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PURPOSE: To develop and internally validate a nomogram combining radiomics signature of primary tumor and fibroglandular tissue (FGT) based on pharmacokinetic dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical factors for preoperative prediction of sentinel lymph node (SLN) status in breast cancer patients. METHODS: This study retrospectively enrolled 186 breast cancer patients who underwent pretreatment pharmacokinetic DCE-MRI with positive (n = 93) and negative (n = 93) SLN. Logistic regression models and radiomics signatures of tumor and FGT were constructed after feature extraction and selection. The radiomics signatures were further combined with independent predictors of clinical factors for constructing a combined model. Prediction performance was assessed by receiver operating characteristic (ROC), calibration, and decision curve analysis. The areas under the ROC curve (AUCs) of models were corrected by 1,000-times bootstrapping method and compared by Delong's test. The added value of each independent model or their combinations was also assessed by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. This report referred to the "Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis" (TRIPOD) statement. RESULTS: The AUCs of the tumor radiomic model (eight features) and the FGT radiomic model (three features) were 0.783 (95% confidence interval [CI], 0.717-0.849) and 0.680 (95% CI, 0.604-0.757), respectively. A higher AUC of 0.799 (95% CI, 0.737-0.862) was obtained by combining tumor and FGT radiomics signatures. By further combining tumor and FGT radiomics signatures with progesterone receptor (PR) status, a nomogram was developed and showed better discriminative ability for SLN status [AUC 0.839 (95% CI, 0.783-0.895)]. The IDI and NRI indices also showed significant improvement when combining tumor, FGT, and PR compared with each independent model or a combination of any two of them (all p < 0.05). CONCLUSION: FGT and clinical factors improved the prediction performance of SLN status in breast cancer. A nomogram integrating the DCE-MRI radiomics signature of tumor and FGT and PR expression achieved good performance for the prediction of SLN status, which provides a potential biomarker for clinical treatment decision-making.
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Objective To evaluate the influence of menopause status on breast fibrograndular tissue (FGT),background parenchymal enhancement volume ratio (BPEv) and intensity ratio (BPEI) by breast MR image automatic quantitative analysis.Methods From 14 033 consecutive patients who underwent breast MR in our center from 2009 to 2012,we randomly selected 101 normal cases (47cases of premenopausal and 54 cases of postmenopausal).Premenopausal status was subclassified into four groups (8 cases in the 1st week and 13 cases in the 2nd,3rd and 4th week,respectively) based on the menstrual cycle.We evaluated FGT,BPEv and BPEI at early (2 minutes),medium (4 minutes) and late (6 minutes) enhanced time phases of breast MRI for quantitative assessment.The FGT,BPEv and BPEI in premenopausal and postmenopausal women were compared using the Mann-Whitney U test.Comparison of each menstrual cycle was made using the Kruskal-Wallis test.Results The FGT,BPEv and middle-late BPEI of postmenopausal women were significantly lower than that of premenopausal women (P＜0.05).The maximum BPEI was in the 1st week,the minimum in the 2nd week in premenopausal women (P＜0.05).The FGT and BPEv in the 1st week were the highest among the 4 weeks,but there was no significant difference.Conclusions The FGT,BPEv and BPEI were significantly decreased after menopause.The optimal time of breast MR examination is in the 2nd week of menstrual cycle.