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2. Orbital Imaging

Author

Gahrmann, Renske, Gardeniers, Mayke, Quaranta Leoni, Francesco M., editor, Verity, David H, editor, and Paridaens, Dion, editor

Published
2024
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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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4. Evaluating glioma growth predictions as a forward ranking problem

Author

van Garderen, Karin A., van der Voort, Sebastian R., Wijnenga, Maarten M. J., Incekara, Fatih, Kapsas, Georgios, Gahrmann, Renske, Alafandi, Ahmad, Smits, Marion, and Klein, Stefan

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

The problem of tumor growth prediction is challenging, but promising results have been achieved with both model-driven and statistical methods. In this work, we present a framework for the evaluation of growth predictions that focuses on the spatial infiltration patterns, and specifically evaluating a prediction of future growth. We propose to frame the problem as a ranking problem rather than a segmentation problem. Using the average precision as a metric, we can evaluate the results with segmentations while using the full spatiotemporal prediction. Furthermore, by separating the model goodness-of-fit from future predictive performance, we show that in some cases, a better fit of model parameters does not guarantee a better the predictive power.

Published
2021

5. WHO 2016 subtyping and automated segmentation of glioma using multi-task deep learning

Author

van der Voort, Sebastian R., Incekara, Fatih, Wijnenga, Maarten M. J., Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W., Tewarie, Rishi Nandoe, Lycklama, Geert J., Hamer, Philip C. De Witt, Eijgelaar, Roelant S., French, Pim J., Dubbink, Hendrikus J., Vincent, Arnaud J. P. E., Niessen, Wiro J., Bent, Martin J. van den, Smits, Marion, and Klein, Stefan

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

Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is a time-consuming task. Leveraging the latest GPU capabilities, we developed a single multi-task convolutional neural network that uses the full 3D, structural, pre-operative MRI scans to can predict the IDH mutation status, the 1p/19q co-deletion status, and the grade of a tumor, while simultaneously segmenting the tumor. We trained our method using the largest, most diverse patient cohort to date containing 1508 glioma patients from 16 institutes. We tested our method on an independent dataset of 240 patients from 13 different institutes, and achieved an IDH-AUC of 0.90, 1p/19q-AUC of 0.85, grade-AUC of 0.81, and a mean whole tumor DICE score of 0.84. Thus, our method non-invasively predicts multiple, clinically relevant parameters and generalizes well to the broader clinical population.

Published
2020

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. Evaluating Glioma Growth Predictions as a Forward Ranking Problem

Author

van Garderen, Karin A., van der Voort, Sebastian R., Wijnenga, Maarten M. J., Incekara, Fatih, Kapsas, Georgios, Gahrmann, Renske, Alafandi, Ahmad, Smits, Marion, Klein, Stefan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crimi, Alessandro, editor, and Bakas, Spyridon, editor

Published
2022
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8. A comprehensive approach to defining the cutoff value of oligometastasis in head and neck squamous cell carcinoma.

Author

Berzenji, Diako, Oude Booijink, Olivier R. G., Gahrmann, Renske, Mast, Hetty, Capala, Marta E., Koppes, Sjors A., van Meerten, Esther, Kremer, Bernd, Baatenburg de Jong, Robert Jan, Offerman, Marinella P. J., and Hardillo, Jose A.

Subjects
SQUAMOUS cell carcinoma, REGRESSION analysis, SURVIVAL rate, METASTASIS, COHORT analysis
Abstract

