no code implementations • 23 Feb 2024 • Yining Zha, Benjamin H. Kann, Zezhong Ye, Anna Zapaishchykova, John He, Shu-Hui Hsu, Jonathan E. Leeman, Kelly J. Fitzgerald, David E. Kozono, Raymond H. Mak, Hugo J. W. L. Aerts
Thus, we explore the potential of delta radiomics from on-treatment magnetic resonance (MR) imaging to track radiation dose response, inform personalized radiotherapy dosing, and predict outcomes.
1 code implementation • 26 Oct 2023 • Shan Chen, Marco Guevara, Shalini Moningi, Frank Hoebers, Hesham Elhalawani, Benjamin H. Kann, Fallon E. Chipidza, Jonathan Leeman, Hugo J. W. L. Aerts, Timothy Miller, Guergana K. Savova, Raymond H. Mak, Maryam Lustberg, Majid Afshar, Danielle S. Bitterman
Results show promise for AI to improve clinician efficiency and patient care through assisting documentation, if used judiciously.
no code implementations • 4 Feb 2022 • Zhongyi Zhang, Jakob Weiss, Jana Taron, Roman Zeleznik, Michael T. Lu, Hugo J. W. L. Aerts
A dataset of 451 CT scans with volumetric liver segmentations of expert readers was used for training a deep learning model.
2 code implementations • 16 Oct 2021 • Zezhong Ye, Jack M. Qian, Ahmed Hosny, Roman Zeleznik, Deborah Plana, Jirapat Likitlersuang, Zhongyi Zhang, Raymond H. Mak, Hugo J. W. L. Aerts, Benjamin H. Kann
The fine-tuned model on chest CTs yielded an AUC: 1. 0 for the internal validation set (n = 53), and AUC: 0. 980 for the external validation set (n = 402).
no code implementations • 28 Feb 2020 • Benjamin Haibe-Kains, George Alexandru Adam, Ahmed Hosny, Farnoosh Khodakarami, MAQC Society Board, Levi Waldron, Bo wang, Chris McIntosh, Anshul Kundaje, Casey S. Greene, Michael M. Hoffman, Jeffrey T. Leek, Wolfgang Huber, Alvis Brazma, Joelle Pineau, Robert Tibshirani, Trevor Hastie, John P. A. Ioannidis, John Quackenbush, Hugo J. W. L. Aerts
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening.
Applications
no code implementations • 24 Mar 2017 • Martin Vallières, Emily Kay-Rivest, Léo Jean Perrin, Xavier Liem, Christophe Furstoss, Hugo J. W. L. Aerts, Nader Khaouam, Phuc Felix Nguyen-Tan, Chang-Shu Wang, Khalil Sultanem, Jan Seuntjens, Issam El Naqa
In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer.