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1. Large-Language-Model Empowered Dose Volume Histogram Prediction for Intensity Modulated Radiotherapy

Author

Dong, Zehao, Chen, Yixin, Gay, Hiram, Hao, Yao, Hugo, Geoffrey D., Samson, Pamela, and Zhao, Tianyu

Subjects
Computer Science - Artificial Intelligence
Abstract

Treatment planning is currently a patient specific, time-consuming, and resource demanding task in radiotherapy. Dose-volume histogram (DVH) prediction plays a critical role in automating this process. The geometric relationship between DVHs in radiotherapy plans and organs-at-risk (OAR) and planning target volume (PTV) has been well established. This study explores the potential of deep learning models for predicting DVHs using images and subsequent human intervention facilitated by a large-language model (LLM) to enhance the planning quality. We propose a pipeline to convert unstructured images to a structured graph consisting of image-patch nodes and dose nodes. A novel Dose Graph Neural Network (DoseGNN) model is developed for predicting DVHs from the structured graph. The proposed DoseGNN is enhanced with the LLM to encode massive knowledge from prescriptions and interactive instructions from clinicians. In this study, we introduced an online human-AI collaboration (OHAC) system as a practical implementation of the concept proposed for the automation of intensity-modulated radiotherapy (IMRT) planning. In comparison to the widely-employed DL models used in radiotherapy, DoseGNN achieved mean square errors that were 80$\%$, 76$\%$ and 41.0$\%$ of those predicted by Swin U-Net Transformer, 3D U-Net CNN and vanilla MLP, respectively. Moreover, the LLM-empowered DoseGNN model facilitates seamless adjustment to treatment plans through interaction with clinicians using natural language.

Published
2024

2. A novel adversarial learning strategy for medical image classification

Author

Fan, Zong, Zhang, Xiaohui, Gasienica, Jacob A., Potts, Jennifer, Ruan, Su, Thorstad, Wade, Gay, Hiram, Song, Pengfei, Wang, Xiaowei, and Li, Hua

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

Deep learning (DL) techniques have been extensively utilized for medical image classification. Most DL-based classification networks are generally structured hierarchically and optimized through the minimization of a single loss function measured at the end of the networks. However, such a single loss design could potentially lead to optimization of one specific value of interest but fail to leverage informative features from intermediate layers that might benefit classification performance and reduce the risk of overfitting. Recently, auxiliary convolutional neural networks (AuxCNNs) have been employed on top of traditional classification networks to facilitate the training of intermediate layers to improve classification performance and robustness. In this study, we proposed an adversarial learning-based AuxCNN to support the training of deep neural networks for medical image classification. Two main innovations were adopted in our AuxCNN classification framework. First, the proposed AuxCNN architecture includes an image generator and an image discriminator for extracting more informative image features for medical image classification, motivated by the concept of generative adversarial network (GAN) and its impressive ability in approximating target data distribution. Second, a hybrid loss function is designed to guide the model training by incorporating different objectives of the classification network and AuxCNN to reduce overfitting. Comprehensive experimental studies demonstrated the superior classification performance of the proposed model. The effect of the network-related factors on classification performance was investigated.

Published
2022

4. Weaving Attention U-net: A Novel Hybrid CNN and Attention-based Method for Organs-at-risk Segmentation in Head and Neck CT Images

Author

Zhang, Zhuangzhuang, Zhao, Tianyu, Gay, Hiram, Zhang, Weixiong, and Sun, Baozhou

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

In radiotherapy planning, manual contouring is labor-intensive and time-consuming. Accurate and robust automated segmentation models improve the efficiency and treatment outcome. We aim to develop a novel hybrid deep learning approach, combining convolutional neural networks (CNNs) and the self-attention mechanism, for rapid and accurate multi-organ segmentation on head and neck computed tomography (CT) images. Head and neck CT images with manual contours of 115 patients were retrospectively collected and used. We set the training/validation/testing ratio to 81/9/25 and used the 10-fold cross-validation strategy to select the best model parameters. The proposed hybrid model segmented ten organs-at-risk (OARs) altogether for each case. The performance of the model was evaluated by three metrics, i.e., the Dice Similarity Coefficient (DSC), Hausdorff distance 95% (HD95), and mean surface distance (MSD). We also tested the performance of the model on the Head and Neck 2015 challenge dataset and compared it against several state-of-the-art automated segmentation algorithms. The proposed method generated contours that closely resemble the ground truth for ten OARs. Our results of the new Weaving Attention U-net demonstrate superior or similar performance on the segmentation of head and neck CT images., Comment: 12 pages, 5 figures

