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Mehmood M, Abbasi SH, Aurangzeb K, Majeed MF, Anwar MS, Alhussein M. A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI. Front Oncol 2023; 13:1225490. [PMID: 38023149 PMCID: PMC10666634 DOI: 10.3389/fonc.2023.1225490] [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: 05/19/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Prostate cancer (PCa) is a major global concern, particularly for men, emphasizing the urgency of early detection to reduce mortality. As the second leading cause of cancer-related male deaths worldwide, precise and efficient diagnostic methods are crucial. Due to high and multiresolution MRI in PCa, computer-aided diagnostic (CAD) methods have emerged to assist radiologists in identifying anomalies. However, the rapid advancement of medical technology has led to the adoption of deep learning methods. These techniques enhance diagnostic efficiency, reduce observer variability, and consistently outperform traditional approaches. Resource constraints that can distinguish whether a cancer is aggressive or not is a significant problem in PCa treatment. This study aims to identify PCa using MRI images by combining deep learning and transfer learning (TL). Researchers have explored numerous CNN-based Deep Learning methods for classifying MRI images related to PCa. In this study, we have developed an approach for the classification of PCa using transfer learning on a limited number of images to achieve high performance and help radiologists instantly identify PCa. The proposed methodology adopts the EfficientNet architecture, pre-trained on the ImageNet dataset, and incorporates three branches for feature extraction from different MRI sequences. The extracted features are then combined, significantly enhancing the model's ability to distinguish MRI images accurately. Our model demonstrated remarkable results in classifying prostate cancer, achieving an accuracy rate of 88.89%. Furthermore, comparative results indicate that our approach achieve higher accuracy than both traditional hand-crafted feature techniques and existing deep learning techniques in PCa classification. The proposed methodology can learn more distinctive features in prostate images and correctly identify cancer.
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Affiliation(s)
- Mubashar Mehmood
- Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan
| | | | - Khursheed Aurangzeb
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | | | | | - Musaed Alhussein
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
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2
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Jaouen T, Souchon R, Moldovan PC, Bratan F, Duran A, Hoang-Dinh A, Di Franco F, Debeer S, Dubreuil-Chambardel M, Arfi N, Ruffion A, Colombel M, Crouzet S, Gonindard-Melodelima C, Rouvière O. Characterization of high-grade prostate cancer at multiparametric MRI using a radiomic-based computer-aided diagnosis system as standalone and second reader. Diagn Interv Imaging 2023; 104:465-476. [PMID: 37345961 DOI: 10.1016/j.diii.2023.04.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 04/16/2023] [Accepted: 04/18/2023] [Indexed: 06/23/2023]
Abstract
PURPOSE The purpose of this study was to develop and test across various scanners a zone-specific region-of-interest (ROI)-based computer-aided diagnosis system (CAD) aimed at characterizing, on MRI, International Society of Urological Pathology (ISUP) grade≥2 prostate cancers. MATERIALS AND METHODS ROI-based quantitative models were selected in multi-vendor training (265 pre-prostatectomy MRIs) and pre-test (112 pre-biopsy MRIs) datasets. The best peripheral and transition zone models were combined and retrospectively assessed in internal (158 pre-biopsy MRIs) and external (104 pre-biopsy MRIs) test datasets. Two radiologists (R1/R2) retrospectively delineated the lesions targeted at biopsy in test datasets. The CAD area under the receiver operating characteristic curve (AUC) for characterizing ISUP≥2 cancers was compared to that of the Prostate Imaging-Reporting and Data System version2 (PI-RADSv2) score prospectively assigned to targeted lesions. RESULTS The best models used the 25th apparent diffusion coefficient (ADC) percentile in transition zone and the 2nd ADC percentile and normalized wash-in rate in peripheral zone. The PI-RADSv2 AUCs were 82% (95% confidence interval [CI]: 74-87) and 86% (95% CI: 81-91) in the internal and external test datasets respectively. They were not different from the CAD AUCs obtained with R1 and R2 delineations, in the internal (82% [95% CI: 76-89], P = 0.95 and 85% [95% CI: 78-91], P = 0.55) and external (82% [95% CI: 74-91], P = 0.41 and 86% [95% CI:78-95], P = 0.98) test datasets. The CAD yielded sensitivities of 86-89% and 90-91%, and specificities of 64-65% and 69-75% in the internal and external test datasets respectively. CONCLUSION The CAD performance for characterizing ISUP grade≥2 prostate cancers on MRI is not different from that of PI-RADSv2 score across two test datasets.
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Affiliation(s)
| | | | - Paul C Moldovan
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France
| | - Flavie Bratan
- Hôpital Saint Joseph Saint Luc, Department of Radiology, Lyon, 69007, France
| | - Audrey Duran
- Univ Lyon, CNRS, Inserm, INSA Lyon, UCBL, CREATIS, UMR5220, U1294, Villeurbanne, 69100, France
| | - Au Hoang-Dinh
- INSERM, LabTAU, U1032, Lyon, 69003, France; Hanoi Medical University, Department of Radiology, Hanoi, 116001, Vietnam
| | - Florian Di Franco
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France
| | - Sabine Debeer
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France
| | - Marine Dubreuil-Chambardel
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France
| | - Nicolas Arfi
- Hôpital Saint Joseph Saint Luc, Department of Urology, Lyon, 69007, France
| | - Alain Ruffion
- Hospices Civils de Lyon, Centre Hospitalier Lyon Sud, Department of Urology, Pierre-Bénite, 69310, France; Equipe 2 - Centre d'Innovation en Cancérologie de Lyon (EA 3738 CICLY), Pierre-Bénite, 69310, France; Université de Lyon, Lyon, 69003, France; Université Lyon 1, Lyon, 69003, France; Faculté de Médecine Lyon Sud, Pierre-Bénite, 69310, France
| | - Marc Colombel
- Université de Lyon, Lyon, 69003, France; Université Lyon 1, Lyon, 69003, France; Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Urology, Lyon, 69003, France; Faculté de Médecine Lyon Est, Lyon, 69003, France
| | - Sébastien Crouzet
- INSERM, LabTAU, U1032, Lyon, 69003, France; Université de Lyon, Lyon, 69003, France; Université Lyon 1, Lyon, 69003, France; Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Urology, Lyon, 69003, France; Faculté de Médecine Lyon Est, Lyon, 69003, France
| | - Christelle Gonindard-Melodelima
- Université Grenoble Alpes, Laboratoire d'Ecologie Alpine, BP 53, Grenoble 38041, France; CNRS, UMR 5553, BP 53, Grenoble, 38041, France
| | - Olivier Rouvière
- INSERM, LabTAU, U1032, Lyon, 69003, France; Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France; Université de Lyon, Lyon, 69003, France; Université Lyon 1, Lyon, 69003, France; Faculté de Médecine Lyon Est, Lyon, 69003, France.
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He M, Cao Y, Chi C, Yang X, Ramin R, Wang S, Yang G, Mukhtorov O, Zhang L, Kazantsev A, Enikeev M, Hu K. Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives. Front Oncol 2023; 13:1189370. [PMID: 37546423 PMCID: PMC10400334 DOI: 10.3389/fonc.2023.1189370] [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: 03/19/2023] [Accepted: 05/30/2023] [Indexed: 08/08/2023] Open
Abstract
Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.
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Affiliation(s)
- Mingze He
- Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Yu Cao
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Changliang Chi
- Department of Urology, The First Hospital of Jilin University (Lequn Branch), Changchun, Jilin, China
| | - Xinyi Yang
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Rzayev Ramin
- Department of Radiology, The Second University Clinic, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Shuowen Wang
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Guodong Yang
- I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Otabek Mukhtorov
- Regional State Budgetary Health Care Institution, Kostroma Regional Clinical Hospital named after Korolev E.I. Avenue Mira, Kostroma, Russia
| | - Liqun Zhang
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, Liaoning, China
| | - Anton Kazantsev
- Regional State Budgetary Health Care Institution, Kostroma Regional Clinical Hospital named after Korolev E.I. Avenue Mira, Kostroma, Russia
| | - Mikhail Enikeev
- Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Kebang Hu
- Department of Urology, The First Hospital of Jilin University (Lequn Branch), Changchun, Jilin, China
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Fernandes MC, Yildirim O, Woo S, Vargas HA, Hricak H. The role of MRI in prostate cancer: current and future directions. MAGMA (NEW YORK, N.Y.) 2022; 35:503-521. [PMID: 35294642 PMCID: PMC9378354 DOI: 10.1007/s10334-022-01006-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 01/16/2022] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
There has been an increasing role of magnetic resonance imaging (MRI) in the management of prostate cancer. MRI already plays an essential role in the detection and staging, with the introduction of functional MRI sequences. Recent advancements in radiomics and artificial intelligence are being tested to potentially improve detection, assessment of aggressiveness, and provide usefulness as a prognostic marker. MRI can improve pretreatment risk stratification and therefore selection of and follow-up of patients for active surveillance. MRI can also assist in guiding targeted biopsy, treatment planning and follow-up after treatment to assess local recurrence. MRI has gained importance in the evaluation of metastatic disease with emerging technology including whole-body MRI and integrated positron emission tomography/MRI, allowing for not only better detection but also quantification. The main goal of this article is to review the most recent advances on MRI in prostate cancer and provide insights into its potential clinical roles from the radiologist's perspective. In each of the sections, specific roles of MRI tailored to each clinical setting are discussed along with its strengths and weakness including already established material related to MRI and the introduction of recent advancements on MRI.
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Affiliation(s)
- Maria Clara Fernandes
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Onur Yildirim
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Sungmin Woo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA.
| | - Hebert Alberto Vargas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Hedvig Hricak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
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Predicting the Grade of Prostate Cancer Based on a Biparametric MRI Radiomics Signature. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2021:7830909. [PMID: 35024015 PMCID: PMC8718299 DOI: 10.1155/2021/7830909] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 10/08/2021] [Accepted: 12/03/2021] [Indexed: 12/12/2022]
Abstract
Purpose This study aimed to investigate the value of biparametric magnetic resonance imaging (bp-MRI)-based radiomics signatures for the preoperative prediction of prostate cancer (PCa) grade compared with visual assessments by radiologists based on the Prostate Imaging Reporting and Data System Version 2.1 (PI-RADS V2.1) scores of multiparametric MRI (mp-MRI). Methods This retrospective study included 142 consecutive patients with histologically confirmed PCa who were undergoing mp-MRI before surgery. MRI images were scored and evaluated by two independent radiologists using PI-RADS V2.1. The radiomics workflow was divided into five steps: (a) image selection and segmentation, (b) feature extraction, (c) feature selection, (d) model establishment, and (e) model evaluation. Three machine learning algorithms (random forest tree (RF), logistic regression, and support vector machine (SVM)) were constructed to differentiate high-grade from low-grade PCa. Receiver operating characteristic (ROC) analysis was used to compare the machine learning-based analysis of bp-MRI radiomics models with PI-RADS V2.1. Results In all, 8 stable radiomics features out of 804 extracted features based on T2-weighted imaging (T2WI) and ADC sequences were selected. Radiomics signatures successfully categorized high-grade and low-grade PCa cases (P < 0.05) in both the training and test datasets. The radiomics model-based RF method (area under the curve, AUC: 0.982; 0.918), logistic regression (AUC: 0.886; 0.886), and SVM (AUC: 0.943; 0.913) in both the training and test cohorts had better diagnostic performance than PI-RADS V2.1 (AUC: 0.767; 0.813) when predicting PCa grade. Conclusions The results of this clinical study indicate that machine learning-based analysis of bp-MRI radiomic models may be helpful for distinguishing high-grade and low-grade PCa that outperformed the PI-RADS V2.1 scores based on mp-MRI. The machine learning algorithm RF model was slightly better.