Background: Patients with limited distant metastatic disease, also known as oligometastasis, show better survival rates than polymetastatic patients, and may be amenable for curative‐intent treatment. The definition of oligometastasis, however, is unknown, and no quantitative analyses on the cutoff value for oligometastasis have been performed before. This study aims to derive specific threshold values for the number of metastases and affected locations that defines oligometastatic disease in head and neck squamous cell carcinoma. Methods: A retrospective cohort study was conducted including all patients diagnosed with distant metastases between 2006 and 2021. For each patient, the number of distant metastases and affected locations was recorded on the basis of the available imaging at the time of diagnosis. Cox regression analyses and a machine‐learning k‐means algorithm were used to determine threshold values. Results: A total of 384 patients untreated for their metastatic foci were analyzed. Most patients (n = 207; 53.9%) had metastasis to one anatomic location, followed by metastases in two anatomic locations (n = 62; 16.1%). The majority of patients had ≥9 metastatic foci (n = 174; 45.3%), followed by one focus (n = 74; 19.3%) and two foci (n = 32; 8.3%). Cox regression and machine‐learning k‐means models showed that although the number of metastases did not predict survival, the number of affected locations did significantly (p <.001), by identifying a threshold of two locations. Conclusions: Contrary to the prevalent dogma, the definition of oligometastasis should not be defined by the number of metastases but rather by the number of affected locations, with a maximum number of affected locations set at two. In this retrospective cohort study that included 384 patients with head and neck squamous cell carcinoma who were untreated for their metastatic foci, quantitative analyses showed that oligometastasis should be defined as distant metastatic dissemination to a maximum of two anatomic locations. In the context of survival, the number of distant metastatic foci was not a significant predictor. [ABSTRACT FROM AUTHOR]

Published
2025
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9. Diffusion MRI Phenotypes Predict Overall Survival Benefit from Anti-VEGF Monotherapy in Recurrent Glioblastoma: Converging Evidence from Phase II Trials

Author

Ellingson, Benjamin M, Gerstner, Elizabeth R, Smits, Marion, Huang, Raymond Y, Colen, Rivka, Abrey, Lauren E, Aftab, Dana T, Schwab, Gisela M, Hessel, Colin, Harris, Robert J, Chakhoyan, Ararat, Gahrmann, Renske, Pope, Whitney B, Leu, Kevin, Raymond, Catalina, Woodworth, Davis C, de Groot, John, Wen, Patrick Y, Batchelor, Tracy T, van den Bent, Martin J, and Cloughesy, Timothy F

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Clinical Trials and Supportive Activities, Orphan Drug, Clinical Research, Precision Medicine, Brain Disorders, Brain Cancer, Biomedical Imaging, Rare Diseases, Neurosciences, Cancer, 6.1 Pharmaceuticals, Angiogenesis Inhibitors, Anilides, Bevacizumab, Biomarkers, Tumor, Diffusion Magnetic Resonance Imaging, Disease-Free Survival, Female, Glioblastoma, Humans, Lomustine, Male, Middle Aged, Neoplasm Recurrence, Local, Pyridines, Quinazolines, Receptors, Vascular Endothelial Growth Factor, Recombinant Fusion Proteins, Vascular Endothelial Growth Factor A, Oncology & Carcinogenesis, Clinical sciences, Oncology and carcinogenesis
Abstract

Purpose: Anti-VEGF therapies remain controversial in the treatment of recurrent glioblastoma (GBM). In the current study, we demonstrate that recurrent GBM patients with a specific diffusion MR imaging signature have an overall survival (OS) advantage when treated with cediranib, bevacizumab, cabozantinib, or aflibercept monotherapy at first or second recurrence. These findings were validated using a separate trial comparing bevacizumab with lomustine.Experimental Design: Patients with recurrent GBM and diffusion MRI from the monotherapy arms of 5 separate phase II clinical trials were included: (i) cediranib (NCT00035656); (ii) bevacizumab (BRAIN Trial, AVF3708g; NCT00345163); (iii) cabozantinib (XL184-201; NCT00704288); (iv) aflibercept (VEGF Trap; NCT00369590); and (v) bevacizumab or lomustine (BELOB; NTR1929). Apparent diffusion coefficient (ADC) histogram analysis was performed prior to therapy to estimate "ADCL," the mean of the lower ADC distribution. Pretreatment ADCL, enhancing volume, and clinical variables were tested as independent prognostic factors for OS.Results: The coefficient of variance (COV) in double baseline ADCL measurements was 2.5% and did not significantly differ (P = 0.4537). An ADCL threshold of 1.24 μm2/ms produced the largest OS differences between patients (HR ∼ 0.5), and patients with an ADCL > 1.24 μm2/ms had close to double the OS in all anti-VEGF therapeutic scenarios tested. Training and validation data confirmed that baseline ADCL was an independent predictive biomarker for OS in anti-VEGF therapies, but not in lomustine, after accounting for age and baseline enhancing tumor volume.Conclusions: Pretreatment diffusion MRI is a predictive imaging biomarker for OS in patients with recurrent GBM treated with anti-VEGF monotherapy at first or second relapse. Clin Cancer Res; 23(19); 5745-56. ©2017 AACR.

