Utility of Second-look Ultrasonography in Distinguishing BI-RADS 4 Calcifications Detected on Mammography: An observational study.

This study aimed to assess the utility of second-look ultrasonography (US) in differentiating breast imaging reporting and data system (BI-RADS) 4 calcifications initially detected on mammography (MG). BI-RADS 4 calcifications have a wide range of positive predictive values. We hypothesized that second-look US would help distinguish BI-RADS 4 calcifications without clinical manifestations and other abnormalities on MG. This study included 1622 pure BI-RADS 4 calcifications in 1510 women (112 patients with bilateral calcifications). The cases were randomly divided into training (85%) and testing (15%) datasets. Two nomograms were developed to differentiate BI-RADS 4 calcifications in the training dataset: the MG-US nomogram, based on multifactorial logistic regression and incorporated clinical information, MG, and second-look US characteristics, and the MG nomogram, based on clinical information and mammographic characteristics. Calibration of the MG-US nomogram was performed using calibration curves. The discriminative ability and clinical utility of both nomograms were compared using the area under the receiver operating characteristic curve (AUC) and the decision analysis curve (DCA) in the test dataset. The clinical information and imaging characteristics were comparable between the training and test datasets. The bias-corrected calibration curves of the MG-US nomogram closely approximate the ideal line for both datasets. In the test dataset, the MG-US nomogram exhibited a higher AUC than the MG nomogram (0.899 vs 0.852, P = .01). DCA demonstrated the superiority of the MG-US nomogram over the MG nomogram. Second-look US features, including ultrasonic calcifications, lesions, and moderate or marked color flow, were valuable for distinguishing BI-RADS 4 calcifications without clinical manifestations and other abnormalities on MG.

Breast Cancer: Multi-b-Value Diffusion Weighted Habitat Imaging in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy.

RATIONALE AND OBJECTIVES: The aim of this study was to ascertain whether the utilization of multiple b-value diffusion-weighted habitat imaging, a technique that depicts tumor heterogeneity, could aid in identifying breast cancer patients who would derive substantial benefit from neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS: This prospective study enrolled 143 women (II-III breast cancer), who underwent multi-b-value diffusion-weighted imaging (DWI) in 3-T magnetic resonance (MR) before NAC. The patient cohort was partitioned into a training set (consisting of 100 patients, of which 36 demonstrated a pathologic complete response [pCR]) and a test set (featuring 43 patients, 16 of whom exhibited pCR). Utilizing the training set, predictive models for pCR, were constructed using different parameters: whole-tumor radiomics (ModelWH), diffusion-weighted habitat-imaging (ModelHabitats), conventional MRI features (ModelCF), along with combined models ModelHabitats+CF. The performance of these models was assessed based on the area under the receiver operating characteristic curve (AUC) and calibration slope. RESULTS: In the prediction of pCR, ModelWH, ModelHabitats, ModelCF, and ModelHabitats+CF achieved AUCs of 0.733, 0.722, 0.705, and 0.756 respectively, within the training set. These scores corresponded to AUCs of 0.625, 0.801, 0.700, and 0.824 respectively in the test set. The DeLong test revealed no significant difference between ModelWH and ModelHabitats (P = 0.182), between ModelHabitats and ModelHabitats+CF (P = 0.113). CONCLUSION: The habitat model we developed, incorporating first-order features along with conventional MRI features, has demonstrated accurate predication of pCR prior to NAC. This model holds the potential to augment decision-making processes in personalized treatment strategies for breast cancer.

Nomogram based on quantitative dynamic contrast-enhanced magnetic resonance imaging, apparent diffusion coefficient, and clinicopathological features for early prediction of pathologic complete response in breast cancer patients receiving neoadjuvant chemotherapy.

