Evaluation of pathological complete response after neoadjuvant systemic treatment of invasive breast cancer using diffusion-weighted imaging compared with dynamic contrast-enhanced based kinetic analysis.

PURPOSE: This study compared the performance of diffusion-weighted imaging (DWI) to dynamic contrast-enhanced (DCE)-MRI for diagnosing pathological complete response (pCR) before surgery. METHOD: Overall, 133 lesions from 133 patients who underwent pre-surgical MRI evaluation after neoadjuvant systemic treatment were included. Two readers blinded to the pathological diagnosis evaluated the images. MR images were obtained using a routine protocol sequence that included DWI and DCE-MRI. DWI of the target lesion was scored using a three-point scale. Kinetic patterns of lesions on DCE-MRI were scored using a four-point scale. The capacities of DWI and kinetic parameters for discriminating pCR and non-pCR were assessed via receiver operating characteristic (ROC) analysis. RESULTS: For DWI scores, ROC analysis showed areas under the ROC curve (AUCs) of 0.84 (95% confidence interval: 0.77-0.90) and 0.85 (0.77-0.90) for readers 1 and 2, respectively; corresponding AUCs of kinetic scores were 0.89 (0.82-0.94) and 0.88 (0.81-0.93). Among the triple-negative subtype, the AUCs of DWI scores were 0.84 (0.70-0.93) and 0.88 (0.75-0.96) for readers 1 and 2, respectively; corresponding AUCs of kinetic scores were 0.94 (0.83-0.99) and 0.93 (0.82-0.99). Among the luminal subtype, the AUCs of DWI scores were 0.85 (0.71-0.94) and 0.82 (0.68-0.92) for readers 1 and 2, respectively; corresponding AUCs of kinetic scores were 0.82 (0.68-0.92) and 0.72 (0.56-0.85). CONCLUSIONS: Our DWI-based visual score and kinetic score showed similar diagnostic performances. Both DWI and kinetic scores tended to perform better in predicting pCR for the triple-negative subtype.

The Rate of Apparent Diffusion Coefficient Change With Diffusion Time on Breast Diffusion-Weighted Imaging Depends on Breast Tumor Types and Molecular Prognostic Biomarker Expression.

INTRODUCTION: The aim of this study was to investigate the variation of apparent diffusion coefficient (ADC) values with diffusion time according to breast tumor type and prognostic biomarkers expression. MATERIALS AND METHODS: A total of 201 patients with known or suspected breast tumors were prospectively enrolled in this study, and 132 breast tumors (86 malignant and 46 benign) were analyzed. Diffusion-weighted imaging scans with 2 diffusion times were acquired on a clinical 3-T magnetic resonance imaging scanner using oscillating and pulsed diffusion-encoding gradients (effective diffusion times, 4.7 and 96.6 milliseconds) and b values of 0 and 700 s/mm2. Diagnostic performances to differentiate malignant and benign breast tumors for ADC values at short and long diffusion times (ADCshort and ADClong), ΔADC (the rate of change in ADC values with diffusion time), ADC0-1000 (ADC value from a standard protocol), and standard reading including dynamic contrast-enhanced magnetic resonance imaging (BI-RADS) were investigated. The correlations of ADCshort, ADClong, and ΔADC values with hormone receptor expression and breast cancer subtypes were also analyzed. RESULTS: The ADC values were lower, and ΔADC was higher in malignant tumors compared with benign tumors. The specificity of ADC values at all diffusion times and ΔADC values for differentiating malignant and benign breast tumors was superior to that of BI-RADS (87.0%-95.7% vs 73.9%), whereas the sensitivity was inferior (87.2%-90.7% vs 100%). Lower ADCshort and ADC0-1000 in ER-positive compared with ER-negative cancers (false discovery rate [FDR]-adjusted P = 0.037 and 0.018, respectively) and lower ADCshort, ADClong, and ADC0-1000 in progesterone receptor-positive compared with progesterone receptor-negative cancers (FDR-adjusted P = 0.037, 0.036, and 0.018, respectively) were found. Ki-67-positive cancers had larger ΔADCs than Ki-67-negative cancers (FDR-adjusted P = 0.018). CONCLUSIONS: The ADC values vary with different diffusion time and vary in correlation with molecular biomarkers, especially Ki-67. Those results suggest that the diffusion time, which should be reported, might be a useful parameter to consider for breast cancer management.

Diffusion MRI of the breast: Current status and future directions.

Diffusion-weighted imaging (DWI) is increasingly being incorporated into routine breast MRI protocols in many institutions worldwide, and there are abundant breast DWI indications ranging from lesion detection and distinguishing malignant from benign tumors to assessing prognostic biomarkers of breast cancer and predicting treatment response. DWI has the potential to serve as a noncontrast MR screening method. Beyond apparent diffusion coefficient (ADC) mapping, which is a commonly used quantitative DWI measure, advanced DWI models such as intravoxel incoherent motion (IVIM), non-Gaussian diffusion MRI, and diffusion tensor imaging (DTI) are extensively exploited in this field, allowing the characterization of tissue perfusion and architecture and improving diagnostic accuracy without the use of contrast agents. This review will give a summary of the clinical literature along with future directions. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:70-90.

Efficiency and reproducibility in pulmonary nodule detection in simulated dose reduction lung CT images.

PURPOSE: To determine the reproducibility and productivity of reduced dose chest computed tomography (CT) using a nodule detection task. MATERIALS AND METHODS: Eighty-eight consecutive non-contrast CT examinations were performed using an automatic exposure system with a reference standard deviation of 8.5. Simulated raw data of a reduced dose scan (standard deviation at 21 and 29) were generated with a dose simulator. Original and simulated raw data were reconstructed to series of 7-mm-thick images (Original, Simulation A, Simulation B). In the first part of the reading experiment, three readers independently interpreted these images (88 cases × 3 series) and recorded the size, type, and location of the pulmonary nodules. The reading time for every case was recorded. In the second part of the experiment, the repeated interpretation of standard dose images was performed by two readers. Concordance or discordance of nodule detection between the first and the repeated reading result was assessed. RESULTS: A statistically significant difference in the detected nodule counts for lesions less than 5 mm by one reader was observed in simulation B images. Discordance of the interpretation result was found only in ground-glass nodules larger than 5 mm detected by one reader in simulation B images. There was no statistically significant difference in the reading time among the three image types. CONCLUSION: Simulated standard deviation 21 images can reproduce the image interpretation result of original images, whereas simulated standard deviation 29 images may compromise the accuracy of nodule assessment. The effect on the reading time was not observed with dose reduction simulation.