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Mayer R, Choyke PL, Simone Ii CB. Editorial for Special Topics: Imaging-Based Diagnosis for Prostate Cancer-State of the Art. Diagnostics (Basel) 2024; 14:2016. [PMID: 39335695 PMCID: PMC11431072 DOI: 10.3390/diagnostics14182016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2024] [Accepted: 09/07/2024] [Indexed: 09/30/2024] Open
Abstract
This Special Topics Issue, "Imaging-based Diagnosis of Prostate Cancer-State of the Art", of Diagnostics compiles 10 select articles [...].
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Affiliation(s)
- Rulon Mayer
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Oncoscore, Garrett Park, MD 20896, USA
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Weidner P, Saar D, Söhn M, Schroeder T, Yu Y, Zöllner FG, Ponelies N, Zhou X, Zwicky A, Rohrbacher FN, Pattabiraman VR, Tanriver M, Bauer A, Ahmed H, Ametamey SM, Riffel P, Seger R, Bode JW, Wade RC, Ebert MPA, Kragelund BB, Burgermeister E. Myotubularin-related-protein-7 inhibits mutant (G12V) K-RAS by direct interaction. Cancer Lett 2024; 588:216783. [PMID: 38462034 DOI: 10.1016/j.canlet.2024.216783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 02/19/2024] [Accepted: 03/03/2024] [Indexed: 03/12/2024]
Abstract
Inhibition of K-RAS effectors like B-RAF or MEK1/2 is accompanied by treatment resistance in cancer patients via re-activation of PI3K and Wnt signaling. We hypothesized that myotubularin-related-protein-7 (MTMR7), which inhibits PI3K and ERK1/2 signaling downstream of RAS, directly targets RAS and thereby prevents resistance. Using cell and structural biology combined with animal studies, we show that MTMR7 binds and inhibits RAS at cellular membranes. Overexpression of MTMR7 reduced RAS GTPase activities and protein levels, ERK1/2 phosphorylation, c-FOS transcription and cancer cell proliferation in vitro. We located the RAS-inhibitory activity of MTMR7 to its charged coiled coil (CC) region and demonstrate direct interaction with the gastrointestinal cancer-relevant K-RASG12V mutant, favouring its GDP-bound state. In mouse models of gastric and intestinal cancer, a cell-permeable MTMR7-CC mimicry peptide decreased tumour growth, Ki67 proliferation index and ERK1/2 nuclear positivity. Thus, MTMR7 mimicry peptide(s) could provide a novel strategy for targeting mutant K-RAS in cancers.
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Affiliation(s)
- Philip Weidner
- Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Daniel Saar
- Structural Biology and NMR Laboratory (SBiNLab) and the Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Michaela Söhn
- Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Torsten Schroeder
- Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Yanxiong Yu
- Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Cooperative Core Facility Animal Scanner ZI, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Norbert Ponelies
- Orthopaedics & Trauma Surgery, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Xiaobo Zhou
- Department of Medicine I, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - André Zwicky
- Laboratory of Organic Chemistry, Department of Chemistry and Applied Bioscience of ETH, Zurich, Switzerland
| | - Florian N Rohrbacher
- Laboratory of Organic Chemistry, Department of Chemistry and Applied Bioscience of ETH, Zurich, Switzerland
| | - Vijaya R Pattabiraman
- Laboratory of Organic Chemistry, Department of Chemistry and Applied Bioscience of ETH, Zurich, Switzerland
| | - Matthias Tanriver
- Laboratory of Organic Chemistry, Department of Chemistry and Applied Bioscience of ETH, Zurich, Switzerland
| | - Alexander Bauer
- Structural Biology and NMR Laboratory (SBiNLab) and the Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Hazem Ahmed
- Institute of Pharmaceutical Sciences, Department of Chemistry and Applied Biosciences of ETH, Zurich, Switzerland
| | - Simon M Ametamey
- Institute of Pharmaceutical Sciences, Department of Chemistry and Applied Biosciences of ETH, Zurich, Switzerland
| | - Philipp Riffel
- Clinic of Radiology and Nuclear Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Rony Seger
- Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Jeffrey W Bode
- Laboratory of Organic Chemistry, Department of Chemistry and Applied Bioscience of ETH, Zurich, Switzerland
| | - Rebecca C Wade
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany; Heidelberg University, Zentrum für Molekulare Biologie (ZMBH), DKFZ-ZMBH Alliance, and Interdisciplinary Center for Scientific Computing (IWR), Heidelberg, Germany
| | - Matthias P A Ebert
- Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; DKFZ-Hector Institute at the University Medical Center, Mannheim, Germany
| | - Birthe B Kragelund
- Structural Biology and NMR Laboratory (SBiNLab) and the Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| | - Elke Burgermeister
- Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
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Mayer R, Raman S, Simone CB. Editorial: Combining multiple non-invasive images and/or biochemical tests to predict prostate cancer aggressiveness. Front Oncol 2023; 13:1156649. [PMID: 36865798 PMCID: PMC9971965 DOI: 10.3389/fonc.2023.1156649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 02/03/2023] [Indexed: 02/16/2023] Open
Affiliation(s)
- Rulon Mayer
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States,Oncoscore, Garrett Park, MD, United States,*Correspondence: Rulon Mayer,
| | - Steven Raman
- Department of Radiology, University of California, Los Angeles Health System, Los Angeles, CA, United States
| | - Charles B. Simone
- Department of Radiation Oncology, New York Proton Center, New York, NY, United States,Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
