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2. Deep learning-based detection of intravenous contrast in computed tomography scans

Author

Ye, Zezhong, Qian, Jack M., Hosny, Ahmed, Zeleznik, Roman, Plana, Deborah, Likitlersuang, Jirapat, Zhang, Zhongyi, Mak, Raymond H., Aerts, Hugo J. W. L., and Kann, Benjamin H.

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

Purpose: Identifying intravenous (IV) contrast use within CT scans is a key component of data curation for model development and testing. Currently, IV contrast is poorly documented in imaging metadata and necessitates manual correction and annotation by clinician experts, presenting a major barrier to imaging analyses and algorithm deployment. We sought to develop and validate a convolutional neural network (CNN)-based deep learning (DL) platform to identify IV contrast within CT scans. Methods: For model development and evaluation, we used independent datasets of CT scans of head, neck (HN) and lung cancer patients, totaling 133,480 axial 2D scan slices from 1,979 CT scans manually annotated for contrast presence by clinical experts. Five different DL models were adopted and trained in HN training datasets for slice-level contrast detection. Model performances were evaluated on a hold-out set and on an independent validation set from another institution. DL models was then fine-tuned on chest CT data and externally validated on a separate chest CT dataset. Results: Initial DICOM metadata tags for IV contrast were missing or erroneous in 1,496 scans (75.6%). The EfficientNetB4-based model showed the best overall detection performance. For HN scans, AUC was 0.996 in the internal validation set (n = 216) and 1.0 in the external validation set (n = 595). The fine-tuned model on chest CTs yielded an AUC: 1.0 for the internal validation set (n = 53), and AUC: 0.980 for the external validation set (n = 402). Conclusion: The DL model could accurately detect IV contrast in both HN and chest CT scans with near-perfect performance.

Published
2021

11. 3D Printed frames to enable reuse and improve the fit of N95 and KN95 respirators

Author

McAvoy, Malia, Bui, Ai-Tram N., Hansen, Christopher, Plana, Deborah, Said, Jordan T., Yu, Zizi, Yang, Helen, Freake, Jacob, Van, Christopher, Krikorian, David, Cramer, Avilash, Smith, Leanne, Jiang, Liwei, Lee, Karen J., Li, Sara J., Beller, Brandon, Huggins, Kimberley, Short, Michael P., Yu, Sherry H., Mostaghimi, Arash, Sorger, Peter K., and LeBoeuf, Nicole R.

Published
2021
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13. 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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14. 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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15. 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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16. 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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17. 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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18. 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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19. 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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20. 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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21. 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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22. De Novo Powered Air-Purifying Respirator Design and Fabrication for Pandemic Response

Author

Massachusetts Institute of Technology. Department of Nuclear Science and Engineering, Kothakonda, Akshay, Atta, Lyla, Plana, Deborah, Ward, Ferrous, Davis, Chris, Cramer, Avilash, Moran, Robert, Freake, Jacob, Tian, Enze, Mazor, Ofer, Gorelik, Pavel, Van, Christopher, Hansen, Christopher, Yang, Helen, Li, Yao, Sinha, Michael S, Li, Ju, Yu, Sherry H, LeBoeuf, Nicole R, Sorger, Peter K, Massachusetts Institute of Technology. Department of Nuclear Science and Engineering, Kothakonda, Akshay, Atta, Lyla, Plana, Deborah, Ward, Ferrous, Davis, Chris, Cramer, Avilash, Moran, Robert, Freake, Jacob, Tian, Enze, Mazor, Ofer, Gorelik, Pavel, Van, Christopher, Hansen, Christopher, Yang, Helen, Li, Yao, Sinha, Michael S, Li, Ju, Yu, Sherry H, LeBoeuf, Nicole R, and Sorger, Peter K