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6
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Rouvière O, Souchon R, Lartizien C, Mansuy A, Magaud L, Colom M, Dubreuil-Chambardel M, Debeer S, Jaouen T, Duran A, Rippert P, Riche B, Monini C, Vlaeminck-Guillem V, Haesebaert J, Rabilloud M, Crouzet S. Detection of ISUP ≥2 prostate cancers using multiparametric MRI: prospective multicentre assessment of the non-inferiority of an artificial intelligence system as compared to the PI-RADS V.2.1 score (CHANGE study). BMJ Open 2022; 12:e051274. [PMID: 35140147 PMCID: PMC8830410 DOI: 10.1136/bmjopen-2021-051274] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
INTRODUCTION Prostate multiparametric MRI (mpMRI) has shown good sensitivity in detecting cancers with an International Society of Urological Pathology (ISUP) grade of ≥2. However, it lacks specificity, and its inter-reader reproducibility remains moderate. Biomarkers, such as the Prostate Health Index (PHI), may help select patients for prostate biopsy. Computer-aided diagnosis/detection (CAD) systems may also improve mpMRI interpretation. Different prototypes of CAD systems are currently developed under the Recherche Hospitalo-Universitaire en Santé / Personalized Focused Ultrasound Surgery of Localized Prostate Cancer (RHU PERFUSE) research programme, tackling challenging issues such as robustness across imaging protocols and magnetic resonance (MR) vendors, and ability to characterise cancer aggressiveness. The study primary objective is to evaluate the non-inferiority of the area under the receiver operating characteristic curve of the final CAD system as compared with the Prostate Imaging-Reporting and Data System V.2.1 (PI-RADS V.2.1) in predicting the presence of ISUP ≥2 prostate cancer in patients undergoing prostate biopsy. METHODS This prospective, multicentre, non-inferiority trial will include 420 men with suspected prostate cancer, a prostate-specific antigen level of ≤30 ng/mL and a clinical stage ≤T2 c. Included men will undergo prostate mpMRI that will be interpreted using the PI-RADS V.2.1 score. Then, they will undergo systematic and targeted biopsy. PHI will be assessed before biopsy. At the end of patient inclusion, MR images will be assessed by the final version of the CAD system developed under the RHU PERFUSE programme. Key secondary outcomes include the prediction of ISUP grade ≥2 prostate cancer during a 3-year follow-up, and the number of biopsy procedures saved and ISUP grade ≥2 cancers missed by several diagnostic pathways combining PHI and MRI findings. ETHICS AND DISSEMINATION Ethical approval was obtained from the Comité de Protection des Personnes Nord Ouest III (ID-RCB: 2020-A02785-34). After publication of the results, access to MR images will be possible for testing other CAD systems. TRIAL REGISTRATION NUMBER NCT04732156.
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Affiliation(s)
- Olivier Rouvière
- Université Lyon 1, Université de Lyon, Lyon, France
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
- LabTau, INSERM U1032, Lyon, France
| | | | - Carole Lartizien
- CREATIS, INSERM U1294, Villeurbanne, France
- CNRS UMR 5220, INSA-Lyon, Villeurbanne, France
| | - Adeline Mansuy
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | - Laurent Magaud
- Service Recherche et Epidémiologie Cliniques, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France
| | - Matthieu Colom
- Direction de la Recherche Clinique et de l'Innovation, Hospices Civils de Lyon, Lyon, France
| | - Marine Dubreuil-Chambardel
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | - Sabine Debeer
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | | | - Audrey Duran
- CREATIS, INSERM U1294, Villeurbanne, France
- CNRS UMR 5220, INSA-Lyon, Villeurbanne, France
| | - Pascal Rippert
- Service Recherche et Epidémiologie Cliniques, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France
| | - Benjamin Riche
- Service de Biostatistique-Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France
- Laboratoire de Biométrie et Biologie Évolutive CNRS UMR 5558, Équipe Biostatistiques Santé, Université de Lyon, Lyon, France
| | | | - Virginie Vlaeminck-Guillem
- Université Lyon 1, Université de Lyon, Lyon, France
- Service de Biochimie et Biologie Moléculaire Sud, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, Pierre Bénite, France
| | - Julie Haesebaert
- Université Lyon 1, Université de Lyon, Lyon, France
- Service Recherche et Epidémiologie Cliniques, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France
- Research on Healthcare Performance (RESHAPE), INSERM U1290, Lyon, France
| | - Muriel Rabilloud
- Université Lyon 1, Université de Lyon, Lyon, France
- Service de Biostatistique-Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, Lyon, France
- Laboratoire de Biométrie et Biologie Évolutive CNRS UMR 5558, Équipe Biostatistiques Santé, Université de Lyon, Lyon, France
| | - Sébastien Crouzet
- Université Lyon 1, Université de Lyon, Lyon, France
- LabTau, INSERM U1032, Lyon, France
- Department of Urology, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
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Rouvière O, Moldovan PC, Vlachomitrou A, Gouttard S, Riche B, Groth A, Rabotnikov M, Ruffion A, Colombel M, Crouzet S, Weese J, Rabilloud M. Combined model-based and deep learning-based automated 3D zonal segmentation of the prostate on T2-weighted MR images: clinical evaluation. Eur Radiol 2022; 32:3248-3259. [PMID: 35001157 DOI: 10.1007/s00330-021-08408-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 09/28/2021] [Accepted: 10/09/2021] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To train and to test for prostate zonal segmentation an existing algorithm already trained for whole-gland segmentation. METHODS The algorithm, combining model-based and deep learning-based approaches, was trained for zonal segmentation using the NCI-ISBI-2013 dataset and 70 T2-weighted datasets acquired at an academic centre. Test datasets were randomly selected among examinations performed at this centre on one of two scanners (General Electric, 1.5 T; Philips, 3 T) not used for training. Automated segmentations were corrected by two independent radiologists. When segmentation was initiated outside the prostate, images were cropped and segmentation repeated. Factors influencing the algorithm's mean Dice similarity coefficient (DSC) and its precision were assessed using beta regression. RESULTS Eighty-two test datasets were selected; one was excluded. In 13/81 datasets, segmentation started outside the prostate, but zonal segmentation was possible after image cropping. Depending on the radiologist chosen as reference, algorithm's median DSCs were 96.4/97.4%, 91.8/93.0% and 79.9/89.6% for whole-gland, central gland and anterior fibromuscular stroma (AFMS) segmentations, respectively. DSCs comparing radiologists' delineations were 95.8%, 93.6% and 81.7%, respectively. For all segmentation tasks, the scanner used for imaging significantly influenced the mean DSC and its precision, and the mean DSC was significantly lower in cases with initial segmentation outside the prostate. For central gland segmentation, the mean DSC was also significantly lower in larger prostates. The radiologist chosen as reference had no significant impact, except for AFMS segmentation. CONCLUSIONS The algorithm performance fell within the range of inter-reader variability but remained significantly impacted by the scanner used for imaging. KEY POINTS • Median Dice similarity coefficients obtained by the algorithm fell within human inter-reader variability for the three segmentation tasks (whole gland, central gland, anterior fibromuscular stroma). • The scanner used for imaging significantly impacted the performance of the automated segmentation for the three segmentation tasks. • The performance of the automated segmentation of the anterior fibromuscular stroma was highly variable across patients and showed also high variability across the two radiologists.
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Affiliation(s)
- Olivier Rouvière
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Pavillon B, 5 place d'Arsonval, F-69437, Lyon, France. .,Université de Lyon, F-69003, Lyon, France. .,Faculté de Médecine Lyon Est, Université Lyon 1, F-69003, Lyon, France. .,INSERM, LabTau, U1032, Lyon, France.
| | - Paul Cezar Moldovan
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Pavillon B, 5 place d'Arsonval, F-69437, Lyon, France
| | - Anna Vlachomitrou
- Philips France, 33 rue de Verdun, CS 60 055, 92156, Suresnes Cedex, France
| | - Sylvain Gouttard
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Pavillon B, 5 place d'Arsonval, F-69437, Lyon, France
| | - Benjamin Riche
- Service de Biostatistique Et Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, F-69003, Lyon, France.,Laboratoire de Biométrie Et Biologie Évolutive, Équipe Biostatistique-Santé, UMR 5558, CNRS, F-69100, Villeurbanne, France
| | - Alexandra Groth
- Philips Research, Röntgenstrasse 24-26, 22335, Hamburg, Germany
| | | | - Alain Ruffion
- Department of Urology, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, F-69310, Pierre-Bénite, France
| | - Marc Colombel
- Université de Lyon, F-69003, Lyon, France.,Faculté de Médecine Lyon Est, Université Lyon 1, F-69003, Lyon, France.,Department of Urology, Hôpital Edouard Herriot, Hospices Civils de Lyon, F-69437, Lyon, France
| | - Sébastien Crouzet
- Department of Urology, Hôpital Edouard Herriot, Hospices Civils de Lyon, F-69437, Lyon, France
| | - Juergen Weese
- Philips Research, Röntgenstrasse 24-26, 22335, Hamburg, Germany
| | - Muriel Rabilloud
- Université de Lyon, F-69003, Lyon, France.,Faculté de Médecine Lyon Est, Université Lyon 1, F-69003, Lyon, France.,Service de Biostatistique Et Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, F-69003, Lyon, France.,Laboratoire de Biométrie Et Biologie Évolutive, Équipe Biostatistique-Santé, UMR 5558, CNRS, F-69100, Villeurbanne, France
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8
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Ferro M, de Cobelli O, Musi G, del Giudice F, Carrieri G, Busetto GM, Falagario UG, Sciarra A, Maggi M, Crocetto F, Barone B, Caputo VF, Marchioni M, Lucarelli G, Imbimbo C, Mistretta FA, Luzzago S, Vartolomei MD, Cormio L, Autorino R, Tătaru OS. Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol 2022; 14:17562872221109020. [PMID: 35814914 PMCID: PMC9260602 DOI: 10.1177/17562872221109020] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 05/30/2022] [Indexed: 12/24/2022] Open
Abstract
Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.
