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2. Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects

Author

Warner, Elisa, Lee, Joonsang, Hsu, William, Syeda-Mahmood, Tanveer, Kahn, Charles, Gevaert, Olivier, and Rao, Arvind

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

Machine learning (ML) applications in medical artificial intelligence (AI) systems have shifted from traditional and statistical methods to increasing application of deep learning models. This survey navigates the current landscape of multimodal ML, focusing on its profound impact on medical image analysis and clinical decision support systems. Emphasizing challenges and innovations in addressing multimodal representation, fusion, translation, alignment, and co-learning, the paper explores the transformative potential of multimodal models for clinical predictions. It also highlights the need for principled assessments and practical implementation of such models, bringing attention to the dynamics between decision support systems and healthcare providers and personnel. Despite advancements, challenges such as data biases and the scarcity of "big data" in many biomedical domains persist. We conclude with a discussion on principled innovation and collaborative efforts to further the mission of seamless integration of multimodal ML models into biomedical practice.

Published
2023

3. Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Author

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

Subjects
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

Published
2022
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5. Clustering-based spatial analysis (CluSA) framework through graph neural network for chronic kidney disease prediction using histopathology images

Author

Lee, Joonsang, Warner, Elisa, Shaikhouni, Salma, Bitzer, Markus, Kretzler, Matthias, Gipson, Debbie, Pennathur, Subramaniam, Bellovich, Keith, Bhat, Zeenat, Gadegbeku, Crystal, Massengill, Susan, Perumal, Kalyani, Saha, Jharna, Yang, Yingbao, Luo, Jinghui, Zhang, Xin, Mariani, Laura, Hodgin, Jeffrey B., and Rao, Arvind

Published
2023
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6. Author Correction: Federated learning enables big data for rare cancer boundary detection

Author

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

Published
2023
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7. Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease

Author

Lee, Joonsang, Warner, Elisa, Shaikhouni, Salma, Bitzer, Markus, Kretzler, Matthias, Gipson, Debbie, Pennathur, Subramaniam, Bellovich, Keith, Bhat, Zeenat, Gadegbeku, Crystal, Massengill, Susan, Perumal, Kalyani, Saha, Jharna, Yang, Yingbao, Luo, Jinghui, Zhang, Xin, Mariani, Laura, Hodgin, Jeffrey B., and Rao, Arvind

Published
2022
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9. List of contributors

Author

Alvarez, Jeffery B., primary, Bibault, Jean-Emmanuel, additional, Burgun, Anita, additional, Cai, Jinzheng, additional, Cao, Zhidong, additional, Chang, Ken, additional, Chen, Jonathan H., additional, Chen, William C., additional, Cho, Mildred, additional, Cho, Peter Jaeho, additional, Cornish, Toby C., additional, Costa, Anthony, additional, Dekker, Andre, additional, Drukker, Karen, additional, Dunn, Jessilyn, additional, Eminaga, Okyaz, additional, Erickson, Bradley J., additional, Fournier, Laure, additional, Gambhir, Sanjiv Sam, additional, Gennatas, Efstathios D., additional, Giger, Maryellen L., additional, Halilaj, Iva, additional, Harrison, Adam P., additional, He, Bryan, additional, Hong, Julian C., additional, Jin, Dakai, additional, Jin, Michael C., additional, Jochems, Arthur, additional, Kalpathy-Cramer, Jayashree, additional, Kapp, Daniel S., additional, Karimzadeh, Mehran, additional, Karnes, William, additional, Lambin, Philippe, additional, Langlotz, Curtis P., additional, Lee, Joonsang, additional, Li, Hui, additional, Liao, Joseph C., additional, Lin, Anthony L., additional, Lin, Rebecca Y., additional, Liu, Yun, additional, Lu, Le, additional, Magnus, David, additional, McIntosh, Chris, additional, Miao, Shun, additional, Min, James K., additional, Neill, Daniel B., additional, Oermann, Eric Karl, additional, Ouyang, David, additional, Peng, Lily, additional, Phene, Sonia, additional, Poirot, Maarten G., additional, Quon, Jennifer L., additional, Ranti, Daniel, additional, Rao, Arvind, additional, Raskar, Ramesh, additional, Rombaoa, Christopher, additional, Rubin, Daniel L., additional, Samarasena, Jason, additional, Seekins, Jayne, additional, Seetharam, Karthik, additional, Shearer, Emily, additional, Sibley, Adam, additional, Singh, Karnika, additional, Singh, Praveer, additional, Sordo, Margarita, additional, Suraweera, Duminda, additional, Valliani, Aly Al-Amyn, additional, van Wijk, Yvonka, additional, Vepakomma, Praneeth, additional, Wang, Bo, additional, Wang, Ge, additional, Wang, Nicholas, additional, Wang, Yirui, additional, Warner, Elisa, additional, Welch, Mattea, additional, Wong, Kimberly, additional, Wu, Zhenqin, additional, Xing, Fuyong, additional, Xing, Lei, additional, Yan, Ke, additional, Yan, Pingkun, additional, Yang, Lu, additional, Yeom, Kristen W., additional, Zachariah, Robin, additional, Zeng, Daniel, additional, Zhang, Lin, additional, Zhang, Ling, additional, Zhang, Xuhong, additional, Zhou, Li, additional, and Zou, James, additional

