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3,116 results on '"Choyke, Peter L."'

1. Location-based Radiology Report-Guided Semi-supervised Learning for Prostate Cancer Detection

Author

Chen, Alex, Lay, Nathan, Harmon, Stephanie, Ozyoruk, Kutsev, Yilmaz, Enis, Wood, Brad J., Pinto, Peter A., Choyke, Peter L., and Turkbey, Baris

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Prostate cancer is one of the most prevalent malignancies in the world. While deep learning has potential to further improve computer-aided prostate cancer detection on MRI, its efficacy hinges on the exhaustive curation of manually annotated images. We propose a novel methodology of semisupervised learning (SSL) guided by automatically extracted clinical information, specifically the lesion locations in radiology reports, allowing for use of unannotated images to reduce the annotation burden. By leveraging lesion locations, we refined pseudo labels, which were then used to train our location-based SSL model. We show that our SSL method can improve prostate lesion detection by utilizing unannotated images, with more substantial impacts being observed when larger proportions of unannotated images are used., Comment: 4 page paper accepted to IEEE International Symposium on Biomedical Imaging (ISBI 2024)

Published
2024

4. Fibroblast activation protein-targeted near-infrared photoimmunotherapy depletes immunosuppressive cancer-associated fibroblasts and remodels local tumor immunity

Author

Akai, Masaaki, Noma, Kazuhiro, Kato, Takuya, Nishimura, Seitaro, Matsumoto, Hijiri, Kawasaki, Kento, Kunitomo, Tomoyoshi, Kobayashi, Teruki, Nishiwaki, Noriyuki, Kashima, Hajime, Kikuchi, Satoru, Ohara, Toshiaki, Tazawa, Hiroshi, Choyke, Peter L., Kobayashi, Hisataka, and Fujiwara, Toshiyoshi

Published
2024
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5. Automated prostate gland segmentation in challenging clinical cases: comparison of three artificial intelligence methods

Author

Johnson, Latrice A., Harmon, Stephanie A., Yilmaz, Enis C., Lin, Yue, Belue, Mason J., Merriman, Katie M., Lay, Nathan S., Sanford, Thomas H., Sarma, Karthik V., Arnold, Corey W., Xu, Ziyue, Roth, Holger R., Yang, Dong, Tetreault, Jesse, Xu, Daguang, Patel, Krishnan R., Gurram, Sandeep, Wood, Bradford J., Citrin, Deborah E., Pinto, Peter A., Choyke, Peter L., and Turkbey, Baris

Published
2024
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7. Automated Detection and Characterization of Small Cell Lung Cancer Liver Metastases on CT

Author

Ty, Sophia, Haque, Fahmida, Desai, Parth, Takahashi, Nobuyuki, Chaudhary, Usamah, Simon, Benjamin, Choyke, Peter L., Thomas, Anish, Turkbey, Baris, Harmon, Stephanie A., Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Finkelstein, Joseph, editor, Moskovitch, Robert, editor, and Parimbelli, Enea, editor

Published
2024
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10. Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study

Author

Belue, Mason J., Harmon, Stephanie A., Yang, Dong, An, Julie Y., Gaur, Sonia, Law, Yan Mee, Turkbey, Evrim, Xu, Ziyue, Tetreault, Jesse, Lay, Nathan S., Yilmaz, Enis C., Phelps, Tim E., Simon, Benjamin, Lindenberg, Liza, Mena, Esther, Pinto, Peter A., Bagci, Ulas, Wood, Bradford J., Citrin, Deborah E., Dahut, William L., Madan, Ravi A., Gulley, James L., Xu, Daguang, Choyke, Peter L., and Turkbey, Baris

Published
2024
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11. Magnetic Resonance Imaging-Guided Biopsy in Active Surveillance of Prostate Cancer

Author

Kinnaird, Adam, Yerram, Nitin K, O’Connor, Luke, Brisbane, Wayne, Sharma, Vidit, Chuang, Ryan, Jayadevan, Rajiv, Ahdoot, Michael, Daneshvar, Michael, Priester, Alan, Delfin, Merdie, Tran, Elizabeth, Barsa, Danielle E, Sisk, Anthony, Reiter, Robert E, Felker, Ely, Raman, Steve, Kwan, Lorna, Choyke, Peter L, Merino, Maria J, Wood, Bradford J, Turkbey, Baris, Pinto, Peter A, and Marks, Leonard S

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Prostate Cancer, Aging, Cancer, Urologic Diseases, Clinical Research, Biomedical Imaging, Aged, Follow-Up Studies, Humans, Image-Guided Biopsy, Magnetic Resonance Imaging, Male, Middle Aged, Neoplasm Grading, Prospective Studies, Prostatectomy, Prostatic Neoplasms, Risk Factors, Watchful Waiting, image-guided biopsy, prostatic neoplasms, observation, magnetic resonance imaging
Abstract

