiveness using the Prostate Imaging Reporting and Data System scoring system (PI-RADS). Recent studies showed that modified signal to clutter ratio (SCR), tumor volume, and eccentricity (elongation or roundness) of prostate tumors correlated with Gleason score (GS). No previous studies have combined the prostate tumor's shape, SCR, tumor volume, in order to predict potential tumor aggressiveness and GS. METHODS MP-MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were obtained, resized, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm [adaptive cosine estimator (ACE)]. Pixel-based blobbing, and labeling were applied to the threshold ACE images. Eccentricity calculation used moments of inertia from the blobs. Tumor volume was computed by counting pixels within multi parametric MRI blobs and tumor outlines based on pathologist assessment of whole mount histology. Pathology assessment of GS was performed on whole mount prostatectomy. The covariance matrix and mean of normal tissue background was computed from normal prostate. Using signatures and normal tissue statistics, the z-score, noise corrected SCR [principal component (PC), modified regularization] from each patient was computed. Eccentricity, tumor volume, and SCR were fitted to GS. Analysis of variance assesses the relationship among the variables. RESULTS A multivariate analysis generated correlation coefficient (0.60 to 0.784) and P value (0.00741 to <0.0001) from fitting two sets of independent variates, namely, tumor eccentricity (the eccentricity for the largest blob, weighted average for the eccentricity) and SCR (removing 3 PCs, removing 4 PCs, modified regularization, and z-score) to GS. The eccentricity t-statistic exceeded the SCR t-statistic. The three-variable fit to GS using tumor volume (histology, MRI) yielded correlation coefficients ranging from 0.724 to 0.819 (P value <<0.05). Tumor volumes generated from histology yielded higher correlation coefficients than MRI volumes. Adding volume to eccentricity and SCR adds little improvement for fitting GS due to higher correlation coefficients among independent variables and little additional, independent information. CONCLUSIONS Combining prostate tumors eccentricity with SCR relatively highly correlates with GS.
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Affiliation(s)
- Rulon Mayer
- University of Pennsylvania, Philadelphia, PA, USA
- OncoScore, Garrett Park, MD, USA
| | | | - Peter Choyke
- National Institutes of Health, Bethesda, MD, USA
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Vitek RA, Huang W, Geiger PG, Heninger E, Lang JM, Jarrard DF, Beebe DJ, Johnson BP. Fresh tissue procurement and preparation for multicompartment and multimodal analysis of the prostate tumor microenvironment. Prostate 2022; 82:836-849. [PMID: 35226381 PMCID: PMC9010374 DOI: 10.1002/pros.24326] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 01/23/2022] [Accepted: 02/08/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Prostatic cancers include a diverse microenvironment of tumor cells, cancer-associated fibroblasts, and immune components. This tumor microenvironment (TME) is a known driving force of tumor survival after treatment, but the standard-of-care tissue freezing or fixation in pathology practice limit the use of available approaches/tools to study the TME's functionality in tumor resistance. Thus, there is a need for approaches that satisfy both clinical and laboratory endpoints for TME study. Here we present methods for clinical case identification, tissue processing, and analytical workflow that are compatible with standard histopathology while enabling molecular and functional interrogation of prostate TME components. METHODS We first performed a small retrospective review to identify cases where submission of alternate prostate tissue slices and a parallel live tissue processing protocol complement traditional histopathology and enable viable multicompartment analysis of the TME. Then, we tested its compatibility with commonly employed methods to study the microenvironment including quantification of components both in situ and after tissue dissociation. We also evaluated tissue digestion conditions and cell isolation techniques to aid various molecular and functional endpoints. RESULTS We identified Gleason Grade Group 3+ clinical cases where tumor volume was sufficient to allow slicing of unfixed tissue and distribution of alternating tissue slices to standard-of-care histopathology and viable multi-modal TME analyses. No single method was found that preserved cellular sub-types for all downstream readouts; instead, tissues were further divided so techniques could be catered to each endpoint. For instance, we show that incorporating the protease dispase into tissue dissociation improves viability for culture and functional analyses but hinders immune cell analysis by flow cytometry. We also found that flow activated cell sorting provides highly pure cell populations for quantitative reverse-transcription polymerase chain reaction and RNA-seq while isolation using antibody-labeled paramagnetic particles facilitated functional coculture experiments. CONCLUSIONS The identification of candidate cases and use of these techniques enable translational research and the development of molecular and functional assays to facilitate prostate TME study without compromising standard-of-care histopathological diagnosis. This allows bridging clinical histopathology and further interrogation of the prostate TME and promises to advance our understanding of tumor biology and unveil new predictive and prognostic markers of prostate cancer progression.
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Affiliation(s)
- Ross A. Vitek
- Department of Pathology and Laboratory MedicineUniversity of WisconsinMadisonWisconsinUSA
- Department of Biomedical EngineeringUniversity of WisconsinMadisonWisconsinUSA
| | - Wei Huang
- Department of Pathology and Laboratory MedicineUniversity of WisconsinMadisonWisconsinUSA
| | - Peter G. Geiger
- Department of Pathology and Laboratory MedicineUniversity of WisconsinMadisonWisconsinUSA
| | - Erika Heninger
- Carbone Cancer CenterUniversity of WisconsinMadisonWisconsinUSA
| | - Joshua M. Lang
- Carbone Cancer CenterUniversity of WisconsinMadisonWisconsinUSA
- Department of MedicineUniversity of WisconsinMadisonWisconsinUSA
| | | | - David J. Beebe
- Department of Pathology and Laboratory MedicineUniversity of WisconsinMadisonWisconsinUSA
- Department of Biomedical EngineeringUniversity of WisconsinMadisonWisconsinUSA
- Carbone Cancer CenterUniversity of WisconsinMadisonWisconsinUSA
| | - Brian P. Johnson
- Department of Pathology and Laboratory MedicineUniversity of WisconsinMadisonWisconsinUSA
- Department of Biomedical EngineeringUniversity of WisconsinMadisonWisconsinUSA
- Department of Pharmacology & ToxicologyMichigan State UniversityEast LansingMichiganUSA
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Diffusion Restriction Comparison between Gleason 4 Fused Glands and Cribriform Glands within Patient Using Whole-Mount Prostate Pathology as Ground Truth. Tomography 2022; 8:635-643. [PMID: 35314630 PMCID: PMC8938782 DOI: 10.3390/tomography8020053] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/13/2022] [Accepted: 02/28/2022] [Indexed: 12/16/2022] Open
Abstract
The presence and extent of cribriform patterned Gleason 4 (G4) glands are associated with poor prognosis following radical prostatectomy. This study used whole-mount prostate histology and multiparametric magnetic resonance imaging (MP-MRI) to evaluate diffusion differences in G4 gland morphology. Fourty-eight patients underwent MP-MRI prior to prostatectomy, of whom 22 patients had regions of both G4 cribriform glands and G4 fused glands (G4CG and G4FG, respectively). After surgery, the prostate was sliced using custom, patient-specific 3D-printed slicing jigs modeled according to the T2-weighted MR image, processed, and embedded in paraffin. Whole-mount hematoxylin and eosin-stained slides were annotated by our urologic pathologist and digitally contoured to differentiate the lumen, epithelium, and stroma. Digitized slides were co-registered to the T2-weighted MRI scan. Linear mixed models were fitted to the MP-MRI data to consider the different hierarchical structures at the patient and slide level. We found that Gleason 4 cribriform glands were more diffusion-restricted than fused glands.
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Mayer R, Simone CB, Turkbey B, Choyke P. Development and testing quantitative metrics from multi-parametric magnetic resonance imaging that predict Gleason score for prostate tumors. Quant Imaging Med Surg 2022; 12:1859-1870. [PMID: 35284265 PMCID: PMC8899928 DOI: 10.21037/qims-21-761] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 10/18/2021] [Indexed: 08/17/2023]
Abstract
BACKGROUND Radiologists currently subjectively examine multi-parametric magnetic resonance imaging (MRI) to detect possible clinically significant lesions using the Prostate Imaging Reporting and Data System (PI-RADS) protocol. The assessment of imaging, however, relies on the experience and judgement of radiologists creating opportunity for inter-reader variability. Quantitative metrics, such as z-score and signal to clutter ratio (SCR), are therefore needed. METHODS Multi-parametric MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were resampled, rescaled, translated, and stitched to form spatially registered multi-parametric cubes for patients undergoing radical prostatectomy. Multi-parametric signatures that characterize prostate tumors were inserted into z-score and SCR. The multispectral covariance matrix was computed for the outlined normal prostate. The z-score from each MRI image was computed and summed. To reduce noise in the covariance matrix, following matrix decomposition, the noisy eigenvectors were removed. Also, regularization and modified regularization was applied to the covariance matrix by minimizing the discrimination score. The filtered and regularized covariance matrices were inserted into the SCR calculation. The z-score and SCR were quantitatively compared to Gleason scores from clinical pathology assessment of the histology of sectioned wholemount prostates. RESULTS Twenty-six consecutive patients were enrolled in this retrospective study. Median patient age was 60 years (range, 49 to 75 years), median prostate-specific antigen (PSA) was 5.8 ng/mL (range, 2.3 to 23.7 ng/mL), and median Gleason score was 7 (range, 6 to 9). A linear fit of the summed z-score against Gleason score found a correlation of R=0.48 and a P value of 0.015. A linear fit of the SCR from regularizing covariance matrix against Gleason score found a correlation of R=0.39 and a P value of 0.058. The SCR employing the modified regularizing covariance matrix against Gleason score found a correlation of R=0.52 and a P value of 0.007. A linear fit of the SCR from filtering out 3 and 4 eigenvectors from the covariance matrix against Gleason score found correlations of R=0.50 and 0.44, respectively, and P values of 0.011 and 0.027, respectively. CONCLUSIONS Z-score and SCR using filtered and regularized covariance matrices derived from spatially registered multi-parametric MRI correlates with Gleason score with highly significant P values.
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Affiliation(s)
- Rulon Mayer
- University of Pennsylvania, Philadelphia, PA, USA
- OncoScore, Garrett Park, MD, USA
| | | | | | - Peter Choyke
- National Institutes of Health, Bethesda, MD, USA
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Mayer R, Simone CB, Turkbey B, Choyke P. Prostate tumor eccentricity predicts Gleason score better than prostate tumor volume. Quant Imaging Med Surg 2022; 12:1096-1108. [PMID: 35111607 DOI: 10.21037/qims-21-466] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 09/03/2021] [Indexed: 12/15/2022]
Abstract
Background Prostate tumor volume predicts biochemical recurrence, metastases, and tumor proliferation. A recent study showed that prostate tumor eccentricity (elongation or roundness) correlated with Gleason score. No studies examined the relationship among the prostate tumor's shape, volume, and potential aggressiveness. Methods Of the 26 patients that were analyzed, 18 had volumes >1 cc for the histology-based study, and 25 took up contrast material for the MRI portion of this study. This retrospective study quantitatively compared tumor eccentricity and volume measurements from pathology assessment sectioned wholemount prostates and multi-parametric MRI to Gleason scores. Multi-parametric MRI (T1, T2, diffusion, dynamic contrast-enhanced images) were resized, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm (Adaptive Cosine Estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. Pixel-based blobbing, and labeling were applied to digitized pathology slides and threshold ACE images. Tumor volumes were measured by counting voxels within the blob. Eccentricity calculation used moments of inertia from the blobs. Results From wholemount prostatectomy slides, fitting two sets of independent variables, prostate tumor eccentricity (largest blob eccentricity, weighted eccentricity, filtered weighted eccentricity) and tumor volume (largest blob volume, average blob volume, filtered average blob volume) to Gleason score in a multivariate analysis, yields correlation coefficient R=0.798 to 0.879 with P<0.01. The eccentricity t-statistic exceeded the volume t-statistic. Fitting histology-based total prostate tumor volume against Gleason score yields R=0.498, P=0.0098. From multi-parametric MRI, the correlation coefficient R between the Gleason score and the largest blob eccentricity for varying thresholds (0.30 to 0.55) ranged from -0.51 to -0.672 (P<0.01). For varying thresholds (0.60 to 0.80) for MRI detection, the R between the largest blob volume eccentricity against the Gleason score ranged from 0.46 to 0.50 (P<0.03). Combining tumor eccentricity and tumor volume in multivariate analysis failed to increase Gleason score prediction. Conclusions Prostate tumor eccentricity, determined by histology or MRI, more accurately predicted Gleason score than prostate tumor volume. Combining tumor eccentricity with volume from histology-based analysis enhanced Gleason score prediction, unlike MRI.
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Affiliation(s)
- Rulon Mayer
- University of Pennsylvania, Philadelphia, PA, USA.,Oncoscore, Garrett Park, MD, USA
| | | | | | - Peter Choyke
- National Institutes of Health, Bethesda, MD, USA
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14
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Correlation of in-vivo imaging with histopathology: A review. Eur J Radiol 2021; 144:109964. [PMID: 34619617 DOI: 10.1016/j.ejrad.2021.109964] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 08/26/2021] [Accepted: 09/17/2021] [Indexed: 11/21/2022]
Abstract
Despite tremendous advancements in in vivo imaging modalities, there remains substantial uncertainty with respect to tumor delineation on in these images. Histopathology remains the gold standard for determining the extent of malignancy, with in vivo imaging to histopathologic correlation enabling spatial comparisons. In this review, the steps necessary for successful imaging to histopathologic correlation are described, including in vivo imaging, resection, fixation, specimen sectioning (sectioning technique, securing technique, orientation matching, slice matching), microtome sectioning and staining, correlation (including image registration) and performance evaluation. The techniques used for each of these steps are also discussed. Hundreds of publications from the past 20 years were surveyed, and 62 selected for detailed analysis. For these 62 publications, each stage of the correlative pathology process (and the sub-steps of specimen sectioning) are listed. A statistical analysis was conducted based on 19 studies that reported target registration error as their performance metric. While some methods promise greater accuracy, they may be expensive. Due to the complexity of the processes involved, correlative pathology studies generally include a small number of subjects, which hinders advanced developments in this field.
