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35 results on '"Bodalal, Zuhir"'

9. List of contributors

Author

Akiyoshi, Takashi, primary, Beets-Tan, Regina, additional, Benson, Sean, additional, Bodalal, Zuhir, additional, Brouwer, Nelleke Pietronella Maria, additional, Burggraaf, Jacobus, additional, Cai, Lishan, additional, Ceelen, Wim P., additional, Chang, Daniel T., additional, De Clercq, Alexander, additional, Debbaut, Charlotte, additional, Delgado, Carlos Alejandro Silvera, additional, Elias, Alexandra, additional, Francke, Kai, additional, Frick, Melissa A., additional, Galema, Hidde A., additional, Hans, Dedecker, additional, Hans, Prenen, additional, Hardiman, Karin, additional, Hazen, Sanne-Marije J.A., additional, Hilling, Denise E., additional, Hoorens, Anne, additional, Hutteman, Merlijn, additional, Ikeda, Masataka, additional, Ito, Masaaki, additional, Jan, Albert, additional, Kanemitsu, Yukihide, additional, Kataoka, Kozo, additional, Keereweer, Stijn, additional, Koudehi, Ghazal Adeli, additional, Kusters, Miranda, additional, Laura, Wuyts, additional, Laure-Anne, Teuwen, additional, Lauwerends, Lorraine J., additional, Loo, Phoebe, additional, Maas, Monique, additional, Marc, Peeters, additional, Meijer, Ruben P.J., additional, Monson, John R.T., additional, Nagtegaal, Iris D., additional, Pollom, Erqi L., additional, Rey, Sergio, additional, Salavati, Hooman, additional, Schito, Luana, additional, Segers, Patrick, additional, Shiozawa, Manabu, additional, Sluckin, Tania C., additional, Smithson, Mary, additional, Struys, Mathieu J.R., additional, Tiernan, Jim P., additional, Tije, ten, additional, Timon, Vandamme, additional, Trebeschi, Stefano, additional, Tsukada, Yuichiro, additional, Vahrmeijer, Alexander L., additional, van Ramshorst, Gabrielle H., additional, Verhoef, Cornelis, additional, Vitzthum, Lucas K., additional, Waktola, Selam, additional, West, Nicholas P., additional, Westwood, Alice C., additional, Willaert, Wouter, additional, and Wright, Jesse P., additional

Published
2022
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10. Imaging of colorectal nodal disease

Author

Cai, Lishan, primary, Bodalal, Zuhir, additional, Trebeschi, Stefano, additional, Waktola, Selam, additional, Sluckin, Tania C., additional, Kusters, Miranda, additional, Maas, Monique, additional, Beets-Tan, Regina, additional, and Benson, Sean, additional

Published
2022
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12. Artificial Intelligence--based Quantification of Pleural Plaque Volume and Association With Lung Function in Asbestos-exposed Patients.

Author

Groot Lipman, Kevin B. W., Boellaard, Thierry N., de Gooijer, Cornedine J., Bogveradze, Nino, Eun Kyoung Hong, Landolfi, Federica, Castagnoli, Francesca, Vakhidova, Nargiza, Smesseim, Illaa, van der Heijden, Ferdi, Beets-Tan, Regina G. H., Wittenberg, Rianne, Bodalal, Zuhir, Burgers, Jacobus A., and Trebeschi, Stefano

Published
2024
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13. Artificial Intelligence–based Quantification of Pleural Plaque Volume and Association With Lung Function in Asbestos-exposed Patients

Author

Groot Lipman, Kevin B.W., primary, Boellaard, Thierry N., additional, de Gooijer, Cornedine J., additional, Bogveradze, Nino, additional, Hong, Eun Kyoung, additional, Landolfi, Federica, additional, Castagnoli, Francesca, additional, Vakhidova, Nargiza, additional, Smesseim, Illaa, additional, van der Heijden, Ferdi, additional, Beets-Tan, Regina G.H., additional, Wittenberg, Rianne, additional, Bodalal, Zuhir, additional, Burgers, Jacobus A., additional, and Trebeschi, Stefano, additional

Published
2023
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14. The Prediction of Biological Features Using Magnetic Resonance Imaging in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis

Author

van der Hulst, Hedda J., primary, Jansen, Robin W., additional, Vens, Conchita, additional, Bos, Paula, additional, Schats, Winnie, additional, de Jong, Marcus C., additional, Martens, Roland M., additional, Bodalal, Zuhir, additional, Beets-Tan, Regina G. H., additional, van den Brekel, Michiel W. M., additional, de Graaf, Pim, additional, and Castelijns, Jonas A., additional

Published
2023
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15. Identifying Genetic Mutation Status in Patients with Colorectal Cancer Liver Metastases Using Radiomics-Based Machine-Learning Models.

