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Chen P, Turco S, Wang Y, Jager A, Daures G, Wijkstra H, Zwart W, Huang P, Mischi M. Can 3D Multiparametric Ultrasound Imaging Predict Prostate Biopsy Outcome? ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:1194-1202. [PMID: 38734528 DOI: 10.1016/j.ultrasmedbio.2024.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/16/2024] [Accepted: 04/14/2024] [Indexed: 05/13/2024]
Abstract
OBJECTIVES To assess the value of 3D multiparametric ultrasound imaging, combining hemodynamic and tissue stiffness quantifications by machine learning, for the prediction of prostate biopsy outcomes. METHODS After signing informed consent, 54 biopsy-naïve patients underwent a 3D dynamic contrast-enhanced ultrasound (DCE-US) recording, a multi-plane 2D shear-wave elastography (SWE) scan with manual sweeping from base to apex of the prostate, and received 12-core systematic biopsies (SBx). 3D maps of 18 hemodynamic parameters were extracted from the 3D DCE-US quantification and a 3D SWE elasticity map was reconstructed based on the multi-plane 2D SWE acquisitions. Subsequently, all the 3D maps were segmented and subdivided into 12 regions corresponding to the SBx locations. Per region, the set of 19 computed parameters was further extended by derivation of eight radiomic features per parameter. Based on this feature set, a multiparametric ultrasound approach was implemented using five different classifiers together with a sequential floating forward selection method and hyperparameter tuning. The classification accuracy with respect to the biopsy reference was assessed by a group-k-fold cross-validation procedure, and the performance was evaluated by the Area Under the Receiver Operating Characteristics Curve (AUC). RESULTS Of the 54 patients, 20 were found with clinically significant prostate cancer (csPCa) based on SBx. The 18 hemodynamic parameters showed mean AUC values varying from 0.63 to 0.75, and SWE elasticity showed an AUC of 0.66. The multiparametric approach using radiomic features derived from hemodynamic parameters only produced an AUC of 0.81, while the combination of hemodynamic and tissue-stiffness quantifications yielded a significantly improved AUC of 0.85 for csPCa detection (p-value < 0.05) using the Gradient Boosting classifier. CONCLUSIONS Our results suggest 3D multiparametric ultrasound imaging combining hemodynamic and tissue-stiffness features to represent a promising diagnostic tool for biopsy outcome prediction, aiding in csPCa localization.
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Affiliation(s)
- Peiran Chen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
| | - Simona Turco
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Yao Wang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University, Hangzhou, China
| | - Auke Jager
- Department of Urology, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Gautier Daures
- Angiogenesis Analytics, JADS Venture Campus, Netherlands
| | - Hessel Wijkstra
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands; Department of Urology, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Wim Zwart
- Angiogenesis Analytics, JADS Venture Campus, Netherlands
| | - Pintong Huang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University, Hangzhou, China
| | - Massimo Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
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Dietrich CF, Correas JM, Cui XW, Dong Y, Havre RF, Jenssen C, Jung EM, Krix M, Lim A, Lassau N, Piscaglia F. EFSUMB Technical Review - Update 2023: Dynamic Contrast-Enhanced Ultrasound (DCE-CEUS) for the Quantification of Tumor Perfusion. ULTRASCHALL IN DER MEDIZIN (STUTTGART, GERMANY : 1980) 2024; 45:36-46. [PMID: 37748503 DOI: 10.1055/a-2157-2587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
Dynamic contrast-enhanced ultrasound (DCE-US) is a technique to quantify tissue perfusion based on phase-specific enhancement after the injection of microbubble contrast agents for diagnostic ultrasound. The guidelines of the European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) published in 2004 and updated in 2008, 2011, and 2020 focused on the use of contrast-enhanced ultrasound (CEUS), including essential technical requirements, training, investigational procedures and steps, guidance regarding image interpretation, established and recommended clinical indications, and safety considerations. However, the quantification of phase-specific enhancement patterns acquired with ultrasound contrast agents (UCAs) is not discussed here. The purpose of this EFSUMB Technical Review is to further establish a basis for the standardization of DCE-US focusing on treatment monitoring in oncology. It provides some recommendations and descriptions as to how to quantify dynamic ultrasound contrast enhancement, and technical explanations for the analysis of time-intensity curves (TICs). This update of the 2012 EFSUMB introduction to DCE-US includes clinical aspects for data collection, analysis, and interpretation that have emerged from recent studies. The current study not only aims to support future work in this research field but also to facilitate a transition to clinical routine use of DCE-US.
