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290 results on '"Cardenas, Carlos E."'

1. Evolving Horizons in Radiotherapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification

Author

Wahid, Kareem A., Cardenas, Carlos E., Marquez, Barbara, Netherton, Tucker J., Kann, Benjamin H., Court, Laurence E., He, Renjie, Naser, Mohamed A., Moreno, Amy C., Fuller, Clifton D., and Fuentes, David

Subjects
Physics - Medical Physics
Abstract

Deep learning has significantly advanced the potential for automated contouring in radiotherapy planning. In this manuscript, guided by contemporary literature, we underscore three key insights: (1) High-quality training data is essential for auto-contouring algorithms; (2) Auto-contouring models demonstrate commendable performance even with limited medical image data; (3) The quantitative performance of auto-contouring is reaching a plateau. Given these insights, we emphasize the need for the radiotherapy research community to embrace data-centric approaches to further foster clinical adoption of auto-contouring technologies., Comment: 13 pages, 4 figures

Published
2023
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2. Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans

Author

Gronberg, Mary P., Beadle, Beth M., Garden, Adam S., Skinner, Heath, Gay, Skylar, Netherton, Tucker, Cao, Wenhua, Cardenas, Carlos E., Chung, Christine, Fuentes, David, Fuller, Clifton D., Howell, Rebecca M., Jhingran, Anuja, Lim, Tze Yee, Marquez, Barbara, Mumme, Raymond, Olanrewaju, Adenike M., Peterson, Christine B., Vazquez, Ivan, Whitaker, Thomas J., Wooten, Zachary, Yang, Ming, and Court, Laurence E.

Subjects
Physics - Medical Physics
Abstract

Purpose: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. Methods: A total of 245 VMAT HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included CT images, target prescriptions, and contours for targets and organs at risk (OARs). The model's performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared to manual flags by 3 HN radiation oncologists. Results: The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D1%, D95%, and D99% across the targets were within -2.53%(SD=1.34%), -0.42%(SD=1.27%), and -0.12%(SD=1.97%), respectively, and the OAR mean and maximum doses were within -0.33Gy(SD=1.40Gy) and -0.96Gy(SD=2.08Gy). For the plan quality assessment study, radiation oncologists flagged 47 OARs for possible plan improvement. There was high interphysician variability; 83% of physician-flagged OARs were flagged by only one of 3 physicians. The comparative dose prediction model flagged 63 OARs, including 30 of 47 physician-flagged OARs. Conclusion: Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality., Comment: updated to reflect the published peer-reviewed article

Published
2022
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7. OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines

Author

Babier, Aaron, Mahmood, Rafid, Zhang, Binghao, Alves, Victor G. L., Barragán-Montero, Ana Maria, Beaudry, Joel, Cardenas, Carlos E., Chang, Yankui, Chen, Zijie, Chun, Jaehee, Diaz, Kelly, Eraso, Harold David, Faustmann, Erik, Gaj, Sibaji, Gay, Skylar, Gronberg, Mary, Guo, Bingqi, He, Junjun, Heilemann, Gerd, Hira, Sanchit, Huang, Yuliang, Ji, Fuxin, Jiang, Dashan, Giraldo, Jean Carlo Jimenez, Lee, Hoyeon, Lian, Jun, Liu, Shuolin, Liu, Keng-Chi, Marrugo, José, Miki, Kentaro, Nakamura, Kunio, Netherton, Tucker, Nguyen, Dan, Nourzadeh, Hamidreza, Osman, Alexander F. I., Peng, Zhao, Muñoz, José Darío Quinto, Ramsl, Christian, Rhee, Dong Joo, Rodriguez, Juan David, Shan, Hongming, Siebers, Jeffrey V., Soomro, Mumtaz H., Sun, Kay, Hoyos, Andrés Usuga, Valderrama, Carlos, Verbeek, Rob, Wang, Enpei, Willems, Siri, Wu, Qi, Xu, Xuanang, Yang, Sen, Yuan, Lulin, Zhu, Simeng, Zimmermann, Lukas, Moore, Kevin L., Purdie, Thomas G., McNiven, Andrea L., and Chan, Timothy C. Y.