Published
2021
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13. A PET Radiomics Model to Predict Refractory Mediastinal Hodgkin Lymphoma

Author

Milgrom, Sarah A., Elhalawani, Hesham, Lee, Joonsang, Wang, Qianghu, Mohamed, Abdallah S. R., Dabaja, Bouthaina S., Pinnix, Chelsea C., Gunther, Jillian R., Court, Laurence, Rao, Arvind, Fuller, Clifton D., Akhtari, Mani, Aristophanous, Michalis, Mawlawi, Osama, Chuang, Hubert H., Sulman, Erik P., Lee, Hun J., Hagemeister, Frederick B., Oki, Yasuhiro, Fanale, Michelle, and Smith, Grace L.

Published
2019
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14. Influence of the mesophyll on stomatal opening

Author

Lee, Joonsang

Subjects
580, Botany
Abstract

This study shows that stomata in the isolated epidermis do not behave like those in the intact leaf and that the mesophyll plays an important role in the mechanism of stomatal opening over short periods. The results suggested that the opening of stomata in isolated epidermis was inferior to that in the intact leaf and was influenced by the concentration of KCl in the medium rather than by light. Stomatal opening in isolated epidermis of Commelina was not only insensitive to light but also unaffected by CO2 in the medium containing 100 mol m-3 KCl. The absence of an effect of light and CO2 on the stomata in isolated epidermis and the evidence that they possessed the potential of those in the intact leaf to open wide, suggested that the mesophyll could be important in influencing stomatal opening in the intact leaf. The solution in which the mesophyll cells were incubated was separated by centrifugation. The medium from cells previously incubated in the light caused the stomata in the isolated epidermis to open but that from cells kept in the dark had no effect. Thus the stimulatory influence of the mesophyll cells in bringing about stomatal opening could be separated from the cells into solution. For ease of description it is tentatively suggested that the putative factor which promotes stomatal opening indicated by the results be called stomatin. Stomatin will be produced when the chloroplasts in the mesophyll are exposed to white light.

Published
1992

15. Head and Neck Radiation Therapy Patterns of Practice Variability Identified as a Challenge to Real-World Big Data: Results From the Learning from Analysis of Multicentre Big Data Aggregation (LAMBDA) Consortium

Author

Caissie, Amanda, primary, Mierzwa, Michelle, additional, Fuller, Clifton David, additional, Rajaraman, Murali, additional, Lin, Alex, additional, MacDonald, Andrew, additional, Popple, Richard, additional, Xiao, Ying, additional, VanDijk, Lisanne, additional, Balter, Peter, additional, Fong, Helen, additional, Xu, Heping, additional, Kovoor, Matthew, additional, Lee, Joonsang, additional, Rao, Arvind, additional, Martel, Mary, additional, Thompson, Reid, additional, Merz, Brandon, additional, Yao, John, additional, and Mayo, Charles, additional

Published
2023
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20. CluSA: Clustering-based Spatial Analysis framework through Graph Neural Network for Chronic Kidney Disease Prediction using Histopathology Images

Author

Lee, Joonsang, primary, Warner, Elisa, additional, Shaikhouni, Salma, additional, Bitzer, Markus, additional, Kretzler, Matthias, additional, Gipson, Debbie, additional, Pennathur, Subramaniam, additional, Bellovich, Keith, additional, Bhat, Zeenat, additional, Gadegbeku, Crystal, additional, Massengill, Susan, additional, Perumal, Kalyani, additional, Saha, Jharna, additional, Yang, Yingbao, additional, Luo, Jinghui, additional, Zhang, Xin, additional, Mariani, Laura, additional, Hodgin, Jeffrey B., additional, and Rao, Arvind, additional

