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2. Validation of a Zero-Shot Learning Natural Language Processing Tool for Data Abstraction from Unstructured Healthcare Data

Author

Kaufmann, Basil, Busby, Dallin, Das, Chandan Krushna, Tillu, Neeraja, Menon, Mani, Tewari, Ashutosh K., and Gorin, Michael A.

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, 68T50, I.2.7
Abstract

Objectives: To describe the development and validation of a zero-shot learning natural language processing (NLP) tool for abstracting data from unstructured text contained within PDF documents, such as those found within electronic health records. Materials and Methods: A data abstraction tool based on the GPT-3.5 model from OpenAI was developed and compared to three physician human abstractors in terms of time to task completion and accuracy for abstracting data on 14 unique variables from a set of 199 de-identified radical prostatectomy pathology reports. The reports were processed by the software tool in vectorized and scanned formats to establish the impact of optical character recognition on data abstraction. The tool was assessed for superiority for data abstraction speed and non-inferiority for accuracy. Results: The human abstractors required a mean of 101s per report for data abstraction, with times varying from 15 to 284 s. In comparison, the software tool required a mean of 12.8 s to process the vectorized reports and a mean of 15.8 to process the scanned reports (P < 0.001). The overall accuracies of the three human abstractors were 94.7%, 97.8%, and 96.4% for the combined set of 2786 datapoints. The software tool had an overall accuracy of 94.2% for the vectorized reports, proving to be non-inferior to the human abstractors at a margin of -10% ($\alpha$=0.025). The tool had a slightly lower accuracy of 88.7% using the scanned reports, proving to be non-inferiority to 2 out of 3 human abstractors. Conclusion: The developed zero-shot learning NLP tool affords researchers comparable levels of accuracy to that of human abstractors, with significant time savings benefits. Because of the lack of need for task-specific model training, the developed tool is highly generalizable and can be used for a wide variety of data abstraction tasks, even outside the field of medicine., Comment: 10 pages, 3 figures, 1 table, 3 supplementary figures

Published
2023

7. List of contributors

Author

Ahn, Joseph C., primary, Anand, Shankara, additional, Arnaout, O., additional, Bandyopadhyay, Anuja, additional, Bartholomew, Erin, additional, Bates, David W., additional, Bender, Sarah M.L., additional, Bhullar, Puneet K., additional, Bhutani, Tina, additional, Boaro, A., additional, Boyer, Edward W., additional, Brown, Ethan D.L., additional, Carreiro, Stephanie, additional, Chahla, Jorge, additional, Chandran, Viji Pulikkel, additional, Cheng, Ching-Yu, additional, Cheung, Carol Y., additional, Choudhary, Anirudh, additional, Choudhury, Avishek, additional, Chung, Mimi, additional, Ciurtin, Coziana, additional, Comfere, Nneka I., additional, Corriveau-Lecavalier, Nick, additional, Côté, Mélina, additional, del Alamo, Diego, additional, Dias, Roger D., additional, Dönnes, Pierre, additional, Duong, Dat, additional, Ebnali, Mahdi, additional, El Sherbini, Adham, additional, ElZarrad, M. Khair, additional, Fakhouri, Tala H., additional, Fakhoury, Marc, additional, Fein, Joshua A., additional, Fischer, Uwe M., additional, Glicksberg, Benjamin S., additional, Goldstein, Cathy, additional, Gonem, Sherif, additional, Green, Darren V.S., additional, Gupta, Raghav, additional, Hakimi, Marwa, additional, Halamka, John D., additional, Han, Christina S., additional, Hannah-Shmouni, Fady, additional, Harrer, Stefan, additional, Hazime, Ali Amer, additional, Howard, Michael A., additional, Hui, Herbert Y.H., additional, Ildardashty, Alexander, additional, J.F. Shaikh, Hashim, additional, Jeliazkov, Jeliazko R., additional, Jones, David T., additional, Juhn, Young J., additional, Jury, Elizabeth C., additional, Kandaswamy, Swaminathan, additional, Kang, Yanna, additional, Kann, Benjamin H., additional, Kaplin, Scott, additional, Karpiak, Joel, additional, Kassir, Elias, additional, Kaur, Harsimran, additional, Khan, Sohil, additional, Khatib, Reem, additional, Kherabi, Yousra, additional, Knake, Lindsey A., additional, Krittanawong, Chayakrit, additional, Kuhn, Veronica C., additional, Kunze, Kyle, additional, Lamarche, Benoît, additional, Laplante, Simon, additional, Lehmann, Lisa Soleymani, additional, Li, Dawei, additional, Liao, Wilson, additional, Likitlersuang, Jirapat, additional, Liu, Qi, additional, Madani, Amin, additional, Malik, Momin M., additional, Mann, Matthias, additional, Masood, Sameer, additional, Mathur, Piyush, additional, McManus, Sean, additional, Menard, Jeffrey, additional, Mivalt, Filip, additional, Murphree, Dennis, additional, Myers, Thomas G., additional, Narayan, Sanjiv M., additional, Natarajan, Vivek, additional, Pandav, Krunal, additional, Parwani, Anil V., additional, Pedraza Bermeo, Adriana Marcela, additional, Peiffer-Smadja, Nathan, additional, Peng, Junjie, additional, Peng, Lily, additional, Peters, Margot S., additional, Petrick, Nicholas, additional, Polce, Evan, additional, Poojari, Pooja Gopal, additional, Price, W. Nicholson, additional, Raghavan, Lavanya, additional, Rajan, Asha K., additional, Ran, An Ran, additional, Rangu, Sowmith, additional, Rashid, Muhammed, additional, Reddy, Charitha D., additional, Rider, Nicholas L., additional, Rigatti, Marc, additional, Rivers, Michael, additional, Robinson, George, additional, Rogers, Albert J., additional, Rogerson, Colin M., additional, Ryu, Euijung, additional, Sahiner, Berkman, additional, Sarnaik, Kunaal, additional, Shafi, Saba, additional, Shah, Vijay H., additional, Shapovalov, Maxim V., additional, Sharma, Samin K, additional, Shekhar, Skand, additional, Shin, Harold, additional, Shouval, Roni, additional, Smith, Kenneth, additional, Sokumbi, Olayemi, additional, Solomon, Benjamin D., additional, Somani, Sulaiman S., additional, Sternke, Matt C., additional, Strauss, Maximillian T., additional, Syrowatka, Ania, additional, Tang, W. H. Wilson, additional, Teven, Chad M., additional, Tewari, Ashutosh Kumar, additional, Thunga, Girish, additional, Urena, Estefania, additional, Verma, Ashish, additional, Vietas, Jay, additional, Waikel, Rebekah L., additional, Wi, Chung-Il, additional, Winter, Meredith C., additional, Wong, Melissa S., additional, Wong, Tien Yin, additional, Wu, Chao-Ping, additional, Yeroushalmi, Samuel, additional, Zenati, Marco A., additional, Zeng, Wen-Feng, additional, and Zheng, Yingfeng, additional

