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Andrew Lagree

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Veterinary and Comparative Oncology|December 23, 2024
Precision in Parsing: Evaluation of an Open-Source Named Entity Recognizer (NER) in Veterinary OncologyChristopher J Pinard, Andrew C Poon, Andrew Lagree, et al.
Breast Cancer Research and Treatment|January 24, 2021
Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patientsDavid W Dodington, Andrew Lagree, Sami Tabbarah, et al.
JCO Clinical Cancer Informatics|October 30, 2024
Identifying Oncology Patients at High Risk for Potentially Preventable Emergency Department Visits Using a Novel DefinitionLauren Fleshner, Sonal Gandhi, Andrew Lagree, et al.
Breast Disease|March 13, 2023
Machine learning analysis of breast ultrasound to classify triple negative and HER2+ breast cancer subtypesRomuald Ferre, Janne Elst, Seanthan Senthilnathan, et al.
Scientific Reports|April 14, 2021
A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networksAndrew Lagree, Majidreza Mohebpour, Nicholas Meti, et al.
The Oncologist|June 11, 2023
Drivers of Emergency Department Use Among Oncology Patients in the Era of Novel Cancer Therapeutics: A Systematic ReviewLauren Fleshner, Andrew Lagree, Audrey Shiner, et al.
International Journal of Radiation Oncology, Biology, Physics|January 27, 2020
Quantitative Thermal Imaging Biomarkers to Detect Acute Skin Toxicity From Breast Radiation Therapy Using Supervised Machine LearningKhadijeh Saednia, Sami Tabbarah, Andrew Lagree, et al.
Journal of Medical Imaging and Radiation Sciences|August 27, 2019
Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and PathomicsWilliam T Tran, Katarzyna Jerzak, Fang-I Lu, et al.
Scientific Reports|June 11, 2022
Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsiesKhadijeh Saednia, Andrew Lagree, Marie A Alera, et al.
Future Science OA|January 10, 2020
Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancerWilliam T Tran, Harini Suraweera, Karina Quaioit, et al.
Pageof 2

Showing results (1-10 of 14) with videos related to

Sort By:
Pageof 2
Veterinary and Comparative Oncology|December 23, 2024
Precision in Parsing: Evaluation of an Open-Source Named Entity Recognizer (NER) in Veterinary OncologyChristopher J Pinard, Andrew C Poon, Andrew Lagree, et al.
Breast Cancer Research and Treatment|January 24, 2021
Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patientsDavid W Dodington, Andrew Lagree, Sami Tabbarah, et al.
JCO Clinical Cancer Informatics|October 30, 2024
Identifying Oncology Patients at High Risk for Potentially Preventable Emergency Department Visits Using a Novel DefinitionLauren Fleshner, Sonal Gandhi, Andrew Lagree, et al.
Breast Disease|March 13, 2023
Machine learning analysis of breast ultrasound to classify triple negative and HER2+ breast cancer subtypesRomuald Ferre, Janne Elst, Seanthan Senthilnathan, et al.
Scientific Reports|April 14, 2021
A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networksAndrew Lagree, Majidreza Mohebpour, Nicholas Meti, et al.
The Oncologist|June 11, 2023
Drivers of Emergency Department Use Among Oncology Patients in the Era of Novel Cancer Therapeutics: A Systematic ReviewLauren Fleshner, Andrew Lagree, Audrey Shiner, et al.
International Journal of Radiation Oncology, Biology, Physics|January 27, 2020
Quantitative Thermal Imaging Biomarkers to Detect Acute Skin Toxicity From Breast Radiation Therapy Using Supervised Machine LearningKhadijeh Saednia, Sami Tabbarah, Andrew Lagree, et al.
Journal of Medical Imaging and Radiation Sciences|August 27, 2019
Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and PathomicsWilliam T Tran, Katarzyna Jerzak, Fang-I Lu, et al.
Scientific Reports|June 11, 2022
Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsiesKhadijeh Saednia, Andrew Lagree, Marie A Alera, et al.
Future Science OA|January 10, 2020
Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancerWilliam T Tran, Harini Suraweera, Karina Quaioit, et al.
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