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237 results on '"Aerts, Hugo J.W.L."'

2. Automated temporalis muscle quantification and growth charts for children through adulthood

Author

Zapaishchykova, Anna, Liu, Kevin X., Saraf, Anurag, Ye, Zezhong, Catalano, Paul J., Benitez, Viviana, Ravipati, Yashwanth, Jain, Arnav, Huang, Julia, Hayat, Hasaan, Likitlersuang, Jirapat, Vajapeyam, Sridhar, Chopra, Rishi B., Familiar, Ariana M., Nabavidazeh, Ali, Mak, Raymond H., Resnick, Adam C., Mueller, Sabine, Cooney, Tabitha M., Haas-Kogan, Daphne A., Poussaint, Tina Y., Aerts, Hugo J.W.L., and Kann, Benjamin H.

Published
2023
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5. Diffusion deep learning for brain age prediction and longitudinal tracking in children through adulthood

Author

Zapaishchykova, Anna, primary, Tak, Divyanshu, additional, Ye, Zezhong, additional, Liu, Kevin X., additional, Likitlersuang, Jirapat, additional, Vajapeyam, Sridhar, additional, Chopra, Rishi B., additional, Seidlitz, Jakob, additional, Bethlehem, Richard A.I., additional, Mak, Raymond H., additional, Mueller, Sabine, additional, Haas-Kogan, Daphne A., additional, Poussaint, Tina Y., additional, Aerts, Hugo J.W.L., additional, and Kann, Benjamin H., additional

Published
2024
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11. Current Status and Future Perspectives on Neoadjuvant Therapy in Lung Cancer

Author

Blumenthal, Gideon M., Bunn, Paul A., Jr., Chaft, Jamie E., McCoach, Caroline E., Perez, Edith A., Scagliotti, Giorgio V., Carbone, David P., Aerts, Hugo J.W.L., Aisner, Dara L., Bergh, Jonas, Berry, Donald A., Jarkowski, Anthony, Botwood, Nicholas, Cross, Darren A.E., Diehn, Max, Drezner, Nicole L., Doebele, Robert C., Blakely, Collin M., Eberhardt, Wilfried E.E., Felip, Enriqueta, Gianni, Luca, Keller, Steven P., Leavey, Patrick J., Malik, Shakun, Pignatti, Francesco, Prowell, Tatiana M., Redman, Mary W., Rizvi, Naiyer A., Rosell, Rafael, Rusch, Valerie, de Ruysscher, Dirk, Schwartz, Lawrence H., Sridhara, Rajeshwari, Stahel, Rolf A., Swisher, Stephen, Taube, Janis M., Travis, William D., Keegan, Patricia, Wiens, Jacinta R., Wistuba, Ignacio I., Wynes, Murry W., Hirsch, Fred R., and Kris, Mark G.

Published
2018
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13. Deep Learning to Estimate Cardiovascular Risk From Chest Radiographs: A Risk Prediction Study.

Author

Weiss, Jakob, Raghu, Vineet K., Paruchuri, Kaavya, Zinzuwadia, Aniket, Natarajan, Pradeep, Aerts, Hugo J.W.L., and Lu, Michael T.

Subjects
CHEST X rays, CARDIOVASCULAR diseases risk factors, DEEP learning, DISEASE risk factors, MAJOR adverse cardiovascular events, MEDICAL record databases
Abstract