PurposeThe underlying premise of prostate cancer active surveillance (AS) is that cancers likely to metastasize will be recognized and eliminated before cancer-related disease can ensue. Our study was designed to determine the prostate cancer upgrading rate when biopsy guided by magnetic resonance imaging (MRGBx) is used before entry and during AS.Materials and methodsThe cohort included 519 men with low- or intermediate-risk prostate cancer who enrolled in prospective studies (NCT00949819 and NCT00102544) between February 2008 and February 2020. Subjects were preliminarily diagnosed with Gleason Grade Group (GG) 1 cancer; AS began when subsequent MRGBx confirmed GG1 or GG2. Participants underwent confirmatory MRGBx (targeted and systematic) followed by surveillance MRGBx approximately every 12 to 24 months. The primary outcome was tumor upgrading to ≥GG3.ResultsUpgrading to ≥GG3 was found in 92 men after a median followup of 4.8 years (IQR 3.1-6.5) after confirmatory MRGBx. Upgrade-free probability after 5 years was 0.85 (95% CI 0.81-0.88). Cancer detected in a magnetic resonance imaging lesion at confirmatory MRGBx increased risk of subsequent upgrading during AS (HR 2.8; 95% CI 1.3-6.0), as did presence of GG2 (HR 2.9; 95% CI 1.1-8.2) In men who upgraded ≥GG3 during AS, upgrading was detected by targeted cores only in 27%, systematic cores only in 25% and both in 47%. In 63 men undergoing prostatectomy, upgrading from MRGBx was found in only 5 (8%).ConclusionsWhen AS begins and follows with MRGBx (targeted and systematic), upgrading rate (≥GG3) is greater when tumor is initially present within a magnetic resonance imaging lesion or when pathology is GG2 than when these features are absent.

Published
2022

19. Adipose Tissue Segmentation in Unlabeled Abdomen MRI using Cross Modality Domain Adaptation

Author

Masoudi, Samira, Anwar, Syed M., Harmon, Stephanie A., Choyke, Peter L., Turkbey, Baris, and Bagci, Ulas

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract

Abdominal fat quantification is critical since multiple vital organs are located within this region. Although computed tomography (CT) is a highly sensitive modality to segment body fat, it involves ionizing radiations which makes magnetic resonance imaging (MRI) a preferable alternative for this purpose. Additionally, the superior soft tissue contrast in MRI could lead to more accurate results. Yet, it is highly labor intensive to segment fat in MRI scans. In this study, we propose an algorithm based on deep learning technique(s) to automatically quantify fat tissue from MR images through a cross modality adaptation. Our method does not require supervised labeling of MR scans, instead, we utilize a cycle generative adversarial network (C-GAN) to construct a pipeline that transforms the existing MR scans into their equivalent synthetic CT (s-CT) images where fat segmentation is relatively easier due to the descriptive nature of HU (hounsfield unit) in CT images. The fat segmentation results for MRI scans were evaluated by expert radiologist. Qualitative evaluation of our segmentation results shows average success score of 3.80/5 and 4.54/5 for visceral and subcutaneous fat segmentation in MR images., Comment: 5 pages,7 figures, EMBC 2020 conference

Published
2020

20. Federated learning improves site performance in multicenter deep learning without data sharing

Author

Sarma, Karthik V, Harmon, Stephanie, Sanford, Thomas, Roth, Holger R, Xu, Ziyue, Tetreault, Jesse, Xu, Daguang, Flores, Mona G, Raman, Alex G, Kulkarni, Rushikesh, Wood, Bradford J, Choyke, Peter L, Priester, Alan M, Marks, Leonard S, Raman, Steven S, Enzmann, Dieter, Turkbey, Baris, Speier, William, and Arnold, Corey W

Subjects
Information and Computing Sciences, Machine Learning, Deep Learning, Humans, Information Dissemination, Privacy, deep learning, federated learning, privacy, generalizability, prostate, Engineering, Medical and Health Sciences, Medical Informatics, Biomedical and clinical sciences, Health sciences, Information and computing sciences
Abstract

ObjectiveTo demonstrate enabling multi-institutional training without centralizing or sharing the underlying physical data via federated learning (FL).Materials and methodsDeep learning models were trained at each participating institution using local clinical data, and an additional model was trained using FL across all of the institutions.ResultsWe found that the FL model exhibited superior performance and generalizability to the models trained at single institutions, with an overall performance level that was significantly better than that of any of the institutional models alone when evaluated on held-out test sets from each institution and an outside challenge dataset.DiscussionThe power of FL was successfully demonstrated across 3 academic institutions while avoiding the privacy risk associated with the transfer and pooling of patient data.ConclusionFederated learning is an effective methodology that merits further study to enable accelerated development of models across institutions, enabling greater generalizability in clinical use.