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15
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Mayer R, Simone CB, Turkbey B, Choyke P. Correlation of prostate tumor eccentricity and Gleason scoring from prostatectomy and multi-parametric-magnetic resonance imaging. Quant Imaging Med Surg 2021; 11:4235-4244. [PMID: 34603979 DOI: 10.21037/qims-21-24] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/22/2021] [Indexed: 01/25/2023]
Abstract
Background Proliferating cancer cells interacting with their microenvironment affects a tumor's spatial shape. Elongation or roundness (eccentricity) of lung, skin, and breast cancers indicates the cancer's relative aggressiveness. Non-invasive determination of the prostate tumor's shape should provide meaningful input for prognostication and clinical management. There are currently few studies of prostate tumor shape, therefore this study examines the relationship between a prostate tumor's eccentricity, derived from spatially registered multi-parametric MRI and histology slides, and Gleason scores. Methods A total of 26 consecutive patients were enrolled in the study. Median patient age was 60 years (range, 49 to 75 years), median PSA was 5.8 ng/mL (range, 2.3 to 23.7 ng/mL, and median Gleason score was 7 (range, 6 to 9). Multi-parametric MRI (T1, T2, Diffusion, Dynamic Contrast Enhanced) were resampled, rescaled, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm (Adaptive Cosine Estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. Also, tumor shape was computed from the histology slides. Blobbing, labeling, and calculation of eccentricity using moments of inertia were applied to the multi-parametric MRI and histology slides. The eccentricity measurements were compared to the Gleason scores from 25 patients. Results From histology slides analysis: the correlation coefficient between the eccentricity for the largest blob and a weighted average eccentricity against the Gleason score ranged from -0.67 to -0.78 for all 18 patients whose tumor volume exceeded 1.0 cc. From multi-parametric MRI analysis: the correlation coefficient between the eccentricity for the largest blob for varying thresholds against the Gleason score ranged from -0.60 to -0.66 for all 25 patients showing contrast uptake in the Dynamic Contrast Enhancement (DCE) MRI. Conclusions Spherical shape prostate adenocarcinoma shows a propensity for higher Gleason score. This novel finding follows lung and breast adenocarcinomas but depart from other primary tumor types. Analysis of multi-parametric MRI can non-invasively determine the prostate tumor's morphology and add critical information for prognostication and disease management. Eccentricity of smaller tumors (<1.0 cc) from MP-MRI correlates well with Gleason score, unlike eccentricity measured using histology of wholemount prostatectomy.
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Affiliation(s)
- Rulon Mayer
- University of Pennsylvania, Philadelphia, PA, USA.,OncoScore, Garrett Park, MD, USA
| | | | | | - Peter Choyke
- National Institutes of Health, Bethesda, MD, USA
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16
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Sandgren K, Nilsson E, Keeratijarut Lindberg A, Strandberg S, Blomqvist L, Bergh A, Friedrich B, Axelsson J, Ögren M, Ögren M, Widmark A, Thellenberg Karlsson C, Söderkvist K, Riklund K, Jonsson J, Nyholm T. Registration of histopathology to magnetic resonance imaging of prostate cancer. Phys Imaging Radiat Oncol 2021; 18:19-25. [PMID: 34258403 PMCID: PMC8254194 DOI: 10.1016/j.phro.2021.03.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 02/16/2021] [Accepted: 03/25/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND AND PURPOSE The diagnostic accuracy of new imaging techniques requires validation, preferably by histopathological verification. The aim of this study was to develop and present a registration procedure between histopathology and in-vivo magnetic resonance imaging (MRI) of the prostate, to estimate its uncertainty and to evaluate the benefit of adding a contour-correcting registration. MATERIALS AND METHODS For twenty-five prostate cancer patients, planned for radical prostatectomy, a 3D-printed prostate mold based on in-vivo MRI was created and an ex-vivo MRI of the specimen, placed inside the mold, was performed. Each histopathology slice was registered to its corresponding ex-vivo MRI slice using a 2D-affine registration. The ex-vivo MRI was rigidly registered to the in-vivo MRI and the resulting transform was applied to the histopathology stack. A 2D deformable registration was used to correct for specimen distortion concerning the specimen's fit inside the mold. We estimated the spatial uncertainty by comparing positions of landmarks in the in-vivo MRI and the corresponding registered histopathology stack. RESULTS Eighty-four landmarks were identified, located in the urethra (62%), prostatic cysts (33%), and the ejaculatory ducts (5%). The median number of landmarks was 3 per patient. We showed a median in-plane error of 1.8 mm before and 1.7 mm after the contour-correcting deformable registration. In patients with extraprostatic margins, the median in-plane error improved from 2.1 mm to 1.8 mm after the contour-correcting deformable registration. CONCLUSIONS Our registration procedure accurately registers histopathology to in-vivo MRI, with low uncertainty. The contour-correcting registration was beneficial in patients with extraprostatic surgical margins.
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Affiliation(s)
- Kristina Sandgren
- Department of Radiation Sciences, Radiophysics, Umea University, Sweden
| | - Erik Nilsson
- Department of Radiation Sciences, Radiophysics, Umea University, Sweden
| | | | - Sara Strandberg
- Department of Radiation Sciences, Diagnostic Radiology, Umea University, Sweden
| | - Lennart Blomqvist
- Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden
| | - Anders Bergh
- Department of Medical Biosciences, Pathology, Umea University, Sweden
| | - Bengt Friedrich
- Department of Surgical and Perioperative Sciences, Urology and Andrology, Umea University, Sweden
| | - Jan Axelsson
- Department of Radiation Sciences, Radiophysics, Umea University, Sweden
| | - Margareta Ögren
- Department of Radiation Sciences, Diagnostic Radiology, Umea University, Sweden
| | - Mattias Ögren
- Department of Radiation Sciences, Diagnostic Radiology, Umea University, Sweden
| | - Anders Widmark
- Department of Radiation Sciences, Oncology, Umea University, Sweden
| | | | - Karin Söderkvist
- Department of Radiation Sciences, Oncology, Umea University, Sweden
| | - Katrine Riklund
- Department of Radiation Sciences, Diagnostic Radiology, Umea University, Sweden
| | - Joakim Jonsson
- Department of Radiation Sciences, Radiophysics, Umea University, Sweden
| | - Tufve Nyholm
- Department of Radiation Sciences, Radiophysics, Umea University, Sweden
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Meyer A, Chlebus G, Rak M, Schindele D, Schostak M, van Ginneken B, Schenk A, Meine H, Hahn HK, Schreiber A, Hansen C. Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105821. [PMID: 33218704 DOI: 10.1016/j.cmpb.2020.105821] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 10/26/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate and reliable segmentation of the prostate gland in MR images can support the clinical assessment of prostate cancer, as well as the planning and monitoring of focal and loco-regional therapeutic interventions. Despite the availability of multi-planar MR scans due to standardized protocols, the majority of segmentation approaches presented in the literature consider the axial scans only. In this work, we investigate whether a neural network processing anisotropic multi-planar images could work in the context of a semantic segmentation task, and if so, how this additional information would improve the segmentation quality. METHODS We propose an anisotropic 3D multi-stream CNN architecture, which processes additional scan directions to produce a high-resolution isotropic prostate segmentation. We investigate two variants of our architecture, which work on two (dual-plane) and three (triple-plane) image orientations, respectively. The influence of additional information used by these models is evaluated by comparing them with a single-plane baseline processing only axial images. To realize a fair comparison, we employ a hyperparameter optimization strategy to select optimal configurations for the individual approaches. RESULTS Training and evaluation on two datasets spanning multiple sites show statistical significant improvement over the plain axial segmentation (p<0.05 on the Dice similarity coefficient). The improvement can be observed especially at the base (0.898 single-plane vs. 0.906 triple-plane) and apex (0.888 single-plane vs. 0.901 dual-plane). CONCLUSION This study indicates that models employing two or three scan directions are superior to plain axial segmentation. The knowledge of precise boundaries of the prostate is crucial for the conservation of risk structures. Thus, the proposed models have the potential to improve the outcome of prostate cancer diagnosis and therapies.
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Affiliation(s)
- Anneke Meyer
- Faculty of Computer Science and Research Campus STIMULATE, University of Magdeburg, Germany.
| | - Grzegorz Chlebus
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany; Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marko Rak
- Faculty of Computer Science and Research Campus STIMULATE, University of Magdeburg, Germany
| | - Daniel Schindele
- Clinic of Urology and Pediatric Urology, University Hospital Magdeburg, Germany
| | - Martin Schostak
- Clinic of Urology and Pediatric Urology, University Hospital Magdeburg, Germany
| | - Bram van Ginneken
- Radboud University Medical Center, Nijmegen, The Netherlands; Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Andrea Schenk
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Hans Meine
- University of Bremen, Medical Image Computing Group, Bremen, Germany; Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Horst K Hahn
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | | | - Christian Hansen
- Faculty of Computer Science and Research Campus STIMULATE, University of Magdeburg, Germany
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Additive Manufacturing of Resected Oral and Oropharyngeal Tissue: A Pilot Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18030911. [PMID: 33494422 PMCID: PMC7908081 DOI: 10.3390/ijerph18030911] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 01/16/2021] [Accepted: 01/18/2021] [Indexed: 11/21/2022]
Abstract
Better visualization of tumor structure and orientation are needed in the postoperative setting. We aimed to assess the feasibility of a system in which oral and oropharyngeal tumors are resected, photographed, 3D modeled, and printed using additive manufacturing techniques. Three patients diagnosed with oral/oropharyngeal cancer were included. All patients underwent preoperative magnetic resonance imaging followed by resection. In the operating room (OR), the resected tissue block was photographed using a smartphone. Digital photos were imported into Agisoft Photoscan to produce a digital 3D model of the resected tissue. Physical models were then printed using binder jetting techniques. The aforementioned process was applied in pilot cases including carcinomas of the tongue and larynx. The number of photographs taken for each case ranged from 63 to 195. The printing time for the physical models ranged from 2 to 9 h, costs ranging from 25 to 141 EUR (28 to 161 USD). Digital photography may be used to additively manufacture models of resected oral/oropharyngeal tumors in an easy, accessible and efficient fashion. The model may be used in interdisciplinary discussion regarding postoperative care to improve understanding and collaboration, but further investigation in prospective studies is required.
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Mayer R, Simone CB, Turkbey B, Choyke P. Algorithms applied to spatially registered multi-parametric MRI for prostate tumor volume measurement. Quant Imaging Med Surg 2021; 11:119-132. [PMID: 33392016 DOI: 10.21037/qims-20-137a] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Prostate tumor volume correlates with critical components of cancer staging such as Gleason score (GS) grade, predicted disease progression, and metastasis. Therefore, non-invasive tumor volume measurement may elevate clinical management. Radiology assessments of multi-parametric MRI (MP-MRI) commonly visually examine individual images to determine possible tumor presence. This study combines registered MP-MRI into a single image that display normal tissue and possible lesions. This study tests and exploits the vector nature of spatially registered MP-MRI by using supervised target detection algorithms (STDA) and color display and psychovisual analysis (CIELAB) to non-invasively estimate prostate tumor volume. Methods MRI, including T1, T2, diffusion [apparent diffusion coefficient (ADC)], dynamic contrast enhanced (DCE) images, were resampled, rescaled, translated, and stitched to form spatially registered Multi-parametric cubes. The multi-parametric or multi-spectral signatures (7-component or T1, T2, ADC, etc.) that characterize the prostate tumors were inserted into target detection algorithms with conical decision surfaces (adaptive cosine estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. In addition, tumor appeared as yellow in color images that were created by assigning red to washout from DCE, green to high B from diffusion, and blue to autonomous diffusion image. The yellow voxels in the three-channel hypercube were visually identified by a reader and recording voxels that exceed a threshold in the b* component of the CIELAB algorithm. The number of reported tumor voxels were converted to volume based on spatial resolution and slice separation. The tumor volume measurements were quantitatively validated by comparing the tumor volume computations to the pathologist's assessment of the histology of sectioned whole mount prostates from 26 consecutive patients with prostate adenocarcinoma who underwent radical prostatectomy. This study analyzed tumors exceeding 1 cc and that also took up contrast material (18 patients). Results High correlation coefficients for tumor volume measurements using supervised target detection and color analysis vs. histology from wholemount prostatectomy were computed (R=0.83 and 0.91, respectively). A linear fit for tumor volume measurements using for supervised target detection and color analysis vs. tumor measurements from radical prostatectomy (after correcting for shrinkage from the radical prostatectomy) results in a slope of 1.02 and 3.02, respectively. A polynomial fit for the color analysis to the histology found (R=0.95). Voxels exceeding a threshold in the b* part of the CIELAB algorithm yielded correlation coefficients (0.71, 0.80) offsets (0.01 cc, -0.63 cc) and slopes (1.99, 0.89) against the wholemount prostatectomy and color analysis, respectively. Conclusions Supervised target detection and color display and analysis applied to registered MP-MRI non-invasively estimates prostate tumor volumes >1 cc and displaying angiogenesis.
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Affiliation(s)
- Rulon Mayer
- Oncoscore, Garrett Park, MD, USA.,University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Peter Choyke
- National Institutes of Health, Bethesda, MD, USA
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Crispin-Ortuzar M, Gehrung M, Ursprung S, Gill AB, Warren AY, Beer L, Gallagher FA, Mitchell TJ, Mendichovszky IA, Priest AN, Stewart GD, Sala E, Markowetz F. Three-Dimensional Printed Molds for Image-Guided Surgical Biopsies: An Open Source Computational Platform. JCO Clin Cancer Inform 2020; 4:736-748. [PMID: 32804543 PMCID: PMC7469624 DOI: 10.1200/cci.20.00026] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/22/2020] [Indexed: 12/14/2022] Open
Abstract
PURPOSE Spatial heterogeneity of tumors is a major challenge in precision oncology. The relationship between molecular and imaging heterogeneity is still poorly understood because it relies on the accurate coregistration of medical images and tissue biopsies. Tumor molds can guide the localization of biopsies, but their creation is time consuming, technologically challenging, and difficult to interface with routine clinical practice. These hurdles have so far hindered the progress in the area of multiscale integration of tumor heterogeneity data. METHODS We have developed an open-source computational framework to automatically produce patient-specific 3-dimensional-printed molds that can be used in the clinical setting. Our approach achieves accurate coregistration of sampling location between tissue and imaging, and integrates seamlessly with clinical, imaging, and pathology workflows. RESULTS We applied our framework to patients with renal cancer undergoing radical nephrectomy. We created personalized molds for 6 patients, obtaining Dice similarity coefficients between imaging and tissue sections ranging from 0.86 to 0.96 for tumor regions and between 0.70 and 0.76 for healthy kidneys. The framework required minimal manual intervention, producing the final mold design in just minutes, while automatically taking into account clinical considerations such as a preference for specific cutting planes. CONCLUSION Our work provides a robust and automated interface between imaging and tissue samples, enabling the development of clinical studies to probe tumor heterogeneity on multiple spatial scales.