Abstract

The rapid spread of COVID-19 and disruption of normal supply chains has resulted in severe shortages of personal protective equipment (PPE), particularly devices with few suppliers such as powered air-purifying respirators (PAPRs). A scarcity of information describing design and performance criteria for PAPRs represents a substantial barrier to mitigating shortages. We sought to apply open-source product development (OSPD) to PAPRs to enable alternative sources of supply and further innovation. We describe the design, prototyping, validation, and user testing of locally manufactured, modular, PAPR components, including filter cartridges and blower units, developed by the Greater Boston Pandemic Fabrication Team (PanFab). Two designs, one with a fully custom-made filter and blower unit housing, and the other with commercially available variants (the “Custom” and “Commercial” designs, respectively) were developed; the components in the Custom design are interchangeable with those in Commercial design, although the form factor differs. The engineering performance of the prototypes was measured and safety validated using National Institutes for Occupational Safety and Health (NIOSH)-equivalent tests on apparatus available under pandemic conditions at university laboratories. Feedback was obtained from four individuals; two clinicians working in ambulatory clinical care and two research technical staff for whom PAPR use is standard occupational PPE; these individuals were asked to compare PanFab prototypes to commercial PAPRs from the perspective of usability and suggest areas for improvement. Respondents rated the PanFab Custom PAPR a 4 to 5 on a 5 Likert-scale 1) as compared to current PPE options, 2) for the sense of security with use in a clinical setting, and 3) for comfort compared to standard, commercially available PAPRs. The three other versions of the designs (with a Commercial blower unit, filter, or both) performed favorably, with survey responses con

Published
2023

26. Supplementary Table 2 from A Proof of Concept for Biomarker-Guided Targeted Therapy against Ovarian Cancer Based on Patient-Derived Tumor Xenografts

Author

Palmer, Adam C., primary, Plana, Deborah, primary, Gao, Hui, primary, Korn, Joshua M., primary, Yang, Guizhi, primary, Green, John, primary, Zhang, Xiamei, primary, Velazquez, Roberto, primary, McLaughlin, Margaret E., primary, Ruddy, David A., primary, Kowal, Colleen, primary, Muszynski, Julie, primary, Bullock, Caroline, primary, Rivera, Stacy, primary, Rakiec, Daniel P., primary, Elliott, GiNell, primary, Fordjour, Paul, primary, Meyer, Ronald, primary, Loo, Alice, primary, Kurth, Esther, primary, Engelman, Jeffrey A., primary, Bitter, Hans, primary, Sellers, William R., primary, Williams, Juliet A., primary, and Sorger, Peter K., primary

Published
2023
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27. Data from A Proof of Concept for Biomarker-Guided Targeted Therapy against Ovarian Cancer Based on Patient-Derived Tumor Xenografts

Author

Palmer, Adam C., primary, Plana, Deborah, primary, Gao, Hui, primary, Korn, Joshua M., primary, Yang, Guizhi, primary, Green, John, primary, Zhang, Xiamei, primary, Velazquez, Roberto, primary, McLaughlin, Margaret E., primary, Ruddy, David A., primary, Kowal, Colleen, primary, Muszynski, Julie, primary, Bullock, Caroline, primary, Rivera, Stacy, primary, Rakiec, Daniel P., primary, Elliott, GiNell, primary, Fordjour, Paul, primary, Meyer, Ronald, primary, Loo, Alice, primary, Kurth, Esther, primary, Engelman, Jeffrey A., primary, Bitter, Hans, primary, Sellers, William R., primary, Williams, Juliet A., primary, and Sorger, Peter K., primary

Published
2023
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28. Supplementary Figure S2 from A Proof of Concept for Biomarker-Guided Targeted Therapy against Ovarian Cancer Based on Patient-Derived Tumor Xenografts

Author

Palmer, Adam C., primary, Plana, Deborah, primary, Gao, Hui, primary, Korn, Joshua M., primary, Yang, Guizhi, primary, Green, John, primary, Zhang, Xiamei, primary, Velazquez, Roberto, primary, McLaughlin, Margaret E., primary, Ruddy, David A., primary, Kowal, Colleen, primary, Muszynski, Julie, primary, Bullock, Caroline, primary, Rivera, Stacy, primary, Rakiec, Daniel P., primary, Elliott, GiNell, primary, Fordjour, Paul, primary, Meyer, Ronald, primary, Loo, Alice, primary, Kurth, Esther, primary, Engelman, Jeffrey A., primary, Bitter, Hans, primary, Sellers, William R., primary, Williams, Juliet A., primary, and Sorger, Peter K., primary

