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Address correspondence to: Neal Shore, MD, Carolina Urologic Research Center, 823 82nd Pkwy, Myrtle Beach, SC 29572, Spain. E-mail: [email protected]
Contents lists available at ScienceDirect
European Journal of Radiology
journal homepage: www.elsevier.com/locate/ejrad
A nomogram for individual prediction of vascular invasion in primary breast T cancer
Fu-sheng Ouyanga,1, Bao-liang Guoa,1, Xi-yi Huangb,1, Li-zhu Ouyangc,1, Cui-ru Zhoua, Rong Zhanga, Mei-lian Wua, Zun-shuai Yanga, Shang-kun Wua, Tian-di Guoa, Shao-ming Yanga, ,
a Department of Radiology, Shunde Hospital of Southern Medical University, Foshan, Guangdong, PR China b Department of Laboratory, Lecong Hospital of Shunde, Foshan, Guangdong, PR China c Department of Ultrasound, Shunde Hospital of Southern Medical University, Foshan, Guangdong, PR China
Magnetic Resonance Imaging Vascular invasion Nomogram
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
Conclusion: The proposed nomogram could be used to predict VI in breast cancer patients, which was helpful for clinical decision-making.
Breast cancer is one of the most common malignant tumors among women world widely. A correct identification of poor prognostic factors for breast cancer may help guiding more aggressive adjuvant treatment protocols . Identification of simple and measurable prognostic
factors is an important issue in clinical decision-making of breast cancer . Ideal prognostic factors would be capable of predicting the short survival in some patients . The major cause of short survival in breast cancer is dispersion of malignant cells from the primary location leading to formation of metastases .
One of the important steps in metastasis is the invasion of vascular
Abbreviations: 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