AIMC Topic: Neoplasm Metastasis
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CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma.
Cancer medicine
Aug 1, 2024
OBJECTIVE: Breast cancer is one of the leading cancer causes among women worldwide. It can be classified as invasive ductal carcinoma (IDC) or metastatic cancer. Early detection of breast cancer is challenging due to the lack of early warning signs. ...
The More, the Better? Modalities of Metastatic Status Extraction on Free Medical Reports Based on Natural Language Processing.
JCO clinical cancer informatics
Aug 1, 2024
Use of Natural Language Processing to Infer Sites of Metastatic Disease From Radiology Reports at Scale.
JCO clinical cancer informatics
May 1, 2024
PURPOSE: To evaluate natural language processing (NLP) methods to infer metastatic sites from radiology reports.
Advancements in oligometastatic breast cancer: a comprehensive review of current strategies and the role of artificial intelligence.
JPMA. The Journal of the Pakistan Medical Association
Apr 1, 2024
In the dynamic landscape of Breast Cancer (BC), Oligo- Metastatic Breast Cancer (OMBC) presents unique challenges and opportunities. This comprehensive review delves into current strategies for addressing OMBC, covering locoregional and site-specific...
Can 3D artificial intelligence models outshine 2D ones in the detection of intracranial metastatic tumors on magnetic resonance images?
Journal of the Chinese Medical Association : JCMA
Oct 1, 2021
BACKGROUND: This study aimed to compare the prediction performance of two-dimensional (2D) and three-dimensional (3D) semantic segmentation models for intracranial metastatic tumors with a volume ≥ 0.3 mL.
Deep Learning for Clinical Image Analyses in Oral Squamous Cell Carcinoma: A Review.
JAMA otolaryngology-- head & neck surgery
Oct 1, 2021
IMPORTANCE: Oral squamous cell carcinoma (SCC) is a lethal malignant neoplasm with a high rate of tumor metastasis and recurrence. Accurate diagnosis, prognosis prediction, and metastasis detection can improve patient outcomes. Deep learning for clin...
Automated model versus treating physician for predicting survival time of patients with metastatic cancer.
Journal of the American Medical Informatics Association : JAMIA
Jun 12, 2021
OBJECTIVE: Being able to predict a patient's life expectancy can help doctors and patients prioritize treatments and supportive care. For predicting life expectancy, physicians have been shown to outperform traditional models that use only a few pred...
Concordance between treatment recommendations provided by IBM Watson for Oncology and a multidisciplinary tumor board for breast cancer in China.
Japanese journal of clinical oncology
Aug 4, 2020
OBJECTIVE: Watson for Oncology (WFO), an artificial intelligence from IBM Corporation, can provide a treatment plan by analyzing patient's disease characteristics. The present study was performed to examine the concordance between treatment recommend...
Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2020
Semi-automatic measurements are performed on FDG PET-CT images to monitor the evolution of metastatic sites in the clinical follow-up of metastatic breast cancer patients. Apart from being time-consuming and prone to subjective approximation, semi-au...
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