Published
2023
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29. Supplementary Materials from A Proof of Concept for Biomarker-Guided Targeted Therapy against Ovarian Cancer Based on Patient-Derived Tumor Xenografts

Author

Palmer, Adam C., primary, Plana, Deborah, primary, Gao, Hui, primary, Korn, Joshua M., primary, Yang, Guizhi, primary, Green, John, primary, Zhang, Xiamei, primary, Velazquez, Roberto, primary, McLaughlin, Margaret E., primary, Ruddy, David A., primary, Kowal, Colleen, primary, Muszynski, Julie, primary, Bullock, Caroline, primary, Rivera, Stacy, primary, Rakiec, Daniel P., primary, Elliott, GiNell, primary, Fordjour, Paul, primary, Meyer, Ronald, primary, Loo, Alice, primary, Kurth, Esther, primary, Engelman, Jeffrey A., primary, Bitter, Hans, primary, Sellers, William R., primary, Williams, Juliet A., primary, and Sorger, Peter K., primary

Published
2023
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30. Supplementary Table 4 from A Proof of Concept for Biomarker-Guided Targeted Therapy against Ovarian Cancer Based on Patient-Derived Tumor Xenografts

Author

Palmer, Adam C., primary, Plana, Deborah, primary, Gao, Hui, primary, Korn, Joshua M., primary, Yang, Guizhi, primary, Green, John, primary, Zhang, Xiamei, primary, Velazquez, Roberto, primary, McLaughlin, Margaret E., primary, Ruddy, David A., primary, Kowal, Colleen, primary, Muszynski, Julie, primary, Bullock, Caroline, primary, Rivera, Stacy, primary, Rakiec, Daniel P., primary, Elliott, GiNell, primary, Fordjour, Paul, primary, Meyer, Ronald, primary, Loo, Alice, primary, Kurth, Esther, primary, Engelman, Jeffrey A., primary, Bitter, Hans, primary, Sellers, William R., primary, Williams, Juliet A., primary, and Sorger, Peter K., primary

Published
2023
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31. Supplementary Table 1 from A Proof of Concept for Biomarker-Guided Targeted Therapy against Ovarian Cancer Based on Patient-Derived Tumor Xenografts

Author

Palmer, Adam C., primary, Plana, Deborah, primary, Gao, Hui, primary, Korn, Joshua M., primary, Yang, Guizhi, primary, Green, John, primary, Zhang, Xiamei, primary, Velazquez, Roberto, primary, McLaughlin, Margaret E., primary, Ruddy, David A., primary, Kowal, Colleen, primary, Muszynski, Julie, primary, Bullock, Caroline, primary, Rivera, Stacy, primary, Rakiec, Daniel P., primary, Elliott, GiNell, primary, Fordjour, Paul, primary, Meyer, Ronald, primary, Loo, Alice, primary, Kurth, Esther, primary, Engelman, Jeffrey A., primary, Bitter, Hans, primary, Sellers, William R., primary, Williams, Juliet A., primary, and Sorger, Peter K., primary

Published
2023
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33. Supplementary Table 3 from A Proof of Concept for Biomarker-Guided Targeted Therapy against Ovarian Cancer Based on Patient-Derived Tumor Xenografts

Author

Palmer, Adam C., primary, Plana, Deborah, primary, Gao, Hui, primary, Korn, Joshua M., primary, Yang, Guizhi, primary, Green, John, primary, Zhang, Xiamei, primary, Velazquez, Roberto, primary, McLaughlin, Margaret E., primary, Ruddy, David A., primary, Kowal, Colleen, primary, Muszynski, Julie, primary, Bullock, Caroline, primary, Rivera, Stacy, primary, Rakiec, Daniel P., primary, Elliott, GiNell, primary, Fordjour, Paul, primary, Meyer, Ronald, primary, Loo, Alice, primary, Kurth, Esther, primary, Engelman, Jeffrey A., primary, Bitter, Hans, primary, Sellers, William R., primary, Williams, Juliet A., primary, and Sorger, Peter K., primary