Published
2021
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6. Cellular states are coupled to genomic and viral heterogeneity in HPV-related oropharyngeal carcinoma

Author

Puram, Sidharth V., Mints, Michael, Pal, Ananya, Qi, Zongtai, Reeb, Ashley, Gelev, Kyla, Barrett, Thomas F., Gerndt, Sophie, Liu, Ping, Parikh, Anuraag S., Ramadan, Salma, Law, Travis, Mroz, Edmund A., Rocco, James W., Adkins, Doug, Thorstad, Wade L., Gay, Hiram A., Ding, Li, Paniello, Randal C., Pipkorn, Patrik, Jackson, Ryan S., Wang, Xiaowei, Mazul, Angela, Chernock, Rebecca, Zevallos, Jose P., Silva-Fisher, Jessica, and Tirosh, Itay

Published
2023
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7. Semi-supervised Semantic Segmentation of Prostate and Organs-at-Risk on 3D Pelvic CT Images

Author

Zhang, Zhuangzhuang, Zhao, Tianyu, Gay, Hiram, Sun, Baozhou, and Zhang, Weixiong

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

Automated segmentation can assist radiotherapy treatment planning by saving manual contouring efforts and reducing intra-observer and inter-observer variations. The recent development of deep learning approaches has revoluted medical data processing, including semantic segmentation, by dramatically improving performance. However, training effective deep learning models usually require a large amount of high-quality labeled data, which are often costly to collect. We developed a novel semi-supervised adversarial deep learning approach for 3D pelvic CT image semantic segmentation. Unlike supervised deep learning methods, the new approach can utilize both annotated and un-annotated data for training. It generates un-annotated synthetic data by a data augmentation scheme using generative adversarial networks (GANs). We applied the new approach to segmenting multiple organs in male pelvic CT images, where CT images without annotations and GAN-synthesized un-annotated images were used in semi-supervised learning. Experimental results, evaluated by three metrics (Dice similarity coefficient, average Hausdorff distance, and average surface Hausdorff distance), showed that the new method achieved either comparable performance with substantially fewer annotated images or better performance with the same amount of annotated data, outperforming the existing state-of-the-art methods., Comment: 16 pages, 4 figures

Published
2020

8. ARPM-net: A novel CNN-based adversarial method with Markov Random Field enhancement for prostate and organs at risk segmentation in pelvic CT images

Author

Zhang, Zhuangzhuang, Zhao, Tianyu, Gay, Hiram, Zhang, Weixiong, and Sun, Baozhou

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, 68T07(Primary), 68T45(Secondary)
Abstract

Purpose: The research is to develop a novel CNN-based adversarial deep learning method to improve and expedite the multi-organ semantic segmentation of CT images, and to generate accurate contours on pelvic CT images. Methods: Planning CT and structure datasets for 120 patients with intact prostate cancer were retrospectively selected and divided for 10-fold cross-validation. The proposed adversarial multi-residual multi-scale pooling Markov Random Field (MRF) enhanced network (ARPM-net) implements an adversarial training scheme. A segmentation network and a discriminator network were trained jointly, and only the segmentation network was used for prediction. The segmentation network integrates a newly designed MRF block into a variation of multi-residual U-net. The discriminator takes the product of the original CT and the prediction/ground-truth as input and classifies the input into fake/real. The segmentation network and discriminator network can be trained jointly as a whole, or the discriminator can be used for fine-tuning after the segmentation network is coarsely trained. Multi-scale pooling layers were introduced to preserve spatial resolution during pooling using less memory compared to atrous convolution layers. An adaptive loss function was proposed to enhance the training on small or low contrast organs. The accuracy of modeled contours was measured with the Dice similarity coefficient (DSC), Average Hausdorff Distance (AHD), Average Surface Hausdorff Distance (ASHD), and relative Volume Difference (VD) using clinical contours as references to the ground-truth. The proposed ARPM-net method was compared to several stateof-the-art deep learning methods., Comment: 21 pages, 8 figures; accepted as a journal article at Medical Physics; abstract presented at AAPM 2020

