Quantitative classification of invasive and noninvasive breast cancer using dynamic magnetic resonance imaging of the mammary gland.

Objectives: Breast cancers are classified as invasive or noninvasive based on histopathological findings. Although time-intensity curve (TIC) analysis using magnetic resonance imaging (MRI) can differentiate benign from malignant disease, its diagnostic ability to quantitatively distinguish between invasive and noninvasive breast cancers has not been determined. In this study, we evaluated the ability of TIC analysis of dynamic MRI data (MRI-TIC) to distinguish between invasive and noninvasive breast cancers. Material and Methods: We collected and analyzed data for 429 cases of epithelial invasive and noninvasive breast carcinomas. TIC features were extracted in washout areas suggestive of malignancy. Results: The graph determining the positive diagnosis rate for invasive and noninvasive cases revealed that the cut-off θi/ni value was 21.6° (invasive: θw > 21.6°, noninvasive: θw ≤ 21.6°). Tissues were classified as invasive or noninvasive using this cut-off value, and each result was compared with the histopathological diagnosis. Using this method, the accuracy of tissue classification by MRI-TIC was 88.6% (380/429), which was higher than that using ultrasound (73.4%, 315/429). Conclusion: MRI-TIC is effective for the classification of invasive vs. noninvasive breast cancer.

Validity of the γ-Ray Evaluation with iodoamphetamine for Cerebral Blood Flow Assessment (REICA) method for quantification of cerebral blood flow including acetazolamide challenge test.

OBJECTIVE: The γ-Ray Evaluation with iodoamphetamine for Cerebral Blood Flow Assessment (REICA) is a new method for quantifying cerebral blood flow (CBF) using single-photon emission computed tomography (SPECT) and [123I]N-isopropyl-p-iodoamphetamine (123I-IMP). The present study aimed to validate the REICA method using data including acetazolamide challenge test. METHODS: The REICA and Graph-Plot (GP) methods were used to calculate mean CBF (mCBF) for 92 acquisitions (rest: 57, stress: 35) and cerebrovascular reactivity (CVR) in 33 patients. To obtain stress data, 15 mg/kg of acetazolamide was injected intravenously 10 min before the administration of 123I-IMP, and blood samples were collected under the same conditions as rest data. The reference standard was the Autoradiograph (ARG) method using arterial blood sampling, and the accuracy of the REICA method was analyzed by comparing it with each method. RESULTS: For mCBF, the correlation coefficients (r) were 0.792 for the REICA method and 0.636 for the GP method. For CVR, r values were 0.660 for the REICA method and 0.578 for the GP method. In both acquisitions, the REICA method had a stronger correlation with the ARG method than the GP method. For mCBF, there was a significant difference in the correlation coefficient between the two correlation coefficients (p < 0.01). CONCLUSIONS: The REICA method was more accurate than the GP method in quantifying CBF and closer to the ARG method. The REICA method, which is a noninvasive method of cerebral blood flow quantification using 123I-IMP, has great medical usefulness.

Utility of Tissue Classification in Invasive Ductal Carcinoma using Dynamic Magnetic Resonance Imaging of the Mammary Gland.

OBJECTIVES: In Japan, invasive ductal carcinomas, which account for 75% of breast cancer cases, are sub-classified as solid, tubule-forming, scirrhous, and other types based on the histopathological findings. Although time-intensity curve (TIC) analysis of magnetic resonance (MR) images has shown diagnostic ability in differentiating benign and malignant tumors, its ability to diagnose different tumor tissue types has not yet been achieved. In this study, we report a histological classification of invasive ductal carcinoma using the TIC analysis of dynamic MR images of the mammary gland. MATERIAL AND METHODS: A total of 312 invasive ductal carcinomas were analyzed, and each tissue type that indicated malignancy in the washout parts of the tumors was classified and characterized using the TIC. RESULTS: The tissue was classified, and the results were then compared to the pathohistological diagnosis. Using this method, the accuracy of tissue classification by quantitative analysis of TIC-MR images was 86.9% (271/312), which was higher than that obtained by ultrasonography 68.9% (215/312). CONCLUSION: This method is effective for classifying tissue types in invasive ductal carcinoma.

[Development of a Computer-aided Diagnosis System to Distinguish between Benign and Malignant Mammary Tumors in Dynamic Magnetic Resonance Images: Automatic Detection of the Position with the Strongest Washout Effect in the Tumor].

We propose a computer-aided diagnostic (CAD) system that uses time-intensity curves to distinguish between benign and malignant mammary tumors. Many malignant tumors show a washout pattern in time-intensity curves. Therefore, we designed a program that automatically detects the position with the strongest washout effect using the technique, such as the subtraction technique, which extracts only the washout area in the tumor, and by scanning data in 2×2 pixel region of interest (ROI). Operation of this independently developed program was verified using a phantom system that simulated tumors. In three cases of malignant tumors, the washout pattern detection rate in images with manually set ROI was ≤6%, whereas the detection rate with our novel method was 100%. In one case of a benign tumor, when the same method was used, we checked that there was no washout effect and detected the persistent pattern. Thus, the distinction between benign and malignant tumors using our method was completely consistent with the pathological diagnoses made. Our novel method is therefore effective for differentiating between benign and malignant mammary tumors in dynamic magnetic resonance images.