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ObjectiveTo establish and validate a new clinical-radiomics nomogram based on the fat-suppressed T2 sequence for differentiating luminal and non-luminal breast cancer.MethodsA total of 593 breast cancer patients who underwent preoperative breast MRI from Jan 2017 to Dec 2020 were enrolled, which were randomly divided into the training (n=474) and test sets (n=119) at the ratio of 8:2. Intratumoral region (ITR) of interest were manually delineated, and peritumoral regions of 3 mm and 5 mm (PTR-3 mm and PTR-5 mm) were automatically obtained by dilating the ITR. Intratumoral and peritumoral radiomics features were extracted from the fat-suppressed T2-weighted images, including first-order statistical features, shape features, texture features, and filtered features. The Mann-Whitney U Test, Z score normalization, K-best method, and least absolute shrinkage and selection operator (LASSO) algorithm were applied to select key features to construct radscores based on ITR, PTR-3 mm, PTR-5 mm, ITR+PTR-3 mm and ITR+ PTR-5 mm. Risk factors were selected by univariate and multivariate logistic regressions and were used to construct a clinical model and a clinical-radiomics model that presented as a nomogram. The performance of models was assessed by sensitivity, specificity, accuracy, the area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves, and decision curve analysis (DCA).ResultsITR+PTR-3 mm radsore and histological grade were selected as risk factors. A clinical-radiomics model was constructed by adding ITR+PTR-3mm radscore to the clinical factor, which was presented as a nomogram. The clinical-radiomics nomogram showed the highest AUC (0.873), sensitivity (72.3%), specificity (78.9%) and accuracy (77.0%) in the training set and the highest AUC (0.851), sensitivity (71.4%), specificity (79.8%) and accuracy (77.3%) in the test set. DCA showed that the clinical-radiomics nomogram had the greatest net clinical benefit compared to the other models.ConclusionThe clinical-radiomics nomogram showed promising clinical application value in differentiating luminal and non-luminal breast cancer.

Purpose: To investigate the potential of radiomics signatures (RSs) from intratumoral and peritumoral regions on multiparametric magnetic resonance imaging (MRI) to noninvasively evaluate HER2 status in breast cancer. Method: In this retrospective study, 992 patients with pathologically confirmed breast cancers who underwent preoperative MRI were enrolled. The breast cancer lesions were segmented manually, and the intratumor region of interest (ROIIntra) was dilated by 2, 4, 6 and 8 mm (ROIPeri2mm, ROIPeri4mm, ROIPeri6mm, and ROIPeri8mm, respectively). Quantitative radiomics features were extracted from dynamic contrast-enhanced T1-weighted imaging (DCE-T1), fat‐saturated T2‐weighted imaging (T2) and diffusion-weighted imaging (DWI). A three-step procedure was performed for feature selection, and RSs were constructed using a support vector machine (SVM) to predict HER2 status. Result: The best single-area RSs for predicting HER2 status were DCE_Peri4mm-RS, T2_Peri4mm-RS, and DWI_Peri4mm-RS, yielding areas under the curve (AUCs) of 0.716 (95% confidence interval (CI), 0.648–0.778), 0.706 (95% CI, 0.637–0.768), and 0.719 (95% CI, 0.651–0.780), respectively, in the test set. The optimal RSs combining intratumoral and peritumoral regions for evaluating HER2 status were DCE-T1_Intra + DCE_Peri4mm-RS, T2_Intra + T2_Peri6mm-RS and DWI_Intra + DWI_Peri4mm-RS, with AUCs of 0.752 (95% CI, 0.686–0.810), 0.754 (95% CI, 0.688–0.812) and 0.725 (95% CI, 0.657–0.786), respectively, in the test set. Combining three sequences in the ROIIntra, ROIPeri2mm, ROIPeri4mm, ROIPeri6mm and ROIPeri8mm areas, the optimal RS was DCE-T1_Peri4mm + T2_Peri4mm + DWI_Peri4mm-RS, achieving an AUC of 0.795 (95% CI, 0.733–0.849) in the test set. Conclusion: This study systematically explored the influence of the intratumoral region, different peritumoral sizes and their combination in radiomics analysis for predicting HER2 status in breast cancer based on multiparametric MRI and found the optimal RS.

