AIMC Topic: Breast Neoplasms

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[Evaluating the accuracy of large language models in answering mammography screening questions in Italian and English: a study based on the Eusobi guidelines.].

Recenti progressi in medicina
INTRODUCTION: Artificial intelligence (AI) is transforming various aspects of everyday life, including healthcare, through large language models (LLMs) like ChatGPT, Gemini, and Copilot. These systems are increasingly used to disseminate medical info...

Enhancing HER2 testing in breast cancer: predicting fluorescence in situ hybridization (FISH) scores from immunohistochemistry images via deep learning.

The journal of pathology. Clinical research
Breast cancer affects millions globally, necessitating precise biomarker testing for effective treatment. HER2 testing is crucial for guiding therapy, particularly with novel antibody-drug conjugates (ADCs) like trastuzumab deruxtecan, which shows pr...

[Prophylactic surgery and genetic counselling: What impact of the artificial intelligence?].

Bulletin du cancer
In the area of cancer predisposition, certain situations may lead to the discussion of prophylactic surgery. This is rarely strictly recommended and depends on the patient's choice. The advantages and disadvantages must be weighed up. The main advant...

An Efficient Lightweight Multi Head Attention Gannet Convolutional Neural Network Based Mammograms Classification.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: This research aims to use deep learning to create automated systems for better breast cancer detection and categorisation in mammogram images, helping medical professionals overcome challenges such as time consumption, feature extraction ...

Using AI to Select Women with Intermediate Breast Cancer Risk for Breast Screening with MRI.

Radiology
Background Combined mammography and MRI screening is not universally accessible for women with intermediate breast cancer risk due to limited MRI resources. Selecting women for MRI by assessing their mammogram may enable more resource-effective scree...

Evaluating the Impact of Changes in Artificial Intelligence-derived Case Scores over Time on Digital Breast Tomosynthesis Screening Outcomes.

Radiology. Artificial intelligence
Purpose To evaluate the change in digital breast tomosynthesis artificial intelligence (DBT-AI) case scores over sequential screenings. Materials and Methods This retrospective review included 21 108 female patients (mean age ± SD, 58.1 years ± 11.5)...

External Validation of a Commercial Artificial Intelligence Algorithm on a Diverse Population for Detection of False Negative Breast Cancers.

Journal of breast imaging
OBJECTIVE: There are limited data on the application of artificial intelligence (AI) on nonenriched, real-world screening mammograms. This work aims to evaluate the ability of AI to detect false negative cancers not detected at the time of screening ...

[Value of the deep learning automated quantification of tumor-stroma ratio in predicting efficacy and prognosis of neoadjuvant therapy for breast cancer based on residual cancer burden grading].

Zhonghua bing li xue za zhi = Chinese journal of pathology
To investigate the prognostic value of deep learning-based automated quantification of tumor-stroma ratio (TSR) in patients undergoing neoadjuvant therapy (NAT) for breast cancer. Specimens were collected from 209 breast cancer patients who receive...