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Affiliation(s)
- Matteo Ferro
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy, via Ripamonti 435 Milano, Italy
| | - Ottavio de Cobelli
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Gennaro Musi
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Francesco del Giudice
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Carrieri
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | | | - Alessandro Sciarra
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Martina Maggi
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Felice Crocetto
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Biagio Barone
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Vincenzo Francesco Caputo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Michele Marchioni
- Department of Medical, Oral and Biotechnological Sciences, G. d’Annunzio, University of Chieti, Chieti, Italy; Urology Unit, ‘SS. Annunziata’ Hospital, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti, Italy
| | - Giuseppe Lucarelli
- Department of Emergency and Organ Transplantation, Urology, Andrology and Kidney Transplantation Unit, University of Bari, Bari, Italy
| | - Ciro Imbimbo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Stefano Luzzago
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - Luigi Cormio
- Urology and Renal Transplantation Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
- Urology Unit, Bonomo Teaching Hospital, Foggia, Italy
| | | | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral Studies, I.O.S.U.D., George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
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9
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Sunoqrot MRS, Selnæs KM, Sandsmark E, Langørgen S, Bertilsson H, Bathen TF, Elschot M. The Reproducibility of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images. Diagnostics (Basel) 2021; 11:diagnostics11091690. [PMID: 34574031 PMCID: PMC8471645 DOI: 10.3390/diagnostics11091690] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 09/08/2021] [Accepted: 09/15/2021] [Indexed: 01/02/2023] Open
Abstract
Volume of interest segmentation is an essential step in computer-aided detection and diagnosis (CAD) systems. Deep learning (DL)-based methods provide good performance for prostate segmentation, but little is known about the reproducibility of these methods. In this work, an in-house collected dataset from 244 patients was used to investigate the intra-patient reproducibility of 14 shape features for DL-based segmentation methods of the whole prostate gland (WP), peripheral zone (PZ), and the remaining prostate zones (non-PZ) on T2-weighted (T2W) magnetic resonance (MR) images compared to manual segmentations. The DL-based segmentation was performed using three different convolutional neural networks (CNNs): V-Net, nnU-Net-2D, and nnU-Net-3D. The two-way random, single score intra-class correlation coefficient (ICC) was used to measure the inter-scan reproducibility of each feature for each CNN and the manual segmentation. We found that the reproducibility of the investigated methods is comparable to manual for all CNNs (14/14 features), except for V-Net in PZ (7/14 features). The ICC score for segmentation volume was found to be 0.888, 0.607, 0.819, and 0.903 in PZ; 0.988, 0.967, 0.986, and 0.983 in non-PZ; 0.982, 0.975, 0.973, and 0.984 in WP for manual, V-Net, nnU-Net-2D, and nnU-Net-3D, respectively. The results of this work show the feasibility of embedding DL-based segmentation in CAD systems, based on multiple T2W MR scans of the prostate, which is an important step towards the clinical implementation.
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Affiliation(s)
- Mohammed R. S. Sunoqrot
- Department of Circulation and Medical Imaging, NTNU—Norwegian University of Science and Technology, 7030 Trondheim, Norway; (K.M.S.); (T.F.B.); (M.E.)
- Correspondence:
| | - Kirsten M. Selnæs
- Department of Circulation and Medical Imaging, NTNU—Norwegian University of Science and Technology, 7030 Trondheim, Norway; (K.M.S.); (T.F.B.); (M.E.)
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030 Trondheim, Norway; (E.S.); (S.L.)
| | - Elise Sandsmark
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030 Trondheim, Norway; (E.S.); (S.L.)
| | - Sverre Langørgen
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030 Trondheim, Norway; (E.S.); (S.L.)
| | - Helena Bertilsson
- Department of Cancer Research and Molecular Medicine, NTNU—Norwegian University of Science and Technology, 7030 Trondheim, Norway;
- Department of Urology, St. Olavs Hospital, Trondheim University Hospital, 7030 Trondheim, Norway
| | - Tone F. Bathen
- Department of Circulation and Medical Imaging, NTNU—Norwegian University of Science and Technology, 7030 Trondheim, Norway; (K.M.S.); (T.F.B.); (M.E.)
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030 Trondheim, Norway; (E.S.); (S.L.)
| | - Mattijs Elschot
- Department of Circulation and Medical Imaging, NTNU—Norwegian University of Science and Technology, 7030 Trondheim, Norway; (K.M.S.); (T.F.B.); (M.E.)
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030 Trondheim, Norway; (E.S.); (S.L.)
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10
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Santoro AA, Di Gianfrancesco L, Racioppi M, Pinto F, Palermo G, Sacco E, Campetella M, Scarciglia E, Bientinesi R, Di Paola V, Totaro A. Multiparametric magnetic resonance imaging of the prostate: Lights and shadows. Urologia 2021; 88:280-286. [PMID: 34075837 DOI: 10.1177/03915603211019982] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Prostate cancer is the second most commonly diagnosed cancer in man. Since the first MRI was performed, enormous progress has been made in diagnosis, treatment, and follow up of PCa, mainly due to multiparametric prostatic MRI (mpMRI). Although mpMRI has become the best imaging tool for identifying PCa, some limitations still exist. Prostate imaging with mpMRI is, to date, the best way to locate suspicious lesions to trigger prostate biopsy, plan active surveillance, or definitive treatment. In case of relapse, mpMRI can help detect local disease and provide specific management. It is well known that there is a subset of patients in whom mpMRI fails to depict csPCa. These missed significant cancers demand great attention. Prostate mpMRI quality depends on several factors related to equipment (including equipment vendor, magnet field and gradient strength, coil set used, software and hardware levels, sequence parameter choices), patient (medications, body habitus, motion, metal implants, rectal gas), and most importantly the radiologic interpretation of images (learning curve effects, subjectivity of observations, interobserver variations, and reporting styles). Inter-reader variability represents a huge current limitation of this method. Therefore, mpMRI remains the best imaging tool available to detect PCa, guiding diagnosis, treatment, and follow up while inter-reader variability represents the best limitation. Radiomics can help identifying imaging biomarkers to help radiologist in detecting significant PCa, reducing examination times, and costs.
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Affiliation(s)
- Agostino Antonio Santoro
- Department of Urology, Catholic University of the Sacred Heart - Fondazione Policlinico Universitario "A. Gemelli" - IRCSS, Rome, Italy
| | - Luca Di Gianfrancesco
- Department of Urology, Catholic University of the Sacred Heart - Fondazione Policlinico Universitario "A. Gemelli" - IRCSS, Rome, Italy
| | - Marco Racioppi
- Department of Urology, Catholic University of the Sacred Heart - Fondazione Policlinico Universitario "A. Gemelli" - IRCSS, Rome, Italy
| | - Francesco Pinto
- Department of Urology, Catholic University of the Sacred Heart - Fondazione Policlinico Universitario "A. Gemelli" - IRCSS, Rome, Italy
| | - Giuseppe Palermo
- Department of Urology, Catholic University of the Sacred Heart - Fondazione Policlinico Universitario "A. Gemelli" - IRCSS, Rome, Italy
| | - Emilio Sacco
- Department of Urology, Catholic University of the Sacred Heart - Fondazione Policlinico Universitario "A. Gemelli" - IRCSS, Rome, Italy
| | - Marco Campetella
- Department of Urology, Catholic University of the Sacred Heart - Fondazione Policlinico Universitario "A. Gemelli" - IRCSS, Rome, Italy
| | - Eros Scarciglia
- Department of Urology, Catholic University of the Sacred Heart - Fondazione Policlinico Universitario "A. Gemelli" - IRCSS, Rome, Italy
| | - Riccardo Bientinesi
- Department of Urology, Catholic University of the Sacred Heart - Fondazione Policlinico Universitario "A. Gemelli" - IRCSS, Rome, Italy
| | - Valerio Di Paola
- Department of Radiology, Catholic University of the Sacred Heart - Fondazione Policlinico Universitario "A. Gemelli" - IRCSS, Rome, Italy
| | - Angelo Totaro
- Department of Urology, Catholic University of the Sacred Heart - Fondazione Policlinico Universitario "A. Gemelli" - IRCSS, Rome, Italy
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11
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Giannini V, Mazzetti S, Cappello G, Doronzio VM, Vassallo L, Russo F, Giacobbe A, Muto G, Regge D. Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers. Diagnostics (Basel) 2021; 11:973. [PMID: 34071215 PMCID: PMC8227686 DOI: 10.3390/diagnostics11060973] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/17/2021] [Accepted: 05/26/2021] [Indexed: 11/17/2022] Open
Abstract
Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this study is to assess the diagnostic performance of non-experienced readers when reporting assisted by the likelihood map generated by a CAD system, and to compare the results with the unassisted interpretation. Three resident radiologists were asked to review multiparametric-MRI of patients with and without PCa, both unassisted and assisted by a CAD system. In both reading sessions, residents recorded all positive cases, and sensitivity, specificity, negative and positive predictive values were computed and compared. The dataset comprised 90 patients (45 with at least one clinically significant biopsy-confirmed PCa). Sensitivity significantly increased in the CAD assisted mode for patients with at least one clinically significant lesion (GS > 6) (68.7% vs. 78.1%, p = 0.018). Overall specificity was not statistically different between unassisted and assisted sessions (94.8% vs. 89.6, p = 0.072). The use of the CAD system significantly increases the per-patient sensitivity of inexperienced readers in the detection of clinically significant PCa, without negatively affecting specificity, while significantly reducing overall reporting time.
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Affiliation(s)
- Valentina Giannini
- Department of Surgical Sciences, University of Turin, 10126 Turin, Italy
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy; (G.C.); (V.M.D.); (L.V.); (F.R.); (D.R.)
| | - Simone Mazzetti
- Department of Surgical Sciences, University of Turin, 10126 Turin, Italy
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy; (G.C.); (V.M.D.); (L.V.); (F.R.); (D.R.)
| | - Giovanni Cappello
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy; (G.C.); (V.M.D.); (L.V.); (F.R.); (D.R.)
| | - Valeria Maria Doronzio
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy; (G.C.); (V.M.D.); (L.V.); (F.R.); (D.R.)
| | - Lorenzo Vassallo
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy; (G.C.); (V.M.D.); (L.V.); (F.R.); (D.R.)
| | - Filippo Russo
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy; (G.C.); (V.M.D.); (L.V.); (F.R.); (D.R.)
| | | | - Giovanni Muto
- Department of Urology, Humanitas University, 10153 Turin, Italy;
| | - Daniele Regge
- Department of Surgical Sciences, University of Turin, 10126 Turin, Italy
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy; (G.C.); (V.M.D.); (L.V.); (F.R.); (D.R.)