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Affiliation(s)
- Christoph F Dietrich
- Department General Internal Medicine, Kliniken Hirslanden Beau Site, Salem und Permanence, Bern, Switzerland
- Zentrum der Inneren Medizin, Johann Wolfgang Goethe Universitätsklinik Frankfurt, Frankfurt, Germany
| | - Jean-Michel Correas
- Department of Adult Radiology, Assistance Publique Hôpitaux de Paris, Necker University Hospital, Paris, France
- Paris Cité University, Paris, France
- CNRS, INSERM Laboratoire d'Imagerie Biomédicale, Sorbonne Université, Paris, France
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Dong
- Department of Ultrasound, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Roald Flesland Havre
- Department of Medicine, National Centre for Ultrasound in Gastroenterology, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Christian Jenssen
- Department of Internal Medicine, Krankenhaus Märkisch Oderland Strausberg/ Wriezen, Wriezen, Germany
- Brandenburg Institute for Clinical Ultrasound (BICUS), Medical University Brandenburg, Neuruppin, Brandenburg, Germany
| | - Ernst Michael Jung
- Institute of Diagnostic Radiology, Interdisciplinary Ultrasound Department, University Hospital Regensburg, Regensburg, Germany
| | - Martin Krix
- Global Medical & Regulatory Affairs, Bracco Imaging, Konstanz, Germany
| | - Adrian Lim
- Department of Imaging, Imperial College London and Healthcare NHS Trust, Charing Cross Hospital Campus, London, United Kingdom of Great Britain and Northern Ireland
| | - Nathalie Lassau
- Imaging Department. Gustave Roussy cancer Campus. Villejuif, France. BIOMAPS. UMR 1281. CEA. CNRS. INSERM, Université Paris-Saclay, France
| | - Fabio Piscaglia
- Division of Internal Medicine, Hepatobiliary and Immunoallergic Diseases, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Dept of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
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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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Wilson PFR, Gilany M, Jamzad A, Fooladgar F, To MNN, Wodlinger B, Abolmaesumi P, Mousavi P. Self-Supervised Learning With Limited Labeled Data for Prostate Cancer Detection in High-Frequency Ultrasound. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2023; 70:1073-1083. [PMID: 37478033 DOI: 10.1109/tuffc.2023.3297840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
Deep learning-based analysis of high-frequency, high-resolution micro-ultrasound data shows great promise for prostate cancer (PCa) detection. Previous approaches to analysis of ultrasound data largely follow a supervised learning (SL) paradigm. Ground truth labels for ultrasound images used for training deep networks often include coarse annotations generated from the histopathological analysis of tissue samples obtained via biopsy. This creates inherent limitations on the availability and quality of labeled data, posing major challenges to the success of SL methods. However, unlabeled prostate ultrasound data are more abundant. In this work, we successfully apply self-supervised representation learning to micro-ultrasound data. Using ultrasound data from 1028 biopsy cores of 391 subjects obtained in two clinical centers, we demonstrate that feature representations learned with this method can be used to classify cancer from noncancer tissue, obtaining an AUROC score of 91% on an independent test set. To the best of our knowledge, this is the first successful end-to-end self-SL (SSL) approach for PCa detection using ultrasound data. Our method outperforms baseline SL approaches, generalizes well between different data centers, and scales well in performance as more unlabeled data are added, making it a promising approach for future research using large volumes of unlabeled data. Our code is publicly available at https://www.github.com/MahdiGilany/SSL_micro_ultrasound.