Published
2017

11. A comprehensive approach to defining the cutoff value of oligometastasis in head and neck squamous cell carcinoma

Author

Berzenji, Diako, Booijink, Olivier R. G. Oude, Gahrmann, Renske, Mast, Hetty, Capala, Marta E., Koppes, Sjors A., van Meerten, Esther, Kremer, Bernd, de Jong, Robert Jan Baatenburg, Offerman, Marinella P. J., Hardillo, Jose A., Berzenji, Diako, Booijink, Olivier R. G. Oude, Gahrmann, Renske, Mast, Hetty, Capala, Marta E., Koppes, Sjors A., van Meerten, Esther, Kremer, Bernd, de Jong, Robert Jan Baatenburg, Offerman, Marinella P. J., and Hardillo, Jose A.

Abstract

Background: Patients with limited distant metastatic disease, also known as oligometastasis, show better survival rates than polymetastatic patients, and may be amenable for curative-intent treatment. The definition of oligometastasis, however, is unknown, and no quantitative analyses on the cutoff value for oligometastasis have been performed before. This study aims to derive specific threshold values for the number of metastases and affected locations that defines oligometastatic disease in head and neck squamous cell carcinoma. Methods: A retrospective cohort study was conducted including all patients diagnosed with distant metastases between 2006 and 2021. For each patient, the number of distant metastases and affected locations was recorded on the basis of the available imaging at the time of diagnosis. Cox regression analyses and a machine-learning k-means algorithm were used to determine threshold values. Results: A total of 384 patients untreated for their metastatic foci were analyzed. Most patients (n = 207; 53.9%) had metastasis to one anatomic location, followed by metastases in two anatomic locations (n = 62; 16.1%). The majority of patients had ≥9 metastatic foci (n = 174; 45.3%), followed by one focus (n = 74; 19.3%) and two foci (n = 32; 8.3%). Cox regression and machine-learning k-means models showed that although the number of metastases did not predict survival, the number of affected locations did significantly (p <.001), by identifying a threshold of two locations. Conclusions: Contrary to the prevalent dogma, the definition of oligometastasis should not be defined by the number of metastases but rather by the number of affected locations, with a maximum number of affected locations set at two.

Published
2024

13. MRI for Differentiation between HPV-Positive and HPV-Negative Oropharyngeal Squamous Cell Carcinoma:A Systematic Review

Author

Chen, Linda L., Lauwers, Iris, Verduijn, Gerda, Philippens, Marielle, Gahrmann, Renske, Capala, Marta E., Petit, Steven, Chen, Linda L., Lauwers, Iris, Verduijn, Gerda, Philippens, Marielle, Gahrmann, Renske, Capala, Marta E., and Petit, Steven

