Personalized breast cancer treatments using artificial intelligence in radiomics and pathomics WT Tran, K Jerzak, FI Lu, J Klein, S Tabbarah, A Lagree, T Wu, ... Journal of medical imaging and radiation sciences 50 (4), S32-S41, 2019 | 71 | 2019 |
Quantitative thermal imaging biomarkers to detect acute skin toxicity from breast radiation therapy using supervised machine learning K Saednia, S Tabbarah, A Lagree, T Wu, J Klein, E Garcia, M Hall, ... International Journal of Radiation Oncology* Biology* Physics 106 (5), 1071-1083, 2020 | 31 | 2020 |
Machine learning frameworks to predict neoadjuvant chemotherapy response in breast cancer using clinical and pathological features N Meti, K Saednia, A Lagree, S Tabbarah, M Mohebpour, A Kiss, FI Lu, ... JCO Clinical Cancer Informatics 5, 66-80, 2021 | 30 | 2021 |
Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients DW Dodington, A Lagree, S Tabbarah, M Mohebpour, A Sadeghi-Naini, ... Breast Cancer Research and Treatment 186, 379-389, 2021 | 28 | 2021 |
Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancer WT Tran, H Suraweera, K Quaioit, D Cardenas, KX Leong, I Karam, ... Future Science OA 6 (1), FSO433, 2019 | 19 | 2019 |
Machine learning analysis of breast ultrasound to classify triple negative and HER2+ breast cancer subtypes R Ferre, J Elst, S Senthilnathan, A Lagree, S Tabbarah, FI Lu, ... Breast Disease 42 (1), 59-66, 2023 | 7 | 2023 |
Quantitative thermal imaging using grey-level run length matrix texture features correlate to radiation-induced skin toxicity V Lin, M Bielecki, P Yogendran, J Sindo, J Gill, T Wu, E Garcia, M Hall, ... Journal of Medical Imaging and Radiation Sciences 50 (2), S6-S7, 2019 | 4 | 2019 |
Predictive models for neoadjuvant chemotherapy response in breast cancer patients using quantitative digital pathology imaging biomarkers T Wu, S Tabbarah, A Lagree, W Tran Journal of medical imaging and radiation sciences 51 (3), S12, 2020 | 2 | 2020 |
COG5 variants lead to complex early onset retinal degeneration, upregulation of PERK and DNA damage S Tabbarah, E Tavares, J Charish, A Vincent, A Paterson, M Di Scipio, ... Scientific Reports 10 (1), 21269, 2020 | 1 | 2020 |
Measurement of Tumour Nuclear Area by Artificial Intelligence is Associated with Residual Cancer Burden Index After Neoadjuvant Chemotherapy in Estrogen Receptor Positive … D Dodington, A Lagree, S Tabbarah, M Mohebpour, A Sadeghi-Naini, ... LABORATORY INVESTIGATION 101 (SUPPL 1), 94-94, 2021 | | 2021 |
Quantitative Digital Pathology Biomarkers of Neoadjuvant Therapy Response in Breast Cancer W Tran, F Lu, S Tabbarah, A Lagree, D Dodington, K Jerzak, S Gandhi, ... Radiotherapy & Oncology 152 (S1), S277-S278, 2020 | | 2020 |
Thermoradiomic Markers of Radiation-Induced Skin Toxicity: Updated Results of a Phase II Study K Saednia, S Tabbarah, A Lagree, T Wu, J Klein, E Garcia, M Hall, ... Journal of Medical Imaging and Radiation Sciences 51 (3), S3, 2020 | | 2020 |
Automatic Tumor Nuclei Detection in Core Biopsies by Artificial Intelligence Can Predict Response to Neoadjuvant Chemotherapy in High Risk Breast Cancer Patients D Dodington, A Lagree, S Tabbarah, T Wu, C Hoey, W Tran, FI Lu LABORATORY INVESTIGATION 100 (SUPPL 1), 134-135, 2020 | | 2020 |
Radiomic Signature Using Quantitative Ultrasound Integrated with Machine Learning for Predicting Clinical Radiosensitivity in Patients with Head-Neck Squamous Cell Carcinoma … M Saifuddin, A Dasgupta, K Fatima, L Sannachi, H Suraweera, K Quiaiot, ... International Journal of Radiation Oncology, Biology, Physics 105 (1), E372, 2019 | | 2019 |
Predicting Recurrence for Patients with Head-Neck Squamous Cell Carcinoma Using Quantitative Ultrasound Based Radiomic Signatures Integrated with Machine Learning K Fatima, L Sannachi, K Quiaiot, H Suraweera, M Saifuddin, A Dasgupta, ... International Journal of Radiation Oncology, Biology, Physics 105 (1), E436-E437, 2019 | | 2019 |