Current guidelines recommend estimating the 10-year risk for major adverse cardiovascular events before deciding whether to initiate statin therapy for the primary prevention of atherosclerotic cardiovascular disease. In the United States, clinicians often estimate the risk using a mathematical model that requires 9 inputs. This article examines whether a single routine chest radiograph can provide comparable information. Visual Abstract. Deep Learning to Estimate Cardiovascular Risk From Chest Radiographs: Current guidelines recommend estimating the 10-year risk for major adverse cardiovascular events before deciding whether to initiate statin therapy for the primary prevention of atherosclerotic cardiovascular disease. In the United States, clinicians often estimate the risk using a mathematical model that requires 9 inputs. This article examines whether a single routine chest radiograph can provide comparable information. Background: Guidelines for primary prevention of atherosclerotic cardiovascular disease (ASCVD) recommend a risk calculator (ASCVD risk score) to estimate 10-year risk for major adverse cardiovascular events (MACE). Because the necessary inputs are often missing, complementary approaches for opportunistic risk assessment are desirable. Objective: To develop and test a deep-learning model (CXR CVD-Risk) that estimates 10-year risk for MACE from a routine chest radiograph (CXR) and compare its performance with that of the traditional ASCVD risk score for implications for statin eligibility. Design: Risk prediction study. Setting: Outpatients potentially eligible for primary cardiovascular prevention. Participants: The CXR CVD-Risk model was developed using data from a cancer screening trial. It was externally validated in 8869 outpatients with unknown ASCVD risk because of missing inputs to calculate the ASCVD risk score and in 2132 outpatients with known risk whose ASCVD risk score could be calculated. Measurements: 10-year MACE predicted by CXR CVD-Risk versus the ASCVD risk score. Results: Among 8869 outpatients with unknown ASCVD risk, those with a risk of 7.5% or higher as predicted by CXR CVD-Risk had higher 10-year risk for MACE after adjustment for risk factors (adjusted hazard ratio [HR], 1.73 [95% CI, 1.47 to 2.03]). In the additional 2132 outpatients with known ASCVD risk, CXR CVD-Risk predicted MACE beyond the traditional ASCVD risk score (adjusted HR, 1.88 [CI, 1.24 to 2.85]). Limitation: Retrospective study design using electronic medical records. Conclusion: On the basis of a single CXR, CXR CVD-Risk predicts 10-year MACE beyond the clinical standard and may help identify individuals at high risk whose ASCVD risk score cannot be calculated because of missing data. Primary Funding Source: None. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Supplementary Figure S3 from Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics

Author

Kazmierski, Michal, primary, Welch, Mattea, primary, Kim, Sejin, primary, McIntosh, Chris, primary, Rey-McIntyre, Katrina, primary, Huang, Shao Hui, primary, Patel, Tirth, primary, Tadic, Tony, primary, Milosevic, Michael, primary, Liu, Fei-Fei, primary, Ryczkowski, Adam, primary, Kazmierska, Joanna, primary, Ye, Zezhong, primary, Plana, Deborah, primary, Aerts, Hugo J.W.L., primary, Kann, Benjamin H., primary, Bratman, Scott V., primary, Hope, Andrew J., primary, and Haibe-Kains, Benjamin, primary

Published
2023
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20. FIGURE 5 from Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics

Author

Kazmierski, Michal, primary, Welch, Mattea, primary, Kim, Sejin, primary, McIntosh, Chris, primary, Rey-McIntyre, Katrina, primary, Huang, Shao Hui, primary, Patel, Tirth, primary, Tadic, Tony, primary, Milosevic, Michael, primary, Liu, Fei-Fei, primary, Ryczkowski, Adam, primary, Kazmierska, Joanna, primary, Ye, Zezhong, primary, Plana, Deborah, primary, Aerts, Hugo J.W.L., primary, Kann, Benjamin H., primary, Bratman, Scott V., primary, Hope, Andrew J., primary, and Haibe-Kains, Benjamin, primary

Published
2023
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21. FIGURE 3 from Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics

Author

Kazmierski, Michal, primary, Welch, Mattea, primary, Kim, Sejin, primary, McIntosh, Chris, primary, Rey-McIntyre, Katrina, primary, Huang, Shao Hui, primary, Patel, Tirth, primary, Tadic, Tony, primary, Milosevic, Michael, primary, Liu, Fei-Fei, primary, Ryczkowski, Adam, primary, Kazmierska, Joanna, primary, Ye, Zezhong, primary, Plana, Deborah, primary, Aerts, Hugo J.W.L., primary, Kann, Benjamin H., primary, Bratman, Scott V., primary, Hope, Andrew J., primary, and Haibe-Kains, Benjamin, primary