Published
2024
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9. Modeling and mitigation of occupational safety risks in dynamic industrial environments

Author

Tewari, Ashutosh and Paiva, Antonio R.

Subjects
Statistics - Applications, Computer Science - Machine Learning
Abstract

Identifying and mitigating safety risks is paramount in a number of industries. In addition to guidelines and best practices, many industries already have safety management systems (SMSs) designed to monitor and reinforce good safety behaviors. The analytic capabilities to analyze the data acquired through such systems, however, are still lacking in terms of their ability to robustly quantify risks posed by various occupational hazards. Moreover, best practices and modern SMSs are unable to account for dynamically evolving environments/behavioral characteristics commonly found in many industrial settings. This article proposes a method to address these issues by enabling continuous and quantitative assessment of safety risks in a data-driven manner. The backbone of our method is an intuitive hierarchical probabilistic model that explains sparse and noisy safety data collected by a typical SMS. A fully Bayesian approach is developed to calibrate this model from safety data in an online fashion. Thereafter, the calibrated model holds necessary information that serves to characterize risk posed by different safety hazards. Additionally, the proposed model can be leveraged for automated decision making, for instance solving resource allocation problems -- targeted towards risk mitigation -- that are often encountered in resource-constrained industrial environments. The methodology is rigorously validated on a simulated test-bed and its scalability is demonstrated on real data from large maintenance projects at a petrochemical plant.