Published
2022
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21. Federated learning enables big data for rare cancer boundary detection

Author

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

Published
2022
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26. A snapshot of medical physics practice patterns

Author

Kisling, Kelly D., primary, Ger, Rachel B., additional, Netherton, Tucker J., additional, Cardenas, Carlos E., additional, Owens, Constance A., additional, Anderson, Brian M., additional, Lee, Joonsang, additional, Rhee, Dong Joo, additional, Edward, Sharbacha S., additional, Gay, Skylar S., additional, He, Yulun, additional, David, Shaquan D., additional, Yang, Jinzhong, additional, Nitsch, Paige L., additional, Balter, Peter A., additional, Urbauer, Diana L., additional, Peterson, Christine B., additional, Court, Laurence E., additional, and Dube, Scott, additional

Published
2018
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28. Cost‐effective immobilization for whole brain radiation therapy

Author

Rubinstein, Ashley E., primary, Ingram, W. Scott, additional, Anderson, Brian M., additional, Gay, Skylar S., additional, Fave, Xenia J., additional, Ger, Rachel B., additional, McCarroll, Rachel E., additional, Owens, Constance A., additional, Netherton, Tucker J., additional, Kisling, Kelly D., additional, Court, Laurence E., additional, Yang, Jinzhong, additional, Li, Yuting, additional, Lee, Joonsang, additional, Mackin, Dennis S., additional, and Cardenas, Carlos E., additional

Published
2017
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39. Large Eddy Simulation of the Effects of Inner Wall Rotation on Heat Transfer in Annular Turbulent Flow.

Author

Lee, JoonSang, Xu, Xiaofeng, and Pletcher, RichardH

Subjects
*HEAT transfer, *TURBULENCE, *FLUID dynamics, *NAVIER-Stokes equations, *FINITE volume method, *NUSSELT number
Abstract

A large eddy simulation has been performed to investigate the effect of swirl on the heat and momentum transfer in an annular pipe flow with a rotating inner wall. The compressible filtered Navier-Stokes equations were solved using a second-order-accurate finite-volume method. Low-Mach-number preconditioning was used to enable the compressible code to work efficiently at low Mach numbers. A dynamic subgrid-scale stress model accounted for the subgrid-scale turbulence. A nonuniform grid in the radial direction yielded very accurate solutions using a reasonable number of grid points. The numerical results are summarized and compared with the experimental results of previous studies. The simulations indicated that the Nusselt number and the wall friction coefficient increased with increasing rotation speed of the wall. It was also observed that the axial velocity profile became flattened and turbulent intensities were enhanced due to swirl. This modification of the turbulent structures was closely related to the increase of the Nusselt number and the friction coefficient. [ABSTRACT FROM AUTHOR]

Published
2004
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40. Bioimaging and biospectra analysis by means of independent component analysis: experimental results

Author

Zhao, Qun, Langley, Jason, Lee, Joonsang, Abell, Justin, and Zhao, Yiping

Abstract

Analysis of bioimaging and biospectra data has received increasingly attention in recent years. Here we will present two experimental results based on independent component analysis (ICA): differentiation of superparamagnetic iron oxide (SPIO) nanoparticles used as contrast agents in magnetic resonance imaging (MRI), and differentiation of mixed chemical analytes by surface-enhanced Raman scattering (SERS). The SPIO nanoparticles have been applied extensively as contrast agent in MRI for tracking of stem cells, targeted detection of cancer, due to its biocompatible and biodegradable features. For differentiation of SPIO from the background signal (e.g. interface between air and tissues), the signal voids from multiple sources makes the task very difficult. To solve this problem, we assume that the number of sensors corresponds to the number of acquisitions with different combinations of MR parameters, i.e., longitudinal and transverse relaxation times. For detection of chemical and biological analytes, the SERS approach has drawn more interest because of its high sensitivity. SERS spectra of mixed analytes were acquired at different locations of a silver nanorod array substrate. Due to the nonuniform diffusion and adsorption of the analytes, these spectra have been successfully used to identify the characteristic SERS spectrum of individual analytes. In both the MRI and SERS data, signal source separation (SPIO or mixed chemical analytes from background signal) was performed on a pixel by pixel basis. The ICA was performed by a spatial analysis using the fast ICA method.