Published
2023
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22. FIGURE 2 from Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics

Author

Kazmierski, Michal, primary, Welch, Mattea, primary, Kim, Sejin, primary, McIntosh, Chris, primary, Rey-McIntyre, Katrina, primary, Huang, Shao Hui, primary, Patel, Tirth, primary, Tadic, Tony, primary, Milosevic, Michael, primary, Liu, Fei-Fei, primary, Ryczkowski, Adam, primary, Kazmierska, Joanna, primary, Ye, Zezhong, primary, Plana, Deborah, primary, Aerts, Hugo J.W.L., primary, Kann, Benjamin H., primary, Bratman, Scott V., primary, Hope, Andrew J., primary, and Haibe-Kains, Benjamin, primary

Published
2023
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23. FIGURE 4 from Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics

Author

Kazmierski, Michal, primary, Welch, Mattea, primary, Kim, Sejin, primary, McIntosh, Chris, primary, Rey-McIntyre, Katrina, primary, Huang, Shao Hui, primary, Patel, Tirth, primary, Tadic, Tony, primary, Milosevic, Michael, primary, Liu, Fei-Fei, primary, Ryczkowski, Adam, primary, Kazmierska, Joanna, primary, Ye, Zezhong, primary, Plana, Deborah, primary, Aerts, Hugo J.W.L., primary, Kann, Benjamin H., primary, Bratman, Scott V., primary, Hope, Andrew J., primary, and Haibe-Kains, Benjamin, primary

Published
2023
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24. Data from Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics

Author

Kazmierski, Michal, primary, Welch, Mattea, primary, Kim, Sejin, primary, McIntosh, Chris, primary, Rey-McIntyre, Katrina, primary, Huang, Shao Hui, primary, Patel, Tirth, primary, Tadic, Tony, primary, Milosevic, Michael, primary, Liu, Fei-Fei, primary, Ryczkowski, Adam, primary, Kazmierska, Joanna, primary, Ye, Zezhong, primary, Plana, Deborah, primary, Aerts, Hugo J.W.L., primary, Kann, Benjamin H., primary, Bratman, Scott V., primary, Hope, Andrew J., primary, and Haibe-Kains, Benjamin, primary

Published
2023
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25. TABLE 1 from Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics

Author

Kazmierski, Michal, primary, Welch, Mattea, primary, Kim, Sejin, primary, McIntosh, Chris, primary, Rey-McIntyre, Katrina, primary, Huang, Shao Hui, primary, Patel, Tirth, primary, Tadic, Tony, primary, Milosevic, Michael, primary, Liu, Fei-Fei, primary, Ryczkowski, Adam, primary, Kazmierska, Joanna, primary, Ye, Zezhong, primary, Plana, Deborah, primary, Aerts, Hugo J.W.L., primary, Kann, Benjamin H., primary, Bratman, Scott V., primary, Hope, Andrew J., primary, and Haibe-Kains, Benjamin, primary

Published
2023
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26. Supplementary Data from Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics

Author

Kazmierski, Michal, primary, Welch, Mattea, primary, Kim, Sejin, primary, McIntosh, Chris, primary, Rey-McIntyre, Katrina, primary, Huang, Shao Hui, primary, Patel, Tirth, primary, Tadic, Tony, primary, Milosevic, Michael, primary, Liu, Fei-Fei, primary, Ryczkowski, Adam, primary, Kazmierska, Joanna, primary, Ye, Zezhong, primary, Plana, Deborah, primary, Aerts, Hugo J.W.L., primary, Kann, Benjamin H., primary, Bratman, Scott V., primary, Hope, Andrew J., primary, and Haibe-Kains, Benjamin, primary

Published
2023
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27. Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics

Author

Kazmierski, Michal, primary, Welch, Mattea, additional, Kim, Sejin, additional, McIntosh, Chris, additional, Rey-McIntyre, Katrina, additional, Huang, Shao Hui, additional, Patel, Tirth, additional, Tadic, Tony, additional, Milosevic, Michael, additional, Liu, Fei-Fei, additional, Ryczkowski, Adam, additional, Kazmierska, Joanna, additional, Ye, Zezhong, additional, Plana, Deborah, additional, Aerts, Hugo J.W.L., additional, Kann, Benjamin H., additional, Bratman, Scott V., additional, Hope, Andrew J., additional, and Haibe-Kains, Benjamin, additional

Published
2023
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29. PET imaging of zirconium-89 labelled cetuximab: A phase I trial in patients with head and neck and lung cancer

Author

van Loon, Judith, Even, Aniek J.G., Aerts, Hugo J.W.L., Öllers, Michel, Hoebers, Frank, van Elmpt, Wouter, Dubois, Ludwig, Dingemans, Anne-Marie C., Lalisang, Roy I., Kempers, Pascal, Brans, Boudewijn, Winnepenninckx, Véronique, Speel, Ernst-Jan, Thunnissen, Eric, Smits, Kim M., Boellaard, Ronald, Vugts, Danielle J., De Ruysscher, Dirk, and Lambin, Philippe

Published
2017
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32. Data from Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer

Author

Rios Velazquez, Emmanuel, primary, Parmar, Chintan, primary, Liu, Ying, primary, Coroller, Thibaud P., primary, Cruz, Gisele, primary, Stringfield, Olya, primary, Ye, Zhaoxiang, primary, Makrigiorgos, Mike, primary, Fennessy, Fiona, primary, Mak, Raymond H., primary, Gillies, Robert, primary, Quackenbush, John, primary, and Aerts, Hugo J.W.L., primary

Published
2023
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41. Supplemental video 2 from NCI Imaging Data Commons

Author

Fedorov, Andrey, primary, Longabaugh, William J.R., primary, Pot, David, primary, Clunie, David A., primary, Pieper, Steve, primary, Aerts, Hugo J.W.L., primary, Homeyer, André, primary, Lewis, Rob, primary, Akbarzadeh, Afshin, primary, Bontempi, Dennis, primary, Clifford, William, primary, Herrmann, Markus D., primary, Höfener, Henning, primary, Octaviano, Igor, primary, Osborne, Chad, primary, Paquette, Suzanne, primary, Petts, James, primary, Punzo, Davide, primary, Reyes, Madelyn, primary, Schacherer, Daniela P., primary, Tian, Mi, primary, White, George, primary, Ziegler, Erik, primary, Shmulevich, Ilya, primary, Pihl, Todd, primary, Wagner, Ulrike, primary, Farahani, Keyvan, primary, and Kikinis, Ron, primary

Published
2023
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42. Supplemental video 1 from NCI Imaging Data Commons

Author

Fedorov, Andrey, primary, Longabaugh, William J.R., primary, Pot, David, primary, Clunie, David A., primary, Pieper, Steve, primary, Aerts, Hugo J.W.L., primary, Homeyer, André, primary, Lewis, Rob, primary, Akbarzadeh, Afshin, primary, Bontempi, Dennis, primary, Clifford, William, primary, Herrmann, Markus D., primary, Höfener, Henning, primary, Octaviano, Igor, primary, Osborne, Chad, primary, Paquette, Suzanne, primary, Petts, James, primary, Punzo, Davide, primary, Reyes, Madelyn, primary, Schacherer, Daniela P., primary, Tian, Mi, primary, White, George, primary, Ziegler, Erik, primary, Shmulevich, Ilya, primary, Pihl, Todd, primary, Wagner, Ulrike, primary, Farahani, Keyvan, primary, and Kikinis, Ron, primary

Published
2023
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43. Supplemental video 4 from NCI Imaging Data Commons

