210 results on '"Buatti, John M"'
Search Results
2. Federated Learning Enables Big Data for Rare Cancer Boundary Detection
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Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J, Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y, Chang, Ken, Balana, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S, Lombardo, Joseph, Palmer, Joshua D, Flanders, Adam E, Dicker, Adam P, Sair, Haris I, Jones, Craig K, Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y, Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A, Mitchell, J Ross, Farinhas, Joaquim, Maldjian, Joseph A, Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C, Reddy, Divya, Holcomb, James, Wagner, Benjamin C, Ellingson, Benjamin M, Cloughesy, Timothy F, Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B, Teixeira, Bernardo C A, Sprenger, Flávia, Menotti, David, Lucio, Diego R, LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W, McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E, Vadmal, Vachan, Waite, Kristin, Colen, Rivka R, Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V M, Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I, Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M, Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R, Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten MJ, Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W, Dubbink, Hendrikus J, Vincent, Arnaud JPE, Bent, Martin J van den, French, Pim J, Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P, Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B, Mistry, Akshitkumar, Thompson, Reid C, Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C, Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G H, Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M, Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F, Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M, Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A, Ogbole, Godwin, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu'aibu, Mustapha, Dorcas, Adeleye, Soneye, Mayowa, Dako, Farouk, Simpson, Amber L, Hamghalam, Mohammad, Peoples, Jacob J, Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y, Boss, Michael A, Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S, Martin, Jason, and Bakas, Spyridon
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Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing., Comment: federated learning, deep learning, convolutional neural network, segmentation, brain tumor, glioma, glioblastoma, FeTS, BraTS
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- 2022
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3. Optimal Needle Placement for Prostate Rotating-Shield Brachytherapy (RSBT)
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Yi, Jirong, Adams, Quentin E., Hopfensperger, Karolyn M., Flynn, Ryan T., Kim, Yusung, Buatti, John M., Xu, Weiyu, and Wu, Xiaodong
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Physics - Medical Physics - Abstract
Purpose: To present an efficient NEEdle Position Optimization (NEEPO) algorithm for prostate rotating shield brachytherapy (RSBT). With RSBT, the increased flexibility beyond conventional high-dose-rate brachytherapy (HDR-BT) due to the partially shielded radiation source has been shown by Adams et al. in 2020 to enable improved urethra sparing (23.1%), enhanced dose escalation (29.9%), or both, with 20 needles without NEEPO-optimized positions. Within this regime of improved dosimetry, we propose in this work that the benefits of RSBT can be maintained while also reducing the number of needles needed for the delivery. The goal of NEEPO is to provide the capability to further increase the dosimetric benefit of RSBT and to minimize the number of needles needed to satisfy a dosimetric goal. Methods: The NEEPO algorithm generates a needle pool for a given patient and then iteratively constructs a subset of needles from the pool based on relative needle importance as determined by total dwell times within needles. The NEEPO algorithm is based on a convex optimization formulation using a quadratic dosimetric penalty function, dwell time regularization by total variation, and a block sparsity regularization term to enable iterative removal of low-importance needles. RSBT treatment plans for 26 patients were generated using single fraction prescriptions with both dose escalation and urethra sparing goals, and compared to baseline HDR-BT treatment plans., Comment: 12 pages
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- 2021
4. Modern Radiation Treatment Planning Parameters and Outcomes in Pediatric Tectal Gliomas
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Khan, Qateeb, Bowar, Breann, Ismael, Heba, Gainey, Jordan, Myers, Bryn, Dlouhy, Brian, Hyer, Daniel, Grafft, Amanda, Khan, Maryam, Buatti, John M., and Kozak, Margaret M.
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- 2024
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5. Surgical management of craniospinal axis malignant peripheral nerve sheath tumors: a single-institution experience and literature review
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Chowdhury, Ajmain, Vivanco-Suarez, Juan, Teferi, Nahom, Belzer, Alex, Al-Kaylani, Hend, Challa, Meron, Lee, Sarah, Buatti, John M., and Hitchon, Patrick
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- 2023
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6. Author Correction: Federated learning enables big data for rare cancer boundary detection
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Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G. Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J., Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y., Chang, Ken, Balaña, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S., Lombardo, Joseph, Palmer, Joshua D., Flanders, Adam E., Dicker, Adam P., Sair, Haris I., Jones, Craig K., Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y., Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A., Mitchell, J. Ross, Farinhas, Joaquim, Maldjian, Joseph A., Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C., Reddy, Divya, Holcomb, James, Wagner, Benjamin C., Ellingson, Benjamin M., Cloughesy, Timothy F., Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B., Teixeira, Bernardo C. A., Sprenger, Flávia, Menotti, David, Lucio, Diego R., LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W., McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E., Vadmal, Vachan, Waite, Kristin, Colen, Rivka R., Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J. Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V. M., Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I., Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M., Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R., Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten M. J., Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W., Dubbink, Hendrikus J., Vincent, Arnaud J. P. E., van den Bent, Martin J., French, Pim J., Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P., Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B., Mistry, Akshitkumar, Thompson, Reid C., Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C., Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G. H., Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M., Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F., Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M., Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A., Ogbole, Godwin, Soneye, Mayowa, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu’aibu, Mustapha, Dorcas, Adeleye, Dako, Farouk, Simpson, Amber L., Hamghalam, Mohammad, Peoples, Jacob J., Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y., Boss, Michael A., Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S., Martin, Jason, and Bakas, Spyridon
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- 2023
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7. A Technique to Enable Efficient Adaptive Radiation Therapy: Automated Contouring of Prostate and Adjacent Organs
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Hyer, Daniel E., Caster, Joseph, Smith, Blake, St-Aubin, Joel, Snyder, Jeffrey, Shepard, Andrew, Zhang, Honghai, Mullan, Sean, Geoghegan, Theodore, George, Benjamin, Byrne, James, Smith, Mark, Buatti, John M., and Sonka, Milan
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- 2024
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8. Survival in Patients With Brain Metastases: Summary Report on the Updated Diagnosis-Specific Graded Prognostic Assessment and Definition of the Eligibility Quotient.
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Sperduto, Paul W, Mesko, Shane, Li, Jing, Cagney, Daniel, Aizer, Ayal, Lin, Nancy U, Nesbit, Eric, Kruser, Tim J, Chan, Jason, Braunstein, Steve, Lee, Jessica, Kirkpatrick, John P, Breen, Will, Brown, Paul D, Shi, Diana, Shih, Helen A, Soliman, Hany, Sahgal, Arjun, Shanley, Ryan, Sperduto, William A, Lou, Emil, Everett, Ashlyn, Boggs, Drexell H, Masucci, Laura, Roberge, David, Remick, Jill, Plichta, Kristin, Buatti, John M, Jain, Supriya, Gaspar, Laurie E, Wu, Cheng-Chia, Wang, Tony JC, Bryant, John, Chuong, Michael, An, Yi, Chiang, Veronica, Nakano, Toshimichi, Aoyama, Hidefumi, and Mehta, Minesh P
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Cancer ,Rare Diseases ,Brain Disorders ,Brain Cancer ,Detection ,screening and diagnosis ,4.2 Evaluation of markers and technologies ,Aged ,Aged ,80 and over ,Brain Neoplasms ,Female ,Humans ,Karnofsky Performance Status ,Male ,Middle Aged ,Multivariate Analysis ,Neoplasm Grading ,Neoplasms ,Precision Medicine ,Prognosis ,Proportional Hazards Models ,Clinical Sciences ,Oncology and Carcinogenesis ,Oncology & Carcinogenesis - Abstract
PurposeConventional wisdom has rendered patients with brain metastases ineligible for clinical trials for fear that poor survival could mask the benefit of otherwise promising treatments. Our group previously published the diagnosis-specific Graded Prognostic Assessment (GPA). Updates with larger contemporary cohorts using molecular markers and newly identified prognostic factors have been published. The purposes of this work are to present all the updated indices in a single report to guide treatment choice, stratify research, and define an eligibility quotient to expand eligibility.MethodsA multi-institutional database of 6,984 patients with newly diagnosed brain metastases underwent multivariable analyses of prognostic factors and treatments associated with survival for each primary site. Significant factors were used to define the updated GPA. GPAs of 4.0 and 0.0 correlate with the best and worst prognoses, respectively.ResultsSignificant prognostic factors varied by diagnosis and new prognostic factors were identified. Those factors were incorporated into the updated GPA with robust separation (P < .01) between subgroups. Survival has improved, but varies widely by GPA for patients with non-small-cell lung, breast, melanoma, GI, and renal cancer with brain metastases from 7-47 months, 3-36 months, 5-34 months, 3-17 months, and 4-35 months, respectively.ConclusionMedian survival varies widely and our ability to estimate survival for patients with brain metastases has improved. The updated GPA (available free at brainmetgpa.com) provides an accurate tool with which to estimate survival, individualize treatment, and stratify clinical trials. Instead of excluding patients with brain metastases, enrollment should be encouraged and those trials should be stratified by the GPA to ensure those trials make appropriate comparisons. Furthermore, we recommend the expansion of eligibility to allow for the enrollment of patients with previously treated brain metastases who have a 50% or greater probability of an additional year of survival (eligibility quotient > 0.50).
