Revolutionary AI Model Reduces Unnecessary Biopsies in Breast Cancer Patients

In a groundbreaking advancement for breast cancer detection, researchers at UT Southwestern Medical Center have unveiled a novel artificial intelligence (AI) model poised to revolutionize the identification of breast cancer metastasis. This innovative noninvasive approach leverages standard magnetic resonance imaging (MRI) combined with sophisticated machine learning techniques to detect axillary metastasis — the spread of cancer cells to the lymph nodes under the arms.

“Most breast cancer deaths are due to metastatic disease, and the first site is usually an axillary lymph node,” explained Dr. Basak Dogan, the study's lead researcher. The findings, published in Radiology: Imaging Cancer, demonstrate that the AI model significantly outperforms traditional MRI and ultrasound in detecting patients with axillary metastasis.

In clinical practice, this AI model could dramatically reduce the number of unnecessary surgical biopsies. According to the study, the AI model could have prevented 51% of benign or unnecessary surgical sentinel node biopsies while accurately identifying 95% of patients with axillary metastasis. “This advancement is crucial because surgical biopsies carry side effects and risks, even though they often result in a negative finding for cancer cells,” Dr. Dogan emphasized. “Enhancing our ability to rule out axillary metastasis during a routine MRI with this model can mitigate those risks and improve clinical outcomes.”

The retrospective study involved dynamic contrast-enhanced breast MRI exams from 350 newly diagnosed breast cancer patients at UT Southwestern and the Moody Center for Breast Health, located on Parkland Health’s main campus in Dallas. All participants had known nodal status. These images, along with a variety of clinical measures, were utilized to train the AI model in identifying axillary metastasis with machine learning techniques.

A notable advantage of this AI model is its integration with standard imaging exams, potentially eliminating the stress and expense of additional tests for many patients. Dr. Dogan highlighted that patients with benign results from traditional MRI exams or needle biopsies are often subjected to sentinel lymph node biopsy due to the possibility of undetected metastasis. “Our research shows that it's possible to accurately identify nonmetastatic patients, benefiting them and allowing physicians to tailor their treatment plans more effectively,” Dr. Dogan said.

This research builds on previous studies at UT Southwestern focused on breast cancer imaging and predictive tools for detecting metastasis. “Our study is a testament to UT Southwestern’s commitment to impactful research addressing real-world health care challenges,” Dr. Dogan remarked. “The development and validation of AI models for medical imaging hold great promise in advancing our fight against breast and other cancers. This new tool represents a significant step forward.”

The team at UT Southwestern continues to refine the image analysis process and aims to incorporate more diverse data to validate their findings further. This promising AI model could soon become a standard practice, significantly enhancing the detection and treatment of breast cancer metastasis while reducing the need for invasive procedures.