Abstract

Human papillomavirus (HPV) is an important risk factor for oropharyngeal squamous cell carcinoma (OPSCC). HPV-positive (HPV+) cases are associated with a different pathophysiology, microstructure, and prognosis compared to HPV-negative (HPV−) cases. This review aimed to investigate the potential of magnetic resonance imaging (MRI) to discriminate between HPV+ and HPV− tumours and predict HPV status in OPSCC patients. A systematic literature search was performed on 15 December 2022 on EMBASE, MEDLINE ALL, Web of Science, and Cochrane according to PRISMA guidelines. Twenty-eight studies (n = 2634 patients) were included. Five, nineteen, and seven studies investigated structural MRI (e.g., T1, T2-weighted), diffusion-weighted MRI, and other sequences, respectively. Three out of four studies found that HPV+ tumours were significantly smaller in size, and their lymph node metastases were more cystic in structure than HPV− ones. Eleven out of thirteen studies found that the mean apparent diffusion coefficient was significantly higher in HPV− than HPV+ primary tumours. Other sequences need further investigation. Fourteen studies used MRI to predict HPV status using clinical, radiological, and radiomics features. The reported areas under the curve (AUC) values ranged between 0.697 and 0.944. MRI can potentially be used to find differences between HPV+ and HPV− OPSCC patients and predict HPV status with reasonable accuracy. Larger studies with external model validation using independent datasets are needed before clinical implementation.

Published
2024

14. Evaluating the predictive value of glioma growth models for low-grade glioma after tumor resection

Author

van Garderen, Karin A., van der Voort, Sebastian R., Wijnenga, Maarten M.J., Incekara, Fatih, Alafandi, Ahmad, Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W., Dubbink, Hendrikus J., Vincent, Arnaud J.P.E., van den Bent, Martin, French, Pim J., Smits, Marion, Klein, Stefan, van Garderen, Karin A., van der Voort, Sebastian R., Wijnenga, Maarten M.J., Incekara, Fatih, Alafandi, Ahmad, Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W., Dubbink, Hendrikus J., Vincent, Arnaud J.P.E., van den Bent, Martin, French, Pim J., Smits, Marion, and Klein, Stefan

Abstract

Tumor growth models have the potential to model and predict the spatiotemporal evolution of glioma in individual patients. Infiltration of glioma cells is known to be faster along the white matter tracts, and therefore structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) can be used to inform the model. However, applying and evaluating growth models in real patient data is challenging. In this work, we propose to formulate the problem of tumor growth as a ranking problem, as opposed to a segmentation problem, and use the average precision (AP) as a performance metric. This enables an evaluation of the spatial pattern that does not require a volume cut-off value. Using the AP metric, we evaluate diffusion-proliferation models informed by structural MRI and DTI, after tumor resection. We applied the models to a unique longitudinal dataset of 14 patients with low-grade glioma (LGG), who received no treatment after surgical resection, to predict the recurrent tumor shape after tumor resection. The diffusion models informed by structural MRI and DTI showed a small but significant increase in predictive performance with respect to homogeneous isotropic diffusion, and the DTI-informed model reached the best predictive performance. We conclude there is a significant improvement in the prediction of the recurrent tumor shape when using a DTI-informed anisotropic diffusion model with respect to istropic diffusion, and that the AP is a suitable metric to evaluate these models. All code and data used in this publication are made publicly available.

Published
2024

16. MRI for Differentiation between HPV-Positive and HPV-Negative Oropharyngeal Squamous Cell Carcinoma: A Systematic Review.

Author

Chen, Linda L., Lauwers, Iris, Verduijn, Gerda, Philippens, Marielle, Gahrmann, Renske, Capala, Marta E., and Petit, Steven

Subjects
HEAD & neck cancer diagnosis, PAPILLOMAVIRUS disease diagnosis, PAPILLOMAVIRUS diseases, SQUAMOUS cell carcinoma, MEDICAL information storage & retrieval systems, RECEIVER operating characteristic curves, RESEARCH funding, OROPHARYNGEAL cancer, MAGNETIC resonance imaging, CANCER patients, SYSTEMATIC reviews, MEDLINE, MEDICAL databases
Abstract

