Deep radiomic features from MRI scans predict survival outcome of recurrent glioblastoma

no code implementations • 15 Nov 2019 • Ahmad Chaddad, Saima Rathore, Mingli Zhang, Christian Desrosiers, Tamim Niazi

This paper proposes to use deep radiomic features (DRFs) from a convolutional neural network (CNN) to model fine-grained texture signatures in the radiomic analysis of recurrent glioblastoma (rGBM).

General Classification

Modeling of Textures to Predict Immune Cell Status and Survival of Brain Tumour Patients

no code implementations • 4 Jun 2022 • Ahmad Chaddad, Mingli Zhang, Lama Hassan, Tamim Niazi

Combined the immune markers with DRFs and clinical variables, Kaplan-Meier estimator and Log-rank test achieved the most significant difference between predicted groups of patients (short-term versus long-term survival) with p\,=\, 4. 31$\times$10$^{-7}$ compared to p\,=\, 0. 03 for Immune cell markers, p\,=\, 0. 07 for clinical variables , and p\,=\, 1. 45$\times$10$^{-5}$ for DRFs.

Can autism be diagnosed with AI?

no code implementations • 5 Jun 2022 • Ahmad Chaddad, Jiali Li, Qizong Lu, Yujie Li, Idowu Paul Okuwobi, Camel Tanougast, Christian Desrosiers, Tamim Niazi

With AI, new radiomic models using the deep learning techniques will be also described.

Texture Classification

Deep radiomic signature with immune cell markers predicts the survival of glioma patients

no code implementations • 9 Jun 2022 • Ahmad Chaddad, Paul Daniel Mingli Zhang, Saima Rathore, Paul Sargos, Christian Desrosiers, Tamim Niazi

These results demonstrate the usefulness of proposed DRFs as non-invasive biomarker for predicting treatment response in patients with brain tumors.