Published
2021

22. A Novel Magnetic Resonance Imaging/Ultrasound Fusion Prostate Biopsy Technique Using Transperineal Ultrasound: An Initial Experience

Author

Enders, Jacob J., Pinto, Peter A., Xu, Sheng, Gomella, Patrick, Rothberg, Michael B., Noun, Jibriel, Blake, Zoe, Daneshvar, Michael, Seifabadi, Reza, Nemirovsky, Daniel, Hazen, Lindsey, Garcia, Charisse, Li, Ming, Gurram, Sandeep, Choyke, Peter L., Merino, Maria J., Toubaji, Antoun, Turkbey, Baris, Varble, Nicole, and Wood, Bradford J.

Published
2023
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28. Artificial Intelligence-Based PTEN Loss Assessment as an Early Predictor of Prostate Cancer Metastasis After Surgery: a Multi-Center Retrospective Study

Author

Patel, Palak, Harmon, Stephanie, Iseman, Rachael, Ludkowski, Olga, Auman, Heidi, Hawley, Sarah, Newcomb, Lisa F., Lin, Daniel W., Nelson, Peter S., Feng, Ziding, Boyer, Hilary D., Tretiakova, Maria S., True, Larry D., Vakar-Lopez, Funda, Carroll, Peter R., Cooperberg, Matthew R., Chan, Emily, Simko, Jeff, Fazli, Ladan, Gleave, Martin, Hurtado-Coll, Antonio, Thompson, Ian M., Troyer, Dean, McKenney, Jesse K., Wei, Wei, Choyke, Peter L., Bratslavsky, Gennady, Turkbey, Baris, Siemens, D. Robert, Squire, Jeremy, Peng, Yingwei P., Brooks, James D., and Jamaspishvili, Tamara

Published
2023
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29. Willingness of patients with biochemically recurrent prostate cancer (BCR) with positive 18F-dcfpyl PET/CT PSMA to monitor without treatment.

Author

Williams, Monique, primary, Hankin, Amy, additional, Hausler, Megan, additional, Mena Gonzalez, Esther, additional, Lindenberg, Liza, additional, Aragon-Ching, Jeanny B., additional, Sena, Laura, additional, Arlen, Philip M., additional, Karzai, Fatima, additional, Choyke, Peter L, additional, and Madan, Ravi Amrit, additional

Published
2024
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30. Long-term engraftment and maturation of autologous iPSC-derived cardiomyocytes in two rhesus macaques

Author

Lin, Yongshun, primary, Sato, Noriko, additional, Hong, Sogun, additional, Nakamura, Kenta, additional, Ferrante, Elisa A., additional, Yu, Zu Xi, additional, Chen, Marcus Y., additional, Nakamura, Daisy S., additional, Yang, Xiulan, additional, Clevenger, Randall R., additional, Hunt, Timothy J., additional, Taylor, Joni L., additional, Jeffries, Kenneth R., additional, Keeran, Karen J., additional, Neidig, Lauren E., additional, Mehta, Atul, additional, Schwartzbeck, Robin, additional, Yu, Shiqin Judy, additional, Kelly, Conor, additional, Navarengom, Keron, additional, Takeda, Kazuyo, additional, Adler, Stephen S., additional, Choyke, Peter L., additional, Zou, Jizhong, additional, Murry, Charles E., additional, Boehm, Manfred, additional, and Dunbar, Cynthia E., additional

Published
2024
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39. Using Prostate Imaging-Reporting and Data System (PI-RADS) Scores to Select an Optimal Prostate Biopsy Method: A Secondary Analysis of the Trio Study

Author

Ahdoot, Michael, Lebastchi, Amir H., Long, Lori, Wilbur, Andrew R., Gomella, Patrick T., Mehralivand, Sherif, Daneshvar, Michael A., Yerram, Nitin K., O’Connor, Luke P., Wang, Alex Z., Gurram, Sandeep, Bloom, Jonathan, Siddiqui, M. Minhaj, Linehan, W. Marston, Merino, Maria, Choyke, Peter L., Pinsky, Paul, Parnes, Howard, Shih, Joanna H., Turkbey, Baris, Wood, Bradford J., and Pinto, Peter A.

Published
2022
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41. Deeply-Supervised CNN for Prostate Segmentation

Author

Zhu, Qikui, Du, Bo, Turkbey, Baris, Choyke, Peter L ., and Yan, Pingkun

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Prostate segmentation from Magnetic Resonance (MR) images plays an important role in image guided interven- tion. However, the lack of clear boundary specifically at the apex and base, and huge variation of shape and texture between the images from different patients make the task very challenging. To overcome these problems, in this paper, we propose a deeply supervised convolutional neural network (CNN) utilizing the convolutional information to accurately segment the prostate from MR images. The proposed model can effectively detect the prostate region with additional deeply supervised layers compared with other approaches. Since some information will be abandoned after convolution, it is necessary to pass the features extracted from early stages to later stages. The experimental results show that significant segmentation accuracy improvement has been achieved by our proposed method compared to other reported approaches., Comment: Due to a crucial sign error in equation 1

Published
2017

42. AI-ADC: Channel and Spatial Attention-Based Contrastive Learning to Generate ADC Maps from T2W MRI for Prostate Cancer Detection.

