A Predictive Nomogram Integrating AI-Assisted Morphological Feature Extraction with Clinical and Ultrasound Parameters for Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer.
Authors
Affiliations (3)
Affiliations (3)
- Department 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. Electronic address: [email protected].
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan. Electronic address: [email protected].
- Department 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; Department of Pharmacy, College of Pharmacy, Tajen University, Pingtung, Taiwan; Department of Medical Laboratory Science and Biotechnology, Fooyin University, Kaohsiung, Taiwan. Electronic address: [email protected].
Abstract
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. 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. 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. 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.