Author

Wesdorp, Nina, Zeeuw, Michiel, van der Meulen, Delanie, van 't Erve, Iris, Bodalal, Zuhir, Roor, Joran, van Waesberghe, Jan Hein, Moos, Shira, van den Bergh, Janneke, Nota, Irene, van Dieren, Susan, Stoker, Jaap, Meijer, Gerrit, Swijnenburg, Rutger-Jan, Punt, Cornelis, Huiskens, Joost, Beets-Tan, Regina, Fijneman, Remond, Marquering, Henk, and Kazemier, Geert

Subjects
DIGITAL image processing, RESEARCH, LIVER tumors, GENETIC mutation, CONFIDENCE intervals, RESEARCH methodology, CANCER chemotherapy, METASTASIS, MACHINE learning, RANDOM forest algorithms, COLORECTAL cancer, CANCER patients, DESCRIPTIVE statistics, RESEARCH funding, COMPUTED tomography, DECISION making in clinical medicine, DISEASE complications
Abstract

Simple Summary: For patients with colorectal cancer with liver metastases, it is important to determine the genetic mutations (e.g., KRAS mutations) of the liver metastases. Around 35–45% of patients with colorectal cancer liver metastases (CRLM) have a KRAS mutation, and genetic mutations are used in treatment planning and prognostication. The aim of this study was to assess if KRAS mutations could be identified on CT scans using radiomics. In the discovery cohort of 255 patients, KRAS mutations could be identified with a good accuracy. In the external validation cohort consisting of 129 patients, the radiomics model performed poorly. These results indicate that radiomics might be used to determine genetic mutations such as KRAS, but foremost emphasize the importance of the external validation of radiomics models. External validation is crucial for the assessment of clinical applicability and should be mandatory in all future studies in the field of radiomics. For patients with colorectal cancer liver metastases (CRLM), the genetic mutation status is important in treatment selection and prognostication for survival outcomes. This study aims to investigate the relationship between radiomics imaging features and the genetic mutation status (KRAS mutation versus no mutation) in a large multicenter dataset of patients with CRLM and validate these findings in an external dataset. Patients with initially unresectable CRLM treated with systemic therapy of the randomized controlled CAIRO5 trial (NCT02162563) were included. All CRLM were semi-automatically segmented in pre-treatment CT scans and radiomics features were calculated from these segmentations. Additionally, data from the Netherlands Cancer Institute (NKI) were used for external validation. A total of 255 patients from the CAIRO5 trial were included. Random Forest, Gradient Boosting, Gradient Boosting + LightGBM, and Ensemble machine-learning classifiers showed AUC scores of 0.77 (95%CI 0.62–0.92), 0.77 (95%CI 0.64–0.90), 0.72 (95%CI 0.57–0.87), and 0.86 (95%CI 0.76–0.95) in the internal test set. Validation of the models on the external dataset with 129 patients resulted in AUC scores of 0.47–0.56. Machine-learning models incorporating CT imaging features could identify the genetic mutation status in patients with CRLM with a good accuracy in the internal test set. However, in the external validation set, the models performed poorly. External validation of machine-learning models is crucial for the assessment of clinical applicability and should be mandatory in all future studies in the field of radiomics. [ABSTRACT FROM AUTHOR]

Published
2023
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25. Pleural plaque volume correlation to lung function and artificial intelligence-driven pleural plaque quantification

Author

Lipman, Kevin Bernardus Wilhelmus Groot, primary, Boellaard, Thierry N., additional, De Gooijer, Cornedine J., additional, Bogveradze, Nino, additional, Hong, Eun Kyoung, additional, Landolfi, Federica, additional, Castagnoli, Francesca, additional, Cavallo, Luciano Castro, additional, Lebedyeva, Natalya, additional, Van Der Heijden, Ferdi, additional, Beets-Tan, Regina G. H., additional, Bodalal, Zuhir, additional, Burgers, Sjaak, additional, and Trebeschi, Stefano, additional

Published
2021
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26. Artificial intelligence-mediated diagnosis of asbestosis

Author

Lipman, Kevin Bernardus Wilhelmus Groot, primary, De Gooijer, Cornedine J., additional, Boellaard, Thierry N., additional, Van Der Heijden, Ferdi, additional, Beets-Tan, Regina G. H., additional, Bodalal, Zuhir, additional, Trebeschi, Stefano, additional, and Burgers, Sjaak, additional

Published
2021
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28. Prognostic Value of Deep Learning-Mediated Treatment Monitoring in Lung Cancer Patients Receiving Immunotherapy

Author

Trebeschi, Stefano, primary, Bodalal, Zuhir, additional, Boellaard, Thierry N., additional, Tareco Bucho, Teresa M., additional, Drago, Silvia G., additional, Kurilova, Ieva, additional, Calin-Vainak, Adriana M., additional, Delli Pizzi, Andrea, additional, Muller, Mirte, additional, Hummelink, Karlijn, additional, Hartemink, Koen J., additional, Nguyen-Kim, Thi Dan Linh, additional, Smit, Egbert F., additional, Aerts, Hugo J. W. L., additional, and Beets-Tan, Regina G. H., additional

Published
2021
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