Author

Ozyoruk, Kutsev Bengisu, Harmon, Stephanie A., Lay, Nathan S., Yilmaz, Enis C., Bagci, Ulas, Citrin, Deborah E., Wood, Bradford J., Pinto, Peter A., Choyke, Peter L., and Turkbey, Baris

Abstract

Background/Objectives: Apparent Diffusion Coefficient (ADC) maps in prostate MRI can reveal tumor characteristics, but their accuracy can be compromised by artifacts related with patient motion or rectal gas associated distortions. To address these challenges, we propose a novel approach that utilizes a Generative Adversarial Network to synthesize ADC maps from T2-weighted magnetic resonance images (T2W MRI). Methods: By leveraging contrastive learning, our model accurately maps axial T2W MRI to ADC maps within the cropped region of the prostate organ boundary, capturing subtle variations and intricate structural details by learning similar and dissimilar pairs from two imaging modalities. We trained our model on a comprehensive dataset of unpaired T2-weighted images and ADC maps from 506 patients. In evaluating our model, named AI-ADC, we compared it against three state-of-the-art methods: CycleGAN, CUT, and StyTr2. Results: Our model demonstrated a higher mean Structural Similarity Index (SSIM) of 0.863 on a test dataset of 3240 2D MRI slices from 195 patients, compared to values of 0.855, 0.797, and 0.824 for CycleGAN, CUT, and StyTr2, respectively. Similarly, our model achieved a significantly lower Fréchet Inception Distance (FID) value of 31.992, compared to values of 43.458, 179.983, and 58.784 for the other three models, indicating its superior performance in generating ADC maps. Furthermore, we evaluated our model on 147 patients from the publicly available ProstateX dataset, where it demonstrated a higher SSIM of 0.647 and a lower FID of 113.876 compared to the other three models. Conclusions: These results highlight the efficacy of our proposed model in generating ADC maps from T2W MRI, showcasing its potential for enhancing clinical diagnostics and radiological workflows. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Editorial for Special Topics: Imaging-Based Diagnosis for Prostate Cancer—State of the Art.

Author

Mayer, Rulon, Choyke, Peter L., and Simone II, Charles B.

Subjects
*PROSTATE-specific membrane antigen, *POSITRON emission tomography, *PROSTATE cancer, *CONTRAST-enhanced magnetic resonance imaging, *MAGNETIC flux density, *PROTON therapy
Abstract

This document is an editorial for a special topics issue of the journal Diagnostics, focusing on imaging-based diagnosis for prostate cancer. The issue includes 10 articles that describe current advances in detecting and assessing prostate tumors using imaging techniques. The editorial discusses the limitations of current diagnostic methods and suggests future trends in prostate cancer diagnostic imaging. It concludes by speculating on longer-term advances, including new biomarkers, focal proton therapy, and the combination of imaging and tumor genomics. The text also includes a table summarizing various articles on the imaging-based diagnosis of prostate cancer. The authors of the text have declared no conflicts of interest. [Extracted from the article]

Published
2024
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48. Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN

Author

Ma, Kevin C., primary, Mena, Esther, additional, Lindenberg, Liza, additional, Lay, Nathan S., additional, Eclarinal, Phillip, additional, Citrin, Deborah E., additional, Pinto, Peter A., additional, Wood, Bradford J., additional, Dahut, William L., additional, Gulley, James L., additional, Madan, Ravi A., additional, Choyke, Peter L., additional, Turkbey, Ismail Baris, additional, and Harmon, Stephanie A., additional

Published
2024
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49. PD36-02 EVALUATING AI ASSISTANCE IN PROSTATE BPMRI INTERPRETATION: A MULTI-READER STUDY

Author

Gelikman, David G., primary, Yilmaz, Enis C., additional, Harmon, Stephanie A., additional, An, Julie Y., additional, Azamat, Sena, additional, Law, Yan Mee, additional, Margolis, Daniel J. A., additional, Marko, Jamie, additional, Panebianco, Valeria, additional, Gaur, Sonia, additional, Bicchetti, Marco, additional, Huang, Erich P., additional, Gurram, Sandeep, additional, Shih, Joanna H., additional, Choyke, Peter L., additional, Wood, Bradford J., additional, Pinto, Peter A., additional, and Turkbey, Baris, additional

Published
2024
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