Published
2020
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10. Neoadjuvant and Adjuvant Pembrolizumab in Resectable Locally Advanced, Human Papillomavirus–Unrelated Head and Neck Cancer: A Multicenter, Phase II Trial

Author

Uppaluri, Ravindra, Campbell, Katie M, Egloff, Ann Marie, Zolkind, Paul, Skidmore, Zachary L, Nussenbaum, Brian, Paniello, Randal C, Rich, Jason T, Jackson, Ryan, Pipkorn, Patrik, Michel, Loren S, Ley, Jessica, Oppelt, Peter, Dunn, Gavin P, Barnell, Erica K, Spies, Nicholas C, Lin, Tianxiang, Li, Tiantian, Mulder, David T, Hanna, Youstina, Cirlan, Iulia, Pugh, Trevor J, Mudianto, Tenny, Riley, Rachel, Zhou, Liye, Jo, Vickie Y, Stachler, Matthew D, Hanna, Glenn J, Kass, Jason, Haddad, Robert, Schoenfeld, Jonathan D, Gjini, Evisa, Lako, Ana, Thorstad, Wade, Gay, Hiram A, Daly, Mackenzie, Rodig, Scott J, Hagemann, Ian S, Kallogjeri, Dorina, Piccirillo, Jay F, Chernock, Rebecca D, Griffith, Malachi, Griffith, Obi L, and Adkins, Douglas R

Subjects
Clinical Research, Sexually Transmitted Infections, Patient Safety, Infectious Diseases, Cancer, Dental/Oral and Craniofacial Disease, Rare Diseases, Adult, Aged, Aged, 80 and over, Antibodies, Monoclonal, Humanized, B7-H1 Antigen, Chemotherapy, Adjuvant, Drug-Related Side Effects and Adverse Reactions, Female, Humans, Interferon-gamma, Lymphocytes, Tumor-Infiltrating, Male, Middle Aged, Neoadjuvant Therapy, Neoplasm Recurrence, Local, Papillomaviridae, Squamous Cell Carcinoma of Head and Neck, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

PurposePembrolizumab improved survival in patients with recurrent or metastatic head and neck squamous-cell carcinoma (HNSCC). The aims of this study were to determine if pembrolizumab would be safe, result in pathologic tumor response (pTR), and lower the relapse rate in patients with resectable human papillomavirus (HPV)-unrelated HNSCC.Patients and methodsNeoadjuvant pembrolizumab (200 mg) was administered and followed 2 to 3 weeks later by surgical tumor ablation. Postoperative (chemo)radiation was planned. Patients with high-risk pathology (positive margins and/or extranodal extension) received adjuvant pembrolizumab. pTR was quantified as the proportion of the resection bed with tumor necrosis, keratinous debris, and giant cells/histiocytes: pTR-0 (

Published
2020

13. Intensification of ADT with enzalutamide in high-risk patients with biochemical relapse following radical prostatectomy undergoing salvage radiation: Initial results from RTOG 3506 (STEEL).

Author

Posadas, Edwin Melencio, primary, Gay, Hiram Alberto, additional, Rodgers, Joseph P, additional, Morgan, Todd Matthew, additional, Xiao, Ying, additional, Yu, James, additional, Michalski, Jeff M., additional, Bouchard, Myriam, additional, Desai, Neil B, additional, Funk, Ryan, additional, Boike, Thomas Pence, additional, Jurgens, Donald James, additional, Wong, Anthony C., additional, Shen, Xinglei, additional, Miyawaki, Lloyd T, additional, Bland, Christopher S, additional, Hairston, John, additional, Sandler, Howard M., additional, Pugh, Stephanie L, additional, and Feng, Felix Y, additional

Published
2024
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15. The Clinical Cell-Cycle Risk (CCR) Score Is Associated With Metastasis After Radiation Therapy and Provides Guidance on When to Forgo Combined Androgen Deprivation Therapy With Dose-Escalated Radiation

Author

Tward, Jonathan, Lenz, Lauren, Flake, Darl D., II, Rajamani, Saradha, Yonover, Paul, Olsson, Carl, Kapoor, Deepak A., Mantz, Constantine, Liauw, Stanley L., Antic, Tatjana, Fabrizio, Michael, Salzstein, Daniel, Shore, Neal, Albertson, Dan, Henderson, Jonathan, Lee, Steve P., Gay, Hiram A., Michalski, Jeff, Hung, Arthur, Raben, David, Garraway, Isla, Lewis, Michael S., Nguyen, Paul L., Marshall, David T., Brawer, Michael K., Stone, Steven, and Cohen, Todd

Published
2022
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21. A medical image classification method based on self‐regularized adversarial learning.