Published
2022

10. Methodology for Testing and Evaluation of Safety Analytics Approaches

Author

Paiva, Antonio R. and Tewari, Ashutosh

Subjects
Statistics - Applications
Abstract

There has been a significant increase in the development of data-driven safety analytics approaches in recent years. In light of these advances it has become imperative to evaluate such approaches in a principled way to determine their merits and limitations. To that end, we propose an evaluation methodology underpinned by a simulated environment that allows for a comprehensive assessment of safety analytics approaches. While assessing those approaches with historical field data is undoubtedly important, such an assessment has limited statistical power because it corresponds to only one realization. The proposed methodology enables validation over a large number of realizations, thereby circumventing the statistical limitations of evaluation on historical data. Moreover, by using a simulated environment one is able to clearly distinguish between the variability in the observed data and differences in performance between safety approaches. A simulated environment does this by comparing the approaches under controlled circumstances, resulting in a fair and systematic evaluation of the potential long-term benefits. We demonstrate the utility of the proposed methodology via a case study that compares a few candidate safety analytics approaches. These approaches differ in how they assimilate field safety data to assess safety risk and suggest mitigative actions. We show that the proposed methodology indeed reveals useful insights and quantifies the relative merits and drawbacks of the different approaches, which would be otherwise difficult to objectively determine in a real-world scenario., Comment: Accepted to Safety Science

Published
2022
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15. Inflammatory Diseases of the Ureter

Author

Dovey, Zachary, Pedraza, Adriana M., Patel, Dhruti, Bhardwaj, Swati, Wagaskar, Vinayak, Gupta, Raghav, Shah, Akash, Tewari, Ashutosh, Abdel-Gawad, Mahmoud, editor, Ali-El-Dein, Bedeir, editor, Barry, John, editor, and Stenzl, Arnulf, editor

Published
2023
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19. Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study.

Author

Leo, Patrick, Janowczyk, Andrew, Elliott, Robin, Janaki, Nafiseh, Bera, Kaustav, Shiradkar, Rakesh, Farré, Xavier, Fu, Pingfu, El-Fahmawi, Ayah, Shahait, Mohammed, Kim, Jessica, Lee, David, Yamoah, Kosj, Rebbeck, Timothy R, Khani, Francesca, Robinson, Brian D, Eklund, Lauri, Jambor, Ivan, Merisaari, Harri, Ettala, Otto, Taimen, Pekka, Aronen, Hannu J, Boström, Peter J, Tewari, Ashutosh, Magi-Galluzzi, Cristina, Klein, Eric, Purysko, Andrei, Nc Shih, Natalie, Feldman, Michael, Gupta, Sanjay, Lal, Priti, and Madabhushi, Anant

Abstract

Existing tools for post-radical prostatectomy (RP) prostate cancer biochemical recurrence (BCR) prognosis rely on human pathologist-derived parameters such as tumor grade, with the resulting inter-reviewer variability. Genomic companion diagnostic tests such as Decipher tend to be tissue destructive, expensive, and not routinely available in most centers. We present a tissue non-destructive method for automated BCR prognosis, termed "Histotyping", that employs computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide. Patients from two institutions (n = 214) were used to train Histotyping for identifying high-risk patients based on six features of glandular morphology extracted from RP specimens. Histotyping was validated for post-RP BCR prognosis on a separate set of n = 675 patients from five institutions and compared against Decipher on n = 167 patients. Histotyping was prognostic of BCR in the validation set (p

Published
2021

20. Diagnostic Performance and Safety of Positron Emission Tomography with 18F-rhPSMA-7.3 in Patients with Newly Diagnosed Unfavourable Intermediate- to Very-high-risk Prostate Cancer: Results from a Phase 3, Prospective, Multicentre Study (LIGHTHOUSE)

Author

Allaf, Mohamad, Andriole, Gerald, Avery, Ryan J., Avril, Norbert, Barker, Helen, Belkoff, Laurence, Budäus, Lars, Cher, Michael L., Chisholm, Diane, Covington, Matthew F., Cox, Ian, Ferrandino, Michael, Fleming, Mark T., Franceschi, Dinko, Gardiner, Peter, Gartrell, Benjamin, Gauden, David, Hasa, Ergela, Hermsen, Rick, Horn, Thomas, Iranpour, Pooya, Jacene, Heather, Jayaratna, Isuru, Joshi, Shreyas S., Kay, Matthew, Kostakoglu, Lale, Kuo, Phillip, Lavely, William, Lokuta, Mary, Lowentritt, Benjamin, Miller, Matthew P., Nix, Jeffrey W., Ogan, Kenneth, Penny, Ross, Piert, Morand, Purysko, Andrei, Ravizzini, Gregory, Saltzstein, Daniel, Savir-Baruch, Bital, Siegel, Barry A., Steuber, Thomas, Twardowski, Przemyslaw, Uchio, Edward, Ulaner, Gary A., Wixom, Jenna M., Yoo, Don, Zukotynski, Katherine, Surasi, Devaki Shilpa, Eiber, Matthias, Maurer, Tobias, Preston, Mark A., Helfand, Brian T., Josephson, David, Tewari, Ashutosh K., Somford, Diederik M., Rais-Bahrami, Soroush, Koontz, Bridget F., Bostrom, Peter J., Chau, Albert, Davis, Phillip, Schuster, David M., and Chapin, Brian F.