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12
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Precise Identification of Prostate Cancer from DWI Using Transfer Learning. SENSORS 2021; 21:s21113664. [PMID: 34070290 PMCID: PMC8197382 DOI: 10.3390/s21113664] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 12/23/2022]
Abstract
Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was 89.2±1.5% with average sensitivity and specificity of 87.5±2.3% and 90.9±1.9%. These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was 91.2±1.3% with sensitivity and specificity of 91.7±1.7% and 90.1±2.8%. Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.
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13
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Rapid Assessment of Acute Ischemic Stroke by Computed Tomography Using Deep Convolutional Neural Networks. J Digit Imaging 2021; 34:637-646. [PMID: 33963421 DOI: 10.1007/s10278-021-00457-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 03/10/2021] [Accepted: 04/27/2021] [Indexed: 01/01/2023] Open
Abstract
Acute stroke is one of the leading causes of disability and death worldwide. Regarding clinical diagnoses, a rapid and accurate procedure is necessary for patients suffering from acute stroke. This study proposes an automatic identification scheme for acute ischemic stroke using deep convolutional neural networks (DCNNs) based on non-contrast computed tomographic (NCCT) images. Our image database for the classification model was composed of 1254 grayscale NCCT images from 96 patients (573 images) with acute ischemic stroke and 121 normal controls (681 images). According to the consensus of critical stroke findings by two neuroradiologists, a gold standard was established and used to train the proposed DCNN using machine-generated image features. Including the earliest DCNN, AlexNet, the popular Inception-v3, and ResNet-101 were proposed. To train the limited data size, transfer learning with ImageNet parameters was also used. The established models were evaluated by tenfold cross-validation and tested on an independent dataset containing 50 patients with acute ischemic stroke (108 images) and 58 normal controls (117 images) from another institution. AlexNet without pretrained parameters achieved an accuracy of 97.12%, a sensitivity of 98.11%, a specificity of 96.08%, and an area under the receiver operating characteristic curve (AUC) of 0.9927. Using transfer learning, transferred AlexNet, transferred Inception-v3, and transferred ResNet-101 achieved accuracies between 90.49 and 95.49%. Tested with a dataset from another institution, AlexNet showed an accuracy of 60.89%, a sensitivity of 18.52%, and a specificity of 100%. Transferred AlexNet, Inception-v3, and ResNet-101 achieved accuracies of 81.77%, 85.78%, and 80.89%, respectively. The proposed DCNN architecture as a computer-aided diagnosis system showed that training from scratch can generate a customized model for a specific scanner, and transfer learning can generate a more generalized model to provide diagnostic suggestions of acute ischemic stroke to radiologists.
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14
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Cutaia G, La Tona G, Comelli A, Vernuccio F, Agnello F, Gagliardo C, Salvaggio L, Quartuccio N, Sturiale L, Stefano A, Calamia M, Arnone G, Midiri M, Salvaggio G. Radiomics and Prostate MRI: Current Role and Future Applications. J Imaging 2021; 7:jimaging7020034. [PMID: 34460633 PMCID: PMC8321264 DOI: 10.3390/jimaging7020034] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 02/07/2021] [Accepted: 02/08/2021] [Indexed: 02/07/2023] Open
Abstract
Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer.
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Affiliation(s)
- Giuseppe Cutaia
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Giuseppe La Tona
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Albert Comelli
- Ri.Med Foundation, Via Bandiera 11, 90133 Palermo, Italy;
| | - Federica Vernuccio
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Francesco Agnello
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Cesare Gagliardo
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Leonardo Salvaggio
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
- Correspondence:
| | - Natale Quartuccio
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90133 Palermo, Italy; (N.Q.); (L.S.); (G.A.)
| | - Letterio Sturiale
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90133 Palermo, Italy; (N.Q.); (L.S.); (G.A.)
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy;
| | - Mauro Calamia
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Gaspare Arnone
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90133 Palermo, Italy; (N.Q.); (L.S.); (G.A.)
| | - Massimo Midiri
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Giuseppe Salvaggio
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
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15
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Jung EM, Wertheimer T, Putz FJ, Jung F, Kammerer S, Pregler B, Luerken L, Stroszczynski C, Beyer L. Contrast enhanced ultrasound (CEUS) with parametric imaging and time intensity curve analysis (TIC) for evaluation of the success of prostate arterial embolization (PAE) in cases of prostate hyperplasia. Clin Hemorheol Microcirc 2020; 76:143-153. [PMID: 32925006 DOI: 10.3233/ch-209202] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
AIM To evaluate the use of dynamic contrast enhanced ultrasound (CEUS) with parametric color-coded imaging and time intensity curve analysis (TIC) for planning and follow-up after prostate arterial embolization (PAE). MATERIAL/METHOD Before and after selective iliacal embolization by PAE with a follow up of 6 months 18 male patients (43-78 years, mean 63±3.5 years) with histopathological proven benign prostate hyperplasia were examined by one experienced examiner. A multifrequency high resolution probe (1-6 MHz) was used for transabdominal ultrasound and CEUS with bolus injections of 2.4 ml sulphur-hexafluoride microbubbles. Independent evaluation of color-coded parametric imaging before and after PAE by in PACS stored DICOM loops from arterial phase (10-15 s) up to 1min were performed. Criteria for successful treatment were reduction of early arterial enhancement by changes of time to peak (TTP) and area under the curve (AUC) by measurements in 8 regions of interest (ROI) of 5 mm in diameter at the margin and in the center and changes from hyperenhancement in parametric imaging (perfusion evaluation of arterial enhancement over 15 s) from red and yellow to blue and green by partial infarctions. Reference imaging method was the contrast high resolution 3 tesla magnetic resonance tomography (MRI) using 3D vibe sequences before and after PAE and for the follow up after 3 and 6 months. RESULTS PAE was technically and clinically successful in all 18 patients with less clinical symptoms and reduction of the gland volume. In all cases color-coded CEUS parametric imaging was able to evaluate partial infarction after embolization with changes from red and yellow to green and blue colors in the embolization areas. Relevant changes could be evaluated for TIC-analysis of CEUS with reduced arterial enhancement in the arterial phase and prolonged enhancement of up to 1 min with significant changes (p = 0.0024). The area under the curve (AUC) decreased from 676±255.04 rU (160 rU-1049 rU) before PAE to 370.43±255.19 rU (45 rU-858 rU) after PAE. Time to peak (TTP) did not change significantly (p = 0.6877); TTP before PAE was 25.82±9.04 s (12.3 s-42.5 s) and after PAE 24.43±9.10 s (12-39 s). Prostate volume decreased significantly (p = 0.0045) from 86.93±34.98 ml (30-139 ml) before PAE to 50.57±26.26 ml (19-117 ml) after PAE. There were no major complications and, in most cases (14/18) a volume reduction of the benign prostate hyperplasia occurred. CONCLUSION Performed by an experienced examiner CEUS with parametric imaging and TIC-analysis is highly useful to further establish prostatic artery embolization (PAE) as a successful minimal invasive treatment of benign prostatic hyperplasia.
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Affiliation(s)
- E M Jung
- Institute of Diagnostic Radiology, University Medical Center Regensburg, Regensburg, Germany
| | - T Wertheimer
- Department for Internal Medicine III, Hematology and Oncology, University Medical Center Regensburg, Regensburg, Germany
| | - F J Putz
- Department of Nephrology, University Medical Center Regensburg, Regensburg, Germany
| | - F Jung
- Brandenburgische Technische Universität Cottbus-Senftenberg, Institute of Biotechnology, Cottbus, Germany
| | - S Kammerer
- Institute of Diagnostic Radiology, University Medical Center Regensburg, Regensburg, Germany
| | - B Pregler
- Institute of Diagnostic Radiology, University Medical Center Regensburg, Regensburg, Germany
| | - L Luerken
- Institute of Diagnostic Radiology, University Medical Center Regensburg, Regensburg, Germany
| | - C Stroszczynski
- Institute of Diagnostic Radiology, University Medical Center Regensburg, Regensburg, Germany
| | - L Beyer
- Institute of Diagnostic Radiology, University Medical Center Regensburg, Regensburg, Germany
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16
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Singhrao K, Fu J, Parikh NR, Mikaeilian AG, Ruan D, Kishan AU, Lewis JH. A generative adversarial network‐based (GAN‐based) architecture for automatic fiducial marker detection in prostate MRI‐only radiotherapy simulation images. Med Phys 2020; 47:6405-6413. [DOI: 10.1002/mp.14498] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 08/02/2020] [Accepted: 09/07/2020] [Indexed: 12/22/2022] Open
Affiliation(s)
- Kamal Singhrao
- Department of Radiation Oncology University of California, Los Angeles Los Angeles CA 90095 USA
| | - Jie Fu
- Department of Radiation Oncology University of California, Los Angeles Los Angeles CA 90095 USA
| | - Neil R. Parikh
- Department of Radiation Oncology University of California, Los Angeles Los Angeles CA 90095 USA
| | - Argin G. Mikaeilian
- Department of Radiation Oncology University of California, Los Angeles Los Angeles CA 90095 USA
| | - Dan Ruan
- Department of Radiation Oncology University of California, Los Angeles Los Angeles CA 90095 USA
| | - Amar U. Kishan
- Department of Radiation Oncology University of California, Los Angeles Los Angeles CA 90095 USA
| | - John H. Lewis
- Department of Radiation Oncology Cedars‐Sinai Medical Center Los Angeles CA 90048 USA
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17
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Gillies RJ, Schabath MB. Radiomics Improves Cancer Screening and Early Detection. Cancer Epidemiol Biomarkers Prev 2020; 29:2556-2567. [PMID: 32917666 DOI: 10.1158/1055-9965.epi-20-0075] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 03/18/2020] [Accepted: 08/31/2020] [Indexed: 11/16/2022] Open
Abstract
Imaging is a key technology in the early detection of cancers, including X-ray mammography, low-dose CT for lung cancer, or optical imaging for skin, esophageal, or colorectal cancers. Historically, imaging information in early detection schema was assessed qualitatively. However, the last decade has seen increased development of computerized tools that convert images into quantitative mineable data (radiomics), and their subsequent analyses with artificial intelligence (AI). These tools are improving diagnostic accuracy of early lesions to define risk and classify malignant/aggressive from benign/indolent disease. The first section of this review will briefly describe the various imaging modalities and their use as primary or secondary screens in an early detection pipeline. The second section will describe specific use cases to illustrate the breadth of imaging modalities as well as the benefits of quantitative image analytics. These will include optical (skin cancer), X-ray CT (pancreatic and lung cancer), X-ray mammography (breast cancer), multiparametric MRI (breast and prostate cancer), PET (pancreatic cancer), and ultrasound elastography (liver cancer). Finally, we will discuss the inexorable improvements in radiomics to build more robust classifier models and the significant limitations to this development, including access to well-annotated databases, and biological descriptors of the imaged feature data.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."