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Clinical Trial Protocol: Developing an Image Classification Algorithm for Prostate Cancer Diagnosis on Three-dimensional Multiparametric Transrectal Ultrasound. EUR UROL SUPPL 2023; 49:32-43. [PMID: 36874606 PMCID: PMC9975006 DOI: 10.1016/j.euros.2022.12.018] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/22/2022] [Indexed: 01/27/2023] Open
Abstract
Introduction and hypothesis The tendency toward population-based screening programs for prostate cancer (PCa) is expected to increase demand for prebiopsy imaging. This study hypothesizes that a machine learning image classification algorithm for three-dimensional multiparametric transrectal prostate ultrasound (3D mpUS) can detect PCa accurately. Design This is a phase 2 prospective multicenter diagnostic accuracy study. A total of 715 patients will be included in a period of approximately 2 yr. Patients are eligible in case of suspected PCa for which prostate biopsy is indicated or in case of biopsy-proven PCa for which radical prostatectomy (RP) will be performed. Exclusion criteria are prior treatment for PCa or contraindications for ultrasound contrast agents (UCAs). Protocol overview Study participants will undergo 3D mpUS, consisting of 3D grayscale, 4D contrast-enhanced ultrasound, and 3D shear wave elastography (SWE). Whole-mount RP histopathology will provide the ground truth to train the image classification algorithm. Patients included prior to prostate biopsy will be used for subsequent preliminary validation. There is a small, anticipated risk for participants associated with the administration of a UCA. Informed consent has to be given prior to study participation, and (serious) adverse events will be reported. Statistical analysis The primary outcome will be the diagnostic performance of the algorithm for detecting clinically significant PCa (csPCa) on a per-voxel and a per-microregion level. Diagnostic performance will be reported as the area under the receiver operating characteristic curve. Clinically significant PCa is defined as the International Society of Urological grade group ≥2. Full-mount RP histopathology will be used as the reference standard. Secondary outcomes will be sensitivity, specificity, negative predictive value, and positive predictive value for csPCa on a per-patient level, evaluated in patients included prior to prostate biopsy, using biopsy results as the reference standard. A further analysis will be performed on the ability of the algorithm to differentiate between low-, intermediate-, and high-risk tumors. Discussion and summary This study aims to develop an ultrasound-based imaging modality for PCa detection. Subsequent head-to-head validation trials with magnetic resonance imaging have to be performed in order to determine its role in clinical practice for risk stratification in patients suspected for PCa.
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Turco S, Tiyarattanachai T, Ebrahimkheil K, Eisenbrey J, Kamaya A, Mischi M, Lyshchik A, Kaffas AE. Interpretable Machine Learning for Characterization of Focal Liver Lesions by Contrast-Enhanced Ultrasound. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2022; 69:1670-1681. [PMID: 35320099 PMCID: PMC9188683 DOI: 10.1109/tuffc.2022.3161719] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
This work proposes an interpretable radiomics approach to differentiate between malignant and benign focal liver lesions (FLLs) on contrast-enhanced ultrasound (CEUS). Although CEUS has shown promise for differential FLLs diagnosis, current clinical assessment is performed only by qualitative analysis of the contrast enhancement patterns. Quantitative analysis is often hampered by the unavoidable presence of motion artifacts and by the complex, spatiotemporal nature of liver contrast enhancement, consisting of multiple, overlapping vascular phases. To fully exploit the wealth of information in CEUS, while coping with these challenges, here we propose combining features extracted by the temporal and spatiotemporal analysis in the arterial phase enhancement with spatial features extracted by texture analysis at different time points. Using the extracted features as input, several machine learning classifiers are optimized to achieve semiautomatic FLLs characterization, for which there is no need for motion compensation and the only manual input required is the location of a suspicious lesion. Clinical validation on 87 FLLs from 72 patients at risk for hepatocellular carcinoma (HCC) showed promising performance, achieving a balanced accuracy of 0.84 in the distinction between benign and malignant lesions. Analysis of feature relevance demonstrates that a combination of spatiotemporal and texture features is needed to achieve the best performance. Interpretation of the most relevant features suggests that aspects related to microvascular perfusion and the microvascular architecture, together with the spatial enhancement characteristics at wash-in and peak enhancement, are important to aid the accurate characterization of FLLs.