Published
2011
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41. DEMARCATE: density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer

Author

Saha, Abhijoy, Banerjee, Sayantan, Kurtek, Sebastian, Narang, Shivali, Lee, Joonsang, Rao, Ganesh, Martinez, Juan, Bharath, Karthik, and Baladandayuthapani, Veerabhadran

Subjects
Tumor heterogeneity, Density estimation, Fisher–Rao metric, Medical imaging, Glioblastoma, Clustering
Abstract

Tumor heterogeneity is a crucial area of cancer research wherein inter- and intra-tumor differences are investigated to assess and monitor disease development and progression, especially in cancer. The proliferation of imaging and linked genomic data has enabled us to evaluate tumor heterogeneity on multiple levels. In this work, we examine magnetic resonance imaging (MRI) in patients with brain cancer to assess image-based tumor heterogeneity. Standard approaches to this problem use scalar summary measures (e.g., intensity-based histogram statistics) that do not adequately capture the complete and finer scale information in the voxel-level data. In this paper, we introduce a novel technique, DEMARCATE (DEnsity-based MAgnetic Resonance image Clustering for Assessing Tumor hEterogeneity) to explore the entire tumor heterogeneity density profiles (THDPs) obtained from the full tumor voxel space. THDPs are smoothed representations of the probability density function of the tumor images. We develop tools for analyzing such objects under the Fisher–Rao Riemannian framework that allows us to construct metrics for THDP comparisons across patients, which can be used in conjunction with standard clustering approaches. Our analyses of The Cancer Genome Atlas (TCGA) based Glioblastoma dataset reveal two significant clusters of patients with marked differences in tumor morphology, genomic characteristics and prognostic clinical outcomes. In addition, we see enrichment of image-based clusters with known molecular subtypes of glioblastoma multiforme, which further validates our representation of tumor heterogeneity and subsequent clustering techniques.

42. DEMARCATE: density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer

Author

Saha, Abhijoy, Banerjee, Sayantan, Kurtek, Sebastian, Narang, Shivali, Lee, Joonsang, Rao, Ganesh, Martinez, Juan, Bharath, Karthik, Baladandayuthapani, Veerabhadran, Saha, Abhijoy, Banerjee, Sayantan, Kurtek, Sebastian, Narang, Shivali, Lee, Joonsang, Rao, Ganesh, Martinez, Juan, Bharath, Karthik, and Baladandayuthapani, Veerabhadran

Abstract

Tumor heterogeneity is a crucial area of cancer research wherein inter- and intra-tumor differences are investigated to assess and monitor disease development and progression, especially in cancer. The proliferation of imaging and linked genomic data has enabled us to evaluate tumor heterogeneity on multiple levels. In this work, we examine magnetic resonance imaging (MRI) in patients with brain cancer to assess image-based tumor heterogeneity. Standard approaches to this problem use scalar summary measures (e.g., intensity-based histogram statistics) that do not adequately capture the complete and finer scale information in the voxel-level data. In this paper, we introduce a novel technique, DEMARCATE (DEnsity-based MAgnetic Resonance image Clustering for Assessing Tumor hEterogeneity) to explore the entire tumor heterogeneity density profiles (THDPs) obtained from the full tumor voxel space. THDPs are smoothed representations of the probability density function of the tumor images. We develop tools for analyzing such objects under the Fisher–Rao Riemannian framework that allows us to construct metrics for THDP comparisons across patients, which can be used in conjunction with standard clustering approaches. Our analyses of The Cancer Genome Atlas (TCGA) based Glioblastoma dataset reveal two significant clusters of patients with marked differences in tumor morphology, genomic characteristics and prognostic clinical outcomes. In addition, we see enrichment of image-based clusters with known molecular subtypes of glioblastoma multiforme, which further validates our representation of tumor heterogeneity and subsequent clustering techniques.