Author

Fan, Zong, Zhang, Xiaohui, Ruan, Su, Thorstad, Wade, Gay, Hiram, Song, Pengfei, Wang, Xiaowei, and Li, Hua

Subjects
IMAGE recognition (Computer vision), POSITRON emission tomography, GENERATIVE adversarial networks, COMPUTER-assisted image analysis (Medicine), DEEP learning, ITERATIVE learning control
Abstract

Background: Deep learning (DL) techniques have been extensively applied in medical image classification. The unique characteristics of medical imaging data present challenges, including small labeled datasets, severely imbalanced class distribution, and significant variations in imaging quality. Recently, generative adversarial network (GAN)‐based classification methods have gained attention for their ability to enhance classification accuracy by incorporating realistic GAN‐generated images as data augmentation. However, the performance of these GAN‐based methods often relies on high‐quality generated images, while large amounts of training data are required to train GAN models to achieve optimal performance. Purpose: In this study, we propose an adversarial learning‐based classification framework to achieve better classification performance. Innovatively, GAN models are employed as supplementary regularization terms to support classification, aiming to address the challenges described above. Methods: The proposed classification framework, GAN‐DL, consists of a feature extraction network (F‐Net), a classifier, and two adversarial networks, specifically a reconstruction network (R‐Net) and a discriminator network (D‐Net). The F‐Net extracts features from input images, and the classifier uses these features for classification tasks. R‐Net and D‐Net have been designed following the GAN architecture. R‐Net employs the extracted feature to reconstruct the original images, while D‐Net is tasked with the discrimination between the reconstructed image and the original images. An iterative adversarial learning strategy is designed to guide model training by incorporating multiple network‐specific loss functions. These loss functions, serving as supplementary regularization, are automatically derived during the reconstruction process and require no additional data annotation. Results: To verify the model's effectiveness, we performed experiments on two datasets, including a COVID‐19 dataset with 13 958 chest x‐ray images and an oropharyngeal squamous cell carcinoma (OPSCC) dataset with 3255 positron emission tomography images. Thirteen classic DL‐based classification methods were implemented on the same datasets for comparison. Performance metrics included precision, sensitivity, specificity, and F1$F_1$‐score. In addition, we conducted ablation studies to assess the effects of various factors on model performance, including the network depth of F‐Net, training image size, training dataset size, and loss function design. Our method achieved superior performance than all comparative methods. On the COVID‐19 dataset, our method achieved 95.4%±0.6%$95.4\%\pm 0.6\%$, 95.3%±0.9%$95.3\%\pm 0.9\%$, 97.7%±0.4%$97.7\%\pm 0.4\%$, and 95.3%±0.9%$95.3\%\pm 0.9\%$ in terms of precision, sensitivity, specificity, and F1$F_1$‐score, respectively. It achieved 96.2%±0.7%$96.2\%\pm 0.7\%$ across all these metrics on the OPSCC dataset. The study to investigate the effects of two adversarial networks highlights the crucial role of D‐Net in improving model performance. Ablation studies further provide an in‐depth understanding of our methodology. Conclusion: Our adversarial‐based classification framework leverages GAN‐based adversarial networks and an iterative adversarial learning strategy to harness supplementary regularization during training. This design significantly enhances classification accuracy and mitigates overfitting issues in medical image datasets. Moreover, its modular design not only demonstrates flexibility but also indicates its potential applicability to various clinical contexts and medical imaging applications. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Quality of Life Implications of Dose-Escalated External Beam Radiation for Localized Prostate Cancer: Results of a Prospective Randomized Phase 3 Clinical Trial, NRG/RTOG 0126