Published
2023
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37. Deep Learning-based Detection of Intravenous Contrast Enhancement on CT Scans

Author

Ye, Zezhong, Qian, Jack M., Hosny, Ahmed, Zeleznik, Roman, Plana, Deborah, Likitlersuang, Jirapat, Zhang, Zhongyi, Mak, Raymond H., Aerts, Hugo J. W. L., Kann, Benjamin H., RS: GROW - R2 - Basic and Translational Cancer Biology, RS: Carim - B06 Imaging, Beeldvorming, and MUMC+: DA BV Research (9)

Subjects
Machine Learning Algorithms, Radiological and Ultrasound Technology, Artificial Intelligence, Radiology, Nuclear Medicine and imaging, Transfer Learning, Supervised Learning, AI in Brief, Convolutional Neural Network (CNN), Contrast Material, CT, Head and Neck
Abstract

Identifying the presence of intravenous contrast material on CT scans is an important component of data curation for medical imaging-based artificial intelligence model development and deployment. Use of intravenous contrast material is often poorly documented in imaging metadata, necessitating impractical manual annotation by clinician experts. Authors developed a convolutional neural network (CNN)-based deep learning platform to identify intravenous contrast enhancement on CT scans. For model development and validation, authors used six independent datasets of head and neck (HN) and chest CT scans, totaling 133 480 axial two-dimensional sections from 1979 scans, which were manually annotated by clinical experts. Five CNN models were trained first on HN scans for contrast enhancement detection. Model performances were evaluated at the patient level on a holdout set and external test set. Models were then fine-tuned on chest CT data and externally validated. This study found that Digital Imaging and Communications in Medicine metadata tags for intravenous contrast material were missing or erroneous for 1496 scans (75.6%). An EfficientNetB4-based model showed the best performance, with areas under the curve (AUCs) of 0.996 and 1.0 in HN holdout (n = 216) and external (n = 595) sets, respectively, and AUCs of 1.0 and 0.980 in the chest holdout (n = 53) and external (n = 402) sets, respectively. This automated, scan-to-prediction platform is highly accurate at CT contrast enhancement detection and may be helpful for artificial intelligence model development and clinical application. Keywords: CT, Head and Neck, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN), Machine Learning Algorithms, Contrast Material Supplemental material is available for this article. © RSNA, 2022.

Published
2022

38. A high-touch outreach model to re-engage patients in mammogram screening.

Author

Ojo, Ayotomiwa, primary, Johnson, Shawn, additional, Erfani, Parsa, additional, Guo, Ruby, additional, Garmilla, Andrea, additional, Saini, Arushi, additional, Benitez, Brian, additional, Salinas, Kevin E, additional, Plana, Deborah, additional, Pena Perez, Amalia, additional, Guzman, Jacqueline, additional, So-Armah, Cynthia, additional, and Gottlieb, Barbara, additional

Published
2022
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39. Plasma-derived extracellular vesicle analysis and deconvolution enable prediction and tracking of melanoma checkpoint blockade outcome

Author

Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Shi, Alvin, Kasumova, Gyulnara G, Michaud, William A, Cintolo-Gonzalez, Jessica, Díaz-Martínez, Marta, Ohmura, Jacqueline, Mehta, Arnav, Chien, Isabel, Frederick, Dennie T, Cohen, Sonia, Plana, Deborah, Johnson, Douglas, Flaherty, Keith T, Sullivan, Ryan J, Kellis, Manolis, Boland, Genevieve M, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Shi, Alvin, Kasumova, Gyulnara G, Michaud, William A, Cintolo-Gonzalez, Jessica, Díaz-Martínez, Marta, Ohmura, Jacqueline, Mehta, Arnav, Chien, Isabel, Frederick, Dennie T, Cohen, Sonia, Plana, Deborah, Johnson, Douglas, Flaherty, Keith T, Sullivan, Ryan J, Kellis, Manolis, and Boland, Genevieve M