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Azzouz A, Hejji L, Kim KH, Kukkar D, Souhail B, Bhardwaj N, Brown RJC, Zhang W. Advances in surface plasmon resonance-based biosensor technologies for cancer biomarker detection. Biosens Bioelectron 2022; 197:113767. [PMID: 34768064 DOI: 10.1016/j.bios.2021.113767] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 10/21/2021] [Accepted: 10/31/2021] [Indexed: 01/25/2023]
Abstract
Surface plasmon resonance approach is a highly useful option to offer optical and label-free detection of target bioanalytes with numerous advantages (e.g., low-cost fabrication, appreciable sensitivity, label-free detection, and outstanding accuracy). As such, it allows early diagnosis of cancer biomarkers to monitor tumor progression and to prevent the recurrence of oncogenic tumors. This work highlights the recent progress in SPR biosensing technology for the diagnosis of various cancer types (e.g., lung, breast, prostate, and ovarian). Further, the performance of various SPR biosensors is also evaluated in terms of the basic quality assurance criteria (e.g., limit of detection (LOD), selectivity, sensor response time, and reusability). Finally, the limitations and future challenges associated with SPR biosensors are also discussed with respect to cancer biomarker detection.
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Affiliation(s)
- Abdelmonaim Azzouz
- Department of Chemistry, Faculty of Science, University of Abdelmalek Essaadi, B.P. 2121, M'Hannech II, 93002, Tétouan, Morocco
| | - Lamia Hejji
- Department of Chemistry, Faculty of Science, University of Abdelmalek Essaadi, B.P. 2121, M'Hannech II, 93002, Tétouan, Morocco
| | - Ki-Hyun Kim
- Department of Civil and Environmental Engineering, Hanyang University, 222 Wangsimni-Ro, Seoul, 04763, South Korea.
| | - Deepak Kukkar
- Department of Nanotechnology, Sri Guru Granth Sahib World University, Fatehgarh Sahib, 140406, Punjab, India
| | - Badredine Souhail
- Department of Chemistry, Faculty of Science, University of Abdelmalek Essaadi, B.P. 2121, M'Hannech II, 93002, Tétouan, Morocco
| | - Neha Bhardwaj
- Department of Biotechnology, University Institute of Engineering Technology (UIET), Panjab University, Chandigarh, India
| | - Richard J C Brown
- Environment Department, National Physical Laboratory, Teddington, TW11 0LW, UK
| | - Wei Zhang
- School of Ecology and Environmental Science, Zhengzhou University, 100 Kexue Avenue, Zhengzhou, Henan, 450001, PR China
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Mendes B, Domingues I, Silva A, Santos J. Prostate Cancer Aggressiveness Prediction Using CT Images. Life (Basel) 2021; 11:life11111164. [PMID: 34833040 PMCID: PMC8618689 DOI: 10.3390/life11111164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/25/2021] [Accepted: 10/26/2021] [Indexed: 11/16/2022] Open
Abstract
Prostate Cancer (PCa) is mostly asymptomatic at an early stage and often painless requiring active surveillance screening. Transrectal Ultrasound Guided Biopsy (TRUS) is the principal method to diagnose PCa following a histological examination by observing cell pattern irregularities and assigning the Gleason Score (GS) according to the recommended guidelines. This procedure presents sampling errors and, being invasive may cause complications to the patients. External Beam Radiotherapy Treatment (EBRT) is presented as curative option for localised and locally advanced disease, as a palliative option for metastatic low-volume disease or after prostatectomy for prostate bed and pelvic nodes salvage. In the EBRT worflow a Computed Tomography (CT) scan is performed as the basis for dose calculations and volume delineations. In this work, we evaluated the use of data-characterization algorithms (radiomics) from CT images for PCa aggressiveness assessment. The fundamental motivation relies on the wide availability of CT images and the need to provide tools to assess EBRT effectiveness. We used Pyradiomics and Local Image Features Extraction (LIFEx) to extract features and search for a radiomic signature within CT images. Finnaly, when applying Principal Component Analysis (PCA) to the features, we were able to show promising results.