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Affiliation(s)
- Robert J Gillies
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida. .,Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Matthew B Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.,Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
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18
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Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study. Cancers (Basel) 2020; 12:cancers12082200. [PMID: 32781640 PMCID: PMC7465024 DOI: 10.3390/cancers12082200] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 07/28/2020] [Accepted: 08/03/2020] [Indexed: 12/21/2022] Open
Abstract
Background: Prostate cancer (PCa) influences its surrounding habitat, which tends to manifest as different phenotypic appearances on magnetic resonance imaging (MRI). This region surrounding the PCa lesion, or the peri-tumoral region, may encode useful information that can complement intra-tumoral information to enable better risk stratification. Purpose: To evaluate the role of peri-tumoral radiomic features on bi-parametric MRI (T2-weighted and Diffusion-weighted) to distinguish PCa risk categories as defined by D’Amico Risk Classification System. Materials and Methods: We studied a retrospective, HIPAA-compliant, 4-institution cohort of 231 PCa patients (n = 301 lesions) who underwent 3T multi-parametric MRI prior to biopsy. PCa regions of interest (ROIs) were delineated on MRI by experienced radiologists following which peri-tumoral ROIs were defined. Radiomic features were extracted within the intra- and peri-tumoral ROIs. Radiomic features differentiating low-risk from: (1) high-risk (L-vs.-H), and (2) (intermediate- and high-risk (L-vs.-I + H)) lesions were identified. Using a multi-institutional training cohort of 151 lesions (D1, N = 116 patients), machine learning classifiers were trained using peri- and intra-tumoral features individually and in combination. The remaining 150 lesions (D2, N = 115 patients) were used for independent hold-out validation and were evaluated using Receiver Operating Characteristic (ROC) analysis and compared with PI-RADS v2 scores. Results: Validation on D2 using peri-tumoral radiomics alone resulted in areas under the ROC curve (AUCs) of 0.84 and 0.73 for the L-vs.-H and L-vs.-I + H classifications, respectively. The best combination of intra- and peri-tumoral features resulted in AUCs of 0.87 and 0.75 for the L-vs.-H and L-vs.-I + H classifications, respectively. This combination improved the risk stratification results by 3–6% compared to intra-tumoral features alone. Our radiomics-based model resulted in a 53% accuracy in differentiating L-vs.-H compared to PI-RADS v2 (48%), on the validation set. Conclusion: Our findings suggest that peri-tumoral radiomic features derived from prostate bi-parametric MRI add independent predictive value to intra-tumoral radiomic features for PCa risk assessment.
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Hamm CA, Beetz NL, Savic LJ, Penzkofer T. [Artificial intelligence and radiomics in MRI-based prostate diagnostics]. Radiologe 2020; 60:48-55. [PMID: 31802148 DOI: 10.1007/s00117-019-00613-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
CLINICAL/METHODICAL ISSUE In view of the diagnostic complexity and the large number of examinations, modern radiology is challenged to identify clinically significant prostate cancer (PCa) with high sensitivity and specificity. Meanwhile overdiagnosis and overtreatment of clinically nonsignificant carcinomas need to be avoided. STANDARD RADIOLOGICAL METHODS Increasingly, international guidelines recommend multiparametric magnetic resonance imaging (mpMRI) as first-line investigation in patients with suspected PCa. METHODICAL INNOVATIONS Image interpretation according to the PI-RADS criteria is limited by interobserver variability. Thus, rapid developments in the field of automated image analysis tools, including radiomics and artificial intelligence (AI; machine learning, deep learning), give hope for further improvement in patient care. PERFORMANCE AI focuses on the automated detection and classification of PCa, but it also attempts to stratify tumor aggressiveness according to the Gleason score. Recent studies present good to very good results in radiomics or AI-supported mpMRI diagnosis. Nevertheless, these systems are not widely used in clinical practice. ACHIEVEMENTS AND PRACTICAL RECOMMENDATIONS In order to apply these innovative technologies, a growing awareness for the need of structured data acquisition, development of robust systems and an increased acceptance of AI as diagnostic support are needed. If AI overcomes these obstacles, it may play a key role in the quantitative and reproducible image-based diagnosis of ever-increasing prostate MRI examination volumes.
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Affiliation(s)
- Charlie Alexander Hamm
- Institute of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Deutschland
| | - Nick Lasse Beetz
- Institute of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Deutschland
| | - Lynn Jeanette Savic
- Institute of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Deutschland
| | - Tobias Penzkofer
- Institute of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Deutschland. .,Berlin Institute of Health, 10178, Berlin, Deutschland.
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20
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Computer-aided diagnosis system for characterizing ISUP grade ≥ 2 prostate cancers at multiparametric MRI: A cross-vendor evaluation. Diagn Interv Imaging 2019; 100:801-811. [DOI: 10.1016/j.diii.2019.06.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2019] [Revised: 05/30/2019] [Accepted: 06/25/2019] [Indexed: 12/28/2022]
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21
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Harmon SA, Tuncer S, Sanford T, Choyke PL, Türkbey B. Artificial intelligence at the intersection of pathology and radiology in prostate cancer. Diagn Interv Radiol 2019; 25:183-188. [PMID: 31063138 PMCID: PMC6521904 DOI: 10.5152/dir.2019.19125] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 03/08/2019] [Accepted: 03/23/2019] [Indexed: 01/30/2023]
Abstract
Pathologic grading plays a key role in prostate cancer risk stratification and treatment selection, traditionally assessed from systemic core needle biopsies sampled throughout the prostate gland. Multiparametric magnetic resonance imaging (mpMRI) has become a well-established clinical tool for detecting and localizing prostate cancer. However, both pathologic and radiologic assessment suffer from poor reproducibility among readers. Artificial intelligence (AI) methods show promise in aiding the detection and assessment of imaging-based tasks, dependent on the curation of high-quality training sets. This review provides an overview of recent advances in AI applied to mpMRI and digital pathology in prostate cancer which enable advanced characterization of disease through combined radiology-pathology assessment.
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Affiliation(s)
- Stephanie A. Harmon
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
| | - Sena Tuncer
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
| | - Thomas Sanford
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
| | - Peter L. Choyke
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
| | - Barış Türkbey
- From the Clinical Research Directorate (S.A.H. ), Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, USA; Molecular Imaging Program (S.A.H.,T.S., P.L.C., B.T.), National Cancer Institute, NIH, Bethesda, MD, USA; Department of Radiology (S.T.), İstanbul University, İstanbul School of Medicine, İstanbul, Turkey
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22
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Ye X, Zhao L, Kang J. Expression and significance of PTEN and Claudin-3 in prostate cancer. Oncol Lett 2019; 17:5628-5634. [PMID: 31186785 PMCID: PMC6507465 DOI: 10.3892/ol.2019.10212] [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: 11/12/2018] [Accepted: 03/20/2019] [Indexed: 12/20/2022] Open
Abstract
Expression and significance of phosphatase and tensin homolog deleted on chromosome 10 (PTEN) and Claudin-3 in the blood of patients with prostate cancer [prostate cancer (PCa)] were investigated. Retrospective analysis of 84 cases of PCa patients confirmed by pathological diagnosis were studied, as the experiment group. Moreover, the physical examination data of 84 healthy volunteers examined in the Affiliated Hospital of Beihua University were the control group. The expression levels of blood in the PTEN and Claudin-3 of both the experiment group and the control group were determined by enzyme-linked immunosorbent assay. According to the blood expression in PTEN and Claudin-3 between both the experiment group and the control group, the test value of the ROC curve in PTEN and Claudin-3 were detected by both single detection and joint detection. The expression levels of PTEN in the experiment group were significantly lower than the control group (P<0.05). The expression levels of Claudin-3 were higher in the experiment group than the control group (P<0.01). The expression levels of PTEN and Claudin-3 in the experiment group were significantly associated with the distant metastasis of cancer cells, preoperative prostate-specific antigen levels, tumor diameter and pathological stages (P<0.01). The expression levels of PTEN in the pathological stage of T1-T2 group was lower than that of the T3-T4 group (P<0.01). The expression levels of PTEN and Claudin-3 are closely related to the distant metastasis of cancer cells, preoperative prostate-specific antigen level, tumor diameter and pathological stage. Combined detection of both PTEN and Claudin-3 can improve the specificity levels of PCa for diagnosis and has an important diagnostic value for PCa. It can be used as a biological indicator for PCa diagnosis, disease severity analysis and efficacy evaluation.
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Affiliation(s)
- Xinglong Ye
- Department of Urology Surgery, The Affiliated Hospital of Beihua University, Jilin, Jilin 132001, P.R. China
| | - Lijing Zhao
- School of Basic Medical Sciences, Jilin Medical College, Jilin, Jilin 132001, P.R. China
| | - Jing Kang
- School of Basic Medical Sciences, Jilin Medical College, Jilin, Jilin 132001, P.R. China
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23
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Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin 2019; 69:127-157. [PMID: 30720861 PMCID: PMC6403009 DOI: 10.3322/caac.21552] [Citation(s) in RCA: 574] [Impact Index Per Article: 114.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.