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Affiliation(s)
- Mireia Crispin-Ortuzar
- Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Marcel Gehrung
- Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Stephan Ursprung
- Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
| | - Andrew B. Gill
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
| | - Anne Y. Warren
- Department of Histopathology, Cambridge University Hospitals National Health Service (NHS) Foundation Trust, Cambridge, United Kingdom
| | - Lucian Beer
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria
| | | | - Thomas J. Mitchell
- Department of Surgery, University of Cambridge, Cambridge, United Kingdom
- Wellcome Trust Sanger Institute, Hinxton, United Kingdom
| | - Iosif A. Mendichovszky
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Andrew N. Priest
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Grant D. Stewart
- Department of Surgery, University of Cambridge, Cambridge, United Kingdom
| | - Evis Sala
- Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
| | - Florian Markowetz
- Cancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
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21
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Liver-specific 3D sectioning molds for correlating in vivo CT and MRI with tumor histopathology in woodchucks (Marmota monax). PLoS One 2020; 15:e0230794. [PMID: 32214365 PMCID: PMC7098627 DOI: 10.1371/journal.pone.0230794] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 03/08/2020] [Indexed: 12/19/2022] Open
Abstract
Purpose To evaluate the spatial registration and correlation of liver and tumor histopathology sections with corresponding in vivo CT and MRI using 3D, liver-specific cutting molds in a woodchuck (Marmota monax) hepatic tumor model. Methods Five woodchucks chronically infected with woodchuck hepatitis virus following inoculation at birth and with confirmed hepatic tumors were imaged by contrast enhanced CT or MRI. Virtual 3D liver or tumor models were generated by segmentation of in vivo CT or MR imaging. A specimen-specific cavity was created inside a block containing cutting slots aligned with an imaging plane using computer-aided design software, and the final cutting molds were fabricated using a 3D printer. Livers were resected two days after initial imaging, fixed with formalin or left unfixed, inserted into the 3D molds, and cut into parallel pieces by passing a sharp blade through the parallel slots in the mold. Histopathology sections were acquired and their spatial overlap with in vivo image slices was quantified using the Dice similarity coefficient (DSC). Results Imaging of the woodchucks revealed heterogeneous hepatic tumors of varying size, number, and location. Specimen-specific 3D molds provided accurate co-localization of histopathology of whole livers, liver lobes, and pedunculated tumors with in vivo CT and MR imaging, with or without tissue fixation. Visual inspection of histopathology sections and corresponding in vivo image slices revealed spatial registration of analogous pathologic features. The mean DSC for all specimens was 0.83+/-0.05. Conclusion Use of specimen-specific 3D molds for en bloc liver dissection provided strong spatial overlap and feature correspondence between in vivo image slices and histopathology sections.
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22
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Wilkinson S, Harmon SA, Terrigino NT, Karzai F, Pinto PA, Madan RA, VanderWeele DJ, Lake R, Atway R, Bright JR, Carrabba NV, Trostel SY, Lis RT, Chun G, Gulley JL, Merino MJ, Choyke PL, Ye H, Dahut WL, Turkbey B, Sowalsky AG. A case report of multiple primary prostate tumors with differential drug sensitivity. Nat Commun 2020; 11:837. [PMID: 32054861 PMCID: PMC7018822 DOI: 10.1038/s41467-020-14657-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 01/23/2020] [Indexed: 12/04/2022] Open
Abstract
Localized prostate cancers are genetically variable and frequently multifocal, comprising spatially distinct regions with multiple independently-evolving clones. To date there is no understanding of whether this variability can influence management decisions for patients with prostate tumors. Here, we present a single case from a clinical trial of neoadjuvant intense androgen deprivation therapy. A patient was diagnosed with a large semi-contiguous tumor by imaging, histologically composed of a large Gleason score 9 tumor with an adjacent Gleason score 7 nodule. DNA sequencing demonstrates these are two independent tumors, as only the Gleason 9 tumor harbors single-copy losses of PTEN and TP53. The PTEN/TP53-deficient tumor demonstrates treatment resistance, selecting for subclones with mutations to the remaining copies of PTEN and TP53, while the Gleason 7 PTEN-intact tumor is almost entirely ablated. These findings indicate that spatiogenetic variability is a major confounder for personalized treatment of patients with prostate cancer.
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Affiliation(s)
- Scott Wilkinson
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA
| | - Stephanie A Harmon
- Molecular Imaging Program, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, 8560 Progress Drive, Frederick, MD, 21701, USA
| | - Nicholas T Terrigino
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA
| | - Fatima Karzai
- Genitourinary Malignancies Branch, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
| | - Ravi A Madan
- Genitourinary Malignancies Branch, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
| | - David J VanderWeele
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA
- Department of Medicine, Feinberg School of Medicine, 420 E. Superior Street, Chicago, IL, 60611, USA
| | - Ross Lake
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA
| | - Rayann Atway
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA
| | - John R Bright
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA
| | - Nicole V Carrabba
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA
| | - Shana Y Trostel
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA
| | - Rosina T Lis
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA
| | - Guinevere Chun
- Genitourinary Malignancies Branch, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
| | - James L Gulley
- Genitourinary Malignancies Branch, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
| | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
| | - Peter L Choyke
- Molecular Imaging Program, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
| | - Huihui Ye
- Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA, 02215, USA
- Department of Pathology and Department of Urology, University of California Los Angeles, 10833 Le Conte Avenue, Los Angeles, CA, 90095, USA
| | - William L Dahut
- Genitourinary Malignancies Branch, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD, 20892, USA
| | - Adam G Sowalsky
- Laboratory of Genitourinary Cancer Pathogenesis, National Cancer Institute, NIH, 37 Convent Drive, Bethesda, MD, 20892, USA.
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23
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Harmon SA, Brown GT, Sanford T, Mehralivand S, Shih JH, Xu S, Merino MJ, Choyke PL, Pinto PA, Wood BJ, McKenney JK, Turkbey B. Spatial density and diversity of architectural histology in prostate cancer: influence on diffusion weighted magnetic resonance imaging. Quant Imaging Med Surg 2020; 10:326-339. [PMID: 32190560 DOI: 10.21037/qims.2020.01.06] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background To assess the influence of specific histopathologic patterns on MRI diffusion characteristics by performing rigorous whole-mount/imaging registration and correlating histologic architectures observed in prostate cancer with diffusion characteristics in prostate MRIs. Methods Fifty-two whole-mount pathology blocks from 15 patients who underwent multiparametric MRI (mpMRI) at a single institution prior to radical prostatectomy were retrospectively analyzed. Regions containing individual morphologic patterns (N=21 patterns, including variations of cribriforming, expansile sheets, single cells, patterns of early intraluminal complexity, and mucin rupture patterns) were digitally annotated by an expert genitourinary pathologist. Distinct tumor foci on each slide were also assigned a Gleason grade and scored as having any high-risk histologic pattern. Digital sections were aligned to MRI using a patient-specific mold and registered using local mean weighted piecewise transformation based on anatomic control points. Density and presence of morphological patterns was correlated to apparent diffusion coefficient (ADC) signal intensity using mixed effects model accounting for nested intra-foci, intra-patient correlation. Influence of intra-tumoral heterogeneity was assessed by affinity propagation clustering (APC) of morphology features and correlated to foci- and cluster-level ADC metrics. Results One hundred eleven distinct tumor foci were evaluated. Beta diversity, reflecting average morphology representation across inter- and intra-foci areas, demonstrated higher intra-tumor diversity within high-risk foci (P<0.05). ADC signal demonstrated an inverse correlation with foci-level Gleason grade (P>0.05), which was strengthened in cluster-level analysis for intra-foci regions containing high-risk morphologies (P=0.017). In voxel-based analysis, dense regions demonstrate lower ADC, but the presence and density for each morphology influenced ADC independently (ANOVA P<0.001). Conclusions Architectural features influence ADC characteristics of MRI, with more complex tumors having lower ADC values regulated by presence and density of specific morphologies.
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Affiliation(s)
- Stephanie A Harmon
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Bethesda, MD, USA.,Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - G Thomas Brown
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Bethesda, MD, USA.,National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Thomas Sanford
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sherif Mehralivand
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Joanna H Shih
- Division of Cancer Treatment and Diagnosis: Biometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sheng Xu
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter L Choyke
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Bradford J Wood
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jesse K McKenney
- Department of Anatomic Pathology, Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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24
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Szewczyk-Bieda M, Wei C, Coll K, Gandy S, Donnan P, Ragupathy SKA, Singh P, Wilson J, Nabi G. A multicentre parallel-group randomised trial assessing multiparametric MRI characterisation and image-guided biopsy of prostate in men suspected of having prostate cancer: MULTIPROS study protocol. Trials 2019; 20:638. [PMID: 31752954 PMCID: PMC6868804 DOI: 10.1186/s13063-019-3746-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2019] [Accepted: 09/23/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND There is growing evidence suggesting that multiparametric magnetic resonance imaging (mpMRI) is a marker for prostate cancer (PCa) aggressiveness and could be used to plan treatment. Improving early detection of clinically significant PCa with pre-biopsy mpMRI would very likely have advantages including optimising the diagnosis and treatment of diseases and diminishing patient anxiety. METHODS AND MATERIALS This is a prospective multicentre study of pre-biopsy mpMRI diagnostic test accuracy with subgroup randomisation at a 1:1 ratio with respect to transrectal ultrasound (TRUS) and MRI/US fusion-guided biopsy or TRUS-only biopsy. It is designed as a single-gate study with a single set of inclusion criteria. The total duration of the recruitment phase was 48 months; however, this has now been extended to 66 months. A sample size of 600 participants is required. DISCUSSION The primary objective is to determine whether mpMRI can improve PCa detection and characterisation. The key secondary objective is to determine whether MRI/US fusion-guided biopsy can reduce the number of false-negative biopsies. Ethical approval was obtained from the East of Scotland Research Ethics Committee 1 (14/ES/1070) on 20 November 2014. The results of this study will be used for publication and presentation in national and international journals and at scientific conferences. TRIAL REGISTRATION ClinicalTrials.gov, NCT02745496. Retrospectively registered on 20 April 2016.
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Affiliation(s)
| | - Cheng Wei
- Division of Imaging Science and Technology, School of Medicine, University of Dundee, Ninewells Hospital, Dundee, DD1 9SY, UK
| | - Katherine Coll
- Tayside Clinical Trials Unit (TCTU), Tayside Medical Science Centre (TASC), University of Dundee, Ninewells Hospital, Dundee, DD1 9SY, UK
| | - Stephen Gandy
- Department of Medical Physics, Ninewells Hospital, Dundee, DD1 9SY, UK
| | - Peter Donnan
- Division of Population Health Genomics, University of Dundee, Dundee, DD2 4BF, UK
| | | | - Paras Singh
- Royal Free London NHS Foundation Trust, Royal Free Hospital, London, NW3 2QG, UK
| | - Jennifer Wilson
- Department of Clinical Pathology, Ninewells Hospital, Dundee, DD1 9SY, UK
| | - Ghulam Nabi
- Division of Imaging Science and Technology, School of Medicine, University of Dundee, Ninewells Hospital, Dundee, DD1 9SY, UK.
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25
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Rutkowski DR, Wells SA, Johnson B, Huang W, Jarrard DF, Lang JM, Cho S, Roldán-Alzate A. Mri-based cancer lesion analysis with 3d printed patient specific prostate cutting guides. AMERICAN JOURNAL OF CLINICAL AND EXPERIMENTAL UROLOGY 2019; 7:215-222. [PMID: 31511828 PMCID: PMC6734042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Accepted: 07/22/2019] [Indexed: 06/10/2023]
Abstract
Purpose: MRI methods have improved diagnosis and treatment planning for prostate cancer. However, validation and standardization is needed to encourage widespread adoption of these methods. The purpose of this study was to improve validation methods by creating a prostate cutting guide and to develop a method for 3D comparison between MRI data and post-prostatectomy histological tissue slices. Methods: Prostate Specific Membrane Antigen (PSMA) Positron Emission Tomography (PET)/MRI was performed on 10 patients with prostate cancer before and after chemohormonal treatment. Post-treatment images were used to design patient-specific prostate cutting guides that were used to create uniform thickness sections of surgically removed prostates. The thickness of the prostate tissue slices matched the imaging slice thickness so that comparisons could be made between MRI results and histopathological study results. A method was also developed to compare post-slicing prostate bulk geometry with the predicted MRI prostate geometry. Results: The prostate cutting guides were used to successfully section the prostate for histopathogical evaluation and slice-by-slice MRI comparison. Surface comparison results displayed an average dimensional difference of 1.99 ± 3.19 mm between MRI and post-prostatectomy slice reconstruction prostate geometries. Conclusion: MRI-based prostate cutting guides were designed, fabricated, and implemented in a study examining the utility and accuracy of MRI for the detection of prostate cancer. Furthermore, a three-dimensional part comparison method was developed, which can be used for validation of MRI with pathological and histological data. Future work will analyze more subjects to examine the effectiveness of these guides for histopathological prostate analysis with MRI and PET/MRI.
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Affiliation(s)
- David R Rutkowski
- Mechanical Engineering, University of WisconsinMadison, WI, United States
- Radiology, University of WisconsinMadison, WI, United States
| | - Shane A Wells
- Radiology, University of WisconsinMadison, WI, United States
| | - Brian Johnson
- Biomedical Engineering, University of WisconsinMadison, WI, United States
| | - Wei Huang
- Pathology, University of WisconsinMadison, WI, United States
| | | | - Joshua M Lang
- Medicine, University of WisconsinMadison, WI, United States
| | - Steve Cho
- Radiology, University of WisconsinMadison, WI, United States
| | - Alejandro Roldán-Alzate
- Mechanical Engineering, University of WisconsinMadison, WI, United States
- Radiology, University of WisconsinMadison, WI, United States
- Biomedical Engineering, University of WisconsinMadison, WI, United States
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26
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Greer MD, Shih JH, Lay N, Barrett T, Bittencourt L, Borofsky S, Kabakus I, Law YM, Marko J, Shebel H, Merino MJ, Wood BJ, Pinto PA, Summers RM, Choyke PL, Turkbey B. Interreader Variability of Prostate Imaging Reporting and Data System Version 2 in Detecting and Assessing Prostate Cancer Lesions at Prostate MRI. AJR Am J Roentgenol 2019; 212:1197-1205. [PMID: 30917023 PMCID: PMC8268760 DOI: 10.2214/ajr.18.20536] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
OBJECTIVE. The purpose of this study was to evaluate agreement among radiologists in detecting and assessing prostate cancer at multiparametric MRI using Prostate Imaging Reporting and Data System version 2 (PI-RADSv2). MATERIALS AND METHODS. Treatment-naïve patients underwent 3-T multipara-metric MRI between April 2012 and June 2015. Among the 163 patients evaluated, 110 underwent prostatectomy after MRI and 53 had normal MRI findings and transrectal ultrasound-guided biopsy results. Nine radiologists participated (three each with high, intermediate, and low levels of experience). Readers interpreted images of 58 patients on average (range, 56-60) using PI-RADSv2. Prostatectomy specimens registered to MRI were ground truth. Interob-server agreement was evaluated with the index of specific agreement for lesion detection and kappa and proportion of agreement for PI-RADS category assignment. RESULTS. The radiologists detected 336 lesions. Sensitivity for index lesions was 80.9% (95% CI, 75.1-85.9%), comparable across reader experience (p = 0.392). Patient-level specificity was experience dependent; highly experienced readers had 84.0% specificity versus 55.2% for all others (p < 0.001). Interobserver agreement was excellent for detecting index lesions (index of specific agreement, 0.871; 95% CI, 0.798-0.923). Agreement on PI-RADSv2 category assignment of index lesions was moderate (κ = 0.419; 95% CI, 0.238-0.595). For individual category assignments, proportion of agreement was slight for PI-RADS category 3 (0.208; 95% CI, 0.086-0.284) but substantial for PI-RADS category 4 (0.674; 95% CI, 0.540-0.776). However, proportion of agreement for T2-weighted PI-RADS 4 in the transition zone was 0.250 (95% CI, 0.108-0.372). Proportion of agreement for category assignment of index lesions on dynamic contrast-enhanced MR images was 0.822 (95% CI, 0.728-0.903), on T2-weighted MR images was 0.515 (95% CI, 0.430-0623), and on DW images was 0.586 (95% CI, 0.495-0.682). Proportion of agreement for dominant lesion was excellent (0.828; 95% CI, 0.742-0.913). CONCLUSION. Radiologists across experience levels had excellent agreement for detecting index lesions and moderate agreement for category assignment of lesions using PI-RADS. Future iterations of PI-RADS should clarify PI-RADS 3 and PI-RADS 4 in the transition zone.