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43. DEMARCATE: density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer

Author

Saha, Abhijoy, Banerjee, Sayantan, Kurtek, Sebastian, Narang, Shivali, Lee, Joonsang, Rao, Ganesh, Martinez, Juan, Bharath, Karthik, Baladandayuthapani, Veerabhadran, Saha, Abhijoy, Banerjee, Sayantan, Kurtek, Sebastian, Narang, Shivali, Lee, Joonsang, Rao, Ganesh, Martinez, Juan, Bharath, Karthik, and Baladandayuthapani, Veerabhadran

Abstract

Tumor heterogeneity is a crucial area of cancer research wherein inter- and intra-tumor differences are investigated to assess and monitor disease development and progression, especially in cancer. The proliferation of imaging and linked genomic data has enabled us to evaluate tumor heterogeneity on multiple levels. In this work, we examine magnetic resonance imaging (MRI) in patients with brain cancer to assess image-based tumor heterogeneity. Standard approaches to this problem use scalar summary measures (e.g., intensity-based histogram statistics) that do not adequately capture the complete and finer scale information in the voxel-level data. In this paper, we introduce a novel technique, DEMARCATE (DEnsity-based MAgnetic Resonance image Clustering for Assessing Tumor hEterogeneity) to explore the entire tumor heterogeneity density profiles (THDPs) obtained from the full tumor voxel space. THDPs are smoothed representations of the probability density function of the tumor images. We develop tools for analyzing such objects under the Fisher–Rao Riemannian framework that allows us to construct metrics for THDP comparisons across patients, which can be used in conjunction with standard clustering approaches. Our analyses of The Cancer Genome Atlas (TCGA) based Glioblastoma dataset reveal two significant clusters of patients with marked differences in tumor morphology, genomic characteristics and prognostic clinical outcomes. In addition, we see enrichment of image-based clusters with known molecular subtypes of glioblastoma multiforme, which further validates our representation of tumor heterogeneity and subsequent clustering techniques.

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44. DEMARCATE: density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer

Author

Saha, Abhijoy, Banerjee, Sayantan, Kurtek, Sebastian, Narang, Shivali, Lee, Joonsang, Rao, Ganesh, Martinez, Juan, Bharath, Karthik, Baladandayuthapani, Veerabhadran, Saha, Abhijoy, Banerjee, Sayantan, Kurtek, Sebastian, Narang, Shivali, Lee, Joonsang, Rao, Ganesh, Martinez, Juan, Bharath, Karthik, and Baladandayuthapani, Veerabhadran

Abstract

Tumor heterogeneity is a crucial area of cancer research wherein inter- and intra-tumor differences are investigated to assess and monitor disease development and progression, especially in cancer. The proliferation of imaging and linked genomic data has enabled us to evaluate tumor heterogeneity on multiple levels. In this work, we examine magnetic resonance imaging (MRI) in patients with brain cancer to assess image-based tumor heterogeneity. Standard approaches to this problem use scalar summary measures (e.g., intensity-based histogram statistics) that do not adequately capture the complete and finer scale information in the voxel-level data. In this paper, we introduce a novel technique, DEMARCATE (DEnsity-based MAgnetic Resonance image Clustering for Assessing Tumor hEterogeneity) to explore the entire tumor heterogeneity density profiles (THDPs) obtained from the full tumor voxel space. THDPs are smoothed representations of the probability density function of the tumor images. We develop tools for analyzing such objects under the Fisher–Rao Riemannian framework that allows us to construct metrics for THDP comparisons across patients, which can be used in conjunction with standard clustering approaches. Our analyses of The Cancer Genome Atlas (TCGA) based Glioblastoma dataset reveal two significant clusters of patients with marked differences in tumor morphology, genomic characteristics and prognostic clinical outcomes. In addition, we see enrichment of image-based clusters with known molecular subtypes of glioblastoma multiforme, which further validates our representation of tumor heterogeneity and subsequent clustering techniques.

Full Text
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45. DEMARCATE: density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer

Author

Saha, Abhijoy, Banerjee, Sayantan, Kurtek, Sebastian, Narang, Shivali, Lee, Joonsang, Rao, Ganesh, Martinez, Juan, Bharath, Karthik, Baladandayuthapani, Veerabhadran, Saha, Abhijoy, Banerjee, Sayantan, Kurtek, Sebastian, Narang, Shivali, Lee, Joonsang, Rao, Ganesh, Martinez, Juan, Bharath, Karthik, and Baladandayuthapani, Veerabhadran