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Affiliation(s)
- Wenya Linda Bi
- Assistant Professor of Neurosurgery, Department of Neurosurgery, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Ahmed Hosny
- Research Scientist, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Matthew B. Schabath
- Associate Member, Department of Cancer EpidemiologyH. Lee Moffitt Cancer Center and Research InstituteTampaFL
| | - Maryellen L. Giger
- Professor of Radiology, Department of RadiologyUniversity of ChicagoChicagoIL
| | - Nicolai J. Birkbak
- Research Associate, The Francis Crick InstituteLondonUnited Kingdom
- Research Associate, University College London Cancer InstituteLondonUnited Kingdom
| | - Alireza Mehrtash
- Research Assistant, Department of Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
- Research Assistant, Department of Electrical and Computer EngineeringUniversity of British ColumbiaVancouverBCCanada
| | - Tavis Allison
- Research Assistant, Department of RadiologyColumbia University College of Physicians and SurgeonsNew YorkNY
- Research Assistant, Department of RadiologyNew York Presbyterian HospitalNew YorkNY
| | - Omar Arnaout
- Assistant Professor of Neurosurgery, Department of Neurosurgery, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Christopher Abbosh
- Research Fellow, The Francis Crick InstituteLondonUnited Kingdom
- Research Fellow, University College London Cancer InstituteLondonUnited Kingdom
| | - Ian F. Dunn
- Associate Professor of Neurosurgery, Department of Neurosurgery, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Raymond H. Mak
- Associate Professor, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Rulla M. Tamimi
- Associate Professor, Department of MedicineBrigham and Women’s Hospital, Dana‐Farber Cancer Institute, Harvard Medical SchoolBostonMA
| | - Clare M. Tempany
- Professor of Radiology, Department of Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Charles Swanton
- Professor, The Francis Crick InstituteLondonUnited Kingdom
- Professor, University College London Cancer InstituteLondonUnited Kingdom
| | - Udo Hoffmann
- Professor of Radiology, Department of RadiologyMassachusetts General Hospital and Harvard Medical SchoolBostonMA
| | - Lawrence H. Schwartz
- Professor of Radiology, Department of RadiologyColumbia University College of Physicians and SurgeonsNew YorkNY
- Chair, Department of RadiologyNew York Presbyterian HospitalNew YorkNY
| | - Robert J. Gillies
- Professor of Radiology, Department of Cancer PhysiologyH. Lee Moffitt Cancer Center and Research InstituteTampaFL
| | - Raymond Y. Huang
- Assistant Professor, Department of Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
| | - Hugo J. W. L. Aerts
- Associate Professor, Departments of Radiation Oncology and Radiology, Brigham and Women’s Hospital, Dana‐Farber Cancer InstituteHarvard Medical SchoolBostonMA
- Professor in AI in Medicine, Radiology and Nuclear Medicine, GROWMaastricht University Medical Centre (MUMC+)MaastrichtThe Netherlands
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24
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Jensen C, Sørensen KS, Jørgensen CK, Nielsen CW, Høy PC, Langkilde NC, Østergaard LR. Prostate zonal segmentation in 1.5T and 3T T2W MRI using a convolutional neural network. J Med Imaging (Bellingham) 2019; 6:014501. [PMID: 30820440 DOI: 10.1117/1.jmi.6.1.014501] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 12/28/2018] [Indexed: 12/22/2022] Open
Abstract
Zonal segmentation of the prostate gland using magnetic resonance imaging (MRI) is clinically important for prostate cancer (PCa) diagnosis and image-guided treatments. A two-dimensional convolutional neural network (CNN) based on the U-net architecture was evaluated for segmentation of the central gland (CG) and peripheral zone (PZ) using a dataset of 40 patients (34 PCa positive and 6 PCa negative) scanned on two different MRI scanners (1.5T GE and 3T Siemens). Images were cropped around the prostate gland to exclude surrounding tissues, resampled to 0.5 × 0.5 × 0.5 mm voxels and z -score normalized before being propagated through the CNN. Performance was evaluated using the Dice similarity coefficient (DSC) and mean absolute distance (MAD) in a fivefold cross-validation setup. Overall performance showed DSC of 0.794 and 0.692, and MADs of 3.349 and 2.993 for CG and PZ, respectively. Dividing the gland into apex, mid, and base showed higher DSC for the midgland compared to apex and base for both CG and PZ. We found no significant difference in DSC between the two scanners. A larger dataset, preferably with multivendor scanners, is necessary for validation of the proposed algorithm; however, our results are promising and have clinical potential.
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Affiliation(s)
- Carina Jensen
- Aalborg University Hospital, Department of Medical Physics, Department of Oncology, Aalborg, Denmark
| | | | | | | | - Pia Christine Høy
- Aalborg University, Department of Health Science and Technology, Aalborg, Denmark
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25
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Jensen C, Carl J, Boesen L, Langkilde NC, Østergaard LR. Assessment of prostate cancer prognostic Gleason grade group using zonal-specific features extracted from biparametric MRI using a KNN classifier. J Appl Clin Med Phys 2019; 20:146-153. [PMID: 30712281 PMCID: PMC6370983 DOI: 10.1002/acm2.12542] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 11/27/2018] [Accepted: 01/11/2019] [Indexed: 12/25/2022] Open
Abstract
Purpose To automatically assess the aggressiveness of prostate cancer (PCa) lesions using zonal‐specific image features extracted from diffusion weighted imaging (DWI) and T2W MRI. Methods Region of interest was extracted from DWI (peripheral zone) and T2W MRI (transitional zone and anterior fibromuscular stroma) around the center of 112 PCa lesions from 99 patients. Image histogram and texture features, 38 in total, were used together with a k‐nearest neighbor classifier to classify lesions into their respective prognostic Grade Group (GG) (proposed by the International Society of Urological Pathology 2014 consensus conference). A semi‐exhaustive feature search was performed (1–6 features in each feature set) and validated using threefold stratified cross validation in a one‐versus‐rest classification setup. Results Classifying PCa lesions into GGs resulted in AUC of 0.87, 0.88, 0.96, 0.98, and 0.91 for GG1, GG2, GG1 + 2, GG3, and GG4 + 5 for the peripheral zone, respectively. The results for transitional zone and anterior fibromuscular stroma were AUC of 0.85, 0.89, 0.83, 0.94, and 0.86 for GG1, GG2, GG1 + 2, GG3, and GG4 + 5, respectively. CONCLUSION This study showed promising results with reasonable AUC values for classification of all GG indicating that zonal‐specific imaging features from DWI and T2W MRI can be used to differentiate between PCa lesions of various aggressiveness.
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Affiliation(s)
- Carina Jensen
- Department of Medical Physics, Oncology, Aalborg University Hospital, Aalborg, Denmark
| | - Jesper Carl
- Department of Oncology, Naestved Sygehus, Zealand University Hospital, Roskilde, Denmark
| | - Lars Boesen
- Department of Urology, Herlev Gentofte University Hospital, Herlev, Denmark
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26
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Shahedi M, Halicek M, Li Q, Liu L, Zhang Z, Verma S, Schuster DM, Fei B. A semiautomatic approach for prostate segmentation in MR images using local texture classification and statistical shape modeling. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2019; 10951:109512I. [PMID: 32528212 PMCID: PMC7289512 DOI: 10.1117/12.2512282] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Segmentation of the prostate in magnetic resonance (MR) images has many applications in image-guided treatment planning and procedures such as biopsy and focal therapy. However, manual delineation of the prostate boundary is a time-consuming task with high inter-observer variation. In this study, we proposed a semiautomated, three-dimensional (3D) prostate segmentation technique for T2-weighted MR images based on shape and texture analysis. The prostate gland shape is usually globular with a smoothly curved surface that could be accurately modeled and reconstructed if the locations of a limited number of well-distributed surface points are known. For a training image set, we used an inter-subject correspondence between the prostate surface points to model the prostate shape variation based on a statistical point distribution modeling. We also studied the local texture difference between prostate and non-prostate tissues close to the prostate surface. To segment a new image, we used the learned prostate shape and texture characteristics to search for the prostate border close to an initially estimated prostate surface. We used 23 MR images for training, and 14 images for testing the algorithm performance. We compared the results to two sets of experts' manual reference segmentations. The measured mean ± standard deviation of error values for the whole gland were 1.4 ± 0.4 mm, 8.5 ± 2.0 mm, and 86 ± 3% in terms of mean absolute distance (MAD), Hausdorff distance (HDist), and Dice similarity coefficient (DSC). The average measured differences between the two experts on the same datasets were 1.5 mm (MAD), 9.0 mm (HDist), and 83% (DSC). The proposed algorithm illustrated a fast, accurate, and robust performance for 3D prostate segmentation. The accuracy of the algorithm is within the inter-expert variability observed in manual segmentation and comparable to the best performance results reported in the literature.
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Affiliation(s)
- Maysam Shahedi
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX
| | - Martin Halicek
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA
| | - Qinmei Li
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX
- Department of Radiology, The Second Affiliated Hospital of Guangzhou, Medical University, Guangzhou, China
| | - Lizhi Liu
- State Key Laboratory of Oncology Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Zhenfeng Zhang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou, Medical University, Guangzhou, China
| | - Sadhna Verma
- Department of Radiology, University of Cincinnati Medical Center and The Veterans Administration Hospital, Cincinnati, OH
| | - David M. Schuster
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
| | - Baowei Fei
- Department of Bioengineering, The University of Texas at Dallas, Richardson, TX
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX
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27
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Yuan Y, Qin W, Buyyounouski M, Ibragimov B, Hancock S, Han B, Xing L. Prostate cancer classification with multiparametric MRI transfer learning model. Med Phys 2019; 46:756-765. [DOI: 10.1002/mp.13367] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 12/14/2018] [Accepted: 12/21/2018] [Indexed: 12/11/2022] Open
Affiliation(s)
- Yixuan Yuan
- Department of Electronic Engineering City University of Hong Kong Kowloon Tong Hong Kong
- Department of Radiation Oncology Stanford University Stanford 94305USA
| | - Wenjian Qin
- Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055People's Republic of China
| | - Mark Buyyounouski
- Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055People's Republic of China
| | - Bulat Ibragimov
- Department of Radiation Oncology Stanford University Stanford 94305USA
| | - Steve Hancock
- Department of Radiation Oncology Stanford University Stanford 94305USA
| | - Bin Han
- Department of Radiation Oncology Stanford University Stanford 94305USA
| | - Lei Xing
- Department of Radiation Oncology Stanford University Stanford 94305USA
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28
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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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Gaur S, Lay N, Harmon SA, Doddakashi S, Mehralivand S, Argun B, Barrett T, Bednarova S, Girometti R, Karaarslan E, Kural AR, Oto A, Purysko AS, Antic T, Magi-Galluzzi C, Saglican Y, Sioletic S, Warren AY, Bittencourt L, Fütterer JJ, Gupta RT, Kabakus I, Law YM, Margolis DJ, Shebel H, Westphalen AC, Wood BJ, Pinto PA, Shih JH, Choyke PL, Summers RM, Turkbey B. Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation. Oncotarget 2018; 9:33804-33817. [PMID: 30333911 PMCID: PMC6173466 DOI: 10.18632/oncotarget.26100] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Accepted: 08/23/2018] [Indexed: 12/31/2022] Open
Abstract
For prostate cancer detection on prostate multiparametric MRI (mpMRI), the Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) and computer-aided diagnosis (CAD) systems aim to widely improve standardization across radiologists and centers. Our goal was to evaluate CAD assistance in prostate cancer detection compared with conventional mpMRI interpretation in a diverse dataset acquired from five institutions tested by nine readers of varying experience levels, in total representing 14 globally spread institutions. Index lesion sensitivities of mpMRI-alone were 79% (whole prostate (WP)), 84% (peripheral zone (PZ)), 71% (transition zone (TZ)), similar to CAD at 76% (WP, p=0.39), 77% (PZ, p=0.07), 79% (TZ, p=0.15). Greatest CAD benefit was in TZ for moderately-experienced readers at PI-RADSv2 <3 (84% vs mpMRI-alone 67%, p=0.055). Detection agreement was unchanged but CAD-assisted read times improved (4.6 vs 3.4 minutes, p<0.001). At PI-RADSv2 ≥ 3, CAD improved patient-level specificity (72%) compared to mpMRI-alone (45%, p<0.001). PI-RADSv2 and CAD-assisted mpMRI interpretations have similar sensitivities across multiple sites and readers while CAD has potential to improve specificity and moderately-experienced radiologists' detection of more difficult tumors in the center of the gland. The multi-institutional evidence provided is essential to future prostate MRI and CAD development.