Simple Summary: Human papillomavirus-positive (HPV+) oropharyngeal squamous cell carcinoma (OPSCC) has a different disease course compared to HPV-negative (HPV−) OPSCC. This systematic review aims to investigate whether magnetic resonance imaging (MRI) can discriminate between HPV+ and HPV− OPSCC or predict HPV status in OPSCC patients using MRI. Our results show that parameters derived from structural MRI and diffusion-weighted MRI are able to discriminate between HPV+ and HPV− cases and predict HPV status with reasonable accuracy. Other MRI sequences have yet to prove their added value for the discrimination and prediction of HPV status in OPSCC patients. Machine learning studies that compared predictive models with and without clinical variables found that performance improved significantly when clinical variables were included in the model. Before the clinical implementation of MRI for HPV status determination, larger studies with external model validation using independent datasets are needed. Human papillomavirus (HPV) is an important risk factor for oropharyngeal squamous cell carcinoma (OPSCC). HPV-positive (HPV+) cases are associated with a different pathophysiology, microstructure, and prognosis compared to HPV-negative (HPV−) cases. This review aimed to investigate the potential of magnetic resonance imaging (MRI) to discriminate between HPV+ and HPV− tumours and predict HPV status in OPSCC patients. A systematic literature search was performed on 15 December 2022 on EMBASE, MEDLINE ALL, Web of Science, and Cochrane according to PRISMA guidelines. Twenty-eight studies (n = 2634 patients) were included. Five, nineteen, and seven studies investigated structural MRI (e.g., T1, T2-weighted), diffusion-weighted MRI, and other sequences, respectively. Three out of four studies found that HPV+ tumours were significantly smaller in size, and their lymph node metastases were more cystic in structure than HPV− ones. Eleven out of thirteen studies found that the mean apparent diffusion coefficient was significantly higher in HPV− than HPV+ primary tumours. Other sequences need further investigation. Fourteen studies used MRI to predict HPV status using clinical, radiological, and radiomics features. The reported areas under the curve (AUC) values ranged between 0.697 and 0.944. MRI can potentially be used to find differences between HPV+ and HPV− OPSCC patients and predict HPV status with reasonable accuracy. Larger studies with external model validation using independent datasets are needed before clinical implementation. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning

Author

van der Voort, Sebastian R, Incekara, Fatih, Wijnenga, Maarten M J, Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W, Nandoe Tewarie, Rishi, Lycklama, Geert J, De Witt Hamer, Philip C, Eijgelaar, Roelant S, French, Pim J, Dubbink, Hendrikus J, Vincent, Arnaud J P E, Niessen, Wiro J, van den Bent, Martin J, Smits, Marion, Klein, Stefan, van der Voort, Sebastian R, Incekara, Fatih, Wijnenga, Maarten M J, Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W, Nandoe Tewarie, Rishi, Lycklama, Geert J, De Witt Hamer, Philip C, Eijgelaar, Roelant S, French, Pim J, Dubbink, Hendrikus J, Vincent, Arnaud J P E, Niessen, Wiro J, van den Bent, Martin J, Smits, Marion, and Klein, Stefan

Abstract

BACKGROUND: Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is time-consuming. Previously, deep learning methods have been developed that can either non-invasively predict the genetic or histological features of glioma, or that can automatically delineate the tumor, but not both tasks at the same time. Here, we present our method that can predict the molecular subtype and grade, while simultaneously providing a delineation of the tumor.METHODS: We developed a single multi-task convolutional neural network that uses the full 3D, structural, pre-operative MRI scans to predict the IDH mutation status, the 1p/19q co-deletion status, and the grade of a tumor, while simultaneously segmenting the tumor. We trained our method using a patient cohort containing 1508 glioma patients from 16 institutes. We tested our method on an independent dataset of 240 patients from 13 different institutes.RESULTS: In the independent test set we achieved an IDH-AUC of 0.90, an 1p/19q co-deletion AUC of 0.85, and a grade AUC of 0.81 (grade II/III/IV). For the tumor delineation, we achieved a mean whole tumor DICE score of 0.84.CONCLUSIONS: We developed a method that non-invasively predicts multiple, clinically relevant features of glioma. Evaluation in an independent dataset shows that the method achieves a high performance and that it generalizes well to the broader clinical population. This first of its kind method opens the door to more generalizable, instead of hyper-specialized, AI methods.

