Using Deep Learning to Predict Axillary Lymph Node Metastasis from US Images of Breast Cancer
Menée à partir de données portant sur des images échographiques réalisées auprès de 834 patientes atteintes d'un cancer du sein primitif, cette étude évalue la performance de trois algorithmes d'apprentissage automatique, utilisant la technologie des réseaux de neurones convolutifs, pour prédire l'absence de métastases ganglionnaires axillaires
During the past 2 decades, management of the axilla in early stage breast cancer has evolved. Results from randomized controlled trials have allowed for the de-escalation of axillary surgery in patients with breast cancer (1,2). The American College of Surgeons Oncology Group Z0011 trial showed that axillary lymph node dissection had no survival benefit when there were one or two positive sentinel lymph nodes in patients with clinical T1 or T2 node-negative breast cancer (2). The omission of an invasive axillary procedure prevents morbidity and complications such as hematoma and lymphedema. Randomized controlled trials for omitting sentinel lymph node biopsy are currently under way in Europe. The SOUND (Sentinel Node versus Observation after Axillary Ultrasound) and INSEMA (Intergroup-Sentinel-Mamma) trials investigate whether sentinel lymph node biopsy could be safely omitted in patients with clinically node-negative breast cancer treated with breast conservation therapy (3). Because imaging plays an important role in patient treatment, imaging features predicting lymph node metastasis have the potential to promote more effective decision making.
Radiology , éditorial, 2018