Subjects
Physics - Medical Physics, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients with head-and-neck cancer and high-quality dose predictions from 19 KBP models that were developed by different research groups during the OpenKBP Grand Challenge. The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans. The predictions and plans were compared to the reference plans via: dose score, which is the average mean absolute voxel-by-voxel difference in dose a model achieved; the deviation in dose-volume histogram (DVH) criterion; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50 to 0.62, which indicates that the quality of the predictions is generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P<0.05; one-sided Wilcoxon test) on 18 of 23 DVH criteria. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for a conventional planning model. This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. In the interest of reproducibility, our data and code is freely available at https://github.com/ababier/open-kbp-opt., Comment: 19 pages, 7 tables, 6 figures

Published
2022
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9. Multi-organ segmentation of abdominal structures from non-contrast and contrast enhanced CT images

Author

Yu, Cenji, Anakwenze, Chidinma P, Zhao, Yao, Martin, Rachael M, Ludmir, Ethan B, S.Niedzielski, Joshua, Qureshi, Asad, Das, Prajnan, Holliday, Emma B, Raldow, Ann C, Nguyen, Callistus M, Mumme, Raymond P, Netherton, Tucker J, Rhee, Dong Joo, Gay, Skylar S, Yang, Jinzhong, Court, Laurence E, and Cardenas, Carlos E

Subjects
Medical and Biological Physics, Biomedical and Clinical Sciences, Clinical Sciences, Physical Sciences, Oncology and Carcinogenesis, Biomedical Imaging, Rare Diseases, Digestive Diseases, Clinical Research, Humans, Tomography, X-Ray Computed, Organs at Risk, Abdomen, Liver, Patient Care Planning, Image Processing, Computer-Assisted
Abstract

Manually delineating upper abdominal organs at risk (OARs) is a time-consuming task. To develop a deep-learning-based tool for accurate and robust auto-segmentation of these OARs, forty pancreatic cancer patients with contrast-enhanced breath-hold computed tomographic (CT) images were selected. We trained a three-dimensional (3D) U-Net ensemble that automatically segments all organ contours concurrently with the self-configuring nnU-Net framework. Our tool's performance was assessed on a held-out test set of 30 patients quantitatively. Five radiation oncologists from three different institutions assessed the performance of the tool using a 5-point Likert scale on an additional 75 randomly selected test patients. The mean (± std. dev.) Dice similarity coefficient values between the automatic segmentation and the ground truth on contrast-enhanced CT images were 0.80 ± 0.08, 0.89 ± 0.05, 0.90 ± 0.06, 0.92 ± 0.03, 0.96 ± 0.01, 0.97 ± 0.01, 0.96 ± 0.01, and 0.96 ± 0.01 for the duodenum, small bowel, large bowel, stomach, liver, spleen, right kidney, and left kidney, respectively. 89.3% (contrast-enhanced) and 85.3% (non-contrast-enhanced) of duodenum contours were scored as a 3 or above, which required only minor edits. More than 90% of the other organs' contours were scored as a 3 or above. Our tool achieved a high level of clinical acceptability with a small training dataset and provides accurate contours for treatment planning.

Published
2022

17. Deep Learning–Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans

Author

Gronberg, Mary P., Beadle, Beth M., Garden, Adam S., Skinner, Heath, Gay, Skylar, Netherton, Tucker, Cao, Wenhua, Cardenas, Carlos E., Chung, Christine, Fuentes, David T., Fuller, Clifton D., Howell, Rebecca M., Jhingran, Anuja, Lim, Tze Yee, Marquez, Barbara, Mumme, Raymond, Olanrewaju, Adenike M., Peterson, Christine B., Vazquez, Ivan, Whitaker, Thomas J., Wooten, Zachary, Yang, Ming, and Court, Laurence E.