Abstract

Tumor heterogeneity is a crucial area of cancer research wherein inter- and intra-tumor differences are investigated to assess and monitor disease development and progression, especially in cancer. The proliferation of imaging and linked genomic data has enabled us to evaluate tumor heterogeneity on multiple levels. In this work, we examine magnetic resonance imaging (MRI) in patients with brain cancer to assess image-based tumor heterogeneity. Standard approaches to this problem use scalar summary measures (e.g., intensity-based histogram statistics) that do not adequately capture the complete and finer scale information in the voxel-level data. In this paper, we introduce a novel technique, DEMARCATE (DEnsity-based MAgnetic Resonance image Clustering for Assessing Tumor hEterogeneity) to explore the entire tumor heterogeneity density profiles (THDPs) obtained from the full tumor voxel space. THDPs are smoothed representations of the probability density function of the tumor images. We develop tools for analyzing such objects under the Fisher–Rao Riemannian framework that allows us to construct metrics for THDP comparisons across patients, which can be used in conjunction with standard clustering approaches. Our analyses of The Cancer Genome Atlas (TCGA) based Glioblastoma dataset reveal two significant clusters of patients with marked differences in tumor morphology, genomic characteristics and prognostic clinical outcomes. In addition, we see enrichment of image-based clusters with known molecular subtypes of glioblastoma multiforme, which further validates our representation of tumor heterogeneity and subsequent clustering techniques.

Full Text
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46. DEMARCATE: density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer

Author

Saha, Abhijoy, Banerjee, Sayantan, Kurtek, Sebastian, Narang, Shivali, Lee, Joonsang, Rao, Ganesh, Martinez, Juan, Bharath, Karthik, Baladandayuthapani, Veerabhadran, Saha, Abhijoy, Banerjee, Sayantan, Kurtek, Sebastian, Narang, Shivali, Lee, Joonsang, Rao, Ganesh, Martinez, Juan, Bharath, Karthik, and Baladandayuthapani, Veerabhadran

Abstract

Tumor heterogeneity is a crucial area of cancer research wherein inter- and intra-tumor differences are investigated to assess and monitor disease development and progression, especially in cancer. The proliferation of imaging and linked genomic data has enabled us to evaluate tumor heterogeneity on multiple levels. In this work, we examine magnetic resonance imaging (MRI) in patients with brain cancer to assess image-based tumor heterogeneity. Standard approaches to this problem use scalar summary measures (e.g., intensity-based histogram statistics) that do not adequately capture the complete and finer scale information in the voxel-level data. In this paper, we introduce a novel technique, DEMARCATE (DEnsity-based MAgnetic Resonance image Clustering for Assessing Tumor hEterogeneity) to explore the entire tumor heterogeneity density profiles (THDPs) obtained from the full tumor voxel space. THDPs are smoothed representations of the probability density function of the tumor images. We develop tools for analyzing such objects under the Fisher–Rao Riemannian framework that allows us to construct metrics for THDP comparisons across patients, which can be used in conjunction with standard clustering approaches. Our analyses of The Cancer Genome Atlas (TCGA) based Glioblastoma dataset reveal two significant clusters of patients with marked differences in tumor morphology, genomic characteristics and prognostic clinical outcomes. In addition, we see enrichment of image-based clusters with known molecular subtypes of glioblastoma multiforme, which further validates our representation of tumor heterogeneity and subsequent clustering techniques.

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47. Low-parameter supervised learning models can discriminate pseudoprogression and true progression in non-perfusion-based MRI.

Author

Warner E, Lee J, Krishnan S, Wang N, Mohammed S, Srinivasan A, Bapuraj J, and Rao A

Subjects
Humans, Disease Progression, Diffusion Magnetic Resonance Imaging methods, Supervised Machine Learning, Magnetic Resonance Imaging, Glioma pathology
Abstract

Discrimination of pseudoprogression and true progression is one challenge to the treatment of malignant gliomas. Although some techniques such as circulating tumor DNA (ctDNA) and perfusion-weighted imaging (PWI) demonstrate promise in distinguishing PsP from TP, we investigate robust and replicable alternatives to distinguish the two entities based on more widely-available media. In this study, we use low-parametric supervised learning techniques based on geographically-weighted regression (GWR) to investigate the utility of both conventional MRI sequences as well as a diffusion-weighted sequence (apparent diffusion coefficient or ADC) in the discrimination of PsP v TP. GWR applied to MRI modality pairs is a unique approach for small sample sizes and is a novel approach in this arena. From our analysis, all modality pairs involving ADC maps, and those involving post-contrast T1 regressed onto T2 showed potential promise. This work on ADC data adds to a growing body of research suggesting the predictive benefits of ADC, and suggests further research on the relationships between post-contrast T1 and T2.Clinical relevance- Few studies have investigated predictive potential of conventional MRI and ADC to detect PsP. Our study adds to the growing research on the topic and presents a new perspective to research by exploiting the utility of ADC in PsP v TP distinction. In addition, our GWR methodology for low-parametric supervised computer vision models demonstrates a unique approach for image processing of small sample sizes.

