المؤلفون: Hirsch, Lukas, Huang, Yu, Luo, Shaojun, Rossi Saccarelli, Carolina, Lo Gullo, Roberto, Daimiel Naranjo, Isaac, Bitencourt, Almir, Onishi, Natsuko, Ko, Eun, Leithner, Doris, Avendano, Daly, Eskreis-Winkler, Sarah, Hughes, Mary, Martinez, Danny, Pinker, Katja, Juluru, Krishna, El-Rowmeim, Amin, Elnajjar, Pierre, Makse, Hernan, Parra, Lucas, Sutton, Elizabeth, Morris, Elizabeth

المصدر: Radiology: Artificial Intelligence. 4(1)

مصطلحات موضوعية: Breast, Convolutional Neural Network (CNN), Deep Learning Algorithms, MRI, Machine Learning Algorithms, Segmentation, Supervised Learning

الوصف: PURPOSE: To develop a deep network architecture that would achieve fully automated radiologist-level segmentation of cancers at breast MRI. MATERIALS AND METHODS: In this retrospective study, 38 229 examinations (composed of 64 063 individual breast scans from 14 475 patients) were performed in female patients (age range, 12-94 years; mean age, 52 years ± 10 [standard deviation]) who presented between 2002 and 2014 at a single clinical site. A total of 2555 breast cancers were selected that had been segmented on two-dimensional (2D) images by radiologists, as well as 60 108 benign breasts that served as examples of noncancerous tissue; all these were used for model training. For testing, an additional 250 breast cancers were segmented independently on 2D images by four radiologists. Authors selected among several three-dimensional (3D) deep convolutional neural network architectures, input modalities, and harmonization methods. The outcome measure was the Dice score for 2D segmentation, which was compared between the network and radiologists by using the Wilcoxon signed rank test and the two one-sided test procedure. RESULTS: The highest-performing network on the training set was a 3D U-Net with dynamic contrast-enhanced MRI as input and with intensity normalized for each examination. In the test set, the median Dice score of this network was 0.77 (interquartile range, 0.26). The performance of the network was equivalent to that of the radiologists (two one-sided test procedures with radiologist performance of 0.69-0.84 as equivalence bounds, P < .001 for both; n = 250). CONCLUSION: When trained on a sufficiently large dataset, the developed 3D U-Net performed as well as fellowship-trained radiologists in detailed 2D segmentation of breast cancers at routine clinical MRI.Keywords: MRI, Breast, Segmentation, Supervised Learning, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning AlgorithmsPublished under a CC BY 4.0 license. Supplemental material is available for this article.

وصف الملف: application/pdf

الوصول الحر: https://escholarship.org/uc/item/734899ptTest

المؤلفون: Dayan, Ittai, Roth, Holger R, Zhong, Aoxiao, Harouni, Ahmed, Gentili, Amilcare, Abidin, Anas Z, Liu, Andrew, Costa, Anthony Beardsworth, Wood, Bradford J, Tsai, Chien-Sung, Wang, Chih-Hung, Hsu, Chun-Nan, Lee, CK, Ruan, Peiying, Xu, Daguang, Wu, Dufan, Huang, Eddie, Kitamura, Felipe Campos, Lacey, Griffin, de Antônio Corradi, Gustavo César, Nino, Gustavo, Shin, Hao-Hsin, Obinata, Hirofumi, Ren, Hui, Crane, Jason C, Tetreault, Jesse, Guan, Jiahui, Garrett, John W, Kaggie, Joshua D, Park, Jung Gil, Dreyer, Keith, Juluru, Krishna, Kersten, Kristopher, Rockenbach, Marcio Aloisio Bezerra Cavalcanti, Linguraru, Marius George, Haider, Masoom A, AbdelMaseeh, Meena, Rieke, Nicola, Damasceno, Pablo F, e Silva, Pedro Mario Cruz, Wang, Pochuan, Xu, Sheng, Kawano, Shuichi, Sriswasdi, Sira, Park, Soo Young, Grist, Thomas M, Buch, Varun, Jantarabenjakul, Watsamon, Wang, Weichung, Tak, Won Young, Li, Xiang, Lin, Xihong, Kwon, Young Joon, Quraini, Abood, Feng, Andrew, Priest, Andrew N, Turkbey, Baris, Glicksberg, Benjamin, Bizzo, Bernardo, Kim, Byung Seok, Tor-Díez, Carlos, Lee, Chia-Cheng, Hsu, Chia-Jung, Lin, Chin, Lai, Chiu-Ling, Hess, Christopher P, Compas, Colin, Bhatia, Deepeksha, Oermann, Eric K, Leibovitz, Evan, Sasaki, Hisashi, Mori, Hitoshi, Yang, Isaac, Sohn, Jae Ho, Murthy, Krishna Nand Keshava, Fu, Li-Chen, de Mendonça, Matheus Ribeiro Furtado, Fralick, Mike, Kang, Min Kyu, Adil, Mohammad, Gangai, Natalie, Vateekul, Peerapon, Elnajjar, Pierre, Hickman, Sarah, Majumdar, Sharmila, McLeod, Shelley L, Reed, Sheridan, Gräf, Stefan, Harmon, Stephanie, Kodama, Tatsuya, Puthanakit, Thanyawee, Mazzulli, Tony, de Lavor, Vitor Lima, Rakvongthai, Yothin, Lee, Yu Rim, Wen, Yuhong, Gilbert, Fiona J, Flores, Mona G, Li, Quanzheng