Abstract

Immune checkpoint inhibitors (ICIs) show promise, but most patients do not respond. We identify and validate biomarkers from extracellular vesicles (EVs), allowing non-invasive monitoring of tumor- intrinsic and host immune status, as well as a prediction of ICI response. We undertook transcriptomic profiling of plasma-derived EVs and tumors from 50 patients with metastatic melanoma receiving ICI, and validated with an independent EV-only cohort of 30 patients. Plasma-derived EV and tumor transcriptomes correlate. EV profiles reveal drivers of ICI resistance and melanoma progression, exhibit differentially expressed genes/pathways, and correlate with clinical response to ICI. We created a Bayesian probabilistic deconvolution model to estimate contributions from tumor and non-tumor sources, enabling interpretation of differentially expressed genes/pathways. EV RNA-seq mutations also segregated ICI response. EVs serve as a non-invasive biomarker to jointly probe tumor-intrinsic and immune changes to ICI, function as predictive markers of ICI responsiveness, and monitor tumor persistence and immune activation.

Published
2022

41. Growth in eligibility criteria content and failure to accrue among National Cancer Institute (NCI)‐affiliated clinical trials.

Author

Peterson, John S., Plana, Deborah, Bitterman, Danielle S., Johnson, Skyler Bryce, Aerts, Hugo J. W. L., and Kann, Benjamin Harris

Subjects
*NATURAL language processing, *CLINICAL trials, *MEDICAL terminology, *WORD frequency, *LOGISTIC regression analysis
Abstract

Background: Cancer trial accrual is a national priority, yet up to 20% of trials fail to accrue. Trial eligibility criteria growth may be associated with accrual failure. We sought to quantify eligibility criteria growth within National Cancer Institute (NCI)‐affiliated trials and determine impact on accrual. Methods: Utilizing the Aggregated Analysis of ClinicalTrials.gov, we analyzed phase II/III interventional NCI‐affiliated trials initiated between 2008 and 2018. Eligibility criteria growth was assessed via number of unique content words within combined inclusion and exclusion criteria. Association between unique word count and accrual failure was evaluated with multivariable logistic regression, adjusting for known predictors of failure. Medical terms associated with accrual failure were identified via natural language processing and categorized. Results: Of 1197 trials, 231 (19.3%) failed due to low accrual. Accrual failure rate increased with eligibility criteria growth, from 11.8% in the lowest decile (12–112 words) to 29.4% in the highest decile (445–750 words). Median eligibility criteria increased over time, from 214 (IQR [23, 282]) unique content words in 2008 to 417 (IQR [289, 514]) in 2018 (r2 = 0.73, P < 0.001). Eligibility criteria growth was independently associated with accrual failure (OR: 1.09 per decile, 95% CI [1.03–1.15], p = 0.004). Eighteen exclusion criteria categories were significantly associated with accrual failure, including renal, pulmonary, and diabetic, among others (Bonferroni‐corrected p < 0.001). Conclusions: Eligibility criteria content growth is increasing dramatically among NCI‐affiliated trials and is strongly associated with accrual failure. These findings support national initiatives to simplify eligibility criteria and suggest that further efforts are warranted to improve cancer trial accrual. [ABSTRACT FROM AUTHOR]

Published
2023
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42. De Novo Powered Air-Purifying Respirator Design and Fabrication for Pandemic Response

Author

Kothakonda, Akshay, primary, Atta, Lyla, additional, Plana, Deborah, additional, Ward, Ferrous, additional, Davis, Chris, additional, Cramer, Avilash, additional, Moran, Robert, additional, Freake, Jacob, additional, Tian, Enze, additional, Mazor, Ofer, additional, Gorelik, Pavel, additional, Van, Christopher, additional, Hansen, Christopher, additional, Yang, Helen, additional, Li, Yao, additional, Sinha, Michael S., additional, Li, Ju, additional, Yu, Sherry H., additional, LeBoeuf, Nicole R., additional, and Sorger, Peter K., additional