Author

Hall, William A., Deshmukh, Snehal, Bruner, Deborah W., Michalski, Jeff M., Purdy, James A., Bosch, Walter, Bahary, Jean-Paul, Patel, Maltibehn P., Parliament, Matthew B., Lock, Michael I., Lau, Harold Y., Souhami, Luis, Fisher, Scot A., Kwok, Young, Seider, Michael J., Vigneault, Eric, Rosenthal, Seth A., Gustafson, Gary S., Gay, Hiram A., Pugh, Stephanie L., Sandler, Howard M., and Movsas, Benjamin

Published
2022
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24. Does the sequence of high-dose rate brachytherapy boost and IMRT for prostate cancer impact early toxicity outcomes? Results from a single institution analysis

Author

Roy, Amit, Brenneman, Randall J., Hogan, Jacob, Barnes, Justin M., Huang, Yi, Morris, Robert, Goddu, Sreekrishna, Altman, Michael, Garcia-Ramirez, Jose, Li, Harold, Zoberi, Jacqueline E., Bullock, Arnold, Kim, Eric, Smith, Zachary, Figenshau, Robert, Andriole, Gerald L., Baumann, Brian C., Michalski, Jeff M., and Gay, Hiram A.

Published
2021
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26. External Beam Radiation Therapy or Brachytherapy With or Without Short-course Neoadjuvant Androgen Deprivation Therapy: Results of a Multicenter, Prospective Study of Quality of Life

Author

Gay, Hiram A, Sanda, Martin G, Liu, Jingxia, Wu, Ningying, Hamstra, Daniel A, Wei, John T, Dunn, Rodney L, Klein, Eric A, Sandler, Howard M, Saigal, Christopher S, Litwin, Mark S, Kuban, Deborah A, Hembroff, Larry, Regan, Meredith M, Chang, Peter, Consortium, Prostate Cancer Outcomes and Satisfaction with Treatment Quality Assessment, Regan, Meredith, Hamstra, Dan, Dunn, Rodney, Northouse, Laurel, Wood, David, Ciezki, Jay, Michalski, Jeff, Andriole, Gerald, Saigal, Christopher, Greenfield, Thomas, Pisters, Louis, Kuban, Deborah, Sandler, Howard, Hu, Jim, Kibel, Adam, Dahl, Douglas, Zietman, Anthony, Kaplan, Irving, Wagner, Andrew, and Michalski, Jeff M

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Cancer, Clinical Trials and Supportive Activities, Aging, Prostate Cancer, Urologic Diseases, Clinical Research, Aged, Aged, 80 and over, Androgen Antagonists, Body Weight, Brachytherapy, Breast, Chi-Square Distribution, Combined Modality Therapy, Depression, Erectile Dysfunction, Fatigue, Hot Flashes, Humans, Male, Middle Aged, Neoadjuvant Therapy, Orgasm, Penile Erection, Prospective Studies, Prostate-Specific Antigen, Prostatic Neoplasms, Quality of Life, Surveys and Questionnaires, Time Factors, Prostate Cancer Outcomes and Satisfaction with Treatment Quality Assessment Consortium, Other Physical Sciences, Oncology & Carcinogenesis, Oncology and carcinogenesis, Theoretical and computational chemistry, Medical and biological physics
Abstract

PurposeThe long-term effects of neoadjuvant androgen deprivation therapy (NADT) with radiation therapy on participant-reported health-related quality of life (HRQOL) have not been characterized in prospective multicenter studies. We evaluated HRQOL for 2 years among participants undergoing radiation therapy (RT) with or without NADT for newly diagnosed, early-stage prostate cancer.Methods and materialsWe analyzed longitudinal cohort data from the Prostate Cancer Outcomes and Satisfaction with Treatment Quality Assessment Consortium to ascertain the HRQOL trajectory of men receiving NADT with external beam RT (EBRT) or brachytherapy. HRQOL was measured using the expanded prostate cancer index composite 26-item questionnaire at 2, 6, 12, and 24 months after the initiation of NADT. We used the χ2 or Fisher exact test to compare the shift in percentages between groups that did or did not receive NADT. Analyses were conducted at the 2-sided 5% significance level.ResultsFor subjects receiving EBRT, questions regarding the ability to have an erection, ability to reach an orgasm, quality of erections, frequency of erections, ability to function sexually, and lack of energy were in a significantly worse dichotomized category for the patients receiving NADT. Comparing the baseline versus 24-month outcomes, 24%, 23%, and 30% of participants receiving EBRT plus NADT shifted to the worse dichotomized category for the ability to reach an orgasm, quality of erections, and ability to function sexually compared with 14%, 13%, and 16% in the EBRT group, respectively.ConclusionsCompared with baseline, at 2 years, participants receiving NADT plus EBRT compared with EBRT alone had worse HRQOL, as measured by the ability to reach orgasm, quality of erections, and ability to function sexually. However, no difference was found in the ability to have an erection, frequency of erections, overall sexual function, hot flashes, breast tenderness/enlargement, depression, lack of energy, or change in body weight. The improved survival in intermediate- and high-risk patients receiving NADT and EBRT necessitates pretreatment counseling of the HRQOL effect of NADT and EBRT.