Published
2023
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22. Comprehensive Quantitative Evaluation of Variability in Magnetic Resonance-Guided Delineation of Oropharyngeal Gross Tumor Volumes and High-Risk Clinical Target Volumes: An R-IDEAL Stage 0 Prospective Study

Author

Cardenas, Carlos E., Blinde, Sanne E., Mohamed, Abdallah S.R., Ng, Sweet Ping, Raaijmakers, Cornelis, Philippens, Marielle, Kotte, Alexis, Al-Mamgani, Abrahim A., Karam, Irene, Thomson, David J., Robbins, Jared, Newbold, Kate, Fuller, Clifton D., and Terhaard, Chris

Published
2022
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23. A Bi-directional, Multi-modality Framework for Segmentation of Brain Structures

Author

Gay, Skylar S., Yu, Cenji, Rhee, Dong Joo, Sjogreen, Carlos, Mumme, Raymond P., Nguyen, Callistus M., Netherton, Tucker J., Cardenas, Carlos E., Court, Laurence E., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Shusharina, Nadya, editor, Heinrich, Mattias P., editor, and Huang, Ruobing, editor

Published
2021
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27. Longitudinal brain volumetrics in glioma survivors

Author

McDonald, Andrew M., primary, Murdaugh, Donna L., additional, Heinzman, Katherine A., additional, Cardan, Rex A., additional, Jacob, Jessica, additional, Fiveash, John B., additional, Nabors, L. Burt, additional, Cooper, Courtney A., additional, Hoyle, John M., additional, Milner, Desmin, additional, and Cardenas, Carlos E., additional

Published
2024
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28. 18FDG positron emission tomography mining for metabolic imaging biomarkers of radiation-induced xerostomia in patients with oropharyngeal cancer

Author

Elhalawani, Hesham, Cardenas, Carlos E., Volpe, Stefania, Barua, Souptik, Stieb, Sonja, Rock, Calvin B., Lin, Timothy, Yang, Pei, Wu, Haijun, Zaveri, Jhankruti, Elgohari, Baher, Abdallah, Lamiaa E., Jethanandani, Amit, Mohamed, Abdallah S.R., Court, Laurence E., Hutcheson, Katherine A., Brandon Gunn, G., Rosenthal, David I., Frank, Steven J., Garden, Adam S., Rao, Arvind, and Fuller, Clifton D.

Published
2021
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31. Landmark‐based auto‐contouring of clinical target volumes for radiotherapy of nasopharyngeal cancer.

Author

Sjogreen, Carlos, Netherton, Tucker J., Lee, Anna, Soliman, Moaaz, Gay, Skylar S., Nguyen, Callistus, Mumme, Raymond, Vazquez, Ivan, Rhee, Dong Joo, Cardenas, Carlos E., Martel, Mary K., Beadle, Beth M., and Court, Laurence Edward

Subjects
NASOPHARYNX cancer, PHYSICIANS, CANCER radiotherapy, ONCOLOGISTS, CANCER patients
Abstract

Background: The delineation of clinical target volumes (CTVs) for radiotherapy for nasopharyngeal cancer is complex and varies based on the location and extent of disease. Purpose: The current study aimed to develop an auto‐contouring solution following one protocol guidelines (NRG‐HN001) that can be adjusted to meet other guidelines, such as RTOG‐0225 and the 2018 International guidelines. Methods: The study used 2‐channel 3‐dimensional U‐Net and nnU‐Net framework to auto‐contour 27 normal structures in the head and neck (H&N) region that are used to define CTVs in the protocol. To define the CTV‐Expansion (CTV1 and CTV2) and CTV‐Overall (the outer envelope of all the CTV contours), we used adjustable morphological geometric landmarks and mimicked physician interpretation of the protocol rules by partially or fully including select anatomic structures. The results were evaluated quantitatively using the dice similarity coefficient (DSC) and mean surface distance (MSD) and qualitatively by independent reviews by two H&N radiation oncologists. Results: The auto‐contouring tool showed high accuracy for nasopharyngeal CTVs. Comparison between auto‐contours and clinical contours for 19 patients with cancers of various stages showed a DSC of 0.94 ± 0.02 and MSD of 0.4 ± 0.4 mm for CTV‐Expansion and a DSC of 0.83 ± 0.02 and MSD of 2.4 ± 0.5 mm for CTV‐Overall. Upon independent review, two H&N physicians found the auto‐contours to be usable without edits in 85% and 75% of cases. In 15% of cases, minor edits were required by both physicians. Thus, one physician rated 100% of the auto‐contours as usable (use as is, or after minor edits), while the other physician rated 90% as usable. The second physician required major edits in 10% of cases. Conclusions: The study demonstrates the ability of an auto‐contouring tool to reliably delineate nasopharyngeal CTVs based on protocol guidelines. The tool was found to be clinically acceptable by two H&N radiation oncology physicians in at least 90% of the cases. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Analyzing the Relationship between Dose and Geometric Agreement Metrics for Auto-Contouring in Head and Neck Normal Tissues.