Published
2023
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21. Independent blinded validation of an AI-based digital histology classifier for prostate cancer recurrence and metastasis risk prediction.

Author

Fay, Magdalena, primary, Liao, Ross, additional, Lone, Zaeem M, additional, Reddy, Chandana A., additional, Muhammad, Hassan, additional, Xie, Chensu, additional, Jain, Parag, additional, Huang, Wei, additional, Basu, Hirak S, additional, Nguyen, Jane, additional, Nair, Sujit S., additional, Chakravarty, Dimple, additional, Williamson, Sean R., additional, Gupta, Shilpa, additional, Weight, Christopher, additional, Roy, Rajat, additional, Wilding, George, additional, Tewari, Ashutosh K., additional, Klein, Eric A., additional, and Mian, Omar Y., additional

Published
2024
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22. A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI

Author

Li, Lin, Shiradkar, Rakesh, Leo, Patrick, Algohary, Ahmad, Fu, Pingfu, Tirumani, Sree Harsha, Mahran, Amr, Buzzy, Christina, Obmann, Verena C, Mansoori, Bahar, El-Fahmawi, Ayah, Shahait, Mohammed, Tewari, Ashutosh, Magi-Galluzzi, Cristina, Lee, David, Lal, Priti, Ponsky, Lee, Klein, Eric, Purysko, Andrei S, and Madabhushi, Anant

Subjects
Aging, Prostate Cancer, Cancer, Biomedical Imaging, Patient Safety, Urologic Diseases, Good Health and Well Being, Aged, Aged, 80 and over, Biomarkers, Tumor, Clinical Decision-Making, Diagnostic Imaging, Disease Management, Humans, Kaplan-Meier Estimate, Magnetic Resonance Imaging, Male, Middle Aged, Neoplasm Recurrence, Local, Neoplasm Staging, Nomograms, Patient Selection, Perioperative Care, Prognosis, Prostatectomy, Prostatic Neoplasms, Retrospective Studies, Workflow, Prostate cancer, Biochemical recurrence, Adverse pathology, Radiomic, MRI, Clinical Sciences, Public Health and Health Services
Abstract

BackgroundWe developed and validated an integrated radiomic-clinicopathologic nomogram (RadClip) for post-surgical biochemical recurrence free survival (bRFS) and adverse pathology (AP) prediction in men with prostate cancer (PCa). RadClip was further compared against extant prognostics tools like CAPRA and Decipher.MethodsA retrospective study of 198 patients with PCa from four institutions who underwent pre-operative 3 Tesla MRI followed by radical prostatectomy, between 2009 and 2017 with a median 35-month follow-up was performed. Radiomic features were extracted from prostate cancer regions on bi-parametric magnetic resonance imaging (bpMRI). Cox Proportional-Hazards (CPH) model warped with minimum redundancy maximum relevance (MRMR) feature selection was employed to select bpMRI radiomic features for bRFS prediction in the training set (D1, N = 71). In addition, a bpMRI radiomic risk score (RadS) and associated nomogram, RadClip, were constructed in D1 and then compared against the Decipher, pre-operative (CAPRA), and post-operative (CAPRA-S) nomograms for bRFS and AP prediction in the testing set (D2, N = 127).Findings"RadClip yielded a higher C-index (0.77, 95% CI 0.65-0.88) compared to CAPRA (0.68, 95% CI 0.57-0.8) and Decipher (0.51, 95% CI 0.33-0.69) and was found to be comparable to CAPRA-S (0.75, 95% CI 0.65-0.85). RadClip resulted in a higher AUC (0.71, 95% CI 0.62-0.81) for predicting AP compared to Decipher (0.66, 95% CI 0.56-0.77) and CAPRA (0.69, 95% CI 0.59-0.79)."InterpretationRadClip was more prognostic of bRFS and AP compared to Decipher and CAPRA. It could help pre-operatively identify PCa patients at low risk of biochemical recurrence and AP and who therefore might defer additional therapy.FundingThe National Institutes of Health, the U.S. Department of Veterans Affairs, and the Department of Defense.