Published
2023

18. Evaluating the predictive value of glioma growth models for low-grade glioma after tumor resection

Author

van Garderen, Karin A., primary, van der Voort, Sebastian R., additional, Wijnenga, Maarten M.J., additional, Incekara, Fatih, additional, Alafandi, Ahmad, 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, additional, French, Pim J., additional, Smits, Marion, additional, and Klein, Stefan, additional

Published
2023
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19. 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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20. Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning

Author

van der Voort, Sebastian R, primary, Incekara, Fatih, additional, Wijnenga, Maarten M J, additional, Kapsas, Georgios, additional, Gahrmann, Renske, additional, Schouten, Joost W, additional, Nandoe Tewarie, Rishi, additional, Lycklama, Geert J, additional, De Witt Hamer, Philip C, additional, Eijgelaar, Roelant S, additional, French, Pim J, additional, Dubbink, Hendrikus J, additional, Vincent, Arnaud J P E, additional, Niessen, Wiro J, additional, van den Bent, Martin J, additional, Smits, Marion, additional, and Klein, Stefan, additional

Published
2022
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21. The impact of different volumetric thresholds to determine progressive disease in patients with recurrent glioblastoma treated with bevacizumab

Author

Gahrmann, Renske, Smits, Marion, Vernhout, René Michel, Taal, Walter, Kapsas, Giorgios, De Groot, Jan Cees, Hanse, Monique, Vos, Maaike, Beerepoot, Laurens Victor, Buter, Jan, Flach, Zwenneke Hendrieke, Van Der Holt, Bronno, Van Den Bent, Martin, Gahrmann, Renske, Smits, Marion, Vernhout, René Michel, Taal, Walter, Kapsas, Giorgios, De Groot, Jan Cees, Hanse, Monique, Vos, Maaike, Beerepoot, Laurens Victor, Buter, Jan, Flach, Zwenneke Hendrieke, Van Der Holt, Bronno, and Van Den Bent, Martin

Abstract

Background: The optimal volumetric threshold for determining progressive disease (PD) in recurrent glioblastoma is yet to be determined. We investigated a range of thresholds in association with overall survival (OS). Methods: First recurrent glioblastoma patients treated with bevacizumab and/or lomustine were included from the phase II BELOB and phase III EORTC26101 trials. Enhancing and nonenhancing tumor volumes were measured at baseline, first (6 weeks), and second (12 weeks) follow-up. Hazard ratios (HRs) for the appearance of new lesions and several thresholds for tumor volume increase were calculated using cox regression analysis. Results were corrected in a multivariate analysis for well-established prognostic factors. Results: At first and second follow-up, 138 and 94 patients respectively, were deemed eligible for analysis of enhancing volumes, while 89 patients were included in the analysis of nonenhancing volumes at first follow-up. New lesions were associated with a significantly worse OS (3.2 versus 11.2 months, HR = 7.03, P <. 001). At first follow-up a threshold of enhancing volume increase of ≥20% provided the highest HR (5.55, p =. 001. At second follow-up, any increase in enhancing volume (≥0%) provided the highest HR (9.00, p <. 001). When measuring nonenhancing volume at first follow-up, only 6 additional patients were scored as PD with the highest HR of ≥25% increase in volume (HR=3.25, p =. 008). Conclusion: Early appearing new lesions were associated with poor OS. Lowering the volumetric threshold for PD at both first and second follow-up improved survival prediction. However, the additional number of patients categorized as PD by lowering the threshold was very low. The per-RANO added change in nonenhancing volumes to the analyses was of limited value.

Published
2022

22. The impact of different volumetric thresholds to determine progressive disease in patients with recurrent glioblastoma treated with bevacizumab

Author

Gahrmann, Renske, primary, Smits, Marion, additional, Vernhout, René Michel, additional, Taal, Walter, additional, Kapsas, Giorgios, additional, de Groot, Jan Cees, additional, Hanse, Monique, additional, Vos, Maaike, additional, Beerepoot, Laurens Victor, additional, Buter, Jan, additional, Flach, Zwenneke Hendrieke, additional, van der Holt, Bronno, additional, and van den Bent, Martin, additional