المصدر: Nature Medicine. 27(10)

مصطلحات موضوعية: Patient Safety, Good Health and Well Being, COVID-19, Electronic Health Records, Humans, Machine Learning, Outcome Assessment, Health Care, Prognosis, SARS-CoV-2, Medical and Health Sciences, Immunology

الوصف: Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.

وصف الملف: application/pdf

الوصول الحر: https://escholarship.org/uc/item/6gc9c8jsTest

المؤلفون: Hirsch, Lukas, Huang, Yu, Luo, Shaojun, Saccarelli, Carolina Rossi, Gullo, Roberto Lo, Naranjo, Isaac Daimiel, Bitencourt, Almir G. V., Onishi, Natsuko, Ko, Eun Sook, Leithner, Doris, Avendano, Daly, Eskreis-Winkler, Sarah, Hughes, Mary, Martinez, Danny F., Pinker, Katja, Juluru, Krishna, El-Rowmeim, Amin E., Elnajjar, Pierre, Morris, Elizabeth A., Makse, Hernan A., Parra, Lucas C, Sutton, Elizabeth J.

مصطلحات موضوعية: Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Physics - Medical Physics, Statistics - Machine Learning

الوصف: Purpose: To develop a deep network architecture that would achieve fully automated radiologist-level segmentation of cancers at breast MRI. Materials and Methods: In this retrospective study, 38229 examinations (composed of 64063 individual breast scans from 14475 patients) were performed in female patients (age range, 12-94 years; mean age, 52 years +/- 10 [standard deviation]) who presented between 2002 and 2014 at a single clinical site. A total of 2555 breast cancers were selected that had been segmented on two-dimensional (2D) images by radiologists, as well as 60108 benign breasts that served as examples of noncancerous tissue; all these were used for model training. For testing, an additional 250 breast cancers were segmented independently on 2D images by four radiologists. Authors selected among several three-dimensional (3D) deep convolutional neural network architectures, input modalities, and harmonization methods. The outcome measure was the Dice score for 2D segmentation, which was compared between the network and radiologists by using the Wilcoxon signed rank test and the two one-sided test procedure. Results: The highest-performing network on the training set was a 3D U-Net with dynamic contrast-enhanced MRI as input and with intensity normalized for each examination. In the test set, the median Dice score of this network was 0.77 (interquartile range, 0.26). The performance of the network was equivalent to that of the radiologists (two one-sided test procedures with radiologist performance of 0.69-0.84 as equivalence bounds, P <= .001 for both; n = 250). Conclusion: When trained on a sufficiently large dataset, the developed 3D U-Net performed as well as fellowship-trained radiologists in detailed 2D segmentation of breast cancers at routine clinical MRI.

الوصول الحر: http://arxiv.org/abs/2009.09827Test

المؤلفون: Santilli, Alice, Panyam, Prashanth, Autz, Arthur, Wray, Rick, Philip, John, Elnajjar, Pierre, Swinburne, Nathaniel, Mayerhoefer, Marius

المصدر: International Journal of Computer Assisted Radiology & Surgery; Nov2023, Vol. 18 Issue 11, p2083-2090, 8p