Published
2021

24. CD8 + T cell targeting of tumor antigens presented by HLA-E

Author

Iyer, Ravi F., primary, Verweij, Marieke C., additional, Nair, Sujit S., additional, Morrow, David, additional, Mansouri, Mandana, additional, Chakravarty, Dimple, additional, Beechwood, Teresa, additional, Meyer, Christine, additional, Uebelhoer, Luke, additional, Lauron, Elvin J., additional, Selseth, Andrea, additional, John, Nessy, additional, Thin, Tin Htwe, additional, Dzedzik, Siarhei, additional, Havenar-Daughton, Colin, additional, Axthelm, Michael K., additional, Douglas, Janet, additional, Korman, Alan, additional, Bhardwaj, Nina, additional, Tewari, Ashutosh K., additional, Hansen, Scott, additional, Malouli, Daniel, additional, Picker, Louis J., additional, and Früh, Klaus, additional

Published
2024
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25. MP41-05 LIQUID-BIOPSY BASED NOVEL mRNA GENOMIC TEST FOR EARLY DETECTION, PROGNOSIS AND MONITORING OF PROSTATE CANCER: A MULTICENTER STUDY

Author

Hashimoto, Yutaka, primary, Verma, Shiv, additional, Deng, Guoren, additional, Chauhan, Dharam P., additional, Nair, Sujit, additional, Chakravarty, Dimple, additional, Yoshino, Hirofumi, additional, Arima, Junya, additional, Tatarano, Shuichi, additional, Liu, Yutong, additional, Hatano, Koji, additional, Shivakumar, Yogesh, additional, Ghosh, Mithua, additional, Krishnappa, Raghunath S., additional, Chandran, Chitra, additional, Ragavan, Narasimhan, additional, Enokida, Hideki, additional, Nonomura, Norio, additional, Balaji, K. C., additional, Rawal, Sudhir K., additional, Tewari, Ashutosh K., additional, Dahiya, Rajvir, additional, and Gupta, Sanjay, additional

Published
2024
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26. MP74-10 AI-BASED RISK-STRATIFICATION TOOL PATHOMIQ_PRAD ANALYZES HISTOPATHOLOGY IMAGES TO ACCURATELY PREDICT METASTASIS IN BLACK MEN AND HELPS DISCOVER CANCER DRIVERS

Author

Chakravarty, Dimple, primary, Muhammad, Hassan, additional, Xie, Chensu, additional, Jain, Parag, additional, Brody, Rachel, additional, Leung, Lawrence, additional, Fatterpekar, Monali, additional, Huang, Wei, additional, Bhardwaj, Swati, additional, Basu, Hirak, additional, Wilding, George, additional, Roy, Rajat, additional, Nair, Sujit S., additional, and Tewari, Ashutosh K., additional

Published
2024
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28. Neoadjuvant clinical trials provide a window of opportunity for cancer drug discovery

Author

Marron, Thomas U., Galsky, Matthew D., Taouli, Bachir, Fiel, Maria Isabel, Ward, Stephen, Kim, Edward, Yankelevitz, David, Doroshow, Deborah, Guttman-Yassky, Emma, Ungar, Benjamin, Mehandru, Saurabh, Golas, Benjamin J., Labow, Daniel, Sfakianos, John, Nair, Sujit S., Chakravarty, Dimple, Buckstein, Michael, Song, Xiaoyu, Kenigsberg, Effi, Gnjatic, Sacha, Brown, Brian D., Sparano, Joseph, Tewari, Ashutosh, Schwartz, Myron, Bhardwaj, Nina, and Merad, Miriam

Published
2022
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30. exRNA Atlas Analysis Reveals Distinct Extracellular RNA Cargo Types and Their Carriers Present across Human Biofluids

Author

Murillo, Oscar D, Thistlethwaite, William, Rozowsky, Joel, Subramanian, Sai Lakshmi, Lucero, Rocco, Shah, Neethu, Jackson, Andrew R, Srinivasan, Srimeenakshi, Chung, Allen, Laurent, Clara D, Kitchen, Robert R, Galeev, Timur, Warrell, Jonathan, Diao, James A, Welsh, Joshua A, Hanspers, Kristina, Riutta, Anders, Burgstaller-Muehlbacher, Sebastian, Shah, Ravi V, Yeri, Ashish, Jenkins, Lisa M, Ahsen, Mehmet E, Cordon-Cardo, Carlos, Dogra, Navneet, Gifford, Stacey M, Smith, Joshua T, Stolovitzky, Gustavo, Tewari, Ashutosh K, Wunsch, Benjamin H, Yadav, Kamlesh K, Danielson, Kirsty M, Filant, Justyna, Moeller, Courtney, Nejad, Parham, Paul, Anu, Simonson, Bridget, Wong, David K, Zhang, Xuan, Balaj, Leonora, Gandhi, Roopali, Sood, Anil K, Alexander, Roger P, Wang, Liang, Wu, Chunlei, Wong, David TW, Galas, David J, Van Keuren-Jensen, Kendall, Patel, Tushar, Jones, Jennifer C, Das, Saumya, Cheung, Kei-Hoi, Pico, Alexander R, Su, Andrew I, Raffai, Robert L, Laurent, Louise C, Roth, Matthew E, Gerstein, Mark B, and Milosavljevic, Aleksandar