Author

Marquez, Barbara, Wooten, Zachary T., Salazar, Ramon M., Peterson, Christine B., Fuentes, David T., Whitaker, T. J., Jhingran, Anuja, Pollard-Larkin, Julianne, Prajapati, Surendra, Beadle, Beth, Cardenas, Carlos E., Netherton, Tucker J., and Court, Laurence E.

Subjects
MEDICAL dosimetry, CANCER radiotherapy, SECONDARY analysis, NECK, TISSUES
Abstract

This study aimed to determine the relationship between geometric and dosimetric agreement metrics in head and neck (H&N) cancer radiotherapy plans. A total 287 plans were retrospectively analyzed, comparing auto-contoured and clinically used contours using a Dice similarity coefficient (DSC), surface DSC (sDSC), and Hausdorff distance (HD). Organs-at-risk (OARs) with ≥200 cGy dose differences from the clinical contour in terms of Dmax (D0.01cc) and Dmean were further examined against proximity to the planning target volume (PTV). A secondary set of 91 plans from multiple institutions validated these findings. For 4995 contour pairs across 19 OARs, 90% had a DSC, sDSC, and HD of at least 0.75, 0.86, and less than 7.65 mm, respectively. Dosimetrically, the absolute difference between the two contour sets was <200 cGy for 95% of OARs in terms of Dmax and 96% in terms of Dmean. In total, 97% of OARs exhibiting significant dose differences between the clinically edited contour and auto-contour were within 2.5 cm PTV regardless of geometric agreement. There was an approximately linear trend between geometric agreement and identifying at least 200 cGy dose differences, with higher geometric agreement corresponding to a lower fraction of cases being identified. Analysis of the secondary dataset validated these findings. Geometric indices are approximate indicators of contour quality and identify contours exhibiting significant dosimetric discordance. For a small subset of OARs within 2.5 cm of the PTV, geometric agreement metrics can be misleading in terms of contour quality. [ABSTRACT FROM AUTHOR]

Published
2024
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33. Comparison of tumor delineation using dual energy computed tomography versus magnetic resonance imaging in head and neck cancer re-irradiation cases

Author

Ng, Sweet Ping, Cardenas, Carlos E, Elhalawani, Hesham, Pollard, Courtney, III, Elgohari, Baher, Fang, Penny, Meheissen, Mohamed, Guha-Thakurta, Nandita, Bahig, Houda, Johnson, Jason M., Kamal, Mona, Garden, Adam S, Reddy, Jay P., Su, Shirley Y., Ferrarotto, Renata, Frank, Steven J., Brandon Gunn, G., Moreno, Amy C., Rosenthal, David I., Fuller, Clifton D., and Phan, Jack

Published
2020
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34. Automatic end‐to‐end VMAT treatment planning for rectal cancers

Author

Huang, Kai, primary, Chung, Christine, additional, Ludmir, Ethan B., additional, Zhang, Lifei, additional, Owens, Constance A., additional, Vega, Jean Gumma‐De La, additional, Duryea, Jack, additional, Zhao, Yao, additional, Chen, Xinru, additional, Fuentes, David, additional, Cardenas, Carlos E., additional, Briere, Tina Marie, additional, Beddar, Sam, additional, Court, Laurence E., additional, and Das, Prajnan, additional

Published
2024
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36. Beyond Acceptable: The Vital Role of Medical Physicists in Ensuring High-Quality Treatment Plans

Author

Lin, Mu-Han, primary, Olsen, Lindsey, additional, Kavanaugh, James A., additional, Jacqmin, Dustin, additional, Lobb, Eric, additional, Yoo, Sua, additional, Berry, Sean L., additional, Pichardo, Jose C., additional, Cardenas, Carlos E., additional, Roper, Justin, additional, Kirk, Maura, additional, Cheung, Joey P., additional, Solberg, Timothy D., additional, Moore, Kevin L., additional, and Kim, Minsun, additional

Published
2024
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41. Fatigue following radiation therapy in nasopharyngeal cancer survivors: A dosimetric analysis incorporating patient report and observer rating