Published
2021
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43. Additional file 2 of 3D Printed frames to enable reuse and improve the fit of N95 and KN95 respirators

Author

McAvoy, Malia, Bui, Ai-Tram N., Hansen, Christopher, Plana, Deborah, Said, Jordan T., Yu, Zizi, Yang, Helen, Freake, Jacob, Van, Christopher, Krikorian, David, Cramer, Avilash, Smith, Leanne, Jiang, Liwei, Lee, Karen J., Li, Sara J., Beller, Brandon, Huggins, Kimberley, Short, Michael P., Yu, Sherry H., Mostaghimi, Arash, Sorger, Peter K., and LeBoeuf, Nicole R.

Abstract

Additional Material 2: Mask frame assembly, donning, and seal check instructions with accompanying diagrams.

Published
2021
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44. Additional file 2 of Assessing the filtration efficiency and regulatory status of N95s and nontraditional filtering face-piece respirators available during the COVID-19 pandemic

Author

Plana, Deborah, Tian, Enze, Cramer, Avilash K., Yang, Helen, Carmack, Mary M., Sinha, Michael S., Bourgeois, Florence T., Yu, Sherry H., Masse, Peter, Boyer, Jon, Kim, Minjune, Mo, Jinhan, LeBoeuf, Nicole R., Li, Ju, and Sorger, Peter K.

Abstract

Additional file 2: Attributes of masks undergoing filtration efficiency testing including manufacturer and model, relevant regulatory standard, shape, presence of exhalation valve, type of tethering device, weight, thickness, and a picture of each mask. Mask numbers correspond to label numbers on Fig. 3 and on the first column of Additional file 4.

Published
2021
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45. Additional file 1 of 3D Printed frames to enable reuse and improve the fit of N95 and KN95 respirators

Author

McAvoy, Malia, Bui, Ai-Tram N., Hansen, Christopher, Plana, Deborah, Said, Jordan T., Yu, Zizi, Yang, Helen, Freake, Jacob, Van, Christopher, Krikorian, David, Cramer, Avilash, Smith, Leanne, Jiang, Liwei, Lee, Karen J., Li, Sara J., Beller, Brandon, Huggins, Kimberley, Short, Michael P., Yu, Sherry H., Mostaghimi, Arash, Sorger, Peter K., and LeBoeuf, Nicole R.

Abstract

Additional Material 1: Individual participant data used to construct Tables 1 and 2.

Published
2021
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46. Additional file 3 of Assessing the filtration efficiency and regulatory status of N95s and nontraditional filtering face-piece respirators available during the COVID-19 pandemic

Author

Plana, Deborah, Tian, Enze, Cramer, Avilash K., Yang, Helen, Carmack, Mary M., Sinha, Michael S., Bourgeois, Florence T., Yu, Sherry H., Masse, Peter, Boyer, Jon, Kim, Minjune, Mo, Jinhan, LeBoeuf, Nicole R., Li, Ju, and Sorger, Peter K.

Abstract

Additional file 3: NIOSH infographic illustrating the correct labeling of N95 masks.

Published
2021
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49. Assessing the filtration efficiency and regulatory status of N95s and nontraditional filtering face-piece respirators available during the COVID-19 pandemic

Author

Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Department of Nuclear Science and Engineering, Massachusetts Institute of Technology. Department of Materials Science and Engineering, Plana, Deborah, Tian, Enze, Cramer, Avilash, Yang, Helen, Carmack, Mary M., Sinha, Michael S., Bourgeois, Florence T., Yu, Sherry H., Masse, Peter, Boyer, Jon, Kim, Minjune, Mo, Jinhan, LeBoeuf, Nicole R., Li, Ju, Sorger, Peter K., Harvard University--MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology. Department of Nuclear Science and Engineering, Massachusetts Institute of Technology. Department of Materials Science and Engineering, Plana, Deborah, Tian, Enze, Cramer, Avilash, Yang, Helen, Carmack, Mary M., Sinha, Michael S., Bourgeois, Florence T., Yu, Sherry H., Masse, Peter, Boyer, Jon, Kim, Minjune, Mo, Jinhan, LeBoeuf, Nicole R., Li, Ju, and Sorger, Peter K.