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Affiliation(s)
- Bruno Mendes
- Centro de Investigação do Instituto Português de Oncologia do Porto (CI-IPOP), Grupo de Física Médica, Radiobiologia e Protecção Radiológica, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- Faculdade de Engenharia da Universidade do Porto (FEUP), 4200-465 Porto, Portugal
- Correspondence:
| | - Inês Domingues
- Centro de Investigação do Instituto Português de Oncologia do Porto (CI-IPOP), Grupo de Física Médica, Radiobiologia e Protecção Radiológica, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- Instituto Superior de Engenharia de Coimbra (ISEC), 3030-199 Coimbra, Portugal
| | - Augusto Silva
- IEETA, Universidade de Aveiro (UA), 3810-193 Aveiro, Portugal;
| | - João Santos
- Centro de Investigação do Instituto Português de Oncologia do Porto (CI-IPOP), Grupo de Física Médica, Radiobiologia e Protecção Radiológica, 4200-072 Porto, Portugal; (I.D.); (J.S.)
- Instituto de Ciências Biomédicas Abel Salazar (ICBAS), 4050-313 Porto, Portugal
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9
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Alves AFF, de Arruda Miranda JR, de Souza SAS, Pereira RV, de Almeida Silvares PR, Yamashita S, Deffune E, de Pina DR. Texture analysis to differentiate anterior cruciate ligament in patients after surgery with platelet-rich plasma. J Orthop Surg Res 2021; 16:283. [PMID: 33910605 PMCID: PMC8080342 DOI: 10.1186/s13018-021-02437-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 04/20/2021] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Platelet-rich plasma (PRP) has been used to favor anterior cruciate ligament (ACL) healing after reconstruction surgeries. However, clinical data are still inconclusive and subjective about PRP. Thus, we propose a quantitative method to demonstrate that PRP produced morphological structure changes. METHODS Thirty-four patients undergoing ACL reconstruction surgery were evaluated and divided into control group (sixteen patients) without PRP application and experiment group (eighteen patients) with intraoperative application of PRP. Magnetic resonance imaging (MRI) scans were performed 3 months after surgery. We used Matlab® and machine learning (ML) in Orange Canvas® to texture analysis (TA) features extraction. Experienced radiologists delimited the regions of interest (RoIs) in the T2-weighted images. Sixty-two texture parameters were extracted, including gray-level co-occurrence matrix and gray level run length. We used the algorithms logistic regression (LR), naive Bayes (NB), and stochastic gradient descent (SGD). RESULTS The accuracy of the classification with NB, LR, and SGD was 83.3%, 75%, 75%, respectively. For the area under the curve, NB, LR, and SGD presented values of 91.7%, 94.4%, 75%, respectively. In clinical evaluations, the groups show similar responses in terms of improvement in pain and increase in the IKDC index (International Knee Documentation Committee) and Lysholm score indices differing only in the assessment of flexion, which presents a significant difference for the group treated with PRP. CONCLUSIONS Here, we demonstrated quantitatively that patients who received PRP presented texture changes when compared to the control group. Thus, our findings suggest that PRP interferes with morphological parameters of the ACL. TRIAL REGISTRATION Protocol no. CAAE 56164316.6.0000.5411.
