Related Experiment Video
Updated: Jul 9, 2026
Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
Published on: February 6, 2020
A Predictive Nomogram Integrating AI-Assisted Morphological Feature Extraction with Clinical and Ultrasound
Wei-Teng Wang1, Zi-Han Yu2, Chen-Pin Chou3
1Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Department of Medical Imaging and Radiology, Shu-Zen Junior College of Medicine and Management, Kaohsiung, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Objective:
To develop and validate a nomogram integrating artificial intelligence (AI)-extracted ultrasound features with clinic pathologic data for non-invasive preoperative prediction of ipsilateral axillary lymph node (IALN) metastasis in breast cancer women.
Materials And Methods:
This retrospective study, approved by the institutional review board with consent waived, included 148 women (mean age, 58 y ± 11) with histologically confirmed invasive breast cancer who underwent preoperative ultrasound between May 2020 and January 2022. A predictive model was developed by integrating AI-extracted ultrasound features (S-Detect, Samsung Medison) with tumor size, IALN size, the proliferation marker Ki-67, and radiologist assessment. Performance was assessed via logistic regression, receiver operating characteristic (ROC) analysis, calibration, and decision-curve analysis (DCA) on internal training (n = 118) and validation (n = 30) cohorts, with accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and harmonic mean of precision and recall (F1 score) calculated at the Youden-optimal threshold.
Results:
IALN metastasis was identified in 57 of 148 patients (38.5%). Eight independent predictors were included in the final model: lesion depth ≥1.0 cm, lesion length ≥2.2 cm, lesion width ≥2.1 cm, IALN long axis ≥2.2 cm, IALN short axis ≥0.7 cm, irregular lesion shape, Ki-67 >20%, and radiologist-assessed IALN involvement. The nomogram achieved AUCs of 0.855 (training) and 0.810 (validation) with excellent calibration (Hosmer-Lemeshow p > 0.05). At the Youden-optimal threshold (0.410), the nomogram achieved stable performance: accuracy 79.7%/80.0%, sensitivity 73.8%/76.9%, specificity 84.8%/82.4%, PPV 75.6%/76.9%, NPV 86.8%/82.4%, and F1 score 0.746/0.769 (training/validation). DCA demonstrated superior net clinical benefit compared to radiologist assessment, particularly within the 20%-60% threshold range.
Conclusion:
An AI-assisted ultrasound nomogram demonstrated robust predictive performance for IALN metastasis, outperformed radiologist assessment, and may optimize preoperative risk stratification and surgical decision-making.