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Affiliation(s)
- Matthew D Greer
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bethesda, MD 20892
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA
| | | | | | | | | | | | | | | | | | - Haytham Shebel
- Department of Radiology, Urology Center, Mansoura University, Mansoura, Egypt
| | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Bradford J Wood
- Center for Interventional Oncology, National Cancer Institute, and Radiologic Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Ronald M Summers
- National Institutes of Health Clinical Center, Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Bethesda, MD
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27
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MR Imaging-Histology Correlation by Tailored 3D-Printed Slicer in Oncological Assessment. CONTRAST MEDIA & MOLECULAR IMAGING 2019; 2019:1071453. [PMID: 31275082 PMCID: PMC6560325 DOI: 10.1155/2019/1071453] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 05/12/2019] [Indexed: 12/14/2022]
Abstract
3D printing and reverse engineering are innovative technologies that are revolutionizing scientific research in the health sciences and related clinical practice. Such technologies are able to improve the development of various custom-made medical devices while also lowering design and production costs. Recent advances allow the printing of particularly complex prototypes whose geometry is drawn from precise computer models designed on in vivo imaging data. This review summarizes a new method for histological sample processing (applicable to e.g., the brain, prostate, liver, and renal mass) which employs a personalized mold developed from diagnostic images through computer-aided design software and 3D printing. Through positioning the custom mold in a coherent manner with respect to the organ of interest (as delineated by in vivo imaging data), the cutting instrument can be precisely guided in order to obtain blocks of tissue which correspond with high accuracy to the slices imaged. This approach appeared crucial for validation of new quantitative imaging tools, for an accurate imaging-histopathological correlation and for the assessment of radiogenomic features extracted from oncological lesions. The aim of this review is to define and describe 3D printing technologies which are applicable to oncological assessment and slicer design, highlighting the radiological and pathological perspective as well as recent applications of this approach for the histological validation of and correlation with MR images.
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28
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Sun Y, Reynolds HM, Parameswaran B, Wraith D, Finnegan ME, Williams S, Haworth A. Multiparametric MRI and radiomics in prostate cancer: a review. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2019; 42:3-25. [PMID: 30762223 DOI: 10.1007/s13246-019-00730-z] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 01/22/2019] [Indexed: 12/30/2022]
Abstract
Multiparametric MRI (mpMRI) is an imaging modality that combines anatomical MR imaging with one or more functional MRI sequences. It has become a versatile tool for detecting and characterising prostate cancer (PCa). The traditional role of mpMRI was confined to PCa staging, but due to the advanced imaging techniques, its role has expanded to various stages in clinical practises including tumour detection, disease monitor during active surveillance and sequential imaging for patient follow-up. Meanwhile, with the growing speed of data generation and the increasing volume of imaging data, it is highly demanded to apply computerised methods to process mpMRI data and extract useful information. Hence quantitative analysis for imaging data using radiomics has become an emerging paradigm. The application of radiomics approaches in prostate cancer has not only enabled automatic localisation of the disease but also provided a non-invasive solution to assess tumour biology (e.g. aggressiveness and the presence of hypoxia). This article reviews mpMRI and its expanding role in PCa detection, staging and patient management. Following that, an overview of prostate radiomics will be provided, with a special focus on its current applications as well as its future directions.
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Affiliation(s)
- Yu Sun
- University of Sydney, Sydney, Australia. .,Peter MacCallum Cancer Centre, Melbourne, Australia.
| | | | | | - Darren Wraith
- Queensland University of Technology, Brisbane, Australia
| | - Mary E Finnegan
- Imperial College Healthcare NHS Trust, London, UK.,Imperial College London, London, UK
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29
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Gaur S, Harmon S, Gupta RT, Margolis DJ, Lay N, Mehralivand S, Merino MJ, Wood BJ, Pinto PA, Shih JH, Choyke PL, Turkbey B. A Multireader Exploratory Evaluation of Individual Pulse Sequence Cancer Detection on Prostate Multiparametric Magnetic Resonance Imaging (MRI). Acad Radiol 2019; 26:5-14. [PMID: 29705281 PMCID: PMC6202287 DOI: 10.1016/j.acra.2018.03.024] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2018] [Revised: 03/19/2018] [Accepted: 03/24/2018] [Indexed: 01/07/2023]
Abstract
RATIONALE AND OBJECTIVES To determine independent contribution of each prostate multiparametric magnetic resonance imaging (mpMRI) sequence to cancer detection when read in isolation. MATERIALS AND METHODS Prostate mpMRI at 3-Tesla with endorectal coil from 45 patients (n = 30 prostatectomy cases, n = 15 controls with negative magnetic resonance imaging [MRI] or biopsy) were retrospectively interpreted. Sequences (T2-weighted [T2W] MRI, diffusion-weighted imaging [DWI], and dynamic contrast-enhanced [DCE] MRI; N = 135) were separately distributed to three radiologists at different institutions. Readers evaluated each sequence blinded to other mpMRI sequences. Findings were correlated to whole-mount pathology. Cancer detection sensitivity, positive predictive value for whole prostate (WP), transition zone, and peripheral zone were evaluated per sequence by reader, with reader concordance measured by index of specific agreement. Cancer detection rates (CDRs) were calculated for combinations of independently read sequences. RESULTS 44 patients were evaluable (cases median prostate-specific antigen 6.83 [ range 1.95-51.13] ng/mL, age 62 [45-71] years; controls prostate-specific antigen 6.85 [2.4-10.87] ng/mL, age 65.5 [47-71] years). Readers had highest sensitivity on DWI (59%) vs T2W MRI (48%) and DCE (23%) in WP. DWI-only positivity (DWI+/T2W-/DCE-) achieved highest CDR in WP (38%), compared to T2W-only (CDR 24%) and DCE-only (CDR 8%). DWI+/T2W+/DCE- achieved CDR 80%, an added benefit of 56.4% from T2W-only and of 42% from DWI-only (P < .0001). All three sequences interpreted independently positive gave highest CDR of 90%. Reader agreement was moderate (index of specific agreement: T2W = 54%, DWI = 58%, DCE = 33%). CONCLUSIONS When prostate mpMRI sequences are interpreted independently by multiple observers, DWI achieves highest sensitivity and CDR in transition zone and peripheral zone. T2W and DCE MRI both add value to detection; mpMRI achieves highest detection sensitivity when all three mpMRI sequences are positive.
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Affiliation(s)
- Sonia Gaur
- Molecular Imaging Program, National Cancer Institute, NIH, 10 Center Drive, Room B3B85, Bethesda, MD 20814, USA. ; ;
| | - Stephanie Harmon
- Clinical Research Directorate/Clinical Monitoring Research Program, Leidos Biomedical Research, Inc., National Cancer Institute, Campus at Frederick, 8560 Progress Drive, Frederick, MD 21707, USA.
| | - Rajan T. Gupta
- Duke University Medical Center, Duke Cancer Institute, Durham, NC 27710, USA.
| | - Daniel J. Margolis
- Weill Cornell Imaging, New York-Presbytarian Hospital, New York, NY 10021, USA.
| | - Nathan Lay
- Computer-Aided Diagnosis Laboratory, Clinical Center, NIH, 10 Center Drive, Bethesda, MD 20814, USA.
| | - Sherif Mehralivand
- Urologic Oncology Branch, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD 20814, USA. ;
| | - Maria J. Merino
- Department of Pathology, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD 20814, USA.
| | - Bradford J. Wood
- Center for Interventional Oncology, Clinical Center, NIH, 10 Center Drive, Bethesda, MD 20814, USA.
| | - Peter A. Pinto
- Urologic Oncology Branch, National Cancer Institute, NIH, 10 Center Drive, Bethesda, MD 20814, USA. ;
| | - Joanna H. Shih
- Biometric Research Branch, National Cancer Institute, NIH, 6130 Executive Plaza, Room 8132, Rockville, MD 20852, USA.
| | - Peter L. Choyke
- Molecular Imaging Program, National Cancer Institute, NIH, 10 Center Drive, Room B3B85, Bethesda, MD 20814, USA. ; ;
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, NIH, 10 Center Drive, Room B3B85, Bethesda, MD 20814, USA. ; ;
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30
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Three-dimensional localization and targeting of prostate cancer foci with imaging and histopathologic correlation. Curr Opin Urol 2018; 28:506-511. [DOI: 10.1097/mou.0000000000000554] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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31
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Mikhail AS, Pritchard WF, Negussie AH, Krishnasamy VP, Amchin DB, Thompson JG, Wakim PG, Woods D, Bakhutashvili I, Esparza-Trujillo JA, Karanian JW, Willis SL, Lewis AL, Levy EB, Wood BJ. Mapping Drug Dose Distribution on CT Images Following Transarterial Chemoembolization with Radiopaque Drug-Eluting Beads in a Rabbit Tumor Model. Radiology 2018; 289:396-404. [PMID: 30106347 PMCID: PMC6219695 DOI: 10.1148/radiol.2018172571] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 05/30/2018] [Accepted: 06/14/2018] [Indexed: 12/15/2022]
Abstract
Purpose To correlate bead location and attenuation on CT images with the quantity and distribution of drug delivered to the liver following transarterial chemoembolization (TACE) with radiopaque drug-eluting beads (DEB) in a rabbit tumor model. Materials and Methods All procedures were performed with a protocol approved by the Institutional Animal Care and Use Committee. TACE was performed in rabbits (n = 4) bearing VX2 liver tumors by using radiopaque DEB (70-150 µm) loaded with doxorubicin (DOX). Livers were resected 1 hour after embolization, immediately frozen, and cut by using liver-specific three-dimensional-printed molds for colocalization of liver specimens and CT imaging. DOX penetration into tissue surrounding beads was evaluated with fluorescence microscopy. DOX levels in liver specimens were predicted by using statistical models correlating DOX content measured in tissue with bead volume and attenuation measured on CT images. Model predictions were then compared with actual measured DOX concentrations to assess the models' predictive power. Results Eluted DOX remained in close proximity (<600 µm) to beads in the liver 1 hour after TACE. Bead volume and attenuation measured on CT images demonstrated positive linear correlations (0.950 and 0.965, respectively) with DOX content in liver specimens. DOX content model predictions based on CT images were accurate compared with actual liver DOX levels at 1 hour. Conclusion CT may be used to estimate drug dose delivery and distribution in the liver following transarterial chemoembolization (TACE) with doxorubicin-loaded radiopaque drug-eluting beads (DEB). Although speculative, this informational map might be helpful in planning and understanding the spatial effects of TACE with DEB. © RSNA, 2018.
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Affiliation(s)
- Andrew S. Mikhail
- From the Center for Interventional Oncology, Radiology and Imaging
Sciences, NIH Clinical Center (A.S.M., W.F.P., A.H.N., V.P.K., D.B.A., J.G.T.,
D.W., I.B., J.A.E.T., J.W.K., E.B.L., B.J.W.), National Institute of Biomedical
Imaging and Bioengineering (B.J.W.), National Cancer Institute Center for Cancer
Research (B.J.W.), and Biostatistics and Clinical Epidemiology Service, Clinical
Center (P.G.W.), National Institutes of Health, 10 Center Dr, Bethesda, MD
20892, and Biocompatibles UK, BTG International Group, Camberley, England
(S.L.W., A.L.L.)
| | - William F. Pritchard
- From the Center for Interventional Oncology, Radiology and Imaging
Sciences, NIH Clinical Center (A.S.M., W.F.P., A.H.N., V.P.K., D.B.A., J.G.T.,
D.W., I.B., J.A.E.T., J.W.K., E.B.L., B.J.W.), National Institute of Biomedical
Imaging and Bioengineering (B.J.W.), National Cancer Institute Center for Cancer
Research (B.J.W.), and Biostatistics and Clinical Epidemiology Service, Clinical
Center (P.G.W.), National Institutes of Health, 10 Center Dr, Bethesda, MD
20892, and Biocompatibles UK, BTG International Group, Camberley, England
(S.L.W., A.L.L.)
| | - Ayele H. Negussie
- From the Center for Interventional Oncology, Radiology and Imaging
Sciences, NIH Clinical Center (A.S.M., W.F.P., A.H.N., V.P.K., D.B.A., J.G.T.,
D.W., I.B., J.A.E.T., J.W.K., E.B.L., B.J.W.), National Institute of Biomedical
Imaging and Bioengineering (B.J.W.), National Cancer Institute Center for Cancer
Research (B.J.W.), and Biostatistics and Clinical Epidemiology Service, Clinical
Center (P.G.W.), National Institutes of Health, 10 Center Dr, Bethesda, MD
20892, and Biocompatibles UK, BTG International Group, Camberley, England
(S.L.W., A.L.L.)
| | - Venkatesh P. Krishnasamy
- From the Center for Interventional Oncology, Radiology and Imaging
Sciences, NIH Clinical Center (A.S.M., W.F.P., A.H.N., V.P.K., D.B.A., J.G.T.,
D.W., I.B., J.A.E.T., J.W.K., E.B.L., B.J.W.), National Institute of Biomedical
Imaging and Bioengineering (B.J.W.), National Cancer Institute Center for Cancer
Research (B.J.W.), and Biostatistics and Clinical Epidemiology Service, Clinical
Center (P.G.W.), National Institutes of Health, 10 Center Dr, Bethesda, MD
20892, and Biocompatibles UK, BTG International Group, Camberley, England
(S.L.W., A.L.L.)
| | - Daniel B. Amchin
- From the Center for Interventional Oncology, Radiology and Imaging
Sciences, NIH Clinical Center (A.S.M., W.F.P., A.H.N., V.P.K., D.B.A., J.G.T.,
D.W., I.B., J.A.E.T., J.W.K., E.B.L., B.J.W.), National Institute of Biomedical
Imaging and Bioengineering (B.J.W.), National Cancer Institute Center for Cancer
Research (B.J.W.), and Biostatistics and Clinical Epidemiology Service, Clinical
Center (P.G.W.), National Institutes of Health, 10 Center Dr, Bethesda, MD
20892, and Biocompatibles UK, BTG International Group, Camberley, England
(S.L.W., A.L.L.)