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- 2020
9. Deep segmentation networks predict survival of non-small cell lung cancer
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Baek, Stephen, He, Yusen, Allen, Bryan G., Buatti, John M., Smith, Brian J., Tong, Ling, Sun, Zhiyu, Wu, Jia, Diehn, Maximilian, Loo, Billy W., Plichta, Kristin A., Seyedin, Steven N., Gannon, Maggie, Cabel, Katherine R., Kim, Yusung, and Wu, Xiaodong
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Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography-computed tomography (PET/CT) images have predictive power on NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new premise for cancer image analysis, with significantly enhanced predictive power compared to other hand-crafted radiomics features. Here we show that CNN trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET/CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-net algorithm has not seen any other clinical information (e.g. survival, age, smoking history) than the images and the corresponding tumor contours provided by physicians. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of progression appear to match with the regions where the U-Net features identified patterns that predicted higher likelihood of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination.
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- 2019
10. Estrogen/progesterone receptor and HER2 discordance between primary tumor and brain metastases in breast cancer and its effect on treatment and survival.
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Sperduto, Paul W, Mesko, Shane, Li, Jing, Cagney, Daniel, Aizer, Ayal, Lin, Nancy U, Nesbit, Eric, Kruser, Tim J, Chan, Jason, Braunstein, Steve, Lee, Jessica, Kirkpatrick, John P, Breen, Will, Brown, Paul D, Shi, Diana, Shih, Helen A, Soliman, Hany, Sahgal, Arjun, Shanley, Ryan, Sperduto, William, Lou, Emil, Everett, Ashlyn, Boggs, Drexell Hunter, Masucci, Laura, Roberge, David, Remick, Jill, Plichta, Kristin, Buatti, John M, Jain, Supriya, Gaspar, Laurie E, Wu, Cheng-Chia, Wang, Tony JC, Bryant, John, Chuong, Michael, Yu, James, Chiang, Veronica, Nakano, Toshimichi, Aoyama, Hidefumi, and Mehta, Minesh P
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Humans ,Breast Neoplasms ,Brain Neoplasms ,Receptor ,erbB-2 ,Receptors ,Progesterone ,Estrogens ,Retrospective Studies ,Biomarkers ,Tumor ,brain metastases ,breast cancer ,estrogen/progesterone/HER2 receptor discordance ,Receptor ,ErbB-2 ,Breast Cancer ,Cancer ,Clinical Trials and Supportive Activities ,Clinical Research ,2.1 Biological and endogenous factors ,Aetiology ,Good Health and Well Being ,Neurosciences ,Oncology and Carcinogenesis ,Oncology & Carcinogenesis - Abstract
BackgroundBreast cancer treatment is based on estrogen receptors (ERs), progesterone receptors (PRs), and human epidermal growth factor receptor 2 (HER2). At the time of metastasis, receptor status can be discordant from that at initial diagnosis. The purpose of this study was to determine the incidence of discordance and its effect on survival and subsequent treatment in patients with breast cancer brain metastases (BCBM).MethodsA retrospective database of 316 patients who underwent craniotomy for BCBM between 2006 and 2017 was created. Discordance was considered present if the ER, PR, or HER2 status differed between the primary tumor and the BCBM.ResultsThe overall receptor discordance rate was 132/316 (42%), and the subtype discordance rate was 100/316 (32%). Hormone receptors (HR, either ER or PR) were gained in 40/160 (25%) patients with HR-negative primary tumors. HER2 was gained in 22/173 (13%) patients with HER2-negative primary tumors. Subsequent treatment was not adjusted for most patients who gained receptors-nonetheless, median survival (MS) improved but did not reach statistical significance (HR, 17-28 mo, P = 0.12; HER2, 15-19 mo, P = 0.39). MS for patients who lost receptors was worse (HR, 27-18 mo, P = 0.02; HER2, 30-18 mo, P = 0.08).ConclusionsReceptor discordance between primary tumor and BCBM is common, adversely affects survival if receptors are lost, and represents a missed opportunity for use of effective treatments if receptors are gained. Receptor analysis of BCBM is indicated when clinically appropriate. Treatment should be adjusted accordingly.Key points1. Receptor discordance alters subtype in 32% of BCBM patients.2. The frequency of receptor gain for HR and HER2 was 25% and 13%, respectively.3. If receptors are lost, survival suffers. If receptors are gained, consider targeted treatment.
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- 2020
11. Beyond an Updated Graded Prognostic Assessment (Breast GPA): A Prognostic Index and Trends in Treatment and Survival in Breast Cancer Brain Metastases From 1985 to Today.
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Sperduto, Paul W, Mesko, Shane, Li, Jing, Cagney, Daniel, Aizer, Ayal, Lin, Nancy U, Nesbit, Eric, Kruser, Tim J, Chan, Jason, Braunstein, Steve, Lee, Jessica, Kirkpatrick, John P, Breen, Will, Brown, Paul D, Shi, Diana, Shih, Helen A, Soliman, Hany, Sahgal, Arjun, Shanley, Ryan, Sperduto, William, Lou, Emil, Everett, Ashlyn, Boggs, Drexell Hunter, Masucci, Laura, Roberge, David, Remick, Jill, Plichta, Kristin, Buatti, John M, Jain, Supriya, Gaspar, Laurie E, Wu, Cheng-Chia, Wang, Tony JC, Bryant, John, Chuong, Michael, Yu, James, Chiang, Veronica, Nakano, Toshimichi, Aoyama, Hidefumi, and Mehta, Minesh P
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Humans ,Breast Neoplasms ,Brain Neoplasms ,BRCA1 Protein ,Prognosis ,Survival Analysis ,Retrospective Studies ,Aged ,Aged ,80 and over ,Middle Aged ,Female ,Rare Diseases ,Clinical Research ,Cancer ,Breast Cancer ,Other Physical Sciences ,Clinical Sciences ,Oncology and Carcinogenesis ,Oncology & Carcinogenesis - Abstract
PurposeBrain metastases are a common sequelae of breast cancer. Survival varies widely based on diagnosis-specific prognostic factors (PF). We previously published a prognostic index (Graded Prognostic Assessment [GPA]) for patients with breast cancer with brain metastases (BCBM), based on cohort A (1985-2007, n = 642), then updated it, reporting the effect of tumor subtype in cohort B (1993-2010, n = 400). The purpose of this study is to update the Breast GPA with a larger contemporary cohort (C) and compare treatment and survival across the 3 cohorts.Methods and materialsA multi-institutional (19), multinational (3), retrospective database of 2473 patients with breast cancer with newly diagnosed brain metastases (BCBM) diagnosed from January 1, 2006, to December 31, 2017, was created and compared with prior cohorts. Associations of PF and treatment with survival were analyzed. Kaplan-Meier survival estimates were compared with log-rank tests. PF were weighted and the Breast GPA was updated such that a GPA of 0 and 4.0 correlate with the worst and best prognoses, respectively.ResultsMedian survival (MS) for cohorts A, B, and C improved over time (from 11, to 14 to 16 months, respectively; P < .01), despite the subtype distribution becoming less favorable. PF significant for survival were tumor subtype, Karnofsky Performance Status, age, number of BCBMs, and extracranial metastases (all P < .01). MS for GPA 0 to 1.0, 1.5-2.0, 2.5-3.0, and 3.5-4.0 was 6, 13, 24, and 36 months, respectively. Between cohorts B and C, the proportion of human epidermal receptor 2 + subtype decreased from 31% to 18% (P < .01) and MS in this subtype increased from 18 to 25 months (P < .01).ConclusionsMS has improved modestly but varies widely by diagnosis-specific PF. New PF are identified and incorporated into an updated Breast GPA (free online calculator available at brainmetgpa.com). The Breast GPA facilitates clinical decision-making and will be useful for stratification of future clinical trials. Furthermore, these data suggest human epidermal receptor 2-targeted therapies improve clinical outcomes in some patients with BCBM.