Published
2022
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24. The Erasmus Glioma Database (EGD): Structural MRI scans, WHO 2016 subtypes, and segmentations of 774 patients with glioma

Author

van der Voort, Sebastian R., primary, 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, and Smits, Marion, additional

Published
2021
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26. SURG-05. THE IMPACT OF SURGERY IN MOLECULARLY DEFINED LOW-GRADE GLIOMA: AN INTEGRATED CLINICAL, RADIOLOGICAL AND MOLECULAR ANALYSIS

Author

Wijnenga, Maarten, primary, French, Pim, additional, Dubbink, Hendrikus, additional, Dinjens, Winand, additional, Atmodimedjo, Peggy, additional, Kros, Johan, additional, Smits, Marion, additional, Gahrmann, Renske, additional, Rutten, Geert-Jan, additional, Verheul, Jeroen, additional, Fleischeuer, Ruth, additional, Dirven, Clemens, additional, Vincent, Arnaud, additional, and van den Bent, Martin, additional

Published
2017
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27. NIMG-01. DIFFUSION MRI PHENOTYPES PREDICT OVERALL SURVIVAL BENEFIT FROM ANTI-VEGF MONOTHERAPY IN GLIOBLASTOMA AT FIRST OR SECOND RELAPSE

Author

Ellingson, Benjamin, primary, Gerstner, Elizabeth, additional, Smits, Marion, additional, Huang, Ray, additional, Colen, Rivka, additional, Abrey, Lauren, additional, Aftab, Dana, additional, Schwab, Gisela, additional, Hessel, Colin, additional, Gahrmann, Renske, additional, Pope, Whitney, additional, de Groot, John, additional, Wen, Patrick, additional, Batchelor, Tracy, additional, van den Bent, Martin, additional, and Cloughesy, Timothy F, additional

Published
2017
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28. The impact of surgery in molecularly defined low-grade glioma: an integrated clinical, radiological, and molecular analysis

Author

Wijnenga, Maarten M J, primary, French, Pim J, additional, Dubbink, Hendrikus J, additional, Dinjens, Winand N M, additional, Atmodimedjo, Peggy N, additional, Kros, Johan M, additional, Smits, Marion, additional, Gahrmann, Renske, additional, Rutten, Geert-Jan, additional, Verheul, Jeroen B, additional, Fleischeuer, Ruth, additional, Dirven, Clemens M F, additional, Vincent, Arnaud J P E, additional, and van den Bent, Martin J, additional

Published
2017
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30. Comparison of 2D (RANO) and volumetric methods for assessment of recurrent glioblastoma treated with bevacizumab—a report from the BELOB trial

Author

Gahrmann, Renske, primary, van den Bent, Martin, additional, van der Holt, Bronno, additional, Vernhout, René Michel, additional, Taal, Walter, additional, Vos, Maaike, additional, de Groot, Jan Cees, additional, Beerepoot, Laurens Victor, additional, Buter, Jan, additional, Flach, Zwenneke Hendrieke, additional, Hanse, Monique, additional, Jasperse, Bas, additional, and Smits, Marion, additional

Published
2017
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31. NIMG-13. RADIOLOGICAL RESPONSE ASSESSMENT IN THE ERA OF BEVACIZUMAB: RANO OR VOLUMETRY? A REPORT FROM THE BELOB TRIAL

Author

Gahrmann, Renske, primary, Bent, Martin van den, additional, Holt, B. van der, additional, Vernhout, R., additional, Taal, W., additional, Taphoorn, Martin, additional, de Groot, J.C., additional, Beerepoot, L., additional, Buter, J., additional, Flach, Z., additional, Hanse, M., additional, Jasperse, B., additional, and Smits, Marion, additional

Published
2016
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32. ATCT-31CENTRAL RADIOLOGY REVIEW OF THE BELOB TRIAL: TREATMENT WITH BEVACIZUMAB IN RECURRENT GLIOBLASTOMA IS ASSOCIATED WITH MORE FREQUENT NON-ENHANCING PROGRESSION

Author

Smits, Marion, primary, Bralten, Linda, additional, Jasperse, Bas, additional, Taal, Walter, additional, Gahrmann, Renske, additional, Walenkamp, Annemiek, additional, Oosterkamp, Rianne, additional, Vernhout, Rene, additional, van der Holt, Bronno, additional, and van den Bent, Martin, additional

Published
2015
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34. Evaluating the Predictive Value of Glioma Growth Models for Low-Grade Glioma After Tumor Resection.