Author

Fedorov, Andrey, primary, Longabaugh, William J.R., primary, Pot, David, primary, Clunie, David A., primary, Pieper, Steve, primary, Aerts, Hugo J.W.L., primary, Homeyer, André, primary, Lewis, Rob, primary, Akbarzadeh, Afshin, primary, Bontempi, Dennis, primary, Clifford, William, primary, Herrmann, Markus D., primary, Höfener, Henning, primary, Octaviano, Igor, primary, Osborne, Chad, primary, Paquette, Suzanne, primary, Petts, James, primary, Punzo, Davide, primary, Reyes, Madelyn, primary, Schacherer, Daniela P., primary, Tian, Mi, primary, White, George, primary, Ziegler, Erik, primary, Shmulevich, Ilya, primary, Pihl, Todd, primary, Wagner, Ulrike, primary, Farahani, Keyvan, primary, and Kikinis, Ron, primary

Published
2023
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44. Supplemental video 3 from NCI Imaging Data Commons

Author

Fedorov, Andrey, primary, Longabaugh, William J.R., primary, Pot, David, primary, Clunie, David A., primary, Pieper, Steve, primary, Aerts, Hugo J.W.L., primary, Homeyer, André, primary, Lewis, Rob, primary, Akbarzadeh, Afshin, primary, Bontempi, Dennis, primary, Clifford, William, primary, Herrmann, Markus D., primary, Höfener, Henning, primary, Octaviano, Igor, primary, Osborne, Chad, primary, Paquette, Suzanne, primary, Petts, James, primary, Punzo, Davide, primary, Reyes, Madelyn, primary, Schacherer, Daniela P., primary, Tian, Mi, primary, White, George, primary, Ziegler, Erik, primary, Shmulevich, Ilya, primary, Pihl, Todd, primary, Wagner, Ulrike, primary, Farahani, Keyvan, primary, and Kikinis, Ron, primary

Published
2023
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45. Data from NCI Imaging Data Commons

Author

Fedorov, Andrey, primary, Longabaugh, William J.R., primary, Pot, David, primary, Clunie, David A., primary, Pieper, Steve, primary, Aerts, Hugo J.W.L., primary, Homeyer, André, primary, Lewis, Rob, primary, Akbarzadeh, Afshin, primary, Bontempi, Dennis, primary, Clifford, William, primary, Herrmann, Markus D., primary, Höfener, Henning, primary, Octaviano, Igor, primary, Osborne, Chad, primary, Paquette, Suzanne, primary, Petts, James, primary, Punzo, Davide, primary, Reyes, Madelyn, primary, Schacherer, Daniela P., primary, Tian, Mi, primary, White, George, primary, Ziegler, Erik, primary, Shmulevich, Ilya, primary, Pihl, Todd, primary, Wagner, Ulrike, primary, Farahani, Keyvan, primary, and Kikinis, Ron, primary

Published
2023
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46. Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline

Author

Ye, Zezhong, primary, Saraf, Anurag, additional, Ravipati, Yashwanth, additional, Hoebers, Frank, additional, Zha, Yining, additional, Zapaishchykova, Anna, additional, Likitlersuang, Jirapat, additional, Tishler, Roy B., additional, Schoenfeld, Jonathan D., additional, Margalit, Danielle N., additional, Haddad, Robert I., additional, Mak, Raymond H., additional, Naser, Mohamed, additional, Wahid, Kareem A., additional, Sahlsten, Jaakko, additional, Jaskari, Joel, additional, Kaski, Kimmo, additional, Mäkitie, Antti A., additional, Fuller, Clifton D., additional, Aerts, Hugo J.W.L., additional, and Kann, Benjamin H., additional

Published
2023
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47. medBERT.de: A comprehensive German BERT model for the medical domain