| | - John G. Thompson
- From the Center for Interventional Oncology, Radiology and Imaging
Sciences, NIH Clinical Center (A.S.M., W.F.P., A.H.N., V.P.K., D.B.A., J.G.T.,
D.W., I.B., J.A.E.T., J.W.K., E.B.L., B.J.W.), National Institute of Biomedical
Imaging and Bioengineering (B.J.W.), National Cancer Institute Center for Cancer
Research (B.J.W.), and Biostatistics and Clinical Epidemiology Service, Clinical
Center (P.G.W.), National Institutes of Health, 10 Center Dr, Bethesda, MD
20892, and Biocompatibles UK, BTG International Group, Camberley, England
(S.L.W., A.L.L.)
| | - Paul G. Wakim
- From the Center for Interventional Oncology, Radiology and Imaging
Sciences, NIH Clinical Center (A.S.M., W.F.P., A.H.N., V.P.K., D.B.A., J.G.T.,
D.W., I.B., J.A.E.T., J.W.K., E.B.L., B.J.W.), National Institute of Biomedical
Imaging and Bioengineering (B.J.W.), National Cancer Institute Center for Cancer
Research (B.J.W.), and Biostatistics and Clinical Epidemiology Service, Clinical
Center (P.G.W.), National Institutes of Health, 10 Center Dr, Bethesda, MD
20892, and Biocompatibles UK, BTG International Group, Camberley, England
(S.L.W., A.L.L.)
| | - David Woods
- From the Center for Interventional Oncology, Radiology and Imaging
Sciences, NIH Clinical Center (A.S.M., W.F.P., A.H.N., V.P.K., D.B.A., J.G.T.,
D.W., I.B., J.A.E.T., J.W.K., E.B.L., B.J.W.), National Institute of Biomedical
Imaging and Bioengineering (B.J.W.), National Cancer Institute Center for Cancer
Research (B.J.W.), and Biostatistics and Clinical Epidemiology Service, Clinical
Center (P.G.W.), National Institutes of Health, 10 Center Dr, Bethesda, MD
20892, and Biocompatibles UK, BTG International Group, Camberley, England
(S.L.W., A.L.L.)
| | - Ivane Bakhutashvili
- From the Center for Interventional Oncology, Radiology and Imaging
Sciences, NIH Clinical Center (A.S.M., W.F.P., A.H.N., V.P.K., D.B.A., J.G.T.,
D.W., I.B., J.A.E.T., J.W.K., E.B.L., B.J.W.), National Institute of Biomedical
Imaging and Bioengineering (B.J.W.), National Cancer Institute Center for Cancer
Research (B.J.W.), and Biostatistics and Clinical Epidemiology Service, Clinical
Center (P.G.W.), National Institutes of Health, 10 Center Dr, Bethesda, MD
20892, and Biocompatibles UK, BTG International Group, Camberley, England
(S.L.W., A.L.L.)
| | - Juan A. Esparza-Trujillo
- From the Center for Interventional Oncology, Radiology and Imaging
Sciences, NIH Clinical Center (A.S.M., W.F.P., A.H.N., V.P.K., D.B.A., J.G.T.,
D.W., I.B., J.A.E.T., J.W.K., E.B.L., B.J.W.), National Institute of Biomedical
Imaging and Bioengineering (B.J.W.), National Cancer Institute Center for Cancer
Research (B.J.W.), and Biostatistics and Clinical Epidemiology Service, Clinical
Center (P.G.W.), National Institutes of Health, 10 Center Dr, Bethesda, MD
20892, and Biocompatibles UK, BTG International Group, Camberley, England
(S.L.W., A.L.L.)
| | - John W. Karanian
- From the Center for Interventional Oncology, Radiology and Imaging
Sciences, NIH Clinical Center (A.S.M., W.F.P., A.H.N., V.P.K., D.B.A., J.G.T.,
D.W., I.B., J.A.E.T., J.W.K., E.B.L., B.J.W.), National Institute of Biomedical
Imaging and Bioengineering (B.J.W.), National Cancer Institute Center for Cancer
Research (B.J.W.), and Biostatistics and Clinical Epidemiology Service, Clinical
Center (P.G.W.), National Institutes of Health, 10 Center Dr, Bethesda, MD
20892, and Biocompatibles UK, BTG International Group, Camberley, England
(S.L.W., A.L.L.)
| | - Sean L. Willis
- From the Center for Interventional Oncology, Radiology and Imaging
Sciences, NIH Clinical Center (A.S.M., W.F.P., A.H.N., V.P.K., D.B.A., J.G.T.,
D.W., I.B., J.A.E.T., J.W.K., E.B.L., B.J.W.), National Institute of Biomedical
Imaging and Bioengineering (B.J.W.), National Cancer Institute Center for Cancer
Research (B.J.W.), and Biostatistics and Clinical Epidemiology Service, Clinical
Center (P.G.W.), National Institutes of Health, 10 Center Dr, Bethesda, MD
20892, and Biocompatibles UK, BTG International Group, Camberley, England
(S.L.W., A.L.L.)
| | - Andrew L. Lewis
- From the Center for Interventional Oncology, Radiology and Imaging
Sciences, NIH Clinical Center (A.S.M., W.F.P., A.H.N., V.P.K., D.B.A., J.G.T.,
D.W., I.B., J.A.E.T., J.W.K., E.B.L., B.J.W.), National Institute of Biomedical
Imaging and Bioengineering (B.J.W.), National Cancer Institute Center for Cancer
Research (B.J.W.), and Biostatistics and Clinical Epidemiology Service, Clinical
Center (P.G.W.), National Institutes of Health, 10 Center Dr, Bethesda, MD
20892, and Biocompatibles UK, BTG International Group, Camberley, England
(S.L.W., A.L.L.)
| | - Elliot B. Levy
- From the Center for Interventional Oncology, Radiology and Imaging
Sciences, NIH Clinical Center (A.S.M., W.F.P., A.H.N., V.P.K., D.B.A., J.G.T.,
D.W., I.B., J.A.E.T., J.W.K., E.B.L., B.J.W.), National Institute of Biomedical
Imaging and Bioengineering (B.J.W.), National Cancer Institute Center for Cancer
Research (B.J.W.), and Biostatistics and Clinical Epidemiology Service, Clinical
Center (P.G.W.), National Institutes of Health, 10 Center Dr, Bethesda, MD
20892, and Biocompatibles UK, BTG International Group, Camberley, England
(S.L.W., A.L.L.)
| | - Bradford J. Wood
- From the Center for Interventional Oncology, Radiology and Imaging
Sciences, NIH Clinical Center (A.S.M., W.F.P., A.H.N., V.P.K., D.B.A., J.G.T.,
D.W., I.B., J.A.E.T., J.W.K., E.B.L., B.J.W.), National Institute of Biomedical
Imaging and Bioengineering (B.J.W.), National Cancer Institute Center for Cancer
Research (B.J.W.), and Biostatistics and Clinical Epidemiology Service, Clinical
Center (P.G.W.), National Institutes of Health, 10 Center Dr, Bethesda, MD
20892, and Biocompatibles UK, BTG International Group, Camberley, England
(S.L.W., A.L.L.)
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32
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Greer MD, Lay N, Shih JH, Barrett T, Bittencourt LK, Borofsky S, Kabakus I, Law YM, Marko J, Shebel H, Mertan FV, Merino MJ, Wood BJ, Pinto PA, Summers RM, Choyke PL, Turkbey B. Computer-aided diagnosis prior to conventional interpretation of prostate mpMRI: an international multi-reader study. Eur Radiol 2018; 28:4407-4417. [PMID: 29651763 PMCID: PMC8023433 DOI: 10.1007/s00330-018-5374-6] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 01/12/2018] [Accepted: 02/06/2018] [Indexed: 12/19/2022]
Abstract
OBJECTIVES To evaluate if computer-aided diagnosis (CAD) prior to prostate multi-parametric MRI (mpMRI) can improve sensitivity and agreement between radiologists. METHODS Nine radiologists (three each high, intermediate, low experience) from eight institutions participated. A total of 163 patients with 3-T mpMRI from 4/2012 to 6/2015 were included: 110 cancer patients with prostatectomy after mpMRI, 53 patients with no lesions on mpMRI and negative TRUS-guided biopsy. Readers were blinded to all outcomes and detected lesions per PI-RADSv2 on mpMRI. After 5 weeks, readers re-evaluated patients using CAD to detect lesions. Prostatectomy specimens registered to MRI were ground truth with index lesions defined on pathology. Sensitivity, specificity and agreement were calculated per patient, lesion level and zone-peripheral (PZ) and transition (TZ). RESULTS Index lesion sensitivity was 78.2% for mpMRI alone and 86.3% for CAD-assisted mpMRI (p = 0.013). Sensitivity was comparable for TZ lesions (78.7% vs 78.1%; p = 0.929); CAD improved PZ lesion sensitivity (84% vs 94%; p = 0.003). Improved sensitivity came from lesions scored PI-RADS < 3 as index lesion sensitivity was comparable at PI-RADS ≥ 3 (77.6% vs 78.1%; p = 0.859). Per patient specificity was 57.1% for CAD and 70.4% for mpMRI (p = 0.003). CAD improved agreement between all readers (56.9% vs 71.8%; p < 0.001). CONCLUSIONS CAD-assisted mpMRI improved sensitivity and agreement, but decreased specificity, between radiologists of varying experience. KEY POINTS • Computer-aided diagnosis (CAD) assists clinicians in detecting prostate cancer on MRI. • CAD assistance improves agreement between radiologists in detecting prostate cancer lesions. • However, this CAD system induces more false positives, particularly for less-experienced clinicians and in the transition zone. • CAD assists radiologists in detecting cancer missed on MRI, suggesting a path for improved diagnostic confidence.
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Affiliation(s)
- Matthew D Greer
- Molecular Imaging Program, NCI, NIH, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA
| | - Nathan Lay
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Joanna H Shih
- Biometric Research Program, NCI, NIH, Bethesda, MD, USA
| | - Tristan Barrett
- Department of Radiology, University of Cambridge School of Medicine, Cambridge, UK
| | | | | | | | - Yan Mee Law
- Singapore General Hospital, Singapore, Singapore
| | - Jamie Marko
- Radiology and Imaging Sciences Department, Clinical Center, NIH, Bethesda, MD, USA
| | - Haytham Shebel
- Department of Radiology, Nephrology Center, Mansoura University, Mansoura, 35516, Egypt
| | - Francesca V Mertan
- Molecular Imaging Program, NCI, NIH, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA
| | | | - Bradford J Wood
- Center for Interventional Oncology, NCI and Radiology Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA
| | - Peter A Pinto
- Urologic Oncology Branch, NCI, NIH, Bethesda, MD, USA
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Peter L Choyke
- Molecular Imaging Program, NCI, NIH, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA
| | - Baris Turkbey
- Molecular Imaging Program, NCI, NIH, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA.
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Greer MD, Shih JH, Barrett T, Bednarova S, Kabakus I, Law YM, Shebel H, Merino MJ, Wood BJ, Pinto PA, Choyke PL, Turkbey B. All over the map: An interobserver agreement study of tumor location based on the PI-RADSv2 sector map. J Magn Reson Imaging 2018; 48:482-490. [PMID: 29341356 PMCID: PMC7983160 DOI: 10.1002/jmri.25948] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Accepted: 12/21/2017] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND Prostate imaging reporting and data system version 2 (PI-RADSv2) recommends a sector map for reporting findings of prostate cancer mulitparametric MRI (mpMRI). Anecdotally, radiologists may demonstrate inconsistent reproducibility with this map. PURPOSE To evaluate interobserver agreement in defining prostate tumor location on mpMRI using the PI-RADSv2 sector map. STUDY TYPE Retrospective. POPULATION Thirty consecutive patients who underwent mpMRI between October, 2013 and March, 2015 and who subsequently underwent prostatectomy with whole-mount processing. FIELD STRENGTH 3T mpMRI with T2 W, diffusion-weighted imaging (DWI) (apparent diffusion coefficient [ADC] and b-2000), dynamic contrast-enhanced (DCE). ASSESSMENT Six radiologists (two high, two intermediate, and two low experience) from six institutions participated. Readers were blinded to lesion location and detected up to four lesions as per PI-RADSv2 guidelines. Readers marked the long-axis of lesions, saved screen-shots of each lesion, and then marked the lesion location on the PI-RADSv2 sector map. Whole-mount prostatectomy specimens registered to the MRI served as ground truth. Index lesions were defined as the highest grade lesion or largest lesion if grades were equivalent. STATISTICAL TEST Agreement was calculated for the exact, overlap, and proportion of agreement. RESULTS Readers detected an average of 1.9 lesions per patient (range 1.6-2.3). 96.3% (335/348) of all lesions for all readers were scored PI-RADS ≥3. Readers defined a median of 2 (range 1-18) sectors per lesion. Agreement for detecting index lesions by screen shots was 83.7% (76.1%-89.9%) vs. 71.0% (63.1-78.3%) overlap agreement on the PI-RADS sector map (P < 0.001). Exact agreement for defining sectors of detected index lesions was only 21.2% (95% confidence interval [CI]: 14.4-27.7%) and rose to 49.0% (42.4-55.3%) when overlap was considered. Agreement on defining the same level of disease (ie, apex, mid, base) was 61.4% (95% CI 50.2-71.8%). DATA CONCLUSION Readers are highly likely to detect the same index lesion on mpMRI, but exhibit poor reproducibility when attempting to define tumor location on the PI-RADSv2 sector map. The poor agreement of the PI-RADSv2 sector map raises concerns its utility in clinical practice. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2018;48:482-490.
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Affiliation(s)
| | - Joanna H. Shih
- Biometric Research Program, NCI, NIH, Bethesda, Maryland, USA
| | - Tristan Barrett
- University of Cambridge School of Medicine, Department of Radiology, Cambridge, UK
| | - Sandra Bednarova
- Institute of Diagnostic Radiology, Department of Medical Area, University of Udine, Udine, Italy
| | | | | | - Haytham Shebel
- Department of Radiology, Urology Center, Mansoura University, Mansoura, Egypt
| | | | - Bradford J. Wood
- Center for Interventional Oncology, NCI and Radiology Imaging Sciences, Clinical Center, NIH, Bethesda, Maryland, USA
| | - Peter A. Pinto
- Urologic Oncology Branch, NCI, NIH, Bethesda, Maryland, USA
| | - Peter L. Choyke
- Molecular Imaging Program, NCI, NIH, Bethesda, Maryland, USA
| | - Baris Turkbey
- Molecular Imaging Program, NCI, NIH, Bethesda, Maryland, USA
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Accurate validation of ultrasound imaging of prostate cancer: a review of challenges in registration of imaging and histopathology. J Ultrasound 2018; 21:197-207. [PMID: 30062440 PMCID: PMC6113189 DOI: 10.1007/s40477-018-0311-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Accepted: 07/11/2018] [Indexed: 01/20/2023] Open
Abstract
As the development of modalities for prostate cancer (PCa) imaging advances, the challenge of accurate registration between images and histopathologic ground truth becomes more pressing. Localization of PCa, rather than detection, requires a pixel-to-pixel validation of imaging based on histopathology after radical prostatectomy. Such a registration procedure is challenging for ultrasound modalities; not only the deformations of the prostate after resection have to be taken into account, but also the deformation due to the employed transrectal probe and the mismatch in orientation between imaging planes and pathology slices. In this work, we review the latest techniques to facilitate accurate validation of PCa localization in ultrasound imaging studies and extrapolate a general strategy for implementation of a registration procedure.