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- 2020
12. Estimating survival in patients with gastrointestinal cancers and brain metastases: An update of the graded prognostic assessment for gastrointestinal cancers (GI-GPA).
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Sperduto, Paul W, Fang, Penny, Li, Jing, Breen, William, Brown, Paul D, Cagney, Daniel, Aizer, Ayal, Yu, James B, Chiang, Veronica, Jain, Supriya, Gaspar, Laurie E, Myrehaug, Sten, Sahgal, Arjun, Braunstein, Steve, Sneed, Penny, Cameron, Brent, Attia, Albert, Molitoris, Jason, Wu, Cheng-Chia, Wang, Tony JC, Lockney, Natalie A, Beal, Kathryn, Parkhurst, Jessica, Buatti, John M, Shanley, Ryan, Lou, Emil, Tandberg, Daniel D, Kirkpatrick, John P, Shi, Diana, Shih, Helen A, Chuong, Michael, Saito, Hirotake, Aoyama, Hidefumi, Masucci, Laura, Roberge, David, and Mehta, Minesh P
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Brain metastases ,End-of-life ,Gastrointestinal cancers ,Prognosis ,Cancer ,Rare Diseases ,Brain Disorders ,Digestive Diseases ,Clinical Research - Abstract
BackgroundPatients with gastrointestinal cancers and brain metastases (BM) represent a unique and heterogeneous population. Our group previously published the Diagnosis-Specific Graded Prognostic Assessment (DS-GPA) for patients with GI cancers (GI-GPA) (1985-2007, n = 209). The purpose of this study is to update the GI-GPA based on a larger contemporary database.MethodsAn IRB-approved consortium database analysis was performed using a multi-institutional (18), multi-national (3) cohort of 792 patients with gastrointestinal (GI) cancers, with newly-diagnosed BM diagnosed between 1/1/2006 and 12/31/2017. Survival was measured from date of first treatment for BM. Multiple Cox regression was used to select and weight prognostic factors in proportion to their hazard ratios. These factors were incorporated into the updated GI-GPA.ResultsMedian survival (MS) varied widely by primary site and other prognostic factors. Four significant factors (KPS, age, extracranial metastases and number of BM) were used to formulate the updated GI-GPA. Overall MS for this cohort remains poor; 8 months. MS by GPA was 3, 7, 11 and 17 months for GPA 0-1, 1.5-2, 2.5-3.0 and 3.5-4.0, respectively. >30% present in the worst prognostic group (GI-GPA of ≤1.0).ConclusionsBrain metastases are not uncommon in GI cancer patients and MS varies widely among them. This updated GI-GPA index improves our ability to estimate survival for these patients and will be useful for therapy selection, end-of-life decision-making and stratification for future clinical trials. A user-friendly, free, on-line app to calculate the GPA score and estimate survival for an individual patient is available at brainmetgpa.com.
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- 2019
13. Survival and prognostic factors in patients with gastrointestinal cancers and brain metastases: have we made progress?
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Sperduto, Paul W, Fang, Penny, Li, Jing, Breen, William, Brown, Paul D, Cagney, Daniel, Aizer, Ayal, Yu, James, Chiang, Veronica, Jain, Supriya, Gaspar, Laurie E, Myrehaug, Sten, Sahgal, Arjun, Braunstein, Steve, Sneed, Penny, Cameron, Brent, Attia, Albert, Molitoris, Jason, Wu, Cheng-Chia, Wang, Tony JC, Lockney, Natalie, Beal, Kathryn, Parkhurst, Jessica, Buatti, John M, Shanley, Ryan, Lou, Emil, Tandberg, Daniel D, Kirkpatrick, John P, Shi, Diana, Shih, Helen A, Chuong, Michael, Saito, Hirotake, Aoyama, Hidefumi, Masucci, Laura, Roberge, David, and Mehta, Minesh P
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Humans ,Gastrointestinal Neoplasms ,Brain Neoplasms ,Prognosis ,Cohort Studies ,Adult ,Aged ,Aged ,80 and over ,Middle Aged ,Female ,Male ,Kaplan-Meier Estimate ,Cancer ,Rare Diseases ,Digestive Diseases ,Clinical Research ,4.1 Discovery and preclinical testing of markers and technologies ,Detection ,screening and diagnosis ,Clinical Sciences ,General Clinical Medicine - Abstract
The literature describing the prognosis of patients with gastrointestinal (GI) cancers and brain metastases (BM) is sparse. Our group previously published a prognostic index, the Graded Prognostic Assessment (GPA) for GI cancer patients with BM, based on 209 patients diagnosed from 1985-2005. The purpose of this analysis is to identify prognostic factors for GI cancer patients with newly diagnosed BM in a larger contemporary cohort. A multi-institutional retrospective IRB-approved database of 792 GI cancer patients with new BM diagnosed from 1/1/2006 to 12/31/2016 was created. Demographic data, clinical parameters, and treatment were correlated with survival and time from primary diagnosis to BM (TPDBM). Kaplan-Meier median survival (MS) estimates were calculated and compared with log-rank tests. The MS from time of first treatment for BM for the prior and current cohorts were 5 and 8 months, respectively (P < 0.001). Eight prognostic factors (age, stage, primary site, resection of primary tumor, Karnofsky Performance Status (KPS), extracranial metastases, number of BM and Hgb were found to be significant for survival, in contrast to only one (KPS) in the prior cohort. In this cohort, the most common primary sites were rectum (24%) and esophagus (23%). Median TPDBM was 22 months. Notably, 37% (267/716) presented with poor prognosis (GPA 0-1.0). Although little improvement in overall survival in this cohort has been achieved in recent decades, survival varies widely and multiple new prognostic factors were identified. Future work will translate these factors into a prognostic index to facilitate clinical decision-making and stratification of future clinical trials.
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- 2019
14. Estimating survival for renal cell carcinoma patients with brain metastases: an update of the Renal Graded Prognostic Assessment tool.