Published
2023
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48. Head and Neck Radiation Therapy Patterns of Practice Variability Identified as a Challenge to Real-World Big Data: Results From the Learning from Analysis of Multicentre Big Data Aggregation (LAMBDA) Consortium.

Author

Caissie A, Mierzwa M, Fuller CD, Rajaraman M, Lin A, MacDonald A, Popple R, Xiao Y, VanDijk L, Balter P, Fong H, Xu H, Kovoor M, Lee J, Rao A, Martel M, Thompson R, Merz B, Yao J, and Mayo C

Abstract

Purpose: Outside of randomized clinical trials, it is difficult to develop clinically relevant evidence-based recommendations for radiation therapy (RT) practice guidelines owing to lack of comprehensive real-world data. To address this knowledge gap, we formed the Learning from Analysis of Multicenter Big Data Aggregation consortium to cooperatively implement RT data standardization, develop software solutions for data analysis, and recommend clinical practice change based on real-world data analyzed. The first phase of this "Big Data" study aimed at characterizing variability in clinical practice patterns of dosimetric data for organs at risk (OARs) that would undermine subsequent use of large-scale, electronically aggregated data to characterize associations with outcomes. Evidence from this study was used as the basis for practical recommendations to improve data quality., Methods and Materials: Dosimetric details of patients with head and neck cancer treated with radiation therapy between 2014 and 2019 were analyzed. Institutional patterns of practice were characterized, including structure nomenclature, volumes, and frequency of contouring. Dose volume histogram (DVH) distributions were characterized and compared with institutional constraints and literature values., Results: Plans for 4664 patients treated to a mean plan dose of 64.4 ± 13.2 Gy in 32 ± 4 fractions were aggregated. Before implementation of TG-263 guidelines in each institution, there was variability in OAR nomenclature across institutions and structures. With evidence from this study, we identified a targeted and practical set of recommendations aimed at improving the quality of real-world data., Conclusions: Quantifying similarities and differences among institutions for OAR structures and DVH metrics is the launching point for next steps to investigate potential relationships between DVH parameters and patient outcomes., (Crown Copyright © 2022 Published by Elsevier Inc. on behalf of American Society for Radiation Oncology.)

Published
2022
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49. Quantification of DCE-MRI: pharmacokinetic parameter ratio between TOI and RR in reference region model.

Author

Lee J

Subjects
Analysis of Variance, Animals, Brain metabolism, Brain Mapping methods, Computer Simulation, Dogs, Models, Statistical, Pharmacokinetics, Positron-Emission Tomography methods, Reference Values, Time Factors, Contrast Media pharmacokinetics, Magnetic Resonance Imaging methods
Abstract

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is performed by obtaining sequential MRI images, before, during, and after the injection of a contrast agent. T(1) weighted MR imaging is used to observe the exchange of contrast agent between the vascular space and extravascular extracellular space (EES), providing information about blood volume and microvascular permeability. Signal intensity is obtained from the sequence of T(1) weighted images and then used to estimate the kinetic parameters in the equation derived from the pharmacokinetic model. In a DCE-MRI study, an accurate knowledge of the arterial input function (AIF) is very important to estimate the kinetic parameters. However, the AIF is usually unknown and it remains very difficult to obtain such information noninvasively. Here we use a reference region model that does not require the information about AIF. Though, this model usually needs literature value for the reference region. In this abstract, without knowledge of AIF, K(trans) in the tissue of interest (TOI) is compared with K(trans) in a reference region (RR). This was done by calculating the ratio K(R) between K(trans) in TOI and RR and the ratio V(R) between v(e) in TOI and RR while the K(trans,RR) was assigned a value ranging from 0.1 to 1.0. It is shown from both simulation and in vivo data set that this ratio is independent of K(trans,RR), implying we are no longer required to get the information about literature value for the reference region.

Published
2010
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