Author

Kamal, Mona, Rosenthal, David I., Batra, Aakash, Volpe, Stefania, Elgohari, Baher, Goepfert, Ryan P., Garden, Adam S., Phan, Jack, Eraj, Salman, Dursteler, Amy, Williams, Bowman, Smith, Joshua, Aymard, Jeremy, Berends, Joel, White, Aubrey, Jethanandani, Amit, Cardenas, Carlos E., Frank, Steven J., Morrison, William H., Sturgis, Erich M., Cleeland, Charles S., Mendoza, Tito R., Mohamed, Abdallah S.R., Fuller, Clifton D., and Gunn, G. Brandon

Published
2019
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44. Radiotherapy dose–volume parameters predict videofluoroscopy-detected dysphagia per DIGEST after IMRT for oropharyngeal cancer: Results of a prospective registry

Author

Kamal, Mona, Mohamed, Abdallah S.R., Volpe, Stefania, Zaveri, Jhankruti, Barrow, Martha Portwood, Gunn, G. Brandon, Lai, Stephen Y., Ferrarotto, Renata, Lewin, Jan S., Rosenthal, David I., Jethanandani, Amit, Meheissen, Mohamed Ahmed Mohamed, Mulder, Samuel L., Cardenas, Carlos E., Fuller, Clifton D., and Hutcheson, Katherine A.

Published
2018
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45. A prospective in silico analysis of interdisciplinary and interobserver spatial variability in post-operative target delineation of high-risk oral cavity cancers: Does physician specialty matter?

Author

Ng, Sweet Ping, Dyer, Brandon A., Kalpathy-Cramer, Jayashree, Mohamed, Abdallah Sherif Radwan, Awan, Musaddiq J., Gunn, G. Brandon, Phan, Jack, Zafereo, Mark, Debnam, J. Matthew, Lewis, Carol M., Colen, Rivka R., Kupferman, Michael E., Guha-Thakurta, Nandita, Canahuate, Guadalupe, Marai, G. Elisabeta, Vock, David, Hamilton, Bronwyn, Holland, John, Cardenas, Carlos E., Lai, Stephen, Rosenthal, David, and Fuller, Clifton David

Published
2018
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46. Prospective in silico study of the feasibility and dosimetric advantages of MRI-guided dose adaptation for human papillomavirus positive oropharyngeal cancer patients compared with standard IMRT

Author

Mohamed, Abdallah S.R., Bahig, Houda, Aristophanous, Michalis, Blanchard, Pierre, Kamal, Mona, Ding, Yao, Cardenas, Carlos E., Brock, Kristy K., Lai, Stephen Y., Hutcheson, Katherine A., Phan, Jack, Wang, Jihong, Ibbott, Geoffrey, Gabr, Refaat E., Narayana, Ponnada A., Garden, Adam S., Rosenthal, David I., Gunn, G. Brandon, and Fuller, Clifton D.

Published
2018
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47. Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes With Built-In Dice Similarity Coefficient Parameter Optimization Function

Author

Cardenas, Carlos E., McCarroll, Rachel E., Court, Laurence E., Elgohari, Baher A., Elhalawani, Hesham, Fuller, Clifton D., Kamal, Mona J., Meheissen, Mohamed A.M., Mohamed, Abdallah S.R., Rao, Arvind, Williams, Bowman, Wong, Andrew, Yang, Jinzhong, and Aristophanous, Michalis

Published
2018
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48. Three-dimensional imaging assessment of anatomic invasion and volumetric considerations for chemo/radiotherapy-based laryngeal preservation in T3 larynx cancer

Author

Kamal, Mona, Ng, Sweet Ping, Eraj, Salman A., Rock, Crosby D., Pham, Brian, Messer, Jay A., Garden, Adam S., Morrison, William H., Phan, Jack, Frank, Steven J., El-Naggar, Adel K., Johnson, Jason M., Ginsberg, Lawrence E., Ferrarotto, Renata, Lewin, Jan S., Hutcheson, Katherine A., Cardenas, Carlos E., Zafereo, Mark E., Lai, Stephen Y., Hessel, Amy C., Weber, Randal S., Gunn, G. Brandon, Fuller, Clifton D., Mohamed, Abdallah S.R., and Rosenthal, David I.

Published
2018
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