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Sperduto, Paul W, Deegan, Brian J, Li, Jing, Jethwa, Krishan R, Brown, Paul D, Lockney, Natalie, Beal, Kathryn, Rana, Nitesh G, Attia, Albert, Tseng, Chia-Lin, Sahgal, Arjun, Shanley, Ryan, Sperduto, William A, Lou, Emil, Zahra, Amir, Buatti, John M, Yu, James B, Chiang, Veronica, Molitoris, Jason K, Masucci, Laura, Roberge, David, Shi, Diana D, Shih, Helen A, Olson, Adam, Kirkpatrick, John P, Braunstein, Steve, Sneed, Penny, and Mehta, Minesh P
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Cancer ,Kidney Disease ,Neurosciences ,Clinical Research ,Rare Diseases ,Brain Disorders ,Brain Cancer ,Adolescent ,Adult ,Aged ,Aged ,80 and over ,Biomarkers ,Tumor ,Brain Neoplasms ,Carcinoma ,Renal Cell ,Combined Modality Therapy ,Female ,Follow-Up Studies ,Humans ,Karnofsky Performance Status ,Kidney Neoplasms ,Male ,Middle Aged ,Prognosis ,Retrospective Studies ,Survival Rate ,Young Adult ,renal cell carcinoma ,brain metastases ,prognosis ,Oncology and Carcinogenesis ,Oncology & Carcinogenesis - Abstract
BackgroundBrain metastases are a common complication of renal cell carcinoma (RCC). Our group previously published the Renal Graded Prognostic Assessment (GPA) tool. In our prior RCC study (n = 286, 1985-2005), we found marked heterogeneity and variation in outcomes. In our recent update in a larger, more contemporary cohort, we identified additional significant prognostic factors. The purpose of this study is to update the original Renal-GPA based on the newly identified prognostic factors.MethodsA multi-institutional retrospective institutional review board-approved database of 711 RCC patients with new brain metastases diagnosed from January 1, 2006 to December 31, 2015 was created. Clinical parameters and treatment were correlated with survival. A revised Renal GPA index was designed by weighting the most significant factors in proportion to their hazard ratios and assigning scores such that the patients with the best and worst prognoses would have a GPA of 4.0 and 0.0, respectively.ResultsThe 4 most significant factors were Karnofsky performance status, number of brain metastases, extracranial metastases, and hemoglobin. The overall median survival was 12 months. Median survival for GPA groups 0-1.0, 1.5-2.0, 2.5-3, and 3.5-4.0 (% n = 25, 27, 30 and 17) was 4, 12, 17, and 35 months, respectively.ConclusionThe updated Renal GPA is a user-friendly tool that will help clinicians and patients better understand prognosis, individualize clinical decision making and treatment selection, provide a means to compare retrospective literature, and provide more robust stratification of future clinical trials in this heterogeneous population. To simplify use of this tool in daily practice, a free online application is available at brainmetgpa.com.
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- 2018
15. The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective
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Press, Robert H, Shu, Hui-Kuo G, Shim, Hyunsuk, Mountz, James M, Kurland, Brenda F, Wahl, Richard L, Jones, Ella F, Hylton, Nola M, Gerstner, Elizabeth R, Nordstrom, Robert J, Henderson, Lori, Kurdziel, Karen A, Vikram, Bhadrasain, Jacobs, Michael A, Holdhoff, Matthias, Taylor, Edward, Jaffray, David A, Schwartz, Lawrence H, Mankoff, David A, Kinahan, Paul E, Linden, Hannah M, Lambin, Philippe, Dilling, Thomas J, Rubin, Daniel L, Hadjiiski, Lubomir, and Buatti, John M
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Medical and Biological Physics ,Physical Sciences ,Biomedical and Clinical Sciences ,Oncology and Carcinogenesis ,Cancer ,Clinical Research ,Clinical Trials and Supportive Activities ,Good Health and Well Being ,Humans ,Magnetic Resonance Imaging ,Magnetic Resonance Spectroscopy ,Neoplasms ,Positron-Emission Tomography ,Radiation Oncology ,Tomography ,X-Ray Computed ,Tumor Hypoxia ,Other Physical Sciences ,Clinical Sciences ,Oncology & Carcinogenesis ,Oncology and carcinogenesis ,Theoretical and computational chemistry ,Medical and biological physics - Abstract
Modern radiation therapy is delivered with great precision, in part by relying on high-resolution multidimensional anatomic imaging to define targets in space and time. The development of quantitative imaging (QI) modalities capable of monitoring biologic parameters could provide deeper insight into tumor biology and facilitate more personalized clinical decision-making. The Quantitative Imaging Network (QIN) was established by the National Cancer Institute to advance and validate these QI modalities in the context of oncology clinical trials. In particular, the QIN has significant interest in the application of QI to widen the therapeutic window of radiation therapy. QI modalities have great promise in radiation oncology and will help address significant clinical needs, including finer prognostication, more specific target delineation, reduction of normal tissue toxicity, identification of radioresistant disease, and clearer interpretation of treatment response. Patient-specific QI is being incorporated into radiation treatment design in ways such as dose escalation and adaptive replanning, with the intent of improving outcomes while lessening treatment morbidities. This review discusses the current vision of the QIN, current areas of investigation, and how the QIN hopes to enhance the integration of QI into the practice of radiation oncology.
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- 2018
16. Reduced blood flow by laser speckle flowgraphy after 125I-plaque brachytherapy for uveal melanoma
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Tamplin, Michelle R., Wang, Jui-Kai, Vitale, Anthony H., Hashimoto, Ryuya, Garvin, Mona K., Binkley, Elaine M., Hyer, Daniel E., Buatti, John M., Boldt, H. Culver, Kardon, Randy H., and Grumbach, Isabella M.
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- 2022
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17. Effect of Targeted Therapies on Prognostic Factors, Patterns of Care, and Survival in Patients With Renal Cell Carcinoma and Brain Metastases
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Sperduto, Paul W, Deegan, Brian J, Li, Jing, Jethwa, Krishan R, Brown, Paul D, Lockney, Natalie, Beal, Kathryn, Rana, Nitesh G, Attia, Albert, Tseng, Chia-Lin, Sahgal, Arjun, Shanley, Ryan, Sperduto, William A, Lou, Emil, Zahra, Amir, Buatti, John M, Yu, James B, Chiang, Veronica, Molitoris, Jason K, Masucci, Laura, Roberge, David, Shi, Diana D, Shih, Helen A, Olson, Adam, Kirkpatrick, John P, Braunstein, Steve, Sneed, Penny, and Mehta, Minesh P
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Biomedical and Clinical Sciences ,Clinical Sciences ,Oncology and Carcinogenesis ,Rare Diseases ,Cancer ,Kidney Disease ,Clinical Research ,Adolescent ,Adult ,Aged ,Aged ,80 and over ,Angiogenesis Inhibitors ,Antineoplastic Agents ,Brain Neoplasms ,Carcinoma ,Renal Cell ,Cause of Death ,Cranial Irradiation ,Cytokines ,Female ,Hemoglobins ,Humans ,Immunotherapy ,Karnofsky Performance Status ,Kidney Neoplasms ,Male ,Middle Aged ,Multivariate Analysis ,Prognosis ,Radiosurgery ,Retrospective Studies ,Young Adult ,Other Physical Sciences ,Oncology & Carcinogenesis ,Oncology and carcinogenesis ,Theoretical and computational chemistry ,Medical and biological physics - Abstract
PurposeTo identify prognostic factors, define evolving patterns of care, and the effect of targeted therapies in a larger contemporary cohort of renal cell carcinoma (RCC) patients with new brain metastases (BM).Methods and materialsA multi-institutional retrospective institutional review board-approved database of 711 RCC patients with new BM diagnosed from January 1, 2006, to December 31, 2015, was created. Clinical parameters and treatment were correlated with median survival and time from primary diagnosis to BM. Multivariable analyses were performed.ResultsThe median survival for the prior/present cohorts was 9.6/12 months, respectively (P
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- 2018
18. The potential role of MR-guided adaptive radiotherapy in pediatric oncology: Results from a SIOPE-COG survey
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Seravalli, Enrica, Kroon, Petra S., Buatti, John M., Hall, Matthew D., Mandeville, Henry C., Marcus, Karen J., Onal, Cem, Ozyar, Enis, Paulino, Arnold C., Paulsen, Frank, Saunders, Daniel, Tsang, Derek S., Wolden, Suzanne L., and Janssens, Geert O.
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- 2021
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19. Lumbar and Thoracic Vertebrae Segmentation in CT Scans Using a 3D Multi-Object Localization and Segmentation CNN.
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Xiong, Xiaofan, Graves, Stephen A., Gross, Brandie A., Buatti, John M., and Beichel, Reinhard R.