مستخلص: Purpose: Neuroendocrine tumors (NETs) are a rare form of cancer that can occur anywhere in the body and commonly metastasizes. The large variance in location and aggressiveness of the tumors makes it a difficult cancer to treat. Assessments of the whole-body tumor burden in a patient image allow for better tracking of disease progression and inform better treatment decisions. Currently, radiologists rely on qualitative assessments of this metric since manual segmentation is unfeasible within a typical busy clinical workflow. Methods: We address these challenges by extending the application of the nnU-net pipeline to produce automatic NET segmentation models. We utilize the ideal imaging type of 68Ga-DOTATATE PET/CT to produce segmentation masks from which to calculate total tumor burden metrics. We provide a human-level baseline for the task and perform ablation experiments of model inputs, architectures, and loss functions. Results: Our dataset is comprised of 915 PET/CT scans and is divided into a held-out test set (87 cases) and 5 training subsets to perform cross-validation. The proposed models achieve test Dice scores of 0.644, on par with our inter-annotator Dice score on a subset 6 patients of 0.682. If we apply our modified Dice score to the predictions, the test performance reaches a score of 0.80. Conclusion: In this paper, we demonstrate the ability to automatically generate accurate NET segmentation masks given PET images through supervised learning. We publish the model for extended use and to support the treatment planning of this rare cancer. [ABSTRACT FROM AUTHOR]

: Copyright of International Journal of Computer Assisted Radiology & Surgery is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Becker, Anton S., Erinjeri, Joseph P., Chaim, Joshua, Kastango, Nicholas, Elnajjar, Pierre, Hricak, Hedvig, Vargas, H. Alberto

المساهمون: Stiftung Professor Dr. Max Cloëtta

المصدر: Journal of Digital Imaging ; volume 35, issue 1, page 1-8 ; ISSN 0897-1889 1618-727X

مصطلحات موضوعية: Computer Science Applications, Radiology, Nuclear Medicine and imaging, Radiological and Ultrasound Technology

الإتاحة: https://doi.org/10.1007/s10278-021-00532-4Test

المؤلفون: Swinburne, Nathaniel C., Yadav, Vivek, Murthy, Krishna Nand Keshava, Elnajjar, Pierre, Shih, Hao-Hsin, Panyam, Prashanth Kumar, Santilli, Alice, Gutman, David C., Pike, Luke, Moss, Nelson S., Stone, Jacqueline, Hatzoglou, Vaios, Shah, Akash, Juluru, Krishna, Shah, Sohrab P., Holodny, Andrei I., Young, Robert J., For The M.S.K. MIND Consortium, Shah, Sohrab, Gao, Jianjiong

المصدر: European Radiology; Sep2023, Vol. 33 Issue 9, p6582-6591, 10p, 4 Diagrams, 2 Charts, 1 Graph

مصطلحات موضوعية: SUPERVISED learning, IMAGE segmentation, ANNOTATIONS, MAGNETIC resonance imaging, RADIOLOGY

مستخلص: Objectives: While fully supervised learning can yield high-performing segmentation models, the effort required to manually segment large training sets limits practical utility. We investigate whether data mined line annotations can facilitate brain MRI tumor segmentation model development without requiring manually segmented training data. Methods: In this retrospective study, a tumor detection model trained using clinical line annotations mined from PACS was leveraged with unsupervised segmentation to generate pseudo-masks of enhancing tumors on T1-weighted post-contrast images (9911 image slices; 3449 adult patients). Baseline segmentation models were trained and employed within a semi-supervised learning (SSL) framework to refine the pseudo-masks. Following each self-refinement cycle, a new model was trained and tested on a held-out set of 319 manually segmented image slices (93 adult patients), with the SSL cycles continuing until Dice score coefficient (DSC) peaked. DSCs were compared using bootstrap resampling. Utilizing the best-performing models, two inference methods were compared: (1) conventional full-image segmentation, and (2) a hybrid method augmenting full-image segmentation with detection plus image patch segmentation. Results: Baseline segmentation models achieved DSC of 0.768 (U-Net), 0.831 (Mask R-CNN), and 0.838 (HRNet), improving with self-refinement to 0.798, 0.871, and 0.873 (each p < 0.001), respectively. Hybrid inference outperformed full image segmentation alone: DSC 0.884 (Mask R-CNN) vs. 0.873 (HRNet), p < 0.001. Conclusions: Line annotations mined from PACS can be harnessed within an automated pipeline to produce accurate brain MRI tumor segmentation models without manually segmented training data, providing a mechanism to rapidly establish tumor segmentation capabilities across radiology modalities. Key Points: • A brain MRI tumor detection model trained using clinical line measurement annotations mined from PACS was leveraged to automatically generate tumor segmentation pseudo-masks. • An iterative self-refinement process automatically improved pseudo-mask quality, with the best-performing segmentation pipeline achieving a Dice score of 0.884 on a held-out test set. • Tumor line measurement annotations generated in routine clinical radiology practice can be harnessed to develop high-performing segmentation models without manually segmented training data, providing a mechanism to rapidly establish tumor segmentation capabilities across radiology modalities. [ABSTRACT FROM AUTHOR]

: Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Juluru, Krishna, Elnajjar, Pierre

المصدر: Techniques in Vascular and Interventional Radiology ; volume 25, issue 4, page 100860 ; ISSN 1089-2516

مصطلحات موضوعية: Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and imaging

الإتاحة: https://doi.org/10.1016/j.tvir.2022.100860Test
https://api.elsevier.com/content/article/PII:S108925162200066X?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S108925162200066X?httpAccept=text/plainTest

المؤلفون: Becker, Anton S., Das, Jeeban P., Woo, Sungmin, Elnajjar, Pierre, Chaim, Joshua, Erinjeri, Joseph P., Hricak, Hedvig, Vargas, Hebert Alberto

المصدر: JCO Clinical Cancer Informatics; 9/9/2022, Vol. 6, p1-8, 8p

مصطلحات موضوعية: WORKFLOW, WORKFLOW software, MAGNETIC resonance imaging, TURNAROUND time, COMPUTED tomography, PROGRAMMING languages

مستخلص: PURPOSE: To evaluate whether a custom programmatic workflow manager reduces reporting turnaround times (TATs) from a body oncologic imaging workflow at a tertiary cancer center. METHODS: A custom software program was developed and implemented in the programming language R. Other aspects of the workflow were left unchanged. TATs were measured over a 12-month period (June-May). The same prior 12-month period served as a historical control. Median TATs of magnetic resonance imaging (MRI) and computed tomography (CT) examinations were compared with a Wilcoxon test. A chi-square test was used to compare the numbers of examinations reported within 24 hours and after 72 hours as well as the proportions of examinations assigned according to individual radiologist preferences. RESULTS: For all MRI and CT examinations (124,507 in 2019/2020 and 138,601 in 2020/2021), the median TAT decreased from 4 (interquartile range: 1-22 hours) to 3 hours (1-17 hours). Reports completed within 24 hours increased from 78% (124,127) to 89% (138,601). For MRI, TAT decreased from 22 (5-49 hours) to 8 hours (2-21 hours), and reports completed within 24 hours increased from 55% (14,211) to 80% (23,744). For CT, TAT decreased from 3 (1-19 hours) to 2 hours (1-13 hours), and reports completed within 24 hours increased from 84% (82,342) to 92% (99,922). Delayed reports (with a TAT > 72 hours) decreased from 17.0% (4,176) to 2.2% (649) for MRI and from 2.5% (2,500) to 0.7% (745) for CT. All differences were statistically significant (P <.001). CONCLUSION: The custom workflow management software program significantly decreased MRI and CT report TATs. Automatic assignment enhances subspecialized radiology and decreases turnaround time @MSKCancerCenter. [ABSTRACT FROM AUTHOR]

: Copyright of JCO Clinical Cancer Informatics is the property of American Society of Clinical Oncology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Juluru, Krishna, Shih, Hao-Hsin, Keshava Murthy, Krishna Nand, Elnajjar, Pierre

المصدر: Radiographics

مصطلحات موضوعية: Informatics, lang, edu

الوصف: Natural language processing (NLP) is a methodology designed to extract concepts and meaning from human-generated unstructured (free-form) text. It is intended to be implemented by using computer algorithms so that it can be run on a corpus of documents quickly and reliably. To enable machine learning (ML) techniques in NLP, free-form text must be converted to a numerical representation. After several stages of preprocessing including tokenization, removal of stop words, token normalization, and creation of a master dictionary, the bag-of-words (BOW) technique can be used to represent each remaining word as a feature of the document. The preprocessing steps simplify the documents but also potentially degrade meaning. The values of the features in BOW can be modified by using techniques such as term count, term frequency, and term frequency–inverse document frequency. Experience and experimentation will guide decisions on which specific techniques will optimize ML performance. These and other NLP techniques are being applied in radiology. Radiologists’ understanding of the strengths and limitations of these techniques will help in communication with data scientists and in implementation for specific tasks. Online supplemental material is available for this article. (©)RSNA, 2021