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Affiliation(s)
- Sonia Gaur
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nathan Lay
- Imaging Biomarkers and Computer-aided Diagnosis Lab, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Stephanie A. Harmon
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Clinical Research Directorate/ Clinical Monitoring Research Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Sreya Doddakashi
- 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
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Department of Urology and Pediatric Urology, University Medical Center Mainz, Mainz, Germany
| | - Burak Argun
- Department of Urology, Acibadem University, Istanbul, Turkey
| | - Tristan Barrett
- Department of Radiology, University of Cambridge, Cambridge, UK
| | | | | | | | - Ali Riza Kural
- Department of Urology, Acibadem University, Istanbul, Turkey
| | - Aytekin Oto
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | | | - Tatjana Antic
- Department of Pathology, University of Chicago, Chicago, IL, USA
| | | | - Yesim Saglican
- Department of Pathology, Acibadem University, Istanbul, Turkey
| | | | - Anne Y. Warren
- Department of Pathology, University of Cambridge, Cambridge, UK
| | | | | | - Rajan T. Gupta
- Department of Radiology, Duke University, Durham, NC, USA
| | - Ismail Kabakus
- Department of Radiology, Hacettepe University, Ankara, Turkey
| | - Yan Mee Law
- Department of Radiology, Singapore General Hospital, Singapore
| | | | - Haytham Shebel
- Department of Radiology, Mansoura University, Mansoura, Egypt
| | - Antonio C. Westphalen
- UCSF Department of Radiology, University of California-San Francisco, San Francisco, CA, USA
| | - Bradford J. Wood
- Center for Interventional Oncology, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Peter A. Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Joanna H. Shih
- Biometric Research Branch, 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
| | - Ronald M. Summers
- Imaging Biomarkers and Computer-aided Diagnosis Lab, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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30
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Kwon D, Reis IM, Breto AL, Tschudi Y, Gautney N, Zavala-Romero O, Lopez C, Ford JC, Punnen S, Pollack A, Stoyanova R. Classification of suspicious lesions on prostate multiparametric MRI using machine learning. J Med Imaging (Bellingham) 2018; 5:034502. [PMID: 30840719 DOI: 10.1117/1.jmi.5.3.034502] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Accepted: 08/06/2018] [Indexed: 01/09/2023] Open
Abstract
We present a radiomics-based approach developed for the SPIE-AAPM-NCI PROSTATEx challenge. The task was to classify clinically significant prostate cancer in multiparametric (mp) MRI. Data consisted of a "training dataset" (330 suspected lesions from 204 patients) and a "test dataset" (208 lesions/140 patients). All studies included T2-weighted (T2-W), proton density-weighted, dynamic contrast enhanced, and diffusion-weighted imaging. Analysis of the images was performed using the MIM imaging platform (MIM Software, Cleveland, Ohio). Prostate and peripheral zone contours were manually outlined on the T2-W images. A workflow for rigid fusion of the aforementioned images to T2-W was created in MIM. The suspicious lesion was outlined using the high b-value image. Intensity and texture features were extracted on four imaging modalities and characterized using nine histogram descriptors: 10%, 25%, 50%, 75%, 90%, mean, standard deviation, kurtosis, and skewness (216 features). Three classification methods were used: classification and regression trees (CART), random forests, and adaptive least absolute shrinkage and selection operator (LASSO). In the held out by the organizers test dataset, the areas under the curve (AUCs) were: 0.82 (random forests), 0.76 (CART), and 0.76 (adaptive LASSO). AUC of 0.82 was the fourth-highest score of 71 entries (32 teams) and the highest for feature-based methods.
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Affiliation(s)
- Deukwoo Kwon
- University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Biostatistics and Bioinformatics Shared Resource, Miami, Florida, United States
| | - Isildinha M Reis
- University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Biostatistics and Bioinformatics Shared Resource, Miami, Florida, United States.,University of Miami Miller School of Medicine, Department of Public Health Sciences, Miami, Florida, United States
| | - Adrian L Breto
- University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States
| | - Yohann Tschudi
- University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States
| | - Nicole Gautney
- University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States
| | - Olmo Zavala-Romero
- University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States
| | - Christopher Lopez
- University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States
| | - John C Ford
- University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States
| | - Sanoj Punnen
- University of Miami Miller School of Medicine, Department of Urology, Miami, Florida, United States
| | - Alan Pollack
- University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States
| | - Radka Stoyanova
- University of Miami Miller School of Medicine, Department of Radiation Oncology, Miami, Florida, United States
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31
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A narrative review and update on management following negative prostate biopsy. Curr Opin Urol 2018; 28:398-402. [PMID: 29702497 DOI: 10.1097/mou.0000000000000509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Prostate cancer has traditionally been diagnosed using systematic transrectal ultrasound-guided biopsy. However, given the inherent nature of sampling, a negative biopsy does not exclude clinically significant prostate cancer (csPCa), and continued controversy exists in the optimal management following initial biopsy. Numerous avenues for evaluation include multiparametric MRI (mpMRI), use of molecular biomarkers, repeat biopsy, and observation. RECENT FINDINGS mpMRI has shown promise in guiding further biopsy management: for individuals with identified target lesions, increased accuracy and detection using combination targeted and systematic sampling has been repeatedly demonstrated in the literature as an effective strategy. For those with negative MRIs and/or negative biomarker (blood, urinary, tissue) studies, increasing evidence has suggested that these individuals may be able to avoid biopsy altogether, albeit at a small risk of missing csPCa. Observation should be based on an individual's risk of csPCa versus their competing health risks, and saturation biopsy reserved for rare cases with high clinical suspicion. SUMMARY Management following an initial negative prostate biopsy requires careful discussion with the patient, their risk tolerance, and threshold for intervention. Although subject to availability, mpMRI and molecular biomarkers may better risk stratify patients, identify target lesions, and in certain cases, spare biopsy altogether.
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32
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Starobinets O, Simko JP, Kuchinsky K, Kornak J, Carroll PR, Greene KL, Kurhanewicz J, Noworolski SM. Characterization and stratification of prostate lesions based on comprehensive multiparametric MRI using detailed whole-mount histopathology as a reference standard. NMR IN BIOMEDICINE 2017; 30:10.1002/nbm.3796. [PMID: 28961382 PMCID: PMC9592076 DOI: 10.1002/nbm.3796] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 07/25/2017] [Accepted: 07/26/2017] [Indexed: 06/07/2023]
Abstract
The purpose of this study was to characterize prostate cancer (PCa) based on multiparametric MR (mpMR) measures derived from MRI, diffusion, spectroscopy, and dynamic contrast-enhanced (DCE) MRI, and to validate mpMRI in detecting PCa and predicting PCa aggressiveness by correlating mpMRI findings with whole-mount histopathology. Seventy-eight men with untreated PCa received 3 T mpMR scans prior to radical prostatectomy. Cancerous regions were outlined, graded, and cancer amount estimated on whole-mount histology. Regions of interest were manually drawn on T2 -weighted images based on histopathology. Logistic regression was used to identify optimal combinations of parameters for the peripheral zone and transition zone to separate: (i) benign from malignant tissues; (ii) Gleason score (GS) ≤3 + 3 disease from ≥GS3 + 4; and (iii) ≤ GS3 + 4 from ≥GS4 + 3 cancers. The performance of the models was assessed using repeated fourfold cross-validation. Additionally, the performance of the logistic regression models created under the assumption that one or more modality has not been acquired was evaluated. Logistic regression models yielded areas under the curve (AUCs) of 1.0 and 0.99 when separating benign from malignant tissues in the peripheral zone and the transition zone, respectively. Within the peripheral zone, combining choline, maximal enhancement slope, apparent diffusion coefficient (ADC), and citrate measures for separating ≤GS3 + 3 from ≥GS3 + 4 PCa yielded AUC = 0.84. Combining creatine, choline, and washout slope yielded AUC = 0.81 for discriminating ≤GS3 + 4 from ≥GS4 + 3 disease. Within the transition zone, combining washout slope, ADC, and creatine yielded AUC = 0.93 for discriminating ≤GS3 + 3 and ≥GS3 + 4 cancers. When separating ≤GS3 + 4 from ≥GS4 + 3 PCa, combining choline and washout slope yielded AUC = 0.92. MpMRI provides excellent separation between benign tissues and PCa, and across PCa tissues of different aggressiveness. The final models prominently feature spectroscopy and DCE-derived metrics, underlining their value within a comprehensive mpMRI examination.
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Affiliation(s)
- Olga Starobinets
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
- Graduate Group in Bioengineering, University of California, San Francisco and Berkeley, USA
| | - Jeffry P Simko
- Department of Pathology, University of California, San Francisco, USA
- Department of Urology, University of California, San Francisco, USA
| | - Kyle Kuchinsky
- Department of Pathology, University of California, San Francisco, USA
| | - John Kornak
- Department of Epidemiology and Biostatistics, University of California, San Francisco, USA
| | - Peter R Carroll
- Department of Urology, University of California, San Francisco, USA
| | - Kirsten L Greene
- Department of Urology, University of California, San Francisco, USA
| | - John Kurhanewicz
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
- Graduate Group in Bioengineering, University of California, San Francisco and Berkeley, USA
| | - Susan M Noworolski
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
- Graduate Group in Bioengineering, University of California, San Francisco and Berkeley, USA
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Abstract
Multi-parametric magnetic resonance imaging (mp-MRI) has an increasingly important role in the diagnosis of prostate cancer. Due to the large amount of data and variations in mp-MRI, tumor detection can be affected by multiple factors, such as the observer's clinical experience, image quality, and appearance of the lesions. In order to improve the quantitative assessment of the disease and reduce the reporting time, various computer-aided diagnosis (CAD) systems have been designed to help radiologists identify lesions. This manuscript presents an overview of the literature regarding prostate CAD using mp-MRI, while focusing on the studies of the most recent five years. Current prostate CAD technologies and their utilization are discussed in this review.