Subjects
Biological Sciences, Genetics, Human Genome, Biotechnology, Adult, Body Fluids, Cell Communication, Cell-Free Nucleic Acids, Circulating MicroRNA, Extracellular Vesicles, Female, Humans, Male, RNA, Reproducibility of Results, Sequence Analysis, RNA, Software, ERCC, deconvolution, exRNA, exosomes, extracellular RNA, extracellular vesicles, lipoproteins, ribonucleoproteins, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

To develop a map of cell-cell communication mediated by extracellular RNA (exRNA), the NIH Extracellular RNA Communication Consortium created the exRNA Atlas resource (https://exrna-atlas.org). The Atlas version 4P1 hosts 5,309 exRNA-seq and exRNA qPCR profiles from 19 studies and a suite of analysis and visualization tools. To analyze variation between profiles, we apply computational deconvolution. The analysis leads to a model with six exRNA cargo types (CT1, CT2, CT3A, CT3B, CT3C, CT4), each detectable in multiple biofluids (serum, plasma, CSF, saliva, urine). Five of the cargo types associate with known vesicular and non-vesicular (lipoprotein and ribonucleoprotein) exRNA carriers. To validate utility of this model, we re-analyze an exercise response study by deconvolution to identify physiologically relevant response pathways that were not detected previously. To enable wide application of this model, as part of the exRNA Atlas resource, we provide tools for deconvolution and analysis of user-provided case-control studies.

Published
2019

32. Effect of Simulation-based Training on Surgical Proficiency and Patient Outcomes: A Randomised Controlled Clinical and Educational Trial

Author

Baig, Umair, Aya, Haleema, Husnain Iqbal, Mohammed, Kum, Francesca, Bultitude, Matthew, Glass, Jonathan, Khan, Azhar, Makanjuola, Jonathan, McCabe, John E., Samsuddin, Azi, McIlhenny, Craig, Brewin, James, Kulkarni, Shashank, Khwaja, Sikandar, Islam, Waliul, Marsh, Howard, Bhat, Taher, Thomas, Benjamin, Cutress, Mark, Housami, Fadi, Nedas, Timothy, Bates, Timothy, Mukherjee, Rono, Graham, Stuart, Bordenave, Matthieu, Coker, Charles, Ahmed, Shwan, Symes, Andrew, Calvert, Robert, Lynch, Ciaran, Long, Ronan, Patterson, Jacob M., Rukin, Nicholas J., Khan, Shahid A., Dasgupta, Ranan, Brown, Stephen, Grey, Ben, Mahmalji, Waseem, Lam, Wayne, Scheitlin, Walter, Saelzler, Norbert, Fiedler, Marcel, Ishikawa, Shuhei, Sasaki, Yoshihiro, Sazawa, Ataru, Shinno, Yuichiro, Mochizuki, Tango, Peter Jessen, Jan, Steiner, Roland, Wendt-Nordahl, Gunnar, Atassi, Nabil, Kohns, Heiko, Cox, Ashley, Rendon, Ricardo, Lawen, Joseph, Bailly, Greg, Marsh, Trevor, Aydın, Abdullatif, Ahmed, Kamran, Abe, Takashige, Raison, Nicholas, Van Hemelrijck, Mieke, Garmo, Hans, Ahmed, Hashim U., Mukhtar, Furhan, Al-Jabir, Ahmed, Brunckhorst, Oliver, Shinohara, Nobuo, Zhu, Wei, Zeng, Guohua, Sfakianos, John P., Gupta, Mantu, Tewari, Ashutosh, Gözen, Ali Serdar, Rassweiler, Jens, Skolarikos, Andreas, Kunit, Thomas, Knoll, Thomas, Moltzahn, Felix, Thalmann, George N., Lantz Powers, Andrea G., Chew, Ben H., Sarica, Kemal, Shamim Khan, Muhammad, and Dasgupta, Prokar

Published
2022
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34. A Machine Learning-Driven Approach to Predict the Outcome of Prostate Biopsy: Identifying Cancer, Clinically Significant Disease, and Unfavorable Pathological Features on Prostate Biopsy

Author

Pfail, John L., Lundon, Dara J., Ratnani, Parita, Wagaskar, Vinayak, Wiklund, Peter, Tewari, Ashutosh K., Arabnia, Hamid, Series Editor, Arabnia, Hamid R., editor, Deligiannidis, Leonidas, editor, Shouno, Hayaru, editor, Tinetti, Fernando G., editor, and Tran, Quoc-Nam, editor

Published
2021
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35. Extracellular vesicles carry transcriptional 'dark matter' revealing tissue‐specific information.