Background: The aim of this study was to develop two nomograms for predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT) for breast cancer based on quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), apparent diffusion coefficient (ADC), and clinicopathological characteristics at two time-points: before and after two cycles of NACT, respectively. Methods: 3.0 T MRI scans were performed before and after 2 cycles of NACT in 215 patients. A total of 74 female patients with stage II-III breast cancer were included. According to univariate and multivariate logistic regression analysis, nomogram model 1 and nomogram model 2 were developed based on the independent predictors for pCR before and after 2 cycles of NACT, respectively. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC) and calibration slope. Results: The independent predictors of pCR were different at the two time points. Both nomograms were found to effectively predict pCR: nomogram model 2 based on Ki67, ΔKtrans%, and ΔADC% after 2 cycles of NACT showed better predictive discrimination [AUC =0.900 (0.829, 0.970) vs. 0.833 (0.736, 0.930)] and calibration ability (mean absolute error of the agreement: 0.017 vs. 0.051) compared to nomogram model 1 based on pre-NACT HER2, Ki67, and Ktrans. Conclusions: Nomograms based on quantitative DCE-MRI parameters, ADC, and clinicopathological characteristics can predict pCR in breast cancer and facilitate individualized decision-making for NACT.

Application of Diffusion Weighted Imaging Techniques for Differentiating Benign and Malignant Breast Lesions.

To explore the value of apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM), and diffusional kurtosis imaging (DKI) based on diffusion weighted magnetic resonance imaging (DW-MRI) in differentiating benign and malignant breast lesions. A total of 215 patients with breast lesions were prospectively collected for breast MR examination. Single exponential, IVIM, and DKI models were calculated using a series of b values. Parameters including ADC, perfusion fraction (f), tissue diffusion coefficient (D), perfusion-related incoherent microcirculation (D*), average kurtosis (MK), and average diffusivity (MD) were compared between benign and malignant lesions. ROC curves were used to analyze the optimal diagnostic threshold of each parameter, and to evaluate the diagnostic efficacy of single and combined parameters. ADC, D, MK, and MD values were significantly different between benign and malignant breast lesions (P<0.001). Among the single parameters, ADC had the highest diagnostic efficiency (sensitivity 91.45%, specificity 82.54%, accuracy 88.84%, AUC 0.915) and the best diagnostic threshold (0.983 µm2/ms). The combination of ADC and MK offered high diagnostic performance (sensitivity 90.79%, specificity 85.71%, accuracy 89.30%, AUC 0.923), but no statistically significant difference in diagnostic performance as compared with single-parameter ADC (P=0.268). The ADC, D, MK, and MD parameters have high diagnostic value in differentiating benign and malignant breast lesions, and of these individual parameters the ADC has the best diagnostic performance. Therefore, our study revealed that the use of ADC alone should be useful for differentiating between benign and malignant breast lesions, whereas the combination of MK and ADC might improve the diagnostic performance to some extent.

Value of Dual-Energy Computed Tomography for Diagnosing Cervical Lymph Node Metastasis in Patients With Papillary Thyroid Cancer.

OBJECTIVE: The objective of this study was to determine the value of dual-energy computed tomography (DECT) for the diagnosis of cervical lymph node metastasis in papillary thyroid cancer. METHODS: The normalized iodine concentration (NIC) and the slope of the spectral Hounsfield unit curve (λHU) in the arterial and venous phases were measured using iodine-overlay images and spectral curves. Quantitative DECT data and qualitative conventional CT data were analyzed by radiologists. RESULTS: The best qualitative parameter for lymph node metastasis detection was obvious node enhancement, and the best quantitative parameter for detection was arterial-phase NIC, which showed high sensitivity, specificity, and accuracy values at an optimal threshold of 25.8%. The best combination of qualitative and quantitative parameters consisted of obvious enhancement and arterial-phase NIC; this combination showed a sensitivity of 90.8% and a specificity of 80.5%. CONCLUSIONS: The DECT quantitative parameters NIC and λHU can be an additional tool to diagnose cervical lymph node metastasis.