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34
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Skorpil M, Brynolfsson P, Engström M. Motion corrected DWI with integrated T2-mapping for simultaneous estimation of ADC, T2-relaxation and perfusion in prostate cancer. Magn Reson Imaging 2017; 39:162-167. [DOI: 10.1016/j.mri.2017.03.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2017] [Revised: 03/08/2017] [Accepted: 03/08/2017] [Indexed: 01/05/2023]
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35
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Summers SM, Chin EJ, April MD, Grisell RD, Lospinoso JA, Kheirabadi BS, Salinas J, Blackbourne LH. Diagnostic accuracy of a novel software technology for detecting pneumothorax in a porcine model. Am J Emerg Med 2017; 35:1285-1290. [PMID: 28400069 DOI: 10.1016/j.ajem.2017.03.073] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 03/21/2017] [Accepted: 03/30/2017] [Indexed: 12/01/2022] Open
Abstract
INTRODUCTION Our objective was to measure the diagnostic accuracy of a novel software technology to detect pneumothorax on Brightness (B) mode and Motion (M) mode ultrasonography. METHODS Ultrasonography fellowship-trained emergency physicians performed thoracic ultrasonography at baseline and after surgically creating a pneumothorax in eight intubated, spontaneously breathing porcine subjects. Prior to pneumothorax induction, we captured sagittal M-mode still images and B-mode videos of each intercostal space with a linear array transducer at 4cm of depth. After collection of baseline images, we placed a chest tube, injected air into the pleural space in 250mL increments, and repeated the ultrasonography for pneumothorax volumes of 250mL, 500mL, 750mL, and 1000mL. We confirmed pneumothorax with intrapleural digital manometry and ultrasound by expert sonographers. We exported collected images for interpretation by the software. We treated each individual scan as a single test for interpretation by the software. RESULTS Excluding indeterminate results, we collected 338M-mode images for which the software demonstrated a sensitivity of 98% (95% confidence interval [CI] 92-99%), specificity of 95% (95% CI 86-99), positive likelihood ratio (LR+) of 21.6 (95% CI 7.1-65), and negative likelihood ratio (LR-) of 0.02 (95% CI 0.008-0.046). Among 364 B-mode videos, the software demonstrated a sensitivity of 86% (95% CI 81-90%), specificity of 85% (81-91%), LR+ of 5.7 (95% CI 3.2-10.2), and LR- of 0.17 (95% CI 0.12-0.22). CONCLUSIONS This novel technology has potential as a useful adjunct to diagnose pneumothorax on thoracic ultrasonography.
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Affiliation(s)
- Shane M Summers
- Department of Emergency Medicine, San Antonio Military Medical Center, JBSA Fort Sam Houston, TX, USA
| | - Eric J Chin
- Department of Emergency Medicine, San Antonio Military Medical Center, JBSA Fort Sam Houston, TX, USA
| | - Michael D April
- Department of Emergency Medicine, San Antonio Military Medical Center, JBSA Fort Sam Houston, TX, USA.
| | - Ronald D Grisell
- United States Army Institute of Surgical Research, JBSA Fort Sam Houston, TX, USA
| | - Joshua A Lospinoso
- Department of Emergency Medicine, San Antonio Military Medical Center, JBSA Fort Sam Houston, TX, USA
| | - Bijan S Kheirabadi
- United States Army Institute of Surgical Research, JBSA Fort Sam Houston, TX, USA
| | - Jose Salinas
- United States Army Institute of Surgical Research, JBSA Fort Sam Houston, TX, USA
| | - Lorne H Blackbourne
- United States Army Medical Department Center and School (AMEDD C&S), JBSA Fort Sam Houston, TX, USA
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Mehrtash A, Sedghi A, Ghafoorian M, Taghipour M, Tempany CM, Wells WM, Kapur T, Mousavi P, Abolmaesumi P, Fedorov A. Classification of Clinical Significance of MRI Prostate Findings Using 3D Convolutional Neural Networks. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2017; 10134. [PMID: 28615793 DOI: 10.1117/12.2277123] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Prostate cancer (PCa) remains a leading cause of cancer mortality among American men. Multi-parametric magnetic resonance imaging (mpMRI) is widely used to assist with detection of PCa and characterization of its aggressiveness. Computer-aided diagnosis (CADx) of PCa in MRI can be used as clinical decision support system to aid radiologists in interpretation and reporting of mpMRI. We report on the development of a convolution neural network (CNN) model to support CADx in PCa based on the appearance of prostate tissue in mpMRI, conducted as part of the SPIE-AAPM-NCI PROSTATEx challenge. The performance of different combinations of mpMRI inputs to CNN was assessed and the best result was achieved using DWI and DCE-MRI modalities together with the zonal information of the finding. On the test set, the model achieved an area under the receiver operating characteristic curve of 0.80.
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Affiliation(s)
- Alireza Mehrtash
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.,Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Alireza Sedghi
- Medical Informatics Laboratory, Queen's University, Kingston, ON, Canada
| | - Mohsen Ghafoorian
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.,Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Mehdi Taghipour
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Clare M Tempany
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - William M Wells
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Tina Kapur
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Parvin Mousavi
- Medical Informatics Laboratory, Queen's University, Kingston, ON, Canada
| | - Purang Abolmaesumi
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Andriy Fedorov
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
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Kriegmair MC, Bergen T, Ritter M, Mandel P, Michel MS, Wittenberg T, Bolenz C. Digital Mapping of the Urinary Bladder: Potential for Standardized Cystoscopy Reports. Urology 2017; 104:235-241. [PMID: 28214573 DOI: 10.1016/j.urology.2017.02.019] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2016] [Revised: 01/16/2017] [Accepted: 02/09/2017] [Indexed: 11/27/2022]
Abstract
OBJECTIVE To develop a standardized digital reporting tool for cystoscopy of the urinary bladder using panoramic imaging. MATERIALS AND METHODS An image processing and stitching software (Endorama) was developed to generate panoramic images from cystoscopy data. In a processing phase, algorithms were modulated and refined by reference to cystoscopy sequences (n = 30). Subsequently, standard systematic cystoscopies (n = 12) were recorded in patients undergoing transurethral resection of a bladder tumor to create panoramic images. RESULTS All sequences were applicable for the development and refinements of the software. Processing increasingly allowed the creation of images illustrating large parts of the bladder and relevant anatomic landmarks in different locations. The pathway covered by the endoscope during the intervention was illustrated as a route in the respective digital image. During the application phase, panoramic images were successfully created in 10 out of 12 cases. The resolution of the images was 4096 × 2048 pixels and the images required a median digital memory of 3.9 MB (3.4-5.7). The panoramic images illustrated 22 relevant findings of which 7 were papillary tumors. CONCLUSION High-quality digital panoramic maps of the urinary bladder were created using specifically processed data of videocystoscopy. In this preliminary series, relevant findings were illustrated in the respective image. Our tool may help improve standardization of cystoscopy reports and reduce interobserver variability.
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Affiliation(s)
| | - Tobias Bergen
- Image Processing and Medical Engineering Department, Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Manuel Ritter
- Department of Urology, University Medical Center Mannheim, Mannheim, Germany
| | - Philipp Mandel
- Department of Urology, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Maurice S Michel
- Department of Urology, University Medical Center Mannheim, Mannheim, Germany
| | - Thomas Wittenberg
- Image Processing and Medical Engineering Department, Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
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Reda I, Shalaby A, Elmogy M, Elfotouh AA, Khalifa F, El-Ghar MA, Hosseini-Asl E, Gimel'farb G, Werghi N, El-Baz A. A comprehensive non-invasive framework for diagnosing prostate cancer. Comput Biol Med 2016; 81:148-158. [PMID: 28063376 DOI: 10.1016/j.compbiomed.2016.12.010] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Revised: 12/16/2016] [Accepted: 12/17/2016] [Indexed: 02/08/2023]
Abstract
Early detection of prostate cancer increases chances of patients' survival. Our automated non-invasive system for computer-aided diagnosis (CAD) of prostate cancer segments the prostate on diffusion-weighted magnetic resonance images (DW-MRI) acquired at different b-values, estimates its apparent diffusion coefficients (ADC), and classifies their descriptors - empirical cumulative distribution functions (CDF) - with a trained deep learning network. To segment the prostate, an evolving geometric (level-set-based) deformable model is guided by a speed function depending on intensity attributes extracted from the DW-MRI with nonnegative matrix factorization (NMF). For a more robust evolution, the attributes are fused with a probabilistic shape prior and estimated spatial dependencies between prostate voxels. To preserve continuity, the ADCs of the segmented prostate volume at different b-values are normalized and refined using a generalized Gauss-Markov random field image model. The CDFs of the refined ADCs at different b-values are considered global water diffusion features and used to distinguish between benign and malignant prostates. A deep learning network of stacked non-negativity-constrained auto-encoders (SNCAE) is trained to classify the benign or malignant prostates on the basis of the constructed CDFs. Our experiments on 53 clinical DW-MRI data sets resulted in 92.3% accuracy, 83.3% sensitivity, and 100% specificity, indicating that the proposed CAD system could be used as a reliable non-invasive diagnostic tool.
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Affiliation(s)
- Islam Reda
- Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt; Bioengineering Department, University of Louisville, Louisville KY 40292, USA
| | - Ahmed Shalaby
- Bioengineering Department, University of Louisville, Louisville KY 40292, USA
| | - Mohammed Elmogy
- Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt; Bioengineering Department, University of Louisville, Louisville KY 40292, USA
| | - Ahmed Abou Elfotouh
- Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
| | - Fahmi Khalifa
- Bioengineering Department, University of Louisville, Louisville KY 40292, USA; Electronics and Communication Engineering Department, Mansoura University, Mansoura, Egypt
| | - Mohamed Abou El-Ghar
- Radiology Department, Urology and Nephrology Center, University of Mansoura, Egypt
| | - Ehsan Hosseini-Asl
- Electrical and Computer Engineering, University of Louisville, Louisville KY 40292, USA
| | - Georgy Gimel'farb
- Department of Computer Science, University of Auckland, Auckland, New Zealand
| | - Naoufel Werghi
- Khalifa University of Science Technology and Research, Abu Dhabi, UAE
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville KY 40292, USA.
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O'Leary DP, Zaheer A, Redmond HP, Corrigan MA. Integration of advances in social media and mHealth technology are pivotal to successful cancer prevention and control. Mhealth 2016; 2:38. [PMID: 28293611 PMCID: PMC5344161 DOI: 10.21037/mhealth.2016.09.02] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Accepted: 09/22/2016] [Indexed: 01/09/2023] Open
Abstract
The successful prevention and treatment of cancer is dependent upon efficient and reliable communication between healthcare workers and patients. Advances in social media and mHealth platforms have provided new ways in which to enhance the sharing of cancer related information. Other benefits of embracing this technology include utilising its analytic capabilities which can process the vast quantity of information generated from genome exploration in a highly efficient manner. The aim of this review is to provide an overview of the rapidly evolving areas through which digital engagement is proving useful in the prevention and control of cancer.
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Affiliation(s)
- D Peter O'Leary
- Cork Breast Research Centre, Cork University Hospital, Wilton, Cork, Ireland
| | - Amir Zaheer
- Cork Breast Research Centre, Cork University Hospital, Wilton, Cork, Ireland
| | - H Paul Redmond
- Cork Breast Research Centre, Cork University Hospital, Wilton, Cork, Ireland
| | - Mark A Corrigan
- Cork Breast Research Centre, Cork University Hospital, Wilton, Cork, Ireland
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