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Affiliation(s)
- Allan Felipe Fattori Alves
- grid.410543.70000 0001 2188 478XMedical School, Sao Paulo State University Julio de Mesquita Filho, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, Botucatu, SP CEP 18618687 Brazil
| | - José Ricardo de Arruda Miranda
- grid.410543.70000 0001 2188 478XInstitute of Bioscience, Sao Paulo State University Julio de Mesquita Filho, R. Prof. Dr. Antônio Celso Wagner Zanin, 250 - Distrito de Rubião Junior, Botucatu, SP CEP 18618687 Brazil
| | - Sérgio Augusto Santana de Souza
- grid.410543.70000 0001 2188 478XInstitute of Bioscience, Sao Paulo State University Julio de Mesquita Filho, R. Prof. Dr. Antônio Celso Wagner Zanin, 250 - Distrito de Rubião Junior, Botucatu, SP CEP 18618687 Brazil
| | - Ricardo Violante Pereira
- grid.410543.70000 0001 2188 478XMedical School, Sao Paulo State University Julio de Mesquita Filho, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, Botucatu, SP CEP 18618687 Brazil
| | - Paulo Roberto de Almeida Silvares
- grid.410543.70000 0001 2188 478XMedical School, Sao Paulo State University Julio de Mesquita Filho, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, Botucatu, SP CEP 18618687 Brazil
| | - Seizo Yamashita
- grid.410543.70000 0001 2188 478XMedical School, Sao Paulo State University Julio de Mesquita Filho, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, Botucatu, SP CEP 18618687 Brazil
| | - Elenice Deffune
- grid.410543.70000 0001 2188 478XMedical School, Sao Paulo State University Julio de Mesquita Filho, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, Botucatu, SP CEP 18618687 Brazil
| | - Diana Rodrigues de Pina
- grid.410543.70000 0001 2188 478XMedical School, Sao Paulo State University Julio de Mesquita Filho, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, Botucatu, SP CEP 18618687 Brazil
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Wildeboer RR, van Sloun RJG, Wijkstra H, Mischi M. Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 189:105316. [PMID: 31951873 DOI: 10.1016/j.cmpb.2020.105316] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 12/09/2019] [Accepted: 01/04/2020] [Indexed: 05/16/2023]
Abstract
Prostate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing, weighing and coupling of multiple images not only places additional burden on the radiologist, it also complicates the reviewing process. Prostate cancer imaging has therefore been an important target for the development of computer-aided diagnostic (CAD) tools. In this survey, we discuss the advances in CAD for prostate cancer over the last decades with special attention to the deep-learning techniques that have been designed in the last few years. Moreover, we elaborate and compare the methods employed to deliver the CAD output to the operator for further medical decision making.
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Affiliation(s)
- Rogier R Wildeboer
- Lab of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, De Zaale, 5600 MB, Eindhoven, the Netherlands.
| | - Ruud J G van Sloun
- Lab of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, De Zaale, 5600 MB, Eindhoven, the Netherlands.
| | - Hessel Wijkstra
- Lab of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, De Zaale, 5600 MB, Eindhoven, the Netherlands; Department of Urology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands
| | - Massimo Mischi
- Lab of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, De Zaale, 5600 MB, Eindhoven, the Netherlands
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11
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Turco S, Frinking P, Wildeboer R, Arditi M, Wijkstra H, Lindner JR, Mischi M. Contrast-Enhanced Ultrasound Quantification: From Kinetic Modeling to Machine Learning. ULTRASOUND IN MEDICINE & BIOLOGY 2020; 46:518-543. [PMID: 31924424 DOI: 10.1016/j.ultrasmedbio.2019.11.008] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 11/13/2019] [Accepted: 11/14/2019] [Indexed: 05/14/2023]
Abstract
Ultrasound contrast agents (UCAs) have opened up immense diagnostic possibilities by combined use of indicator dilution principles and dynamic contrast-enhanced ultrasound (DCE-US) imaging. UCAs are microbubbles encapsulated in a biocompatible shell. With a rheology comparable to that of red blood cells, UCAs provide an intravascular indicator for functional imaging of the (micro)vasculature by quantitative DCE-US. Several models of the UCA intravascular kinetics have been proposed to provide functional quantitative maps, aiding diagnosis of different pathological conditions. This article is a comprehensive review of the available methods for quantitative DCE-US imaging based on temporal, spatial and spatiotemporal analysis of the UCA kinetics. The recent introduction of novel UCAs that are targeted to specific vascular receptors has advanced DCE-US to a molecular imaging modality. In parallel, new kinetic models of increased complexity have been developed. The extraction of multiple quantitative maps, reflecting complementary variables of the underlying physiological processes, requires an integrative approach to their interpretation. A probabilistic framework based on emerging machine-learning methods represents nowadays the ultimate approach, improving the diagnostic accuracy of DCE-US imaging by optimal combination of the extracted complementary information. The current value and future perspective of all these advances are critically discussed.
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Affiliation(s)
- Simona Turco
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
| | | | - Rogier Wildeboer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Marcel Arditi
- École polytechnique fédérale de Lausanne, Lausanne, Switzerland
| | - Hessel Wijkstra
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Jonathan R Lindner
- Knight Cardiovascular Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Massimo Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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