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35
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Gaur S, Harmon S, Rosenblum L, Greer MD, Mehralivand S, Coskun M, Merino MJ, Wood BJ, Shih JH, Pinto PA, Choyke PL, Turkbey B. Can Apparent Diffusion Coefficient Values Assist PI-RADS Version 2 DWI Scoring? A Correlation Study Using the PI-RADSv2 and International Society of Urological Pathology Systems. AJR Am J Roentgenol 2018; 211:W33-W41. [PMID: 29733695 PMCID: PMC7984719 DOI: 10.2214/ajr.17.18702] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
OBJECTIVE The purposes of this study were to assess correlation of apparent diffusion coefficient (ADC) and normalized ADC (ratio of tumor to nontumor tissue) with the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) and updated International Society of Urological Pathology (ISUP) categories and to determine how to optimally use ADC metrics for objective assistance in categorizing lesions within PI-RADSv2 guidelines. MATERIALS AND METHODS In this retrospective study, 100 patients (median age, 62 years; range, 44-75 years; prostate-specific antigen level, 7.18 ng/mL; range, 1.70-84.56 ng/mL) underwent 3-T multiparametric MRI of the prostate with an endorectal coil. Mean ADC was extracted from ROIs based on subsequent prostatectomy specimens. Histopathologic analysis revealed 172 lesions (113 peripheral, 59 transition zone). Two radiologists blinded to histopathologic outcome assigned PI-RADSv2 categories. Kendall tau was used to correlate ADC metrics with PI-RADSv2 and ISUP categories. ROC curves were used to assess the utility of ADC metrics in differentiating each reader's PI-RADSv2 DWI category 4 or 5 assessment in the whole prostate and by zone. RESULTS ADC metrics negatively correlated with ISUP category in the whole prostate (ADC, τ = -0.21, p = 0.0002; normalized ADC, τ = -0.21, p = 0.0001). Moderate negative correlation was found in expert PI-RADSv2 DWI categories (ADC, τ = -0.34; normalized ADC, τ = -0.31; each p < 0.0001) maintained across zones. In the whole prostate, AUCs of ADC and normalized ADC were 87% and 82% for predicting expert PI-RADSv2 DWI category 4 or 5. A derived optimal cutoff ADC less than 1061 and normalized ADC less than 0.65 achieved positive predictive values of 83% and 84% for correct classification of PI-RADSv2 DWI category 4 or 5 by an expert reader. Consistent relations and predictive values were found by an independent novice reader. CONCLUSION ADC and normalized ADC inversely correlate with PI-RADSv2 and ISUP categories and can serve as quantitative metrics to assist with assigning PI-RADSv2 DWI category 4 or 5.
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Affiliation(s)
- Sonia Gaur
- 1 Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, Rm B3B85, Bethesda, MD 20814
| | - Stephanie Harmon
- 2 Clinical Research Directorate, Clinical Monitoring Research Program, Leidos Biomedical Research, National Cancer Institute, Frederick, MD
| | - Lauren Rosenblum
- 1 Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, Rm B3B85, Bethesda, MD 20814
| | - Matthew D Greer
- 1 Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, Rm B3B85, Bethesda, MD 20814
| | - Sherif Mehralivand
- 3 Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Mehmet Coskun
- 4 İzmir Katip Çelebi University, Atatürk Training and Research Hospital, Izmir, Turkey
| | - Maria J Merino
- 1 Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, Rm B3B85, Bethesda, MD 20814
| | - Bradford J Wood
- 5 Center for Interventional Oncology, Clinical Center, National Institutes of Health, Bethesda, MD
| | - Joanna H Shih
- 6 Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, MD
| | - Peter A Pinto
- 3 Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Peter L Choyke
- 1 Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, Rm B3B85, Bethesda, MD 20814
| | - Baris Turkbey
- 1 Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr, Rm B3B85, Bethesda, MD 20814
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McGarry SD, Hurrell SL, Iczkowski KA, Hall W, Kaczmarowski AL, Banerjee A, Keuter T, Jacobsohn K, Bukowy JD, Nevalainen MT, Hohenwalter MD, See WA, LaViolette PS. Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate Cancer. Int J Radiat Oncol Biol Phys 2018; 101:1179-1187. [PMID: 29908785 PMCID: PMC6190585 DOI: 10.1016/j.ijrobp.2018.04.044] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 04/10/2018] [Accepted: 04/16/2018] [Indexed: 12/22/2022]
Abstract
PURPOSE This study aims to combine multiparametric magnetic resonance imaging (MRI) and digitized pathology with machine learning to generate predictive maps of histologic features for prostate cancer localization. METHODS AND MATERIALS Thirty-nine patients underwent MRI prior to prostatectomy. After surgery, tissue was sliced according to MRI orientation using patient-specific 3-dimensionally printed slicing jigs. Whole-mount sections were annotated by our pathologist and digitally contoured to differentiate the lumen and epithelium. Slides were co-registered to the T2-weighted MRI scan. A learning curve was generated to determine the number of patients required for a stable machine-learning model. Patients were randomly stratified into 2 training sets and 1 test set. Two partial least-squares regression models were trained, each capable of predicting lumen and epithelium density. Predicted density values were calculated for each patient in the test dataset, mapped into the MRI space, and compared between regions confirmed as high-grade prostate cancer. RESULTS The learning-curve analysis showed that a stable fit was achieved with data from 10 patients. Maps indicated that regions of increased epithelium and decreased lumen density, generated from each independent model, corresponded with pathologist-annotated regions of high-grade cancer. CONCLUSIONS We present a radio-pathomic approach to mapping prostate cancer. We find that the maps are useful for highlighting high-grade tumors. This technique may be relevant for dose-painting strategies in prostate radiation therapy.
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Affiliation(s)
- Sean D McGarry
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Sarah L Hurrell
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | | | - William Hall
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Amy L Kaczmarowski
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Anjishnu Banerjee
- Department of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Tucker Keuter
- Department of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Kenneth Jacobsohn
- Department of Urological Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - John D Bukowy
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Marja T Nevalainen
- Department of Pathology, Medical College of Wisconsin, Milwaukee, Wisconsin; Department of Pharmacology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Mark D Hohenwalter
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - William A See
- Department of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin; Department of Urological Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Peter S LaViolette
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin; Biomedical Engineering, Medical College of Wisconsin and Marquette University, Milwaukee, Wisconsin.
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Priester A, Wu H, Khoshnoodi P, Schneider D, Zhang Z, Asvadi NH, Sisk A, Raman S, Reiter R, Grundfest W, Marks LS, Natarajan S. Registration Accuracy of Patient-Specific, Three-Dimensional-Printed Prostate Molds for Correlating Pathology With Magnetic Resonance Imaging. IEEE Trans Biomed Eng 2018; 66:14-22. [PMID: 29993431 DOI: 10.1109/tbme.2018.2828304] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE This investigation was performed to evaluate the registration accuracy between magnetic resonance imaging (MRI) and pathology using three-dimensional (3-D) printed molds. METHODS Tissue-mimicking prostate phantoms were manufactured with embedded fiducials. The fiducials were used to measure and compare target registration error (TRE) between phantoms that were sliced by hand versus phantoms that were sliced within 3-D-printed molds. Subsequently, ten radical prostatectomy specimens were placed inside molds, scanned with MRI, and then sliced. The ex vivo scan was used to assess the true location of whole mount (WM) slides relative to in vivo MRI. The TRE between WM and in vivo MRI was measured using anatomic landmarks. RESULTS Manually sliced phantoms had a 4.1-mm mean TRE, whereas mold-sliced phantoms had a 1.9-mm mean TRE. Similarly, mold-assisted slicing reduced mean angular misalignment around the left-right (LR) anatomic axis from 10.7° to 4.5°. However, ex vivo MRI revealed that excised prostates were misaligned within molds, including a mean 14° rotation about the LR axis. The mean in-plane TRE was 3.3 mm using molds alone and 2.2 mm after registration was corrected with ex vivo MRI. CONCLUSION Patient-specific molds improved accuracy relative to manual slicing techniques in a phantom model. However, the registration accuracy of surgically resected specimens was limited by their imperfect fit within molds. This limitation can be overcome with the addition of ex vivo imaging. SIGNIFICANCE The accuracy of 3-D-printed molds was characterized, quantifying their utility for facilitating MRI-pathology registration.
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Improved Magnetic Resonance Imaging-Pathology Correlation With Imaging-Derived, 3D-Printed, Patient-Specific Whole-Mount Molds of the Prostate. Invest Radiol 2018; 52:507-513. [PMID: 28379863 DOI: 10.1097/rli.0000000000000372] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
OBJECTIVES The aim of this study was to compare the anatomical registration of preoperative magnetic resonance imaging (MRI) and prostate whole-mount obtained with 3D-printed, patient-specific, MRI-derived molds (PSM) versus conventional whole-mount sectioning (WMS). MATERIALS AND METHODS Based on an a priori power analysis, this institutional review board-approved study prospectively included 50 consecutive men who underwent 3 T multiparametric prostate MRI followed by radical prostatectomy. Two blinded and independent readers (R1 and R2) outlined the contours of the prostate, tumor, peripheral, and transition zones in the MRI scans using regions of interest. These were compared with the corresponding regions of interest from the whole-mounted histopathology, the reference standard, using PSM whole-mount results obtained in the study group (n = 25) or conventional WMS in the control group (n = 25). The spatial overlap across the MRI and histology data sets was calculated using the Dice similarity coefficient (DSC) for the prostate overall (DSCprostate), tumor (DSCtumor), peripheral (DSCPZ), and transition (DSCTZ) zone. Results in the study and control groups were compared using Wilcoxon rank sum test. RESULTS The MRI histopathology anatomical registration for the prostate gland overall, tumor, peripheral, and transition zones were significantly superior with the use of PSMs (DSCs for R1: 0.95, 0.86, 0.84, and 0.89; for R2: 0.93, 0.75, 0.78, and 0.85, respectively) than with the use of standard WMS (R1: 0.85, 0.46, 0.66, and 0.69; R2: 0.85, 0.46, 0.66, and 0.69) (P < 0.0001). CONCLUSIONS The use of PSMs for prostate specimen whole-mount sectioning provides significantly superior anatomical registration of in vivo multiparametric MRI and ex vivo prostate whole-mounts than conventional WMS.
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Abstract
Imaging provides an insight into biological patho-mechanisms of diseases. However, the link between the imaging phenotype and the underlying molecular processes is often not well understood. Methods such as metabolomics and proteomics reveal detailed information about these processes. Unfortunately, they provide no spatial information and thus cannot be easily correlated with functional imaging. We have developed an image-guided milling machine and unique workflows to precisely isolate tissue samples based on imaging data. The tissue samples remain cooled during the entire procedure, preventing sample degradation. This enables us to correlate, at an unprecedented spatial precision, comprehensive imaging information with metabolomics and proteomics data, leading to a better understanding of diseases. Phenotypic heterogeneity is commonly observed in diseased tissue, specifically in tumors. Multimodal imaging technologies can reveal tissue heterogeneity noninvasively in vivo, enabling imaging-based profiling of receptors, metabolism, morphology, or function on a macroscopic scale. In contrast, in vitro multiomics, immunohistochemistry, or histology techniques accurately characterize these heterogeneities in the cellular and subcellular scales in a more comprehensive but ex vivo manner. The complementary in vivo and ex vivo information would provide an enormous potential to better characterize a disease. However, this requires spatially accurate coregistration of these data by image-driven sampling as well as fast sample-preparation methods. Here, a unique image-guided milling machine and workflow for precise extraction of tissue samples from small laboratory animals or excised organs has been developed and evaluated. The samples can be delineated on tomographic images as volumes of interest and can be extracted with a spatial accuracy better than 0.25 mm. The samples remain cooled throughout the procedure to ensure metabolic stability, a precondition for accurate in vitro analysis.
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Mayer R, Simone CB, Skinner W, Turkbey B, Choykey P. Pilot study for supervised target detection applied to spatially registered multiparametric MRI in order to non-invasively score prostate cancer. Comput Biol Med 2018; 94:65-73. [PMID: 29407999 DOI: 10.1016/j.compbiomed.2018.01.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2017] [Revised: 01/16/2018] [Accepted: 01/16/2018] [Indexed: 01/21/2023]
Abstract
BACKGROUND Gleason Score (GS) is a validated predictor of prostate cancer (PCa) disease progression and outcomes. GS from invasive needle biopsies suffers from significant inter-observer variability and possible sampling error, leading to underestimating disease severity ("underscoring") and can result in possible complications. A robust non-invasive image-based approach is, therefore, needed. PURPOSE Use spatially registered multi-parametric MRI (MP-MRI), signatures, and supervised target detection algorithms (STDA) to non-invasively GS PCa at the voxel level. METHODS AND MATERIALS This study retrospectively analyzed 26 MP-MRI from The Cancer Imaging Archive. The MP-MRI (T2, Diffusion Weighted, Dynamic Contrast Enhanced) were spatially registered to each other, combined into stacks, and stitched together to form hypercubes. Multi-parametric (or multi-spectral) signatures derived from a training set of registered MP-MRI were transformed using statistics-based Whitening-Dewhitening (WD). Transformed signatures were inserted into STDA (having conical decision surfaces) applied to registered MP-MRI determined the tumor GS. The MRI-derived GS was quantitatively compared to the pathologist's assessment of the histology of sectioned whole mount prostates from patients who underwent radical prostatectomy. In addition, a meta-analysis of 17 studies of needle biopsy determined GS with confusion matrices and was compared to the MRI-determined GS. RESULTS STDA and histology determined GS are highly correlated (R = 0.86, p < 0.02). STDA more accurately determined GS and reduced GS underscoring of PCa relative to needle biopsy as summarized by meta-analysis (p < 0.05). CONCLUSION This pilot study found registered MP-MRI, STDA, and WD transforms of signatures shows promise in non-invasively GS PCa and reducing underscoring with high spatial resolution.
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Affiliation(s)
- Rulon Mayer
- OncoScore, Garrett Park, MD 20896, USA; University of Pennsylvania, Philadelphia, PA 19104, USA.
