We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us

Download Mobile App




Machine Learning Method Can Help Women Avoid Unnecessary Breast Surgery

By HospiMedica International staff writers
Posted on 19 Mar 2019
A team of researchers from the Geisel School of Medicine at Dartmouth (Hanover, NH, USA) has developed a machine learning method to predict atypical ductal hyperplasia (ADH) upgrade to cancer.

ADH, a breast lesion associated with a four- to five-fold increase in the risk of breast cancer, is mainly found using mammography and identified on core needle biopsy. More...
Despite multiple passes of the lesion during biopsy, only portions of the lesions are sampled. Other variable factors influence sampling and accuracy such that the presence of cancer may be underestimated by 10-45%. Currently, surgical removal is recommended for all ADH cases found on core needle biopsies to determine if the lesion is cancerous. About 20-30% of ADH cases are upgraded to cancer after surgical excision. However, this means that 70-80% of women undergo a costly and invasive surgical procedure for a benign (but high-risk) lesion.

The new machine learning method to predict ADH upgrade to cancer can potentially help clinicians and low-risk patients decide whether active surveillance and hormonal therapy is a reasonable alternative to surgical excision. An evaluation of the model by the researchers showed that the machine learning approach can identify 98% of all malignant cases prior to surgery while sparing from surgery 16% of women who otherwise would have undergone an unnecessary operation for a benign lesion. The researchers now plan to expand the scope of their model by including other high-risk breast lesions such as lobular neoplasia, papillomas, and radial scars. They also plan on further validating their approach on large external datasets using state and national breast cancer registries, and collaborating with other medical centers.

"Our results suggest there are robust clinical differences between women at low versus high risk for ADH upgrade to cancer based on core needle biopsy data that allowed our machine learning model to reliably predict malignancy upgrades in our dataset," said Saeed Hassanpour, PhD, who led the Dartmouth research team. "This study also identified important clinical variables involved in ADH upgrade risk."

"Our model can potentially help patients and clinicians choose an alternative management approach in low-risk cases," added Hassanpour. "In the era of personalized medicine, such models can be desirable for patients who value a shared decision-making approach with the ability to choose between surgical excision for certainty versus surveillance to avoid cost, stress, and potential side effects in women at low risk for upgrade of ADH to cancer."

Related Links:
Geisel School of Medicine at Dartmouth


Gold Member
Blood Gas Analyzer
i-Check200
Radiology Monitor
MDNC-6121 Barco Nio Color 5.8MP
Immobilization System
Cranial 4Pi Immobilization
Glucose Meter
StatStrip®
Read the full article by registering today, it's FREE! It's Free!
Register now for FREE to HospiMedica.com and get access to news and events that shape the world of Hospital Medicine.
  • Free digital version edition of HospiMedica International sent by email on regular basis
  • Free print version of HospiMedica International magazine (available only outside USA and Canada).
  • Free and unlimited access to back issues of HospiMedica International in digital format
  • Free HospiMedica International Newsletter sent every week containing the latest news
  • Free breaking news sent via email
  • Free access to Events Calendar
  • Free access to LinkXpress new product services
  • REGISTRATION IS FREE AND EASY!
Click here to Register








Channels

Artificial Intelligence

view channel
Image: Artificial intelligence (AI) standalone performance and reader performance with versus without AI assistance. (A) Receiver operating characteristics (ROC) curve for AI standalone performance in the US dataset (AUC 0.899, 95% CI 0.858 to 0.939). (B) ROC curve for AI standalone performance in the Korean dataset (AUC 0.963, 95% CI 0.946 to 0.975). (C) Pooled reader ROC without (AUC 0.718) versus with (AUC 0.852) AI assistance in the Korean dataset; P<0.001. AUC, area under the receiver operating characteristics curve. (Leonard Sunwoo et al., Journal of NeuroInterventional Surgery (2026). DOI: 10.1136/jnis-2026-025339)

AI Improves Non-Contrast CT Interpretation for Time-Sensitive Stroke Assessment

Acute ischemic stroke occurs when a blood vessel in the brain becomes blocked, requiring rapid diagnosis to enable timely reperfusion therapy. Emergency departments often use computed tomography angiography... Read more

Critical Care

view channel
Image Credit: 123RF

AI-Enhanced Handheld Ultrasound Improves Carotid Plaque Detection

Handheld ultrasound can miss small or faint carotid plaque, creating uncertainty in community screening. This gap makes it harder for frontline clinicians to decide who needs confirmatory imaging, closer... Read more

Surgical Techniques

view channel
Image: Associate Professor Menglin Chen studies how the light-sensitive nanoparticles affect living cells. The screen shows calcium being released inside a cell after nanoparticles taken up by the cell are exposed to blue light. Calcium plays an important role in cellular signaling, and the experiment helps the researchers understand how the nanoparticles can translate light into biological activity. (Photo courtesy of Aarhus University, Johanne Holm Jensen)

Light-Activated Nanoparticles May Offer New Approach to Retinal Prostheses

Retinitis pigmentosa is a degenerative retinal disorder in which photoreceptors progressively die, reducing visual signals to the brain while leaving surviving inner retinal circuits underused.... Read more

Point of Care

view channel
Image Credit: 123RF

Continuous Glucose Monitoring Identifies Cardiometabolic Risk in Adults Without Diabetes

Dysglycemia—abnormal blood glucose regulation—can fluctuate throughout the day and often escape conventional screening. Clinicians typically rely on fasting plasma glucose and hemoglobin A1c, which offer... Read more

Business

view channel
Image: LigaSure RAS Maryland, designed for the Valleylab FT10 platform on Hugo RAS, seals and cuts vessels, tissue, and lymphatics up to 7 mm in diameter (Photo courtesy of Medtronic)

Medtronic Receives FDA Clearance for Vessel-Sealing Instrument for Robotic Surgery

As robotic-assisted surgery expands across U.S. hospitals, teams increasingly seek energy instruments with the familiarity and performance of tools used in open and laparoscopic procedures.... Read more
Copyright © 2000-2026 Globetech Media. All rights reserved.