Published
2017

27. Management of Muscle-Invasive Bladder Cancer During a Pandemic: Impact of Treatment Delay on Survival Outcomes for Patients Treated With Definitive Concurrent Chemoradiotherapy

Author

Fischer-Valuck, Benjamin W., Michalski, Jeff M., Harton, Joanna G., Birtle, Alison, Christodouleas, John P., Efstathiou, Jason A., Arora, Vivek K., Kim, Eric H., Knoche, Eric M., Pachynski, Russell K., Picus, Joel, Rao, Yuan James, Reimers, Melissa, Roth, Bruce J., Sargos, Paul, Smith, Zachary L., Zaghloul, Mohamed S., Gay, Hiram A., Patel, Sagar A., and Baumann, Brian C.

Published
2021
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29. Who Benefits From a Prostate Rectal Spacer? Secondary Analysis of a Phase III Trial

Author

Quinn, Thomas J., Daignault-Newton, Stephanie, Bosch, Walter, Mariados, Neil, Sylvester, John, Shah, Dhiren, Gross, Eric, Hudes, Richard, Beyer, David, Kurtzman, Steven, Bogart, Jeffrey, Hsi, R. Alex, Kos, Michael, Ellis, Rodney, Logsdon, Mark, Zimberg, Shawn, Forsythe, Kevin, Zhang, Hong, Soffen, Edward, Francke, Patrick, Mantz, Constantine, DeWeese, Theodore, Gay, Hiram A., Michalski, Jeff, and Hamstra, Daniel A.

Published
2020
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30. Beyond Average: α-Particle Distribution and Dose Heterogeneity in Bone Metastatic Prostate Cancer

Author

Benabdallah, Nadia, primary, Lu, Peng, additional, Abou, Diane S., additional, Zhang, Hanwen, additional, Ulmert, David, additional, Hobbs, Robert F., additional, Gay, Hiram A., additional, Simons, Brian W., additional, Saeed, Muhammad A., additional, Rogers, Buck E., additional, Jha, Abhinav K., additional, Tai, Yuan-Chuan, additional, Malone, Christopher D., additional, Ippolito, Joseph E., additional, Michalski, Jeff, additional, Jennings, Jack W., additional, Baumann, Brian C., additional, Pachynski, Russell K., additional, and Thorek, Daniel L.J., additional

Published
2023
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31. Lessons Learned From Hurricane Maria in Puerto Rico: Practical Measures to Mitigate the Impact of a Catastrophic Natural Disaster on Radiation Oncology Patients

Author

Gay, Hiram A., Santiago, Roberto, Gil, Betty, Remedios, Carlos, Montes, Pedro J., López-Araujo, Javier, Chévere, Carlos M., Imbert, Winston S., White, Julia, Arthur, Douglas W., Horton, Janet K., Jagsi, Reshma, Rabinovich, Rachel, Beriwal, Sushil, Viswanathan, Akila, Erickson, Beth A., Rengan, Ramesh, Palma, David, Loo, Billy W., Jr., Kavanaugh, James A., Bradley, Jeff, Yom, Sue S., Harari, Paul M., and Lee Burnett, Omer, III

Published
2019
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34. Treatment of pagetoid reticulosis with intensity modulated radiation therapy

Author

Mount, Charles, Ferraro, Daniel, Gru, Alejandro, Bradbury, Matthew, Cornelius, Lynn, and Gay, Hiram

Abstract

Pagetoid reticulosis, also known as Woringer-Kolopp disease, is a rare variant of cutaneous T-cell lymphoma usually presenting as an isolated patch or plaque on the extremities.  Immunohistochemical staining of T-cell markers can be variable, but as the name implies the epidermotropic lymphocytes consistently display a “pagetoid” appearance.  The following case demonstrates clearance of this condition with intensity modulated radiation therapy, whereas most cases are managed with electron beam therapy if radiation therapy is implemented.

Published
2014

36. CT-based online adaptive radiotherapy improves target coverage and organ at risk (OAR) avoidance in stereotactic body radiation therapy (SBRT) for prostate cancer

Author

Waters, Michael, primary, Price, Alex, additional, Laugeman, Eric, additional, Henke, Lauren, additional, Hugo, Geoff, additional, Stowe, Hayley, additional, Andruska, Neal, additional, Brenneman, Randall, additional, Hao, Yao, additional, Green, Olga, additional, Robinson, Clifford, additional, Gay, Hiram, additional, Michalski, Jeff, additional, and Baumann, Brian C., additional

Published
2023
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37. Abstract PO-012: Spatial transcriptomic analysis of HPV-related and HPV-unrelated head and neck squamous cell carcinoma

Author

Barrett, Thomas F., primary, Simkin, Dor, additional, Greenwald, Alissa R., additional, Parikh, Anuraag, additional, Gay, Hiram, additional, Apicelli, Anthony, additional, Adkins, Douglas, additional, Thorstad, Wade, additional, Rich, Jason T., additional, Paniello, Randal C., additional, Zolkind, Paul, additional, Pipkorn, Patrik, additional, Jackson, Ryan S., additional, Chernock, Rebecca, additional, Tirosh, Itay, additional, and Puram, Sidharth V., additional

Published
2023
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38. Neoadjuvant Androgen Deprivation Therapy Leads to Immediate Impairment of Vitality/Hormonal and Sexual Quality of Life: Results of a Multicenter Prospective Study

Author

Gay, Hiram Alberto, Michalski, Jeff M, Hamstra, Daniel A, Wei, John T, Dunn, Rodney L, Klein, Eric A, Sandler, Howard M, Saigal, Chris, Litwin, Mark, Kuban, Deborah, Hembroff, Larry, Chang, Peter, Sanda, Martin G, and Consortium, PROSTQA

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Clinical Research, Urologic Diseases, Aging, Prostate Cancer, Cancer, Biopsy, Needle, Humans, Image-Guided Biopsy, Male, Prospective Studies, Prostatic Neoplasms, Quality of Life, Unnecessary Procedures, PROSTQA Consortium, Urology & Nephrology, Clinical sciences
Abstract

ObjectiveTo evaluate the immediate effects of neoadjuvant androgen deprivation therapy (NADT) on health-related quality of life (HRQOL) among patients undergoing radiation therapy (RT) for newly diagnosed prostate cancer.MethodsThe Prostate Cancer Outcomes and Satisfaction with Treatment Quality Assessment Consortium is a prospective multi-institutional study. HRQOL is measured with the Expanded Prostate Cancer Index Composite-26 questionnaire. Differences in patient-reported HRQOL were observed between pretreatment and 2 months after NADT start (and before definitive RT) with significant differences evaluated by paired t test.ResultsFrom among 450 patients who completed the Expanded Prostate Cancer Index Composite-26 before and 2 months after NADT start, 71 received NADT before proceeding with definitive RT. Patients receiving NADT experienced significant impairment in vitality/hormonal (P

Published
2013

41. nab-Paclitaxel and cisplatin followed by cisplatin and radiation (Arm 1) and nab-paclitaxel followed by cetuximab and radiation (Arm 2) for locally advanced head and neck squamous-cell carcinoma: a multicenter, non-randomized phase 2 trial

Author

Oppelt, Peter, Ley, Jessica, Daly, Mackenzie, Rich, Jason, Paniello, Randal, Jackson, Ryan S., Pipkorn, Patrik, Liu, Jingxia, Gay, Hiram, Palka, Kevin, Neupane, Prakash, Powell, Steven, Spanos, William C., Gitau, Mark, Zevallos, Jose, Thorstad, Wade, and Adkins, Douglas

Published
2021
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43. A multi-institutional phase 2 trial of prostate stereotactic body radiation therapy (SBRT) using continuous real-time evaluation of prostate motion with patient-reported quality of life

Author

Jackson, William C., Dess, Robert T., Litzenberg, Dale W., Li, Pin, Schipper, Matthew, Rosenthal, Seth A., Chang, Garrick C., Horwitz, Eric M., Price, Robert A., Michalski, Jeff M., Gay, Hiram A., Wei, John T., Feng, Mary, Feng, Felix Y., Sandler, Howard M., Wallace, Robert E., Spratt, Daniel E., and Hamstra, Daniel A.