Author

Dogra, Navneet, Chen, Tzu‐Yi, Gonzalez‐Kozlova, Edgar, Miceli, Rebecca, Cordon‐Cardo, Carlos, Tewari, Ashutosh K., Losic, Bojan, and Stolovitzky, Gustavo

Abstract

From eukaryotes to prokaryotes, all cells secrete extracellular vesicles (EVs) as part of their regular homeostasis, intercellular communication, and cargo disposal. Accumulating evidence suggests that small EVs carry functional small RNAs, potentially serving as extracellular messengers and liquid‐biopsy markers. Yet, the complete transcriptomic landscape of EV‐associated small RNAs during disease progression is poorly delineated due to critical limitations including the protocols used for sequencing, suboptimal alignment of short reads (20–50 nt), and uncharacterized genome annotations—often denoted as the 'dark matter' of the genome. In this study, we investigate the EV‐associated small unannotated RNAs that arise from endogenous genes and are part of the genomic 'dark matter', which may play a key emerging role in regulating gene expression and translational mechanisms. To address this, we created a distinct small RNAseq dataset from human prostate cancer & benign tissues, and EVs derived from blood (pre‐ & post‐prostatectomy), urine, and human prostate carcinoma epithelial cell line. We then developed an unsupervised data‐based bioinformatic pipeline that recognizes biologically relevant transcriptional signals irrespective of their genomic annotation. Using this approach, we discovered distinct EV‐RNA expression patterns emerging from the un‐annotated genomic regions (UGRs) of the transcriptomes associated with tissue‐specific phenotypes. We have named these novel EV‐associated small RNAs as 'EV‐UGRsʼ or "EV‐dark matter". Here, we demonstrate that EV‐UGR gene expressions are downregulated by ∼100 fold (FDR < 0.05) in the circulating serum EVs from aggressive prostate cancer subjects. Remarkably, these EV‐UGRs expression signatures were regained (upregulated) after radical prostatectomy in the same follow‐up patients. Finally, we developed a stem‐loop RT‐qPCR assay that validated prostate cancer‐specific EV‐UGRs for selective fluid‐based diagnostics. Overall, using an unsupervised data driven approach, we investigate the 'dark matter' of EV‐transcriptome and demonstrate that EV‐UGRs carry tissue‐specific Information that significantly alters pre‐ and post‐prostatectomy in the prostate cancer patients. Although further validation in randomized clinical trials is required, this new class of EV‐RNAs hold promise in liquid‐biopsy by avoiding highly invasive biopsy procedures in prostate cancer. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Development and Validation of a Novel Integrated Clinical-Genomic Risk Group Classification for Localized Prostate Cancer

Author

Spratt, Daniel E, Zhang, Jingbin, Santiago-Jiménez, María, Dess, Robert T, Davis, John W, Den, Robert B, Dicker, Adam P, Kane, Christopher J, Pollack, Alan, Stoyanova, Radka, Abdollah, Firas, Ross, Ashley E, Cole, Adam, Uchio, Edward, Randall, Josh M, Nguyen, Hao, Zhao, Shuang G, Mehra, Rohit, Glass, Andrew G, Lam, Lucia LC, Chelliserry, Jijumon, du Plessis, Marguerite, Choeurng, Voleak, Aranes, Maria, Kolisnik, Tyler, Margrave, Jennifer, Alter, Jason, Jordan, Jennifer, Buerki, Christine, Yousefi, Kasra, Haddad, Zaid, Davicioni, Elai, Trabulsi, Edouard J, Loeb, Stacy, Tewari, Ashutosh, Carroll, Peter R, Weinmann, Sheila, Schaeffer, Edward M, Klein, Eric A, Karnes, R Jeffrey, Feng, Felix Y, and Nguyen, Paul L

Subjects
Urologic Diseases, Prevention, Clinical Research, Cancer, Prostate Cancer, Genetics, Human Genome, Aging, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Aged, Genomics, Humans, Male, Middle Aged, Prognosis, Prostatic Neoplasms, Risk, Clinical Sciences, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