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THORACIC vertebrae ,LUMBAR vertebrae ,COMPUTED tomography ,CONVOLUTIONAL neural networks ,VERTEBRAE ,CERVICAL cancer - Abstract
Radiation treatment of cancers like prostate or cervix cancer requires considering nearby bone structures like vertebrae. In this work, we present and validate a novel automated method for the 3D segmentation of individual lumbar and thoracic vertebra in computed tomography (CT) scans. It is based on a single, low-complexity convolutional neural network (CNN) architecture which works well even if little application-specific training data are available. It is based on volume patch-based processing, enabling the handling of arbitrary scan sizes. For each patch, it performs segmentation and an estimation of up to three vertebrae center locations in one step, which enables utilizing an advanced post-processing scheme to achieve high segmentation accuracy, as required for clinical use. Overall, 1763 vertebrae were used for the performance assessment. On 26 CT scans acquired for standard radiation treatment planning, a Dice coefficient of 0.921 ± 0.047 (mean ± standard deviation) and a signed distance error of 0.271 ± 0.748 mm was achieved. On the large-sized publicly available VerSe2020 data set with 129 CT scans depicting lumbar and thoracic vertebrae, the overall Dice coefficient was 0.940 ± 0.065 and the signed distance error was 0.109 ± 0.301 mm. A comparison to other methods that have been validated on VerSe data showed that our approach achieved a better overall segmentation performance. [ABSTRACT FROM AUTHOR]
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- 2024
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20. Long-term outcome comparison for standard fractionation (>59 Gy) versus hyperfractionated (>45 Gy) radiotherapy plus concurrent chemotherapy for limited-stage small-cell lung cancer
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Watkins, John M., Russo, J. Kyle, Andresen, Nicholas, Rountree, Coyt R., Zahra, Amir, Mott, Sarah L., Herr, Daniel J., O’Keefe, Jacy, Allen, Bryan G., Sharma, Anand K., and Buatti, John M.
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- 2020
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21. Quantitative Imaging in Cancer Clinical Trials
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Yankeelov, Thomas E, Mankoff, David A, Schwartz, Lawrence H, Lieberman, Frank S, Buatti, John M, Mountz, James M, Erickson, Bradley J, Fennessy, Fiona MM, Huang, Wei, Kalpathy-Cramer, Jayashree, Wahl, Richard L, Linden, Hannah M, Kinahan, Paul E, Zhao, Binsheng, Hylton, Nola M, Gillies, Robert J, Clarke, Laurence, Nordstrom, Robert, and Rubin, Daniel L
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Biomedical and Clinical Sciences ,Clinical Sciences ,Oncology and Carcinogenesis ,Cancer ,Clinical Trials and Supportive Activities ,Clinical Research ,Biomedical Imaging ,Bioengineering ,Detection ,screening and diagnosis ,4.2 Evaluation of markers and technologies ,4.4 Population screening ,Good Health and Well Being ,Clinical Trials as Topic ,Evaluation Studies as Topic ,Humans ,Molecular Imaging ,Molecular Targeted Therapy ,Neoplasms ,Oncology & Carcinogenesis ,Clinical sciences ,Oncology and carcinogenesis - Abstract
As anticancer therapies designed to target specific molecular pathways have been developed, it has become critical to develop methods to assess the response induced by such agents. Although traditional, anatomic CT, and MRI examinations are useful in many settings, increasing evidence suggests that these methods cannot answer the fundamental biologic and physiologic questions essential for assessment and, eventually, prediction of treatment response in the clinical trial setting, especially in the critical period soon after treatment is initiated. To optimally apply advances in quantitative imaging methods to trials of targeted cancer therapy, new infrastructure improvements are needed that incorporate these emerging techniques into the settings where they are most likely to have impact. In this review, we first elucidate the needs for therapeutic response assessment in the era of molecularly targeted therapy and describe how quantitative imaging can most effectively provide scientifically and clinically relevant data. We then describe the tools and methods required to apply quantitative imaging and provide concrete examples of work making these advances practically available for routine application in clinical trials. We conclude by proposing strategies to surmount barriers to wider incorporation of these quantitative imaging methods into clinical trials and, eventually, clinical practice. Our goal is to encourage and guide the oncology community to deploy standardized quantitative imaging techniques in clinical trials to further personalize care for cancer patients and to provide a more efficient path for the development of improved targeted therapies.
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- 2016
22. Head and Neck Cancer Segmentation in FDG PET Images: Performance Comparison of Convolutional Neural Networks and Vision Transformers
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Xiong, Xiaofan, primary, Smith, Brian J., additional, Graves, Stephen A., additional, Graham, Michael M., additional, Buatti, John M., additional, and Beichel, Reinhard R., additional
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- 2023
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23. Estimating survival in patients with gastrointestinal cancers and brain metastases: An update of the graded prognostic assessment for gastrointestinal cancers (GI-GPA)
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Sperduto, Paul W., Fang, Penny, Li, Jing, Breen, William, Brown, Paul D., Cagney, Daniel, Aizer, Ayal, Yu, James B., Chiang, Veronica, Jain, Supriya, Gaspar, Laurie E., Myrehaug, Sten, Sahgal, Arjun, Braunstein, Steve, Sneed, Penny, Cameron, Brent, Attia, Albert, Molitoris, Jason, Wu, Cheng-Chia, Wang, Tony J.C., Lockney, Natalie A., Beal, Kathryn, Parkhurst, Jessica, Buatti, John M., Shanley, Ryan, Lou, Emil, Tandberg, Daniel D., Kirkpatrick, John P., Shi, Diana, Shih, Helen A., Chuong, Michael, Saito, Hirotake, Aoyama, Hidefumi, Masucci, Laura, Roberge, David, and Mehta, Minesh P.
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- 2019
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24. Magnetic resonance imaging of iron metabolism with T2* mapping predicts an enhanced clinical response to pharmacological ascorbate in patients with GBM
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Petronek, Michael S., primary, Monga, Varun, additional, Bodeker, Kellie L., additional, Kwofie, Michael, additional, Lee, Chu-Yu, additional, Mapuskar, Kranti A., additional, Stolwijk, Jeffrey M., additional, Zaher, Amira, additional, Wagner, Brett A., additional, Smith, Mark C., additional, Vollstedt, Sandy, additional, Brown, Heather, additional, Chandler, Meghan L., additional, Lorack, Amanda C., additional, Wulfekuhle, Jared S., additional, Sarkaria, Jann N., additional, Flynn, Ryan T., additional, Greenlee, Jeremy D.W., additional, Howard, Matthew A., additional, Smith, Brian J., additional, Jones, Karra A., additional, Buettner, Garry R., additional, Cullen, Joseph J., additional, St-Aubin, Joel, additional, Buatti, John M., additional, Magnotta, Vincent A., additional, Spitz, Douglas R., additional, and Allen, Bryan G., additional
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- 2023
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25. A Technique to Enable Efficient Adaptive Radiation Therapy: Automated Contouring of Prostate and Adjacent Organs
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Hyer, Daniel E., primary, Caster, Joseph, additional, Smith, Blake, additional, St-Aubin, Joel, additional, Snyder, Jeffrey, additional, Shepard, Andrew, additional, Zhang, Honghai, additional, Mullan, Sean, additional, Geoghegan, Theodore, additional, George, Benjamin, additional, Byrne, James, additional, Smith, Mark, additional, Buatti, John M., additional, and Sonka, Milan, additional
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- 2023
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26. Is More Always Better? An Assessment of the Impact of Lymph Node Yield on Outcome for Clinically Localized Prostate Cancer with Low/Intermediate Risk Pathology (pT2-3a/pN0) Managed with Prostatectomy Alone
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Seyedin, Steven N., Mitchell, Darrion L., Mott, Sarah L., Russo, J. Kyle, Tracy, Chad R., Snow, Anthony N., Parkhurst, Jessica R., Smith, Mark C., Buatti, John M., and Watkins, John M.
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- 2019
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27. Predictive power of deep-learning segmentation based prognostication model in non-small cell lung cancer
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Gainey, Jordan C., primary, He, Yusen, additional, Zhu, Robert, additional, Baek, Stephen S., additional, Wu, Xiaodong, additional, Buatti, John M., additional, Allen, Bryan G., additional, Smith, Brian J., additional, and Kim, Yusung, additional
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- 2023
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28. Incidental prostate cancer diagnosed at radical cystoprostatectomy for bladder cancer: disease-specific outcomes and survival
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Kaelberer, Joshua B., O'Donnell, Michael A., Mitchell, Darrion L., Snow, Anthony N., Mott, Sarah L., Buatti, John M., Smith, Mark C., and Watkins, John M.