ObjectiveTo investigate the value of predicting axillary lymph node (ALN) metastasis based on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinico-radiological characteristics in breast cancer.MethodsA total of 473 breast cancer patients who underwent preoperative DCE-MRI from Jan 2017 to Dec 2020 were enrolled. These patients were randomly divided into training (n=378) and testing sets (n=95) at 8:2 ratio. Intratumoral regions (ITRs) of interest were manually delineated, and peritumoral regions of 3 mm (3 mmPTRs) were automatically obtained by morphologically dilating the ITR. Radiomics features were extracted, and ALN metastasis-related radiomics features were selected by the Mann-Whitney U test, Z score normalization, variance thresholding, K-best algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. Clinico-radiological risk factors were selected by logistic regression and were also used to construct predictive models combined with radiomics features. Then, 5 models were constructed, including ITR, 3 mmPTR, ITR+3 mmPTR, clinico-radiological and combined (ITR+3 mmPTR+ clinico-radiological) models. The performance of models was assessed by sensitivity, specificity, accuracy, F1 score and area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves and decision curve analysis (DCA).ResultsA total of 2264 radiomics features were extracted from each region of interest (ROI), 3 and 10 radiomics features were selected for the ITR and 3 mmPTR, respectively. 5 clinico-radiological risk factors were selected, including lesion size, human epidermal growth factor receptor 2 (HER2) expression, vascular cancer thrombus status, MR-reported ALN status, and time-signal intensity curve (TIC) type. In the testing set, the combined model showed the highest AUC (0.839), specificity (74.2%), accuracy (75.8%) and F1 Score (69.3%) among the 5 models. DCA showed that it had the greatest net clinical benefit compared to the other models.ConclusionThe intra- and peritumoral radiomics models based on DCE-MRI could be used to predict ALN metastasis in breast cancer, especially for the combined model with clinico-radiological characteristics showing promising clinical application value.

Breast tumor segmentation plays a crucial role in subsequent disease diagnosis, and most algorithms need interactive prior to firstly locate tumors and perform segmentation based on tumor-centric candidates. In this paper, we propose a fully convolutional network to achieve automatic segmentation of breast tumor in an end-to-end manner. Considering the diversity of shape and size for malignant tumors in the digital mammograms, we introduce multiscale image information into the fully convolutional dense network architecture to improve the segmentation precision. Multiple sampling rates of atrous convolution are concatenated to acquire different field-of-views of image features without adding additional number of parameters to avoid over fitting. Weighted loss function is also employed during training according to the proportion of the tumor pixels in the entire image, in order to weaken unbalanced classes problem. Qualitative and quantitative comparisons demonstrate that the proposed algorithm can achieve automatic tumor segmentation and has high segmentation precision for various size and shapes of tumor images without preprocessing and postprocessing. [ABSTRACT FROM AUTHOR]

The aim of the study was to investigate the value of sequential application of molybdenum target X-ray, multislice spiral computed tomography (MSCT) and magnetic resonance imaging (MRI) in the preoperative evaluation of breast-conserving surgeries. In total, 76 patients with indications for breast-conserving surgery due to complicated breast cancer participated in the study and were assigned to either control or observation group (n=38 per group). The patients in the control group were evaluated with two sets of random combinations of molybdenum target X-ray, MSCT or MRI with ultrasound inspection, whereas the patients in the observation group were evaluated by sequential inspection methods of molybdenum target X-ray, MSCT and MRI. A comparison of surgery outcomes, incidence of complications, rate of positive surgical margins, and recurrence and survival rates in the groups during a follow-up period of 24 months was made. Comparisons of the preoperative evaluation results for tumor number, average maximum diameter, number of lymphatic metastatic groups and number of metastatic lymph nodes in the observation group showed the numbers to be significantly higher than those in the control group (P<0.05). Conversely, the comparisons of age, tumor distribution and T-staging yielded no significant differences, validating the analysis. The percentage of successful breast-conserving surgeries in the observation group was significantly higher than that in the control group, while the incidence of complications in the observation group was lower (P<0.05). The rate of positive surgical margins and the recurrence rate of cancer in the observation group were lower than those in the control group, and the survival rate in the observation group was higher, with differences having statistical significance (P<0.05). In conclusion, the sequential application of molybdenum target X-ray, MSCT and MRI during the preoperative evaluation for breast-conserving surgery positively affects the success rate of the procedure improving the diagnostic accuracy and therapeutic effects. [ABSTRACT FROM AUTHOR]