A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients

• Published in: Scientific reports 2022-05, Vol.12 (1), p.7914-7914, Article 7914
• Main Authors: Bove, Samantha, Comes, Maria Colomba, Lorusso, Vito, Cristofaro, Cristian, Didonna, Vittorio, Gatta, Gianluca, Giotta, Francesco, La Forgia, Daniele, Latorre, Agnese, Pastena, Maria Irene, Petruzzellis, Nicole, Pomarico, Domenico, Rinaldi, Lucia, Tamborra, Pasquale, Zito, Alfredo, Fanizzi, Annarita, Massafra, Raffaella
• Format: Article
• Language: English
• Subjects: 631/67/1347; 631/67/322; Axilla - pathology; Biopsy; Breast cancer; Breast Neoplasms - pathology; Female; Humanities and Social Sciences; Humans; Lymph nodes; Lymph Nodes - pathology; Lymphatic Metastasis - pathology; Metastases; Metastasis; multidisciplinary; Patients; Radiomics; Science; Science (multidisciplinary); Sentinel Lymph Node Biopsy - methods; Triple Negative Breast Neoplasms - pathology; Tumors; Ultrasonic imaging; Ultrasound
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-11876-4

In breast cancer patients, an accurate detection of the axillary lymph node metastasis status is essential for reducing distant metastasis occurrence probabilities. In case of patients resulted negative at both clinical and instrumental examination, the nodal status is commonly evaluated performing the sentinel lymph-node biopsy, that is a time-consuming and expensive intraoperative procedure for the sentinel lymph-node (SLN) status assessment. The aim of this study was to predict the nodal status of 142 clinically negative breast cancer patients by means of both clinical and radiomic features extracted from primary breast tumor ultrasound images acquired at diagnosis. First, different regions of interest (ROIs) were segmented and a radiomic analysis was performed on each ROI. Then, clinical and radiomic features were evaluated separately developing two different machine learning models based on an SVM classifier. Finally, their predictive power was estimated jointly implementing a soft voting technique. The experimental results showed that the model obtained by combining clinical and radiomic features provided the best performances, achieving an AUC value of 88.6%, an accuracy of 82.1%, a sensitivity of 100% and a specificity of 78.2%. The proposed model represents a promising non-invasive procedure for the SLN status prediction in clinically negative patients.