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Gvozdenko AA, Blinov AV, Slyadneva KS, Blinova AA, Golik AB, Maglakelidze DG. X-Ray Contrast Magnetic Diagnostic Tool Based on a Three-Component Nanosystem. RUSS J GEN CHEM+ 2022. [DOI: 10.1134/s1070363222060305] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Ye N, Yang Q, Chen Z, Teng C, Liu P, Liu X, Xiong Y, Lin X, Li S, Li X. Classification of Gliomas and Germinomas of the Basal Ganglia by Transfer Learning. Front Oncol 2022; 12:844197. [PMID: 35311111 PMCID: PMC8928458 DOI: 10.3389/fonc.2022.844197] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 02/11/2022] [Indexed: 12/20/2022] Open
Abstract
Background Germ cell tumors (GCTs) are neoplasms derived from reproductive cells, mostly occurring in children and adolescents at 10 to 19 years of age. Intracranial GCTs are classified histologically into germinomas and non-germinomatous germ cell tumors. Germinomas of the basal ganglia are difficult to distinguish based on symptoms or routine MRI images from gliomas, even for experienced neurosurgeons or radiologists. Meanwhile, intracranial germinoma has a lower incidence rate than glioma in children and adults. Therefore, we established a model based on pre-trained ResNet18 with transfer learning to better identify germinomas of the basal ganglia. Methods This retrospective study enrolled 73 patients diagnosed with germinoma or glioma of the basal ganglia. Brain lesions were manually segmented based on both T1C and T2 FLAIR sequences. The T1C sequence was used to build the tumor classification model. A 2D convolutional architecture and transfer learning were implemented. ResNet18 from ImageNet was retrained on the MRI images of our cohort. Class activation mapping was applied for the model visualization. Results The model was trained using five-fold cross-validation, achieving a mean AUC of 0.88. By analyzing the class activation map, we found that the model's attention was focused on the peri-tumoral edema region of gliomas and tumor bulk for germinomas, indicating that differences in these regions may help discriminate these tumors. Conclusions This study showed that the T1C-based transfer learning model could accurately distinguish germinomas from gliomas of the basal ganglia preoperatively.
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Affiliation(s)
- Ningrong Ye
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Qi Yang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Ziyan Chen
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Chubei Teng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Peikun Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Xi Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Yi Xiong
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Xuelei Lin
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Shouwei Li
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China
| | - Xuejun Li
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
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Schrammen PL, Ghaffari Laleh N, Echle A, Truhn D, Schulz V, Brinker TJ, Brenner H, Chang-Claude J, Alwers E, Brobeil A, Kloor M, Heij LR, Jäger D, Trautwein C, Grabsch HI, Quirke P, West NP, Hoffmeister M, Kather JN. Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology. J Pathol 2021; 256:50-60. [PMID: 34561876 DOI: 10.1002/path.5800] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 08/01/2021] [Accepted: 09/03/2021] [Indexed: 12/17/2022]
Abstract
Deep learning is a powerful tool in computational pathology: it can be used for tumor detection and for predicting genetic alterations based on histopathology images alone. Conventionally, tumor detection and prediction of genetic alterations are two separate workflows. Newer methods have combined them, but require complex, manually engineered computational pipelines, restricting reproducibility and robustness. To address these issues, we present a new method for simultaneous tumor detection and prediction of genetic alterations: The Slide-Level Assessment Model (SLAM) uses a single off-the-shelf neural network to predict molecular alterations directly from routine pathology slides without any manual annotations, improving upon previous methods by automatically excluding normal and non-informative tissue regions. SLAM requires only standard programming libraries and is conceptually simpler than previous approaches. We have extensively validated SLAM for clinically relevant tasks using two large multicentric cohorts of colorectal cancer patients, Darmkrebs: Chancen der Verhütung durch Screening (DACHS) from Germany and Yorkshire Cancer Research Bowel Cancer Improvement Programme (YCR-BCIP) from the UK. We show that SLAM yields reliable slide-level classification of tumor presence with an area under the receiver operating curve (AUROC) of 0.980 (confidence interval 0.975, 0.984; n = 2,297 tumor and n = 1,281 normal slides). In addition, SLAM can detect microsatellite instability (MSI)/mismatch repair deficiency (dMMR) or microsatellite stability/mismatch repair proficiency with an AUROC of 0.909 (0.888, 0.929; n = 2,039 patients) and BRAF mutational status with an AUROC of 0.821 (0.786, 0.852; n = 2,075 patients). The improvement with respect to previous methods was validated in a large external testing cohort in which MSI/dMMR status was detected with an AUROC of 0.900 (0.864, 0.931; n = 805 patients). In addition, SLAM provides human-interpretable visualization maps, enabling the analysis of multiplexed network predictions by human experts. In summary, SLAM is a new simple and powerful method for computational pathology that could be applied to multiple disease contexts. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
| | | | - Amelie Echle
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Daniel Truhn