Purpose It is clinically challenging to integrate genomic-classifier results that report a numeric risk of recurrence into treatment recommendations for localized prostate cancer, which are founded in the framework of risk groups. We aimed to develop a novel clinical-genomic risk grouping system that can readily be incorporated into treatment guidelines for localized prostate cancer. Materials and Methods Two multicenter cohorts (n = 991) were used for training and validation of the clinical-genomic risk groups, and two additional cohorts (n = 5,937) were used for reclassification analyses. Competing risks analysis was used to estimate the risk of distant metastasis. Time-dependent c-indices were constructed to compare clinicopathologic risk models with the clinical-genomic risk groups. Results With a median follow-up of 8 years for patients in the training cohort, 10-year distant metastasis rates for National Comprehensive Cancer Network (NCCN) low, favorable-intermediate, unfavorable-intermediate, and high-risk were 7.3%, 9.2%, 38.0%, and 39.5%, respectively. In contrast, the three-tier clinical-genomic risk groups had 10-year distant metastasis rates of 3.5%, 29.4%, and 54.6%, for low-, intermediate-, and high-risk, respectively, which were consistent in the validation cohort (0%, 25.9%, and 55.2%, respectively). C-indices for the clinical-genomic risk grouping system (0.84; 95% CI, 0.61 to 0.93) were improved over NCCN (0.73; 95% CI, 0.60 to 0.86) and Cancer of the Prostate Risk Assessment (0.74; 95% CI, 0.65 to 0.84), and 30% of patients using NCCN low/intermediate/high would be reclassified by the new three-tier system and 67% of patients would be reclassified from NCCN six-tier (very-low- to very-high-risk) by the new six-tier system. Conclusion A commercially available genomic classifier in combination with standard clinicopathologic variables can generate a simple-to-use clinical-genomic risk grouping that more accurately identifies patients at low, intermediate, and high risk for metastasis and can be easily incorporated into current guidelines to better risk-stratify patients.

Published
2018

39. AI-powered real-time annotations during urologic surgery: The future of training and quality metrics

Author

Zuluaga, Laura, primary, Rich, Jordan Miller, additional, Gupta, Raghav, additional, Pedraza, Adriana, additional, Ucpinar, Burak, additional, Okhawere, Kennedy E., additional, Saini, Indu, additional, Dwivedi, Priyanka, additional, Patel, Dhruti, additional, Zaytoun, Osama, additional, Menon, Mani, additional, Tewari, Ashutosh, additional, and Badani, Ketan K., additional

Published
2023
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43. A multi-institutional study of 1,111 men with 4K score, multiparametric magnetic resonance imaging, and prostate biopsy

Author

Thomas, Jamie, Atluri, Shrikanth, Zucker, Isaac, Reis, Isildinha, Kwon, Deukwoo, Kim, Eric, Tewari, Ashutosh, Patel, Vipul, Wagaskar, Vinayak, Konety, Badrinath, Kasraeian, Ali, Czarniecki, Stefan, Thoreson, Gregory, Soodana-Prakash, Nachiketh, Ritch, Chad, Nahar, Bruno, Gonzalgo, Mark, Kava, Bruce, Parekh, Dipen, and Punnen, Sanoj

Published
2023
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50. 18F-Fluciclovine Positron Emission Tomography in Men With Biochemical Recurrence of Prostate Cancer After Radical Prostatectomy and Planning to Undergo Salvage Radiation Therapy: Results from LOCATE

Author

Adler, Lee P., Andriole, Gerald L., Belkoff, Laurence H., Burzon, Daniel, Chau, Albert, Dato, Paul, Duan, Fenghai, Farwell, Michael, Fogelson, Stephen, Gardiner, Peter, Hanna, Lucy, Hoffman, John M., Intenzo, Charles, Josephson, David, Kaminetsky, Jed, Kipper, Michael, Kostakoglu, Lale, Krynyckyi, Borys, Linder, Karen E., Mahmood, Umar, Marques, Helga, Mankoff, David, McConathy, Jonathan, Melnick, John, Miller, Matthew P., Oh, William, Philips, Shaile, Rose, Judith, Savir-Baruch, Bital, Schuster, David M., Siegel, Barry A., Stevens, Daniel J., Tewari, Ashutosh, Twardowski, Przemyslaw, Ward, Penelope, Wasserman, Martha, Weick, Sharon, (Michael) Yu, Jian Q., Solanki, Abhishek A., Liauw, Stanley L., Michalski, Jeff, Tward, Jonathan D., Vapiwala, Neha, and Teoh, Eugene J.

Published
2020
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