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- 2016
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29. Machine learning with the TCGA-HNSC dataset: improving usability by addressing inconsistency, sparsity, and high-dimensionality
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Rendleman, Michael C., Buatti, John M., Braun, Terry A., Smith, Brian J., Nwakama, Chibuzo, Beichel, Reinhard R., Brown, Bart, and Casavant, Thomas L.
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- 2019
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30. Federated learning enables big data for rare cancer boundary detection
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Pati, Sarthak, primary, Baid, Ujjwal, additional, Edwards, Brandon, additional, Sheller, Micah, additional, Wang, Shih-Han, additional, Reina, G. Anthony, additional, Foley, Patrick, additional, Gruzdev, Alexey, additional, Karkada, Deepthi, additional, Davatzikos, Christos, additional, Sako, Chiharu, additional, Ghodasara, Satyam, additional, Bilello, Michel, additional, Mohan, Suyash, additional, Vollmuth, Philipp, additional, Brugnara, Gianluca, additional, Preetha, Chandrakanth J., additional, Sahm, Felix, additional, Maier-Hein, Klaus, additional, Zenk, Maximilian, additional, Bendszus, Martin, additional, Wick, Wolfgang, additional, Calabrese, Evan, additional, Rudie, Jeffrey, additional, Villanueva-Meyer, Javier, additional, Cha, Soonmee, additional, Ingalhalikar, Madhura, additional, Jadhav, Manali, additional, Pandey, Umang, additional, Saini, Jitender, additional, Garrett, John, additional, Larson, Matthew, additional, Jeraj, Robert, additional, Currie, Stuart, additional, Frood, Russell, additional, Fatania, Kavi, additional, Huang, Raymond Y., additional, Chang, Ken, additional, Balaña, Carmen, additional, Capellades, Jaume, additional, Puig, Josep, additional, Trenkler, Johannes, additional, Pichler, Josef, additional, Necker, Georg, additional, Haunschmidt, Andreas, additional, Meckel, Stephan, additional, Shukla, Gaurav, additional, Liem, Spencer, additional, Alexander, Gregory S., additional, Lombardo, Joseph, additional, Palmer, Joshua D., additional, Flanders, Adam E., additional, Dicker, Adam P., additional, Sair, Haris I., additional, Jones, Craig K., additional, Venkataraman, Archana, additional, Jiang, Meirui, additional, So, Tiffany Y., additional, Chen, Cheng, additional, Heng, Pheng Ann, additional, Dou, Qi, additional, Kozubek, Michal, additional, Lux, Filip, additional, Michálek, Jan, additional, Matula, Petr, additional, Keřkovský, Miloš, additional, Kopřivová, Tereza, additional, Dostál, Marek, additional, Vybíhal, Václav, additional, Vogelbaum, Michael A., additional, Mitchell, J. Ross, additional, Farinhas, Joaquim, additional, Maldjian, Joseph A., additional, Yogananda, Chandan Ganesh Bangalore, additional, Pinho, Marco C., additional, Reddy, Divya, additional, Holcomb, James, additional, Wagner, Benjamin C., additional, Ellingson, Benjamin M., additional, Cloughesy, Timothy F., additional, Raymond, Catalina, additional, Oughourlian, Talia, additional, Hagiwara, Akifumi, additional, Wang, Chencai, additional, To, Minh-Son, additional, Bhardwaj, Sargam, additional, Chong, Chee, additional, Agzarian, Marc, additional, Falcão, Alexandre Xavier, additional, Martins, Samuel B., additional, Teixeira, Bernardo C. A., additional, Sprenger, Flávia, additional, Menotti, David, additional, Lucio, Diego R., additional, LaMontagne, Pamela, additional, Marcus, Daniel, additional, Wiestler, Benedikt, additional, Kofler, Florian, additional, Ezhov, Ivan, additional, Metz, Marie, additional, Jain, Rajan, additional, Lee, Matthew, additional, Lui, Yvonne W., additional, McKinley, Richard, additional, Slotboom, Johannes, additional, Radojewski, Piotr, additional, Meier, Raphael, additional, Wiest, Roland, additional, Murcia, Derrick, additional, Fu, Eric, additional, Haas, Rourke, additional, Thompson, John, additional, Ormond, David Ryan, additional, Badve, Chaitra, additional, Sloan, Andrew E., additional, Vadmal, Vachan, additional, Waite, Kristin, additional, Colen, Rivka R., additional, Pei, Linmin, additional, Ak, Murat, additional, Srinivasan, Ashok, additional, Bapuraj, J. Rajiv, additional, Rao, Arvind, additional, Wang, Nicholas, additional, Yoshiaki, Ota, additional, Moritani, Toshio, additional, Turk, Sevcan, additional, Lee, Joonsang, additional, Prabhudesai, Snehal, additional, Morón, Fanny, additional, Mandel, Jacob, additional, Kamnitsas, Konstantinos, additional, Glocker, Ben, additional, Dixon, Luke V. M., additional, Williams, Matthew, additional, Zampakis, Peter, additional, Panagiotopoulos, Vasileios, additional, Tsiganos, Panagiotis, additional, Alexiou, Sotiris, additional, Haliassos, Ilias, additional, Zacharaki, Evangelia I., additional, Moustakas, Konstantinos, additional, Kalogeropoulou, Christina, additional, Kardamakis, Dimitrios M., additional, Choi, Yoon Seong, additional, Lee, Seung-Koo, additional, Chang, Jong Hee, additional, Ahn, Sung Soo, additional, Luo, Bing, additional, Poisson, Laila, additional, Wen, Ning, additional, Tiwari, Pallavi, additional, Verma, Ruchika, additional, Bareja, Rohan, additional, Yadav, Ipsa, additional, Chen, Jonathan, additional, Kumar, Neeraj, additional, Smits, Marion, additional, van der Voort, Sebastian R., additional, Alafandi, Ahmed, additional, Incekara, Fatih, additional, Wijnenga, Maarten M. J., additional, Kapsas, Georgios, additional, Gahrmann, Renske, additional, Schouten, Joost W., additional, Dubbink, Hendrikus J., additional, Vincent, Arnaud J. P. E., additional, van den Bent, Martin J., additional, French, Pim J., additional, Klein, Stefan, additional, Yuan, Yading, additional, Sharma, Sonam, additional, Tseng, Tzu-Chi, additional, Adabi, Saba, additional, Niclou, Simone P., additional, Keunen, Olivier, additional, Hau, Ann-Christin, additional, Vallières, Martin, additional, Fortin, David, additional, Lepage, Martin, additional, Landman, Bennett, additional, Ramadass, Karthik, additional, Xu, Kaiwen, additional, Chotai, Silky, additional, Chambless, Lola B., additional, Mistry, Akshitkumar, additional, Thompson, Reid C., additional, Gusev, Yuriy, additional, Bhuvaneshwar, Krithika, additional, Sayah, Anousheh, additional, Bencheqroun, Camelia, additional, Belouali, Anas, additional, Madhavan, Subha, additional, Booth, Thomas C., additional, Chelliah, Alysha, additional, Modat, Marc, additional, Shuaib, Haris, additional, Dragos, Carmen, additional, Abayazeed, Aly, additional, Kolodziej, Kenneth, additional, Hill, Michael, additional, Abbassy, Ahmed, additional, Gamal, Shady, additional, Mekhaimar, Mahmoud, additional, Qayati, Mohamed, additional, Reyes, Mauricio, additional, Park, Ji Eun, additional, Yun, Jihye, additional, Kim, Ho Sung, additional, Mahajan, Abhishek, additional, Muzi, Mark, additional, Benson, Sean, additional, Beets-Tan, Regina G. H., additional, Teuwen, Jonas, additional, Herrera-Trujillo, Alejandro, additional, Trujillo, Maria, additional, Escobar, William, additional, Abello, Ana, additional, Bernal, Jose, additional, Gómez, Jhon, additional, Choi, Joseph, additional, Baek, Stephen, additional, Kim, Yusung, additional, Ismael, Heba, additional, Allen, Bryan, additional, Buatti, John M., additional, Kotrotsou, Aikaterini, additional, Li, Hongwei, additional, Weiss, Tobias, additional, Weller, Michael, additional, Bink, Andrea, additional, Pouymayou, Bertrand, additional, Shaykh, Hassan F., additional, Saltz, Joel, additional, Prasanna, Prateek, additional, Shrestha, Sampurna, additional, Mani, Kartik M., additional, Payne, David, additional, Kurc, Tahsin, additional, Pelaez, Enrique, additional, Franco-Maldonado, Heydy, additional, Loayza, Francis, additional, Quevedo, Sebastian, additional, Guevara, Pamela, additional, Torche, Esteban, additional, Mendoza, Cristobal, additional, Vera, Franco, additional, Ríos, Elvis, additional, López, Eduardo, additional, Velastin, Sergio A., additional, Ogbole, Godwin, additional, Soneye, Mayowa, additional, Oyekunle, Dotun, additional, Odafe-Oyibotha, Olubunmi, additional, Osobu, Babatunde, additional, Shu’aibu, Mustapha, additional, Dorcas, Adeleye, additional, Dako, Farouk, additional, Simpson, Amber L., additional, Hamghalam, Mohammad, additional, Peoples, Jacob J., additional, Hu, Ricky, additional, Tran, Anh, additional, Cutler, Danielle, additional, Moraes, Fabio Y., additional, Boss, Michael A., additional, Gimpel, James, additional, Veettil, Deepak Kattil, additional, Schmidt, Kendall, additional, Bialecki, Brian, additional, Marella, Sailaja, additional, Price, Cynthia, additional, Cimino, Lisa, additional, Apgar, Charles, additional, Shah, Prashant, additional, Menze, Bjoern, additional, Barnholtz-Sloan, Jill S., additional, Martin, Jason, additional, and Bakas, Spyridon, additional