Author

van Garderen KA, van der Voort SR, Wijnenga MMJ, Incekara F, Alafandi A, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent M, French PJ, Smits M, and Klein S

Subjects
Humans, Diffusion Tensor Imaging methods, Magnetic Resonance Imaging, Anisotropy, Brain Neoplasms diagnostic imaging, Brain Neoplasms surgery, Brain Neoplasms pathology, Glioma diagnostic imaging, Glioma surgery, Glioma pathology
Abstract

Tumor growth models have the potential to model and predict the spatiotemporal evolution of glioma in individual patients. Infiltration of glioma cells is known to be faster along the white matter tracts, and therefore structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) can be used to inform the model. However, applying and evaluating growth models in real patient data is challenging. In this work, we propose to formulate the problem of tumor growth as a ranking problem, as opposed to a segmentation problem, and use the average precision (AP) as a performance metric. This enables an evaluation of the spatial pattern that does not require a volume cut-off value. Using the AP metric, we evaluate diffusion-proliferation models informed by structural MRI and DTI, after tumor resection. We applied the models to a unique longitudinal dataset of 14 patients with low-grade glioma (LGG), who received no treatment after surgical resection, to predict the recurrent tumor shape after tumor resection. The diffusion models informed by structural MRI and DTI showed a small but significant increase in predictive performance with respect to homogeneous isotropic diffusion, and the DTI-informed model reached the best predictive performance. We conclude there is a significant improvement in the prediction of the recurrent tumor shape when using a DTI-informed anisotropic diffusion model with respect to istropic diffusion, and that the AP is a suitable metric to evaluate these models. All code and data used in this publication are made publicly available.

Published
2024
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35. Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning.

Author

van der Voort SR, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Nandoe Tewarie R, Lycklama GJ, De Witt Hamer PC, Eijgelaar RS, French PJ, Dubbink HJ, Vincent AJPE, Niessen WJ, van den Bent MJ, Smits M, and Klein S

Subjects
Humans, Magnetic Resonance Imaging methods, Chromosome Aberrations, Isocitrate Dehydrogenase genetics, Mutation, Neoplasm Grading, Brain Neoplasms diagnostic imaging, Brain Neoplasms genetics, Brain Neoplasms pathology, Deep Learning, Glioma diagnostic imaging, Glioma genetics, Glioma pathology
Abstract

Background: Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is time-consuming. Previously, deep learning methods have been developed that can either non-invasively predict the genetic or histological features of glioma, or that can automatically delineate the tumor, but not both tasks at the same time. Here, we present our method that can predict the molecular subtype and grade, while simultaneously providing a delineation of the tumor., Methods: We developed a single multi-task convolutional neural network that uses the full 3D, structural, preoperative MRI scans to predict the IDH mutation status, the 1p/19q co-deletion status, and the grade of a tumor, while simultaneously segmenting the tumor. We trained our method using a patient cohort containing 1508 glioma patients from 16 institutes. We tested our method on an independent dataset of 240 patients from 13 different institutes., Results: In the independent test set, we achieved an IDH-AUC of 0.90, an 1p/19q co-deletion AUC of 0.85, and a grade AUC of 0.81 (grade II/III/IV). For the tumor delineation, we achieved a mean whole tumor Dice score of 0.84., Conclusions: We developed a method that non-invasively predicts multiple, clinically relevant features of glioma. Evaluation in an independent dataset shows that the method achieves a high performance and that it generalizes well to the broader clinical population. This first-of-its-kind method opens the door to more generalizable, instead of hyper-specialized, AI methods., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.)

Published
2023
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