Using the apparent diffusion coefficient histogram analysis to predict response to neoadjuvant chemotherapy in patients with breast cancer: comparison among three region of interest selection methods.

Background: The apparent diffusion coefficient (ADC) value using histogram analysis is helpful to predict responses to neoadjuvant chemotherapy (NAC) in breast cancer. However, the measurement method has not reached a consensus. This study was to assess the diagnostic performance of the ADC histogram analysis at predicting patient response prior to NAC in breast cancer patients using different region of interest (ROI) selection methods. Methods: A total of 75 patients who underwent diffusion weighted imaging (DWI) prior to NAC were retrospectively enrolled from February 2017 to December 2019. Images were measured using small 2-dimensional (2D) ROI, large 2D ROI, and volume ROI methods. The measurement time and ROI size were recorded. Histopathologic responses were acquired using the Miller-Payne grading system after surgery. The inter- and intra-observer repeatability was analyzed and the ADC histogram values from the three ROI methods were compared. The efficacy of each method at predicting patient response prior to NAC was assessed using the area under the receiver operating characteristic curve (AUC) for the whole study population and subgroups according to molecular subtype. Results: Among the 75 enrolled patients, 26 (34.67%) were responsive to NAC therapy. The ADC histogram values were significantly different among the three ROI methods (P≤0.038). Inter- and intra-observer repeatability of the large 2D ROI method and the volume ROI method was generally greater than that observed with the 2D ROI method. The 10% ADC value of the large 2D ROI method showed the greatest AUC (0.701) in the whole study population and in the luminal subgroup (AUC 0.804). The volume ROI method required significantly more time than the other two ROI methods (P<0.001). Conclusions: The small 2D ROI method is not appropriate for predicting response prior to NAC in breast cancer patients due to the poor repeatability. When choosing the ROI method and the histogram parameters for predicting response prior to NAC in breast cancer patients using ADC-derived histogram analysis, 10% of the large 2D ROI method is recommended, especially in luminal A subtype patients.

Positive Predictive Value of Mammographic Lymphography in the Evaluation of Patients with Breast Cancer: A Preliminary Study.

RATIONALE AND OBJECTIVES: The study aimed to investigate the positive predictive value (PPV) of mammographic lymphography (MLG) for assessing malignant breast disease and lymphatic metastasis in patients in a typical clinical setting. MATERIALS AND METHODS: Patients who underwent mammography with Breast Imaging Reporting and Data System (BI-RADS) category 4 or 5 lesions and had abnormal mammographic findings in the upper-outer quadrant of the breast were enrolled. Next, MLG was performed. A water-soluble agent was subcutaneously injected into the upper-outer periareolar region of the bilateral breast, and mammography was then performed. Morphologic characteristics, including lymphatic vessel development, the presence of lymphatic vessel defects, and dilation, were recorded for evaluation. RESULTS: Fifty-one patients with BI-RADS category 4 lesions and 40 patients with BI-RADS category 5 lesions were included in the study. Sixty-one patients were found to have malignant disease, whereas 30 patients were found to have benign disease. Morphologic characteristics were recorded for evaluation. The interobserver agreement was evaluated and was classified as excellent according to kappa analysis. The PPV of MLG characteristics for malignant breast disease and lymphatic metastasis was analyzed by logistic regression, and the presentation of a lymphatic vessel defect was the most predictive characteristic of a malignancy (PPV: 0.89; P value: 0.02) in patients with BI-RADS category 4 lesions. Meanwhile, in patients with malignant breast disease, the PPVs for predicting lymphatic metastasis with lymphatic vessel defect and dilation were 0.50 (P value: 0.02) and 0.67 (P value: <0.01), respectively. CONCLUSION: The assessment of morphologic characteristics by MLG has the potential to predict malignant breast disease and lymphatic metastasis.

Dual Energy Spectral CT Imaging for Colorectal Cancer Grading: A Preliminary Study.

OBJECTIVES: To assess the diagnostic value of dual energy spectral CT imaging for colorectal cancer grading using the quantitative iodine density measurements in both arterial phase (AP) and venous phase (VP). METHODS: 81 colorectal cancer patients were divided into two groups based on their pathological findings: a low grade group including well (n = 13) and moderately differentiated cancer (n = 24), and a high grade group including poorly differentiated (n = 42) and signet ring cell cancer (n = 2). Iodine density (ID) in the lesions was derived from the iodine-based material decomposition (MD) image and normalized to that in the psoas muscle to obtain normalized iodine density (NID). The difference in ID and NID between AP and VP was calculated. RESULTS: The ID and NID values of the low grade cancer group were, 14.65 ± 3.38 mg/mL and 1.70 ± 0.33 in AP, and 21.90 ± 3.11 mg/mL and 2.05 ± 0.32 in VP, respectively. The ID and NID values for the high grade cancer group were 20.63 ± 3.72 mg/mL and 2.95 ± 0.72 in AP, and 26.27 ± 3.10mg/mL and 3.51 ± 1.12 in VP, respectively. There was significant difference for ID and NID between the low grade and high grade cancer groups in both AP and VP (all p<0.001). ROC analysis indicated that NID of 1.92 in AP provided 70.3% sensitivity and 97.7% specificity in differentiating low grade cancer from high grade cancer. CONCLUSIONS: The quantitative measurement of iodine density in AP and VP can provide useful information to differentiate low grade colorectal cancer from high grade colorectal cancer with NID in AP providing the greatest diagnostic value.

Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient.

PURPOSE: To investigate the utility of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in capturing breast lesion heterogeneity and determine which ADC metric may help best differentiate benign from malignant breast mass lesions at 3.0T magnetic resonance imaging (MRI). MATERIALS AND METHODS: We retrospectively included 101 women with breast mass lesions (benign:malignant = 36:65) who underwent 3.0T diffusion-weighted imaging (DWI) and subsequently had histopathologic confirmation. ADC histogram parameters, including the mean, minimum, maximum, 10th/25th/50th/75th/90th percentile, skewness, kurtosis, and entropy ADCs, were derived for the whole-lesion volume in each patient. Mann-Whitney U-test, univariate and multivariate logistic regression, area under the receiver-operating characteristic curve (Az ), intraclass correlation coefficient (ICC), and Bland-Altman test were used for statistical analysis. RESULTS: Mean, minimum, maximum, and 10th/25th/50th/75th/90th percentile ADCs were significantly lower (all P < 0.0001), while skewness and entropy ADCs were significantly higher (P < 0.001 and P = 0.001, respectively) in malignant lesions compared with benign ones. The Az values of minimum and 25th percentile ADCs were significantly higher than that of mean ADC (P = 0.0194 and P = 0.0154, respectively) or that of median ADC (P = 0.0300 and P = 0.0401, respectively), indicating that minimum and 25th percentile ADCs may be more accurate for lesion discrimination. Multivariate logistic regression showed that the minimum ADC was the unique independent predictor of breast malignancy. Minimum and 25th percentile ADCs had excellent interobserver agreement (ICC = 0.943 and 0.989, respectively; narrow width of 95% limits of agreement). CONCLUSION: These results suggest that whole-lesion ADC histogram analysis may facilitate the differentiation between benign and malignant breast mass lesions.