Canadian Preparation and Review Course in Clinical and Radiation Oncology

Nov 27th, 2021Dec 2nd, 2021
Online
https://www.ottawaradcme.com/courses/83
This annual refresher course is designed to provide radiation oncology residents, clinicians, and other members of the community with a comprehensive review and update of clinical practice across most subdisciplines of radiation oncology.
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High breast density tied to impaired recurrence-free cancer survival
By Amerigo Allegretto
Recurrence-free breast cancer survival is less likely in women with high breast density compared with women with low density.
April 19, 2024
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Can ChatGPT ‘data mine’ patient reports for stroke registries?
By Will Morton
ChatGPT-4 appears useful for extracting details from reports on thrombectomy procedures in stroke patients, a labor-intensive and error-prone task currently performed by human readers.
April 19, 2024
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PET/MRI reveals sex differences in patients with autism
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PET/MRI shows that young women with autism spectrum disorder have increased brain metabolism associated with translocator protein.
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Radiologist consultation improves patients' LCS attitudes
By Kate Madden Yee
Consulting with a radiologist boosts patients' attitudes toward lung cancer screening (LCS) and their interest in quitting smoking, researchers have reported.
April 18, 2024
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Pisano speaks with AuntMinnie.com on new ARPA-H leadership role
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Etta Pisano, MD, spoke with AuntMinnie.com on leading the Advancing Clinical Trials Readiness initiative, part of ARPA-H.
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AI proposed to help radiologists detect child abuse injuries
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A team at Michigan State University has developed an AI model designed to detect rib fractures in children under 3 years old.
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Test set images with ground truth (teal, red) and model predictions (green, yellow), with true positives (green), false positives (yellow), and false negatives (red). Predictions from the 6x-YOLOv5 ensemble trained on histogram equalized input images with a y = 0.2 avalanche scheme, achieving 0.536 precision, 0.795 recall, and 0.723 F2 score. Image and caption courtesy of Scientific Reports.
Tailored lung cancer screening model helps determine nodule follow-up
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Some of the top domains for users of point-of-care ultrasound (POCUS) incorporate the technology for lung ultrasound applications.
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CT-based deep learning identifies patients at further lung cancer risk
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ChatGPT-4 matches radiologists in flagging errors on reports
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ChatGPT-4 matched radiologists when tasked with detecting errors in imaging reports, with the study suggesting the technology may be useful for improving reporting accuracy.
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Deep-learning model determines breast tumor staging
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A deep-learning model based on mammographic images can determine tumor staging and sentinel lymph node metastasis in breast cancer patients.
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Researchers explain the data preparation and structure of a proposed CNN-based deep learning model using mammography features to determine breast tumor staging. The image is available for use under a Creative Commons license: CC BY-NC-ND 4.0 DEED Attribution-NonCommercial-NoDerivs 4.0 International.
FAPI-PET shows promise in patients with lung cancer
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FAPI-PET imaging could emerge as a new tool for assessing patients with single pulmonary tumors, especially in those with negative results on other scans.
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F-18 FDG and Ga-68 FAPI-46 images of a 51-year-old woman with adenocarcinoma with lepidic growth pattern in right upper lobe. (A) Maximum-intensity-projection PET images. The lesion had F-18 FDG uptake below blood pool level but was strongly Ga-68 FAPI-46-positive. CT-guided biopsy led to pathologic diagnosis of adenocarcinoma, and the patient was treated by stereotactic body radiation therapy because of functional inoperability. Image courtesy of the Journal of Nuclear Medicine.
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