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- 2022
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31. Initial clinical applications treating pediatric and adolescent patients using MR-guided radiotherapy
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Kozak, Margaret M., primary, Crompton, David, additional, Gross, Brandie A., additional, Harshman, Lyndsay, additional, Dickens, David, additional, Snyder, Jeffrey, additional, Shepard, Andrew, additional, St-Aubin, Joël, additional, Dunkerley, David, additional, Hyer, Daniel, additional, and Buatti, John M., additional
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- 2022
- Full Text
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32. Pharmacological ascorbate improves the response to platinum-based chemotherapy in advanced stage non-small cell lung cancer
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Furqan, Muhammad, primary, Abu-Hejleh, Taher, additional, Stephens, Laura M., additional, Hartwig, Stacey M., additional, Mott, Sarah L., additional, Pulliam, Casey F., additional, Petronek, Michael, additional, Henrich, John B., additional, Fath, Melissa A., additional, Houtman, Jon C., additional, Varga, Steven M., additional, Bodeker, Kellie L., additional, Bossler, Aaron D., additional, Bellizzi, Andrew M., additional, Zhang, Jun, additional, Monga, Varun, additional, Mani, Hariharasudan, additional, Ivanovic, Marina, additional, Smith, Brian J., additional, Byrne, Margaret M., additional, Zeitler, William, additional, Wagner, Brett A., additional, Buettner, Garry R., additional, Cullen, Joseph J., additional, Buatti, John M., additional, Spitz, Douglas R., additional, and Allen, Bryan G., additional
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- 2022
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33. Abstract 811: Pharmacologic ascorbate opens a therapeutic window for ATM inhibition and radiotherapy in colorectal cancer
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Callaghan, Cameron M., primary, Abukhiran, Ibrahim M., additional, Van Rheeden, Richard V., additional, Kalen, Amanda L., additional, Rodman, Samuel N., additional, Petronek, Michael S., additional, Mapuskar, Kranti A., additional, Mott, Sarah L., additional, Coleman, Mitchell C., additional, Goswami, Prabhat C., additional, Buatti, John M., additional, Allen, Bryan G., additional, Spitz, Douglas R., additional, and Caster, Joseph M., additional
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- 2022
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34. An almost linear time algorithm for field splitting in radiation therapy
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Wu, Xiaodong, Dou, Xin, Bayouth, John E., and Buatti, John M.
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- 2013
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35. Clinical Implementational and Site-Specific Workflows for a 1.5T MR-Linac
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Dunkerley, David A. P., primary, Hyer, Daniel E., additional, Snyder, Jeffrey E., additional, St-Aubin, Joël J., additional, Anderson, Carryn M., additional, Caster, Joseph M., additional, Smith, Mark C., additional, Buatti, John M., additional, and Yaddanapudi, Sridhar, additional
- Published
- 2022
- Full Text
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36. Prostate-Specific Membrane Antigen (PSMA) Theranostics for Treatment of Oligometastatic Prostate Cancer
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Plichta, Kristin A., primary, Graves, Stephen A., additional, and Buatti, John M., additional
- Published
- 2021
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37. A Recursive Partitioning Analysis Demonstrating Risk Subsets for 8-Year Biochemical Relapse After Margin-Positive Radical Prostatectomy Without Adjuvant Hormone or Radiation Therapy
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Seyedin, Steven N., primary, Watkins, John M., additional, Mayo, Zachary, additional, Snow, Anthony N., additional, Laszewski, Michael, additional, Russo, J. Kyle, additional, Mott, Sarah L., additional, Tracy, Chad R., additional, Smith, Mark C., additional, Buatti, John M., additional, and Caster, Joseph M., additional
- Published
- 2021
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38. Utilization of Pharmacological Ascorbate to Enhance Hydrogen Peroxide-Mediated Radiosensitivity in Cancer Therapy
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Mehdi, Zain, primary, Petronek, Michael S., additional, Stolwijk, Jeffrey M., additional, Mapuskar, Kranti A., additional, Kalen, Amanda L., additional, Buettner, Garry R., additional, Cullen, Joseph J., additional, Spitz, Douglas R., additional, Buatti, John M., additional, and Allen, Bryan G., additional
- Published
- 2021
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39. Why an Increasing Number of Unmatched Residency Positions in Radiation Oncology? A Survey of Fourth-Year Medical Students
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Blitzer, Grace C., primary, Parekh, Akash D., additional, Chen, Shuai, additional, Taparra, Kekoa, additional, Kahn, Jenna M., additional, Fields, Emma C., additional, Stahl, John M., additional, Rosenberg, Stephen A., additional, Buatti, John M., additional, Laucis, Anna M., additional, Wang, Yichu, additional, Mayhew, David L., additional, McDonald, Andrew M., additional, Harari, Paul M., additional, and Brower, Jeffrey V., additional
- Published
- 2021
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40. Mitochondrial Superoxide Dismutase in Cisplatin-Induced Kidney Injury
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Mapuskar, Kranti A., primary, Steinbach, Emily J., additional, Zaher, Amira, additional, Riley, Dennis P., additional, Beardsley, Robert A., additional, Keene, Jeffery L., additional, Holmlund, Jon T., additional, Anderson, Carryn M., additional, Zepeda-Orozco, Diana, additional, Buatti, John M., additional, Spitz, Douglas R., additional, and Allen, Bryan G., additional
- Published
- 2021
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41. The potential role of MR-guided adaptive radiotherapy in pediatric oncology: Results from a SIOPE-COG survey
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Klinische Fysica RT, Cancer, MS Radiotherapie, Seravalli, Enrica, Kroon, Petra S., Buatti, John M., Hall, Matthew D., Mandeville, Henry C., Marcus, Karen J., Onal, Cem, Ozyar, Enis, Paulino, Arnold C., Paulsen, Frank, Saunders, Daniel, Tsang, Derek S., Wolden, Suzanne L., Janssens, Geert O., Klinische Fysica RT, Cancer, MS Radiotherapie, Seravalli, Enrica, Kroon, Petra S., Buatti, John M., Hall, Matthew D., Mandeville, Henry C., Marcus, Karen J., Onal, Cem, Ozyar, Enis, Paulino, Arnold C., Paulsen, Frank, Saunders, Daniel, Tsang, Derek S., Wolden, Suzanne L., and Janssens, Geert O.