العلاقة: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8415041Test/

الإتاحة: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8415041Test/

المؤلفون: Dayan, Ittai, Roth, Holger, Zhong, Aoxiao, Harouni, Ahmed, Gentili, Amilcare, Abidin, Anas, Liu, Andrew, Costa, Anthony Beardsworth, Wood, Bradford J., Tsai, Chien-Sung, Wang, Chih-Hung, Hsu, Chun-Nan, Lee, CK, Ruan, Peiying, Xu, Daguang, Wu, Dufan, Huang, Eddie, Kitamura, Felipe Campos, Lacey, Griffin, Corradi, Gustavo César de Antônio, Nino Furnieles, Gustavo, Shin, Hao-Hsin, Obinata, Hirofumi, Ren, Hui, Crane, Jason C., Tetreault, Jesse, Guan, Jiahui, Garrett, John W., Kaggie, Josh D, Park, Jung Gil, Dreyer, Keith, Juluru, Krishna, Kersten, Kristopher, Rockenbach, Marcio Aloisio Bezerra Cavalcanti, Linguraru, Marius George, Haider, Masoom A., AbdelMaseeh, Meena, Rieke, Nicola, Damasceno, Pablo F., Silva, Pedro Mario Cruz e, Wang, Pochuan, Xu, Sheng, Kawano, Shuichi, Sriswasdi, Sira, Park, Soo-Young, Grist, Thomas M, Buch, Varun, Jantarabenjakul, Watsamon, Wang, Weichung, Tak, Won Young, Li, Xiang, Lin, Xihong, Kwon, Young Joon, Quraini, Abood, Feng, Andrew, Priest, Andrew N, Turkbey, Baris, Glicksberg, Benjamin, Canedo Bizzo, Bernardo, Kim, Byung Seok, Tor-Díez, Carlos, Lee, Chia-Cheng, Hsu, Chia-Jung, Lin, Chin, Lai, Chiu-Ling, Hess, Christopher P., Compas, Colin, Bhatia, Deepeksha, Oermann, Eric K, Leibovitz, Evan, Sasaki, Hisashi, Mori, Hitoshi, Yang, Isaac, Sohn, Jae Ho, Murthy, Krishna Nand Keshava, Fu, Li-Chen, Mendonça, Matheus Ribeiro Furtado de, Fralick, Mike, Kang, Min Kyu, Adil, Mohammad, Gangai, Natalie, Vateekul, Peerapon, Elnajjar, Pierre, Hickman, Sarah, Majumdar, Sharmila, McLeod, Shelley L., Reed, Sheridan, Graf, Stefan, Harmon, Stephanie, Kodama, Tatsuya, Puthanakit, Thanyawee, Mazzulli, Tony, Lavor, Vitor de Lima, Rakvongthai, Yothin, Lee, Yu Rim, Wen, Yuhong, Gilbert, Fiona J, Flores, Mona G., Li, Quanzheng

الوصف: Federated learning (FL) is a method for training artificial intelligence (AI) models with data from multiple sources while maintaining the anonymity of the data, thus removing many barriers to data sharing. Here we use data from 20 institutes across the globe to train a FL model, called “EXAM” (EMR CXR AI Model), that predicts future oxygen requirements of symptomatic COVID-19 patients using inputs of vital signs, laboratory data, and chest X-rays. EXAM achieved an average area under the curve (AUC) greater than 0.92 for both 24/72h predictions, and it provided an average improvement in the avg. AUC of 16%, and an average increase in generalizability of 38% when compared to models trained at a single site using the same site’s data (‘local models’). For predicting mechanical ventilation (MV) treatment (or death) at 24h at the independent test site, EXAM achieved a sensitivity of 0.950 and a specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for predicting clinical outcomes in COVID-19 patients, setting the stage for broader use of FL in healthcare. ; Accepted Manuscript

وصف الملف: application/vnd.openxmlformats-officedocument.wordprocessingml.document

العلاقة: Nature Medicine; https://doi.org/10.1038/s41591-021-01506-3Test; Dayan, Ittai, Holger R. Roth, Aoxiao Zhong, Ahmed Harouni, Amilcare Gentili, Anas Z. Abidin, Andrew Liu, et al. 2021. “Federated Learning for Predicting Clinical Outcomes in Patients with COVID-19.” Nature Medicine 27 (10): 1735–43.; https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37374279Test

الإتاحة: https://doi.org/10.1038/s41591-021-01506-3Test
https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37374279Test