Author

Bressem, Keno K., Papaioannou, Jens Michalis, Grundmann, Paul, Borchert, Florian, Adams, Lisa C., Liu, Leonhard, Busch, Felix, Xu, Lina, Loyen, Jan P., Niehues, Stefan M., Augustin, Moritz, Grosser, Lennart, Makowski, Marcus R., Aerts, Hugo J.W.L., Löser, Alexander, Bressem, Keno K., Papaioannou, Jens Michalis, Grundmann, Paul, Borchert, Florian, Adams, Lisa C., Liu, Leonhard, Busch, Felix, Xu, Lina, Loyen, Jan P., Niehues, Stefan M., Augustin, Moritz, Grosser, Lennart, Makowski, Marcus R., Aerts, Hugo J.W.L., and Löser, Alexander

Abstract

This paper presents medBERT.de, a pre-trained German BERT model specifically designed for the German medical domain. The model has been trained on a large corpus of 4.7 Million German medical documents and has been shown to achieve new state-of-the-art performance on eight different medical benchmarks covering a wide range of disciplines and medical document types. In addition to evaluating the overall performance of the model, this paper also conducts a more in-depth analysis of its capabilities. We investigate the impact of data deduplication on the model's performance, as well as the potential benefits of using more efficient tokenization methods. Our results indicate that domain-specific models such as medBERT.de are particularly useful for longer texts, and that deduplication of training data does not necessarily lead to improved performance. Furthermore, we found that efficient tokenization plays only a minor role in improving model performance, and attribute most of the improved performance to the large amount of training data. To encourage further research, the pre-trained model weights and new benchmarks based on radiological data are made publicly available for use by the scientific community.

Published
2023

48. Natural Language Processing to Automatically Extract the Presence and Severity of Esophagitis in Notes of Patients Undergoing Radiotherapy

Author

Chen, Shan, Guevara, Marco, Ramirez, Nicolas, Murray, Arpi, Warner, Jeremy L., Aerts, Hugo J.W.L., Miller, Timothy A., Savova, Guergana K., Mak, Raymond H., Bitterman, Danielle S., Chen, Shan, Guevara, Marco, Ramirez, Nicolas, Murray, Arpi, Warner, Jeremy L., Aerts, Hugo J.W.L., Miller, Timothy A., Savova, Guergana K., Mak, Raymond H., and Bitterman, Danielle S.

Abstract

PURPOSE: Radiotherapy (RT) toxicities can impair survival and quality of life, yet remain understudied. Real-world evidence holds potential to improve our understanding of toxicities, but toxicity information is often only in clinical notes. We developed natural language processing (NLP) models to identify the presence and severity of esophagitis from notes of patients treated with thoracic RT. METHODS: Our corpus consisted of a gold-labeled data set of 1,524 clinical notes from 124 patients with lung cancer treated with RT, manually annotated for Common Terminology Criteria for Adverse Events (CTCAE) v5.0 esophagitis grade, and a silver-labeled data set of 2,420 notes from 1,832 patients from whom toxicity grades had been collected as structured data during clinical care. We fine-tuned statistical and pretrained Bidirectional Encoder Representations from Transformers-based models for three esophagitis classification tasks: task 1, no esophagitis versus grade 1-3; task 2, grade =1 versus >1; and task 3, no esophagitis versus grade 1 versus grade 2-3. Transferability was tested on 345 notes from patients with esophageal cancer undergoing RT. RESULTS: Fine-tuning of PubMedBERT yielded the best performance. The best macro-F1 was 0.92, 0.82, and 0.74 for tasks 1, 2, and 3, respectively. Selecting the most informative note sections during fine-tuning improved macro-F1 by =2% for all tasks. Silver-labeled data improved the macro-F1 by =3% across all tasks. For the esophageal cancer notes, the best macro-F1 was 0.73, 0.74, and 0.65 for tasks 1, 2, and 3, respectively, without additional fine-tuning. CONCLUSION: To our knowledge, this is the first effort to automatically extract esophagitis toxicity severity according to CTCAE guidelines from clinical notes. This provides proof of concept for NLP-based automated detailed toxicity monitoring in expanded domains.

Published
2023

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