- Department of Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Volkmar Schulz
- Department of Physics of Molecular Imaging Systems, Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany.,Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.,Comprehensive Diagnostic Center Aachen (CDCA), University Hospital Aachen, Aachen, Germany.,Hyperion Hybrid Imaging Systems GmbH, Aachen, Germany
| | - Titus J Brinker
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Cancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Elizabeth Alwers
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alexander Brobeil
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.,Tumor Bank Unit, Tissue Bank of the National Center for Tumor Diseases, Heidelberg, Germany
| | - Matthias Kloor
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
| | - Lara R Heij
- Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands.,Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany.,Department of Surgery and Transplantation, University Hospital RWTH Aachen, Aachen, Germany
| | - Dirk Jäger
- Medical Oncology, National Center of Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
| | - Christian Trautwein
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Heike I Grabsch
- Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands.,Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Philip Quirke
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Nicholas P West
- Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.,German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.,Medical Oncology, National Center of Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.,Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK
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Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives. Diagnostics (Basel) 2021; 11:diagnostics11020354. [PMID: 33672608 PMCID: PMC7924061 DOI: 10.3390/diagnostics11020354] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 02/16/2021] [Accepted: 02/17/2021] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.
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Mayer R, Simone CB, Turkbey B, Choyke P. Algorithms applied to spatially registered multi-parametric MRI for prostate tumor volume measurement. Quant Imaging Med Surg 2021; 11:119-132. [PMID: 33392016 DOI: 10.21037/qims-20-137a] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Prostate tumor volume correlates with critical components of cancer staging such as Gleason score (GS) grade, predicted disease progression, and metastasis. Therefore, non-invasive tumor volume measurement may elevate clinical management. Radiology assessments of multi-parametric MRI (MP-MRI) commonly visually examine individual images to determine possible tumor presence. This study combines registered MP-MRI into a single image that display normal tissue and possible lesions. This study tests and exploits the vector nature of spatially registered MP-MRI by using supervised target detection algorithms (STDA) and color display and psychovisual analysis (CIELAB) to non-invasively estimate prostate tumor volume. Methods MRI, including T1, T2, diffusion [apparent diffusion coefficient (ADC)], dynamic contrast enhanced (DCE) images, were resampled, rescaled, translated, and stitched to form spatially registered Multi-parametric cubes. The multi-parametric or multi-spectral signatures (7-component or T1, T2, ADC, etc.) that characterize the prostate tumors were inserted into target detection algorithms with conical decision surfaces (adaptive cosine estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. In addition, tumor appeared as yellow in color images that were created by assigning red to washout from DCE, green to high B from diffusion, and blue to autonomous diffusion image. The yellow voxels in the three-channel hypercube were visually identified by a reader and recording voxels that exceed a threshold in the b* component of the CIELAB algorithm. The number of reported tumor voxels were converted to volume based on spatial resolution and slice separation. The tumor volume measurements were quantitatively validated by comparing the tumor volume computations to the pathologist's assessment of the histology of sectioned whole mount prostates from 26 consecutive patients with prostate adenocarcinoma who underwent radical prostatectomy. This study analyzed tumors exceeding 1 cc and that also took up contrast material (18 patients). Results High correlation coefficients for tumor volume measurements using supervised target detection and color analysis vs. histology from wholemount prostatectomy were computed (R=0.83 and 0.91, respectively). A linear fit for tumor volume measurements using for supervised target detection and color analysis vs. tumor measurements from radical prostatectomy (after correcting for shrinkage from the radical prostatectomy) results in a slope of 1.02 and 3.02, respectively. A polynomial fit for the color analysis to the histology found (R=0.95). Voxels exceeding a threshold in the b* part of the CIELAB algorithm yielded correlation coefficients (0.71, 0.80) offsets (0.01 cc, -0.63 cc) and slopes (1.99, 0.89) against the wholemount prostatectomy and color analysis, respectively. Conclusions Supervised target detection and color display and analysis applied to registered MP-MRI non-invasively estimates prostate tumor volumes >1 cc and displaying angiogenesis.
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Affiliation(s)
- Rulon Mayer
- Oncoscore, Garrett Park, MD, USA.,University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Peter Choyke
- National Institutes of Health, Bethesda, MD, USA
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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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