- Published
- 2021
42. Radiation-Induced DNA Single-Strand Breaks in Freshly Isolated Human Leukocytes
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Buatti, John M., Rivero, Luis R., and Jorgensen, Timothy J.
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- 1992
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43. Reduced blood flow by laser speckle flowgraphy after 125I-plaque brachytherapy for uveal melanoma.
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Tamplin, Michelle R., Wang, Jui-Kai, Vitale, Anthony H., Hashimoto, Ryuya, Garvin, Mona K., Binkley, Elaine M., Hyer, Daniel E., Buatti, John M., Boldt, H. Culver, Kardon, Randy H., and Grumbach, Isabella M.
- Subjects
LASERS ,CROSS-sectional method ,UVEA ,MELANOMA ,UVEA cancer ,IODINE radioisotopes ,RESEARCH funding ,RADIOISOTOPE brachytherapy ,HEMODYNAMICS ,CARDIOVASCULAR disease diagnosis ,BLOOD flow measurement - Abstract
Background: To determine whether reductions in retinal and choroidal blood flow measured by laser speckle flowgraphy are detected after 125I-plaque brachytherapy for uveal melanoma.Methods: In a cross-sectional study, retinal and choroidal blood flow were measured using laser speckle flowgraphy in 25 patients after treatment with 125I-plaque brachytherapy for uveal melanoma. Flow was analyzed in the peripapillary region by mean blur rate as well as in the entire image area with a novel superpixel-based method. Relationships between measures were determined by Spearman correlation.Results: Significant decreases in laser speckle blood flow were observed in both the retinal and choroidal vascular beds of irradiated, but not fellow, eyes. Overall, 24 of 25 patients had decreased blood flow compared to their fellow eye, including 5 of the 6 patients imaged within the first 6 months following brachytherapy. A significant negative correlation between blood flow and time from therapy was present.Conclusions: Decreases in retinal and choroidal blood flow by laser speckle flowgraphy were detected within the first 6 months following brachytherapy. Reduced retinal and choroidal blood flow may be an early indicator of microangiographic response to radiation therapy. [ABSTRACT FROM AUTHOR]- Published
- 2022
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44. Temporal Relationship Between Visual Field, Retinal and Microvascular Pathology Following 125I-Plaque Brachytherapy for Uveal Melanoma
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Tamplin, Michelle R., primary, Deng, Wenxiang, additional, Garvin, Mona K., additional, Binkley, Elaine M., additional, Hyer, Daniel E., additional, Buatti, John M., additional, Ledolter, Johannes, additional, Boldt, H. Culver, additional, Kardon, Randy H., additional, and Grumbach, Isabella M., additional
- Published
- 2021
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45. Magnetic resonance imaging (MRI) of pharmacological ascorbate-induced iron redox state as a biomarker in subjects undergoing radio-chemotherapy
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Cushing, Cameron M., primary, Petronek, Michael S., additional, Bodeker, Kellie L., additional, Vollstedt, Sandy, additional, Brown, Heather A., additional, Opat, Emyleigh, additional, Hollenbeck, Nancy J., additional, Shanks, Thomas, additional, Berg, Daniel J., additional, Smith, Brian J., additional, Smith, Mark C., additional, Monga, Varun, additional, Furqan, Muhammad, additional, Howard, Matthew A., additional, Greenlee, Jeremy D., additional, Mapuskar, Kranti A., additional, St-Aubin, Joel, additional, Flynn, Ryan T., additional, Cullen, Joseph J., additional, Buettner, Garry R., additional, Spitz, Douglas R., additional, Buatti, John M., additional, Allen, Bryan G., additional, and Magnotta, Vincent A., additional
- Published
- 2021
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46. Stereotactic radiotherapy of appropriately selected meningiomas and metastatic brain tumor beds with gamma knife icon versus volumetric modulated arc therapy
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Buatti, Jacob S., primary, Buatti, John M., additional, Yaddanapudi, Sridhar, additional, Pennington, Edward C., additional, Wang, Dongxu, additional, Gross, Brandie, additional, St‐Aubin, Joël J., additional, Hyer, Daniel E., additional, Smith, Mark C., additional, and Flynn, Ryan T., additional
- Published
- 2020
- Full Text
- View/download PDF
47. Effects of vessel geometry and catheter position on dose delivery in intracoronary brachytherapy
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Wahle, Andreas, Lopez, John J., Pennington, Edward C., Meeks, Sanford L., Braddy, Kathleen C., Fox, James M., Brennan, Theresa M.H., Buatti, John M., Rossen, James D., and Sonka, Milan
- Subjects
Radioisotope brachytherapy -- Evaluation ,Atherosclerosis -- Care and treatment ,Biological sciences ,Business ,Computers ,Health care industry - Abstract
In-stent restenosis is commonly observed in coronary arteries after intervention. Intravascular brachytherapy has been found effective in reducing the recurrence of restenosis after stent placement. Conventional dosing models for brachytherapy with beta ([beta]) radiation neglect vessel geometry as well as the position of the delivery catheter. This paper demonstrates in computer simulations on phantoms and on in vivo patient data that the estimated dose distribution varies substantially in curved vessels. In simulated phantoms of 50-mm length with a shape corresponding to a 60[degrees]-180[degrees] segment of a respectively sized torus, the average dose in 2-mm depth was decreased by 2.70%-7.48% at the outer curvature and increased by 2.95 %-9.70 % at the inner curvature as compared with a straight phantom. In vivo data were represented in a geometrically correct three-dimensional model that was derived by fusion of intravascular ultrasound (IVUS) and biplane angiography. These data were compared with a simplified tubular model reflecting common assumptions of conventional dosing schemes. The simplified model yielded significantly lower estimates of the delivered radiation and the dose variability as compared with a geometrically correct model (p < 0.001). The estimated dose in ten vessel segments of eight patients was on average 8.76 % lower at the lumen/plaque and 6.52 % lower at the media/adventitia interfaces (simplified tubular model relative to geometrically correct model). The differences in dose estimates between the two models were significantly higher in the right coronary artery as compared with the left coronary artery (p < 0.001). Index Terms--Coronary atherosclerosis, dose-distribution models, in-stent restenosis, intravascular brachytherapy, multi-modality imaging.
- Published
- 2003
48. Radioresistance in Glioblastoma and the Development of Radiosensitizers
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Ali, Md Yousuf, primary, Oliva, Claudia R., additional, Noman, Abu Shadat M., additional, Allen, Bryan G., additional, Goswami, Prabhat C., additional, Zakharia, Yousef, additional, Monga, Varun, additional, Spitz, Douglas R., additional, Buatti, John M., additional, and Griguer, Corinne E., additional
- Published
- 2020
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49. Clinical Trial Design and Development Work Group Within the Quantitative Imaging Network
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Jones, Ella F., primary, Buatti, John M., additional, Shu, Hui-Kuo, additional, Wahl, Richard L., additional, Kurland, Brenda F., additional, Linden, Hannah M., additional, Mankoff, David A., additional, Rubin, Daniel L., additional, Tata, Darrell, additional, Nordstrom, Robert J., additional, Hadjiyski, Lubomir, additional, Holdhoff, Matthias, additional, and Schwartz, Lawrence H., additional
- Published
- 2020
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50. Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images
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Smith, Brian J., primary, Buatti, John M., additional, Bauer, Christian, additional, Ulrich, Ethan J., additional, Ahmadvand, Payam, additional, Budzevich, Mikalai M., additional, Gillies, Robert J., additional, Goldgof, Dmitry, additional, Grkovski, Milan, additional, Hamarneh, Ghassan, additional, Kinahan, Paul E., additional, Muzi, John P., additional, Muzi, Mark, additional, Laymon, Charles M., additional, Mountz, James M., additional, Nehmeh, Sadek, additional, Oborski, Matthew J., additional, Zhao, Binsheng, additional, Sunderland, John J., additional, and Beichel, Reinhard R., additional
- Published
- 2020
- Full Text
- View/download PDF
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