Abstract

Background:The COVID-19 pandemic has severely disrupted supply chains for many types of Personal Protective Equipment (PPE), particularly surgical N95 filtering facepiece respirators (FFRs; “masks”). As a consequence, an Emergency Use Authorization (EUA) from the FDA has allowed use of industrial N95 respirators and importation of N95-type masks manufactured to international standards; these include KN95 masks from China and FFP2 masks from the European Union. Methods: We conducted a survey of masks in the inventory of major academic medical centers in Boston, MA to determine provenance and manufacturer or supplier. We then assembled a testing apparatus at a university laboratory and performed a modified test of filtration performance using KCl and ambient particulate matter on masks from hospital inventories; an accompanying website shows how to build and use the testing apparatus. Results: Over 100 different makes and models of traditional and nontraditional filtering facepiece respirators (N95-type masks) were in the inventory of surveyed U.S. teaching hospitals as opposed to 2–5 models under normal circumstances. A substantial number of unfamiliar masks are from unknown manufacturers. Many are not correctly labelled and do not perform to accepted standards and a subset are obviously dangerous; many of these masks are likely to be counterfeit. Due to the absence of publicly available information on mask suppliers and inconsistent labeling of KN95 masks, it is difficult to distinguish between legitimate and counterfeit products. Conclusions: Many FFRs available for procurement during the COVID-19 pandemic do not provide levels of fit and filtration similar to those of N95 masks and are not acceptable for use in healthcare settings. Based on these results, and in consultation with occupational health officers, we make six recommendations to assist end users in acquiring legitimate products. Institutions should always assess masks from non-traditional supply chains

Published
2021

50. Plasma-derived extracellular vesicle analysis and deconvolution enable prediction and tracking of melanoma checkpoint blockade outcome

Author

Shi, Alvin, Kasumova, Gyulnara G, Michaud, William A, Cintolo-Gonzalez, Jessica, Díaz-Martínez, Marta, Ohmura, Jacqueline, Mehta, Arnav, Chien, Isabel, Frederick, Dennie T, Cohen, Sonia, Plana, Deborah, Johnson, Douglas, Flaherty, Keith T, Sullivan, Ryan J, Kellis, Manolis, Boland, Genevieve M, Shi, Alvin, Kasumova, Gyulnara G, Michaud, William A, Cintolo-Gonzalez, Jessica, Díaz-Martínez, Marta, Ohmura, Jacqueline, Mehta, Arnav, Chien, Isabel, Frederick, Dennie T, Cohen, Sonia, Plana, Deborah, Johnson, Douglas, Flaherty, Keith T, Sullivan, Ryan J, Kellis, Manolis, and Boland, Genevieve M

Abstract

Immune checkpoint inhibitors (ICIs) show promise, but most patients do not respond. We identify and validate biomarkers from extracellular vesicles (EVs), allowing non-invasive monitoring of tumor- intrinsic and host immune status, as well as a prediction of ICI response. We undertook transcriptomic profiling of plasma-derived EVs and tumors from 50 patients with metastatic melanoma receiving ICI, and validated with an independent EV-only cohort of 30 patients. Plasma-derived EV and tumor transcriptomes correlate. EV profiles reveal drivers of ICI resistance and melanoma progression, exhibit differentially expressed genes/pathways, and correlate with clinical response to ICI. We created a Bayesian probabilistic deconvolution model to estimate contributions from tumor and non-tumor sources, enabling interpretation of differentially expressed genes/pathways. EV RNA-seq mutations also segregated ICI response. EVs serve as a non-invasive biomarker to jointly probe tumor-intrinsic and immune changes to ICI, function as predictive markers of ICI responsiveness, and monitor tumor persistence and immune activation.

Published
2021

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