Naoko Mori1,2, Hiroyuki Abe3, Shunji Mugikura4,5, Minoru Miyashita6, Yu Mori7, Yo Oguma8, Minami Hirasawa4, Satoko Sato9, Kei Takase4. 1. Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, Seiryo 1-1, Sendai, 980-8574, Japan. naokomori7127@gmail.com. 2. Department of Radiology, The University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL, 60637, USA. naokomori7127@gmail.com. 3. Department of Radiology, The University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL, 60637, USA. 4. Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, Seiryo 1-1, Sendai, 980-8574, Japan. 5. Department of Image Statistics, Tohoku Medical Megabank Organization, Tohoku University, Seiryo 2-1, Sendai, 980-8574, Japan. 6. Department of Surgical Oncology, Tohoku University Graduate School of Medicine, Seiryo 1-1, Sendai, 980-8574, Japan. 7. Department of Orthopaedic Surgery, Tohoku University Graduate School of Medicine, Seiryo 1-1, Sendai, 980-8574, Japan. 8. Tohoku University School of Medicine, Seiryo 1-1, Sendai, 980-8574, Japan. 9. Department of Anatomic Pathology, Tohoku University Graduate School of Medicine, Seiryo 1-1, Sendai, 980-8574, Japan.
Abstract
PURPOSE: To investigate effective model composed of features from ultrafast dynamic contrast-enhanced magnetic resonance imaging (UF-MRI) for distinguishing low- from non-low-grade ductal carcinoma in situ (DCIS) lesions or DCIS lesions upgraded to invasive carcinoma (upgrade DCIS lesions) among lesions diagnosed as DCIS on pre-operative biopsy. MATERIALS AND METHODS: Eighty-six consecutive women with 86 DCIS lesions diagnosed by biopsy underwent UF-MRI including pre- and 18 post-contrast ultrafast scans (temporal resolution of 3 s/phase). The last phase of UF-MRI was used to perform 3D segmentation. The time point at 6 s after the aorta started to enhance was used to obtain subtracted images. From the 3D segmentation and subtracted images, enhancement, shape, and texture features were calculated and compared between low- and non-low-grade or upgrade DCIS lesions using univariate analysis. Feature selection by least absolute shrinkage and selection operator (LASSO) algorithm and k-fold cross-validation were performed to evaluate the diagnostic performance. RESULTS: Surgical specimens revealed 16 low-grade DCIS lesions, 37 non-low-grade lesions and 33 upgrade DCIS lesions. In univariate analysis, five shape and seven texture features were significantly different between low- and non-low-grade lesions or upgrade DCIS lesions, whereas enhancement features were not. The six features including surface/volume ratio, irregularity, diff variance, uniformity, sum average, and variance were selected using LASSO algorism and the mean area under the receiver operating characteristic curve for training and validation folds were 0.88 and 0.88, respectively. CONCLUSION: The model with shape and texture features of UF-MRI could effectively distinguish low- from non-low-grade or upgrade DCIS lesions.
PURPOSE: To investigate effective model composed of features from ultrafast dynamic contrast-enhanced magnetic resonance imaging (UF-MRI) for distinguishing low- from non-low-grade ductal carcinoma in situ (DCIS) lesions or DCIS