A nomogram for individual prediction of vascular invasion in primary breast cancer. Issue 110 (January 2019)

• Record Type: Journal Article
• Authors: Ouyang, Fu-sheng; Guo, Bao-liang; Huang, Xi-yi; Ouyang, Li-zhu; Zhou, Cui-ru; Zhang, Rong; Wu, Mei-lian; Yang, Zun-shuai; Wu, Shang-kun; Guo, Tian-di; Yang, Shao-ming; Hu, Qiu-gen
• Is Part Of: European journal of radiology. Issue 110(2019)
• Page Start: 30
• Page End: 38
• Publication Date: 2019-01
• Subjects: LASSO least absolute shrinkage and selection operator -- AUC area under the curve -- CI confidence Interval -- PPV positive prediction value -- NPV negative prediction value -- DCE dynamic contrast-enhanced -- DWI diffusion-weighted imaging -- ALNM axillary lymph node metastasis -- TIC time-intensity curve -- ER estrogen receptor -- PR progesterone receptor -- HER2 human epidermal growth factor receptor -- LVI lymphovascular invasion -- LI lymphatic invasion -- VI vascular invasion -- IDC invasive ductal carcinoma; Breast cancer -- Magnetic Resonance Imaging -- Vascular invasion -- Nomogram; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2018.11.013
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Vascular invasion has been reported as a strong prognostic factor in patients with breast cancer. We identified 10 clinicopathologic and radiological features associated with vascular invasion in breast cancer. The nomogram for individual risk prediction for vascular invasion showed excellent discrimination and calibration. Abstract: Objectives: To explore the feasibility of preoperative prediction of vascular invasion (VI) in breast cancer patients using nomogram based on multiparametric MRI and pathological reports. Methods: We retrospectively collected 200 patients with confirmed breast cancer between January 2016 and January 2018. All patients underwent MRI examinations before the surgery. VI was identified by postoperative pathology. The 200 patients were randomly divided into training (n = 100) and validation datasets (n = 100) at a ratio of 1:1. Least absolute shrinkage and selection operator (LASSO) regression was used to select predictors most associated with VI of breast cancer. A nomogram was constructed to calculate the area under the curve (AUC) of receiver operating characteristics, sensitivity, specificity, accuracy, positive prediction value (PPV) and negative prediction value (NPV). We bootstrapped the data for 2000 times without setting the random seed to obtain corrected results. Results: VI was observed in 79 patients (39.5%). LASSO selected 10 predictors associated with VI. In the training dataset, the AUC for nomogram was 0.94 (95% confidence interval [CI]: 0.89–0.99, the sensitivity was 78.9% (95%CI: 72.4%–89.1%), the specificity was 95.3% (95%CI: 89.1%–100.0%), the accuracy was 86.0% (95%CI: 82.0%–92.0%), the PPV was 95.7% (95%CI: 90.0%–100.0%), and the NPV was 77.4% (95%CI: 67.8%–87.0%). In the validation dataset, the AUC for nomogram was 0.89 (95%CI: 0.83–0.95), the sensitivity was 70.3% (95%CI: 60.7%–79.2%), the specificity was 88.9% (95%CI: 80.0%–97.1%), the accuracy was 77.0% (95%CI: 70.0%–83.0%), the PPV was 91.8% (95%CI: 85.3%–98.0%), and the NPV was 62.7% (95%CI: 51.7%–74.0%). The nomogram calibration curve shows good agreement between the predicted probability and the actual probability. Conclusion: The proposed nomogram could be used to predict VI in breast cancer patients, which was helpful for clinical decision-making.