Published
2018
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44. Sexual quality of life following prostate intensity modulated radiation therapy (IMRT) with a rectal/prostate spacer: Secondary analysis of a phase 3 trial

Author

Hamstra, Daniel A., Mariados, Neil, Sylvester, John, Shah, Dhiren, Gross, Eric, Hudes, Richard, Beyer, David, Kurtzman, Steven, Bogart, Jeffrey, Hsi, R. Alex, Kos, Michael, Ellis, Rodney, Logsdon, Mark, Zimberg, Shawn, Forsythe, Kevin, Zhang, Hong, Soffen, Edward, Francke, Patrick, Mantz, Constantine, Rossi, Peter, DeWeese, Theodore, Daignault-Newton, Stephanie, Fischer-Valuck, Benjamin W., Chundury, Anupama, Gay, Hiram A., Bosch, Walter, and Michalski, Jeff

Published
2018
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45. Effect of Brachytherapy With External Beam Radiation Therapy Versus Brachytherapy Alone for Intermediate-Risk Prostate Cancer: NRG Oncology RTOG 0232 Randomized Clinical Trial

Author

Michalski, Jeff M., primary, Winter, Kathryn A., additional, Prestidge, Bradley R., additional, Sanda, Martin G., additional, Amin, Mahul, additional, Bice, William S., additional, Gay, Hiram A., additional, Ibbott, Geoffrey S., additional, Crook, Juanita M., additional, Catton, Charles N., additional, Raben, Adam, additional, Bosch, Walter, additional, Beyer, David C., additional, Frank, Steven J., additional, Papagikos, Michael A., additional, Rosenthal, Seth A., additional, Barthold, H. Joseph, additional, Roach, Mack, additional, Moughan, Jennifer, additional, and Sandler, Howard M., additional

Published
2023
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47. External Beam Radiation Therapy or Brachytherapy With or Without Short-course Neoadjuvant Androgen Deprivation Therapy: Results of a Multicenter, Prospective Study of Quality of Life

Author

Regan, Meredith, Hembroff, Larry, Wei, John T., Hamstra, Dan, Dunn, Rodney, Northouse, Laurel, Wood, David, Klein, Eric A., Ciezki, Jay, Michalski, Jeff, Andriole, Gerald, Litwin, Mark S., Saigal, Christopher, Greenfield, Thomas, Pisters, Louis, Kuban, Deborah, Sandler, Howard, Hu, Jim, Kibel, Adam, Dahl, Douglas, Zietman, Anthony, Chang, Peter, Kaplan, Irving, Wagner, Andrew, Sanda, Martin G., Gay, Hiram A., Liu, Jingxia, Wu, Ningying, Hamstra, Daniel A., Dunn, Rodney L., Sandler, Howard M., Saigal, Christopher S., Kuban, Deborah A., Regan, Meredith M., and Michalski, Jeff M.

Published
2017
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49. Continued Benefit to Rectal Separation for Prostate Radiation Therapy: Final Results of a Phase III Trial

Author

Hamstra, Daniel A., Mariados, Neil, Sylvester, John, Shah, Dhiren, Karsh, Lawrence, Hudes, Richard, Beyer, David, Kurtzman, Steven, Bogart, Jeffrey, Hsi, R. Alex, Kos, Michael, Ellis, Rodney, Logsdon, Mark, Zimberg, Shawn, Forsythe, Kevin, Zhang, Hong, Soffen, Edward, Francke, Patrick, Mantz, Constantine, Rossi, Peter, DeWeese, Theodore, Daignault-Newton, Stephanie, Fischer-Valuck, Benjamin W., Chundury, Anupama, Gay, Hiram, Bosch, Walter, and Michalski, Jeff

Published
2017
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