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Gibson E, Hu Y, Huisman HJ, Barratt DC. Designing image segmentation studies: Statistical power, sample size and reference standard quality. Med Image Anal 2017; 42:44-59. [PMID: 28772163 PMCID: PMC5666910 DOI: 10.1016/j.media.2017.07.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Revised: 04/03/2017] [Accepted: 07/21/2017] [Indexed: 11/18/2022]
Abstract
Segmentation algorithms are typically evaluated by comparison to an accepted reference standard. The cost of generating accurate reference standards for medical image segmentation can be substantial. Since the study cost and the likelihood of detecting a clinically meaningful difference in accuracy both depend on the size and on the quality of the study reference standard, balancing these trade-offs supports the efficient use of research resources. In this work, we derive a statistical power calculation that enables researchers to estimate the appropriate sample size to detect clinically meaningful differences in segmentation accuracy (i.e. the proportion of voxels matching the reference standard) between two algorithms. Furthermore, we derive a formula to relate reference standard errors to their effect on the sample sizes of studies using lower-quality (but potentially more affordable and practically available) reference standards. The accuracy of the derived sample size formula was estimated through Monte Carlo simulation, demonstrating, with 95% confidence, a predicted statistical power within 4% of simulated values across a range of model parameters. This corresponds to sample size errors of less than 4 subjects and errors in the detectable accuracy difference less than 0.6%. The applicability of the formula to real-world data was assessed using bootstrap resampling simulations for pairs of algorithms from the PROMISE12 prostate MR segmentation challenge data set. The model predicted the simulated power for the majority of algorithm pairs within 4% for simulated experiments using a high-quality reference standard and within 6% for simulated experiments using a low-quality reference standard. A case study, also based on the PROMISE12 data, illustrates using the formulae to evaluate whether to use a lower-quality reference standard in a prostate segmentation study.
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Affiliation(s)
- Eli Gibson
- Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; Centre for Medical Image Computing, The Engineering Front Building, University College London, Malet Place, London, WC1E 6BT, United Kingdom.
| | - Yipeng Hu
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Henkjan J Huisman
- Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Dean C Barratt
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
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Development of a Patient-specific Tumor Mold Using Magnetic Resonance Imaging and 3-Dimensional Printing Technology for Targeted Tissue Procurement and Radiomics Analysis of Renal Masses. Urology 2017; 112:209-214. [PMID: 29056576 DOI: 10.1016/j.urology.2017.08.056] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Revised: 05/19/2017] [Accepted: 08/31/2017] [Indexed: 12/13/2022]
Abstract
OBJECTIVE To implement a platform for colocalization of in vivo quantitative multiparametric magnetic resonance imaging features with ex vivo surgical specimens of patients with renal masses using patient-specific 3-dimensional (3D)-printed tumor molds, which may aid in targeted tissue procurement and radiomics and radiogenomic analyses. MATERIALS AND METHODS Volumetric segmentation of 6 renal masses was performed with 3D Slicer (http://www.slicer.org) to create a 3D tumor model. A slicing guide template was created with specialized software, which included notches corresponding to the anatomic locations of the magnetic resonance images. The tumor model was subtracted from the slicing guide to create a depression in the slicing guide corresponding to the exact size and shape of the tumor. A customized, tumor-specific, slicing guide was then printed using a 3D printer. After partial nephrectomy, the surgical specimen was bivalved through the preselected magnetic resonance imaging (MRI) plane. A thick slab of the tumor was obtained, fixed, and processed as a whole-mount slide and was correlated to multiparametric MRI findings. RESULTS All patients successfully underwent partial nephrectomy and adequate fitting of the tumor specimens within the 3D mold was achieved in all tumors. Distinct in vivo MRI features corresponded to unique pathologic characteristics in the same tumor. The average cost of printing each mold was US$160.7 ± 111.1 (range: US$20.9-$350.7). CONCLUSION MRI-based preoperative 3D printing of tumor-specific molds allow for accurate sectioning of the tumor after surgical resection and colocalization of in vivo imaging features with tissue-based analysis in radiomics and radiogenomic studies.
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Turkbey B, Mena E, Lindenberg L, Adler S, Bednarova S, Berman R, Ton AT, McKinney Y, Eclarinal P, Hill C, Afari G, Bhattacharyya S, Mease RC, Merino MJ, Jacobs PM, Wood BJ, Pinto PA, Pomper MG, Choyke PL. 18F-DCFBC Prostate-Specific Membrane Antigen-Targeted PET/CT Imaging in Localized Prostate Cancer: Correlation With Multiparametric MRI and Histopathology. Clin Nucl Med 2017; 42:735-740. [PMID: 28806263 PMCID: PMC5703072 DOI: 10.1097/rlu.0000000000001804] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE To assess the ability of (N-[N-[(S)-1,3-dicarboxypropyl]carbamoyl]-4-F-fluorobenzyl-L-cysteine) (F-DCFBC), a prostate-specific membrane antigen-targeted PET agent, to detect localized prostate cancer lesions in correlation with multiparametric MRI (mpMRI) and histopathology. METHODS This Health Insurance Portability and Accountability Act of 1996-compliant, prospective, institutional review board-approved study included 13 evaluable patients with localized prostate cancer (median age, 62.8 years [range, 51-74 years]; median prostate-specific antigen, 37.5 ng/dL [range, 3.26-216 ng/dL]). Patients underwent mpMRI and F-DCFBC PET/CT within a 3 months' window. Lesions seen on mpMRI were biopsied under transrectal ultrasound/MRI fusion-guided biopsy, or a radical prostatectomy was performed. F-DCFBC PET/CT and mpMRI were evaluated blinded and separately for tumor detection on a lesion basis. For PET image analysis, MRI and F-DCFBC PET images were fused by using software registration; imaging findings were correlated with histology, and uptake of F-DCFBC in tumors was compared with uptake in benign prostatic hyperplasia nodules and normal peripheral zone tissue using the 80% threshold SUVmax. RESULTS A total of 25 tumor foci (mean size, 1.8 cm; median size, 1.5 cm; range, 0.6-4.7 cm) were histopathologically identified in 13 patients. Sensitivity rates of F-DCFBC PET/CT and mpMRI were 36% and 96%, respectively, for all tumors. For index lesions, the largest tumor with highest Gleason score, sensitivity rates of F-DCFBC PET/CT and mpMRI were 61.5% and 92%, respectively. The average SUVmax for primary prostate cancer was higher (5.8 ± 4.4) than that of benign prostatic hyperplasia nodules (2.1 ± 0.3) or that of normal prostate tissue (2.1 ± 0.4) at 1 hour postinjection (P = 0.0033). CONCLUSIONS The majority of index prostate cancers are detected with F-DCFBC PET/CT, and this may be a prognostic indicator based on uptake and staging. However, for detecting prostate cancer with high sensitivity, it is important to combine prostate-specific membrane antigen PET/CT with mpMRI.
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Affiliation(s)
- Baris Turkbey
- From the *Molecular Imaging Program, National Cancer Institute, Bethesda; †Clinical Research Directorate/Clinical Monitoring Research Program, Leidos Biomedical Research, Inc, Frederick, MD; ‡Institute of Diagnostic Radiology, Department of Medical Area, University of Udine, Udine, Italy; §Office of the Clinical Director/Center for Cancer Research/National Cancer Institute, Bethesda; ∥Applied/Developmental Research Directorate, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick; ¶Office of the Pharmaceutical Quality, FDA/CDER, Silver Spring; **Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institutions, Baltimore; ††Laboratory of Pathology, National Cancer Institute, Bethesda; ‡‡Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville; §§Center for Interventional Oncology, National Cancer Institute and Clinical Center, and Radiology Imaging Sciences, National Institutes of Health, Bethesda; and ∥∥Urologic Oncology Branch, National Cancer Institute, Bethesda, MD
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Chang YCC, Ackerstaff E, Tschudi Y, Jimenez B, Foltz W, Fisher C, Lilge L, Cho H, Carlin S, Gillies RJ, Balagurunathan Y, Yechieli RL, Subhawong T, Turkbey B, Pollack A, Stoyanova R. Delineation of Tumor Habitats based on Dynamic Contrast Enhanced MRI. Sci Rep 2017; 7:9746. [PMID: 28851989 PMCID: PMC5575347 DOI: 10.1038/s41598-017-09932-5] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Accepted: 08/01/2017] [Indexed: 02/03/2023] Open
Abstract
Tumor heterogeneity can be elucidated by mapping subregions of the lesion with differential imaging characteristics, called habitats. Dynamic Contrast Enhanced (DCE-)MRI can depict the tumor microenvironments by identifying areas with variable perfusion and vascular permeability, since individual tumor habitats vary in the rate and magnitude of the contrast uptake and washout. Of particular interest is identifying areas of hypoxia, characterized by inadequate perfusion and hyper-permeable vasculature. An automatic procedure for delineation of tumor habitats from DCE-MRI was developed as a two-part process involving: (1) statistical testing in order to determine the number of the underlying habitats; and (2) an unsupervised pattern recognition technique to recover the temporal contrast patterns and locations of the associated habitats. The technique is examined on simulated data and DCE-MRI, obtained from prostate and brain pre-clinical cancer models, as well as clinical data from sarcoma and prostate cancer patients. The procedure successfully identified habitats previously associated with well-perfused, hypoxic and/or necrotic tumor compartments. Given the association of tumor hypoxia with more aggressive tumor phenotypes, the obtained in vivo information could impact management of cancer patients considerably.
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Affiliation(s)
| | - Ellen Ackerstaff
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Yohann Tschudi
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, 33136, USA
| | - Bryan Jimenez
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, 33136, USA
| | - Warren Foltz
- STTARR Innovation Centre, Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Carl Fisher
- Department of Medical Biophysics, University of Toronto and Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Lothar Lilge
- Department of Medical Biophysics, University of Toronto and Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - HyungJoon Cho
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Sean Carlin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Robert J Gillies
- Cancer Imaging and Metabolism, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | | | - Raphael L Yechieli
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, 33136, USA
| | - Ty Subhawong
- Department of Radiology, University of Miami Miller School of Medicine, Miami, FL, 33136, USA
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, 33136, USA
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, 33136, USA.
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Kwak JT, Sankineni S, Xu S, Turkbey B, Choyke PL, Pinto PA, Moreno V, Merino M, Wood BJ. Prostate Cancer: A Correlative Study of Multiparametric MR Imaging and Digital Histopathology. Radiology 2017; 285:147-156. [PMID: 28582632 DOI: 10.1148/radiol.2017160906] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To correlate multiparametric magnetic resonance (MR) imaging and quantitative digital histopathologic analysis (DHA) of the prostate. Materials and Methods This retrospective study was approved by the local institutional review board and was HIPAA compliant. Forty patients (median age, 60 years; age range, 44-71 years) who underwent prostate MR imaging consisting of T2-weighted and diffusion-weighted (DW) MR imaging along with subsequent robot-assisted radical prostatectomy gave informed consent to be included. Whole-mount tissue specimens were obtained with a patient-specific mold, and DHA was performed to assess the lumen, epithelium, stroma, and epithelial nucleus. These DHA images were registered with MR images and were correlated on a per-voxel basis. The relationship between MR imaging and DHA was assessed by using a linear mixed-effects model and the Pearson correlation coefficient. Results T2-weighted MR imaging, apparent diffusion coefficient (ADC) of DW imaging, and high-b-value DW imaging were significantly related to specific DHA parameters (P < .01). For instance, lumen density (ie, the percentage area of tissue components) was associated with T2-weighted MR imaging (slope = 0.36 ± 0.05 [standard error], γ = 0.35), ADC (slope = 0.47 ± 0.05, γ = 0.50), and high-b-value DW imaging (slope = -0.44 ± 0.05, γ = -0.44). Differences between regions harboring benign tissue and those harboring malignant tissue were observed at MR imaging and DHA (P < .01). Gleason score was significantly associated with MR imaging and DHA parameters (P < .05). For example, it was positively related to high-b-value DW imaging (slope = 0.21 ± 0.16, γ = 0.18) and negatively related to lumen density (slope = -0.19 ± 0.18, γ = -0.35). Conclusion Overall, significant associations were observed between MR imaging and DHA, regardless of prostate anatomy. © RSNA, 2017 Online supplemental material is available for this article.
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Affiliation(s)
- Jin Tae Kwak
- From the Department of Computer Science and Engineering, Sejong University, Seoul, Korea (J.T.K.); Molecular Imaging Program, National Cancer Institute (S.S., B.T., P.L.C.), Center for Interventional Oncology (S.X., B.J.W.), Urologic Oncology Branch, National Cancer Institute (P.A.P.), and Laboratory of Pathology, National Cancer Institute (V.M., M.M.), National Institutes of Health, 10 Center Dr, Room 1C341, Bethesda, MD 20892
| | - Sandeep Sankineni
- From the Department of Computer Science and Engineering, Sejong University, Seoul, Korea (J.T.K.); Molecular Imaging Program, National Cancer Institute (S.S., B.T., P.L.C.), Center for Interventional Oncology (S.X., B.J.W.), Urologic Oncology Branch, National Cancer Institute (P.A.P.), and Laboratory of Pathology, National Cancer Institute (V.M., M.M.), National Institutes of Health, 10 Center Dr, Room 1C341, Bethesda, MD 20892
| | - Sheng Xu
- From the Department of Computer Science and Engineering, Sejong University, Seoul, Korea (J.T.K.); Molecular Imaging Program, National Cancer Institute (S.S., B.T., P.L.C.), Center for Interventional Oncology (S.X., B.J.W.), Urologic Oncology Branch, National Cancer Institute (P.A.P.), and Laboratory of Pathology, National Cancer Institute (V.M., M.M.), National Institutes of Health, 10 Center Dr, Room 1C341, Bethesda, MD 20892
| | - Baris Turkbey
- From the Department of Computer Science and Engineering, Sejong University, Seoul, Korea (J.T.K.); Molecular Imaging Program, National Cancer Institute (S.S., B.T., P.L.C.), Center for Interventional Oncology (S.X., B.J.W.), Urologic Oncology Branch, National Cancer Institute (P.A.P.), and Laboratory of Pathology, National Cancer Institute (V.M., M.M.), National Institutes of Health, 10 Center Dr, Room 1C341, Bethesda, MD 20892
| | - Peter L Choyke
- From the Department of Computer Science and Engineering, Sejong University, Seoul, Korea (J.T.K.); Molecular Imaging Program, National Cancer Institute (S.S., B.T., P.L.C.), Center for Interventional Oncology (S.X., B.J.W.), Urologic Oncology Branch, National Cancer Institute (P.A.P.), and Laboratory of Pathology, National Cancer Institute (V.M., M.M.), National Institutes of Health, 10 Center Dr, Room 1C341, Bethesda, MD 20892
| | - Peter A Pinto
- From the Department of Computer Science and Engineering, Sejong University, Seoul, Korea (J.T.K.); Molecular Imaging Program, National Cancer Institute (S.S., B.T., P.L.C.), Center for Interventional Oncology (S.X., B.J.W.), Urologic Oncology Branch, National Cancer Institute (P.A.P.), and Laboratory of Pathology, National Cancer Institute (V.M., M.M.), National Institutes of Health, 10 Center Dr, Room 1C341, Bethesda, MD 20892
| | - Vanessa Moreno
- From the Department of Computer Science and Engineering, Sejong University, Seoul, Korea (J.T.K.); Molecular Imaging Program, National Cancer Institute (S.S., B.T., P.L.C.), Center for Interventional Oncology (S.X., B.J.W.), Urologic Oncology Branch, National Cancer Institute (P.A.P.), and Laboratory of Pathology, National Cancer Institute (V.M., M.M.), National Institutes of Health, 10 Center Dr, Room 1C341, Bethesda, MD 20892
| | - Maria Merino
- From the Department of Computer Science and Engineering, Sejong University, Seoul, Korea (J.T.K.); Molecular Imaging Program, National Cancer Institute (S.S., B.T., P.L.C.), Center for Interventional Oncology (S.X., B.J.W.), Urologic Oncology Branch, National Cancer Institute (P.A.P.), and Laboratory of Pathology, National Cancer Institute (V.M., M.M.), National Institutes of Health, 10 Center Dr, Room 1C341, Bethesda, MD 20892
| | - Bradford J Wood
- From the Department of Computer Science and Engineering, Sejong University, Seoul, Korea (J.T.K.); Molecular Imaging Program, National Cancer Institute (S.S., B.T., P.L.C.), Center for Interventional Oncology (S.X., B.J.W.), Urologic Oncology Branch, National Cancer Institute (P.A.P.), and Laboratory of Pathology, National Cancer Institute (V.M., M.M.), National Institutes of Health, 10 Center Dr, Room 1C341, Bethesda, MD 20892
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Wildeboer RR, Schalk SG, Demi L, Wijkstra H, Mischi M. Three-dimensional histopathological reconstruction as a reliable ground truth for prostate cancer studies. Biomed Phys Eng Express 2017. [DOI: 10.1088/2057-1976/aa7073] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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47
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Bourne RM, Bailey C, Johnston EW, Pye H, Heavey S, Whitaker H, Siow B, Freeman A, Shaw GL, Sridhar A, Mertzanidou T, Hawkes DJ, Alexander DC, Punwani S, Panagiotaki E. Apparatus for Histological Validation of In Vivo and Ex Vivo Magnetic Resonance Imaging of the Human Prostate. Front Oncol 2017; 7:47. [PMID: 28393049 PMCID: PMC5364138 DOI: 10.3389/fonc.2017.00047] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Accepted: 03/08/2017] [Indexed: 01/30/2023] Open
Abstract
This article describes apparatus to aid histological validation of magnetic resonance imaging studies of the human prostate. The apparatus includes a 3D-printed patient-specific mold that facilitates aligned in vivo and ex vivo imaging, in situ tissue fixation, and tissue sectioning with minimal organ deformation. The mold and a dedicated container include MRI-visible landmarks to enable consistent tissue positioning and minimize image registration complexity. The inclusion of high spatial resolution ex vivo imaging aids in registration of in vivo MRI and histopathology data.
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Affiliation(s)
- Roger M. Bourne
- Discipline of Medical Radiation Sciences, Faculty of Health Sciences, University of Sydney, Sydney, NSW, Australia
| | - Colleen Bailey
- Centre for Medical Image Computing, University College London, London, UK
| | | | - Hayley Pye
- Centre for Molecular Intervention, University College London, London, UK
| | - Susan Heavey
- Centre for Molecular Intervention, University College London, London, UK
| | - Hayley Whitaker
- Centre for Molecular Intervention, University College London, London, UK
| | - Bernard Siow
- Centre for Advanced Biomedical Imaging, University College London, London, UK
| | - Alex Freeman
- Department of Research Pathology, University College London, London, UK
| | - Greg L. Shaw
- Division of Surgery and Interventional Science, University College London, London, UK
- Department of Urology, University College London Hospitals, London, UK
| | - Ashwin Sridhar
- Division of Surgery and Interventional Science, University College London, London, UK
- Department of Urology, University College London Hospitals, London, UK
| | - Thomy Mertzanidou
- Centre for Medical Image Computing, University College London, London, UK
| | - David J. Hawkes
- Centre for Medical Image Computing, University College London, London, UK
| | | | - Shonit Punwani
- Centre for Medical Imaging, University College London, London, UK
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Mertzanidou T, Hipwell JH, Reis S, Hawkes DJ, Ehteshami Bejnordi B, Dalmis M, Vreemann S, Platel B, van der Laak J, Karssemeijer N, Hermsen M, Bult P, Mann R. 3D volume reconstruction from serial breast specimen radiographs for mapping between histology and 3D whole specimen imaging. Med Phys 2017; 44:935-948. [PMID: 28064435 PMCID: PMC6849622 DOI: 10.1002/mp.12077] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Revised: 11/10/2016] [Accepted: 12/18/2016] [Indexed: 11/11/2022] Open
Abstract
PURPOSE In breast imaging, radiological in vivo images, such as x-ray mammography and magnetic resonance imaging (MRI), are used for tumor detection, diagnosis, and size determination. After excision, the specimen is typically sliced into slabs and a small subset is sampled. Histopathological imaging of the stained samples is used as the gold standard for characterization of the tumor microenvironment. A 3D volume reconstruction of the whole specimen from the 2D slabs could facilitate bridging the gap between histology and in vivo radiological imaging. This task is challenging, however, due to the large deformation that the breast tissue undergoes after surgery and the significant undersampling of the specimen obtained in histology. In this work, we present a method to reconstruct a coherent 3D volume from 2D digital radiographs of the specimen slabs. METHODS To reconstruct a 3D breast specimen volume, we propose the use of multiple target neighboring slices, when deforming each 2D slab radiograph in the volume, rather than performing pairwise registrations. The algorithm combines neighborhood slice information with free-form deformations, which enables a flexible, nonlinear deformation to be computed subject to the constraint that a coherent 3D volume is obtained. The neighborhood information provides adequate constraints, without the need for any additional regularization terms. RESULTS The volume reconstruction algorithm is validated on clinical mastectomy samples using a quantitative assessment of the volume reconstruction smoothness and a comparison with a whole specimen 3D image acquired for validation before slicing. Additionally, a target registration error of 5 mm (comparable to the specimen slab thickness of 4 mm) was obtained for five cases. The error was computed using manual annotations from four observers as gold standard, with interobserver variability of 3.4 mm. Finally, we illustrate how the reconstructed volumes can be used to map histology images to a 3D specimen image of the whole sample (either MRI or CT). CONCLUSIONS Qualitative and quantitative assessment has illustrated the benefit of using our proposed methodology to reconstruct a coherent specimen volume from serial slab radiographs. To our knowledge, this is the first method that has been applied to clinical breast cases, with the goal of reconstructing a whole specimen sample. The algorithm can be used as part of the pipeline of mapping histology images to ex vivo and ultimately in vivo radiological images of the breast.
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Affiliation(s)
- Thomy Mertzanidou
- Centre for Medical Image ComputingUniversity College LondonWC1E 6BTLondonUK
| | - John H. Hipwell
- Centre for Medical Image ComputingUniversity College LondonWC1E 6BTLondonUK
| | - Sara Reis
- Centre for Medical Image ComputingUniversity College LondonWC1E 6BTLondonUK
| | - David J. Hawkes
- Centre for Medical Image ComputingUniversity College LondonWC1E 6BTLondonUK
| | | | - Mehmet Dalmis
- Diagnostic Image Analysis GroupRadboud University Medical Center6500 HBNijmegenThe Netherlands
| | - Suzan Vreemann
- Diagnostic Image Analysis GroupRadboud University Medical Center6500 HBNijmegenThe Netherlands
| | - Bram Platel
- Diagnostic Image Analysis GroupRadboud University Medical Center6500 HBNijmegenThe Netherlands
| | - Jeroen van der Laak
- Diagnostic Image Analysis GroupRadboud University Medical Center6500 HBNijmegenThe Netherlands
| | - Nico Karssemeijer
- Diagnostic Image Analysis GroupRadboud University Medical Center6500 HBNijmegenThe Netherlands
| | - Meyke Hermsen
- Department of PathologyRadboud University Medical Center6500 HBNijmegenThe Netherlands
| | - Peter Bult
- Department of PathologyRadboud University Medical Center6500 HBNijmegenThe Netherlands
| | - Ritse Mann
- Department of RadiologyRadboud University Medical Center6500 HBNijmegenThe Netherlands
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49
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Muller BG, Swaan A, de Bruin DM, van den Bos W, Schreurs AW, Faber DJ, Zwartkruis ECH, Rozendaal L, Vis AN, Nieuwenhuijzen JA, van Moorselaar RJA, van Leeuwen TG, de la Rosette JJMCH. Customized Tool for the Validation of Optical Coherence Tomography in Differentiation of Prostate Cancer. Technol Cancer Res Treat 2017; 16:57-65. [PMID: 26818025 PMCID: PMC5616116 DOI: 10.1177/1533034615626614] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Revised: 11/20/2015] [Accepted: 12/16/2015] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE To design and demonstrate a customized tool to generate histologic sections of the prostate that directly correlate with needle-based optical coherence tomography pullback measurements. MATERIALS AND METHODS A customized tool was created to hold the prostatectomy specimens during optical coherence tomography measurements and formalin fixation. Using the tool, the prostate could be sliced into slices of 4 mm thickness through the optical coherence tomography measurement trajectory. In this way, whole-mount pathology slides were produced in exactly the same location as the optical coherence tomography measurements were performed. Full 3-dimensional optical coherence tomography pullbacks were fused with the histopathology slides using the 3-dimensional imaging software AMIRA, and images were compared. RESULTS A radical prostatectomy was performed in a patient (age: 68 years, prostate-specific antigen: 6.0 ng/mL) with Gleason score 3 + 4 = 7 in 2/5 biopsy cores on the left side (15%) and Gleason score 3 + 4 = 7 in 1/5 biopsy cores on the right side (5%). Histopathology after radical prostatectomy showed an anterior located pT2cNx adenocarcinoma (Gleason score 3 + 4 = 7). Histopathological prostate slides were produced using the customized tool for optical coherence tomography measurements, fixation, and slicing of the prostate specimens. These slides correlated exactly with the optical coherence tomography images. Various structures, for example, Gleason 3 + 4 prostate cancer, stroma, healthy glands, and cystic atrophy with septae, could be identified both on optical coherence tomography and on the histopathological prostate slides. CONCLUSION We successfully designed and applied a customized tool to process radical prostatectomy specimens to improve the coregistration of whole mount histology sections to fresh tissue optical coherence tomography pullback measurements. This technique will be crucial in validating the results of optical coherence tomography imaging studies with histology and can easily be applied in other solid tissues as well, for example, lung, kidney, breast, and liver. This will help improve the efficacy of optical coherence tomography in cancer detection and staging in solid organs.
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Affiliation(s)
- B. G. Muller
- Department of Urology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - A. Swaan
- Department of Urology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
- Department of Biomedical Engineering and Physics, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - D. M. de Bruin
- Department of Urology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
- Department of Biomedical Engineering and Physics, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - W. van den Bos
- Department of Urology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - A. W. Schreurs
- Department of Instrumental Services, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - D. J. Faber
- Department of Biomedical Engineering and Physics, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - E. C. H. Zwartkruis
- Department of Pathology, VU University Medical Center, Free University, Amsterdam, the Netherlands
| | - L. Rozendaal
- Department of Pathology, VU University Medical Center, Free University, Amsterdam, the Netherlands
| | - A. N. Vis
- Department of Urology, VU University Medical Center, Free University, Amsterdam, the Netherlands
| | - J. A. Nieuwenhuijzen
- Department of Urology, VU University Medical Center, Free University, Amsterdam, the Netherlands
| | - R. J. A. van Moorselaar
- Department of Urology, VU University Medical Center, Free University, Amsterdam, the Netherlands
| | - T. G. van Leeuwen
- Department of Biomedical Engineering and Physics, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
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50
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McGrath DM, Lee J, Foltz WD, Samavati N, Jewett MAS, van der Kwast T, Chung P, Ménard C, Brock KK. Technical Note: Method to correlate whole-specimen histopathology of radical prostatectomy with diagnostic MR imaging. Med Phys 2016; 43:1065-72. [PMID: 26936694 DOI: 10.1118/1.4941016] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Validation of MRI-guided tumor boundary delineation for targeted prostate cancer therapy is achieved via correlation with gold-standard histopathology of radical prostatectomy specimens. Challenges to accurate correlation include matching the pathology sectioning plane with the in vivo imaging slice plane and correction for the deformation that occurs between in vivo imaging and histology. A methodology is presented for matching of the histological sectioning angle and position to the in vivo imaging slices. METHODS Patients (n = 4) with biochemical failure following external beam radiotherapy underwent diagnostic MRI to confirm localized recurrence of prostate cancer, followed by salvage radical prostatectomy. High-resolution 3-D MRI of the ex vivo specimens was acquired to determine the pathology sectioning angle that best matched the in vivo imaging slice plane, using matching anatomical features and implanted fiducials. A novel sectioning device was developed to guide sectioning at the correct angle, and to assist the insertion of reference dye marks to aid in histopathology reconstruction. RESULTS The percentage difference in the positioning of the urethra in the ex vivo pathology sections compared to the positioning in in vivo images was reduced from 34% to 7% through slicing at the best match angle. Reference dye marks were generated, which were visible in ex vivo imaging, in the tissue sections before and after processing, and in histology sections. CONCLUSIONS The method achieved an almost fivefold reduction in the slice-matching error and is readily implementable in combination with standard MRI technology. The technique will be employed to generate datasets for correlation of whole-specimen prostate histopathology with in vivo diagnostic MRI using 3-D deformable registration, allowing assessment of the sensitivity and specificity of MRI parameters for prostate cancer. Although developed specifically for prostate, the method is readily adaptable to other types of whole tissue specimen, such as mastectomy or liver resection.
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Affiliation(s)
- Deirdre M McGrath
- Radiation Medicine Program, Princess Margaret Hospital, University Health Network, Toronto, Ontario M5G 2M9, Canada
| | - Jenny Lee
- Radiation Medicine Program, Princess Margaret Hospital, University Health Network, Toronto, Ontario M5G 2M9, Canada
| | - Warren D Foltz
- Radiation Medicine Program, Princess Margaret Hospital, University Health Network, Toronto, Ontario M5G 2M9, Canada
| | - Navid Samavati
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3G9, Canada
| | - Michael A S Jewett
- Departments of Surgery (Urology) and Surgical Oncology, Princess Margaret Cancer Centre, University Health Network and University of Toronto, Toronto, Ontario M5G 2M9, Canada
| | - Theo van der Kwast
- Pathology Department, University Health Network, Toronto, Ontario M5G 2C4, Canada
| | - Peter Chung
- Radiation Medicine Program, Princess Margaret Hospital, University Health Network and the University of Toronto, Toronto, Ontario M5G 2M9, Canada
| | - Cynthia Ménard
- Radiation Medicine Program, Princess Margaret Hospital, University Health Network and the University of Toronto, Toronto, Ontario M5G 2M9, Canada and Centre Hospitalier de l'Université de Montréal, 1058 Rue Saint-Denis, Montréal, Québec H2X 3J4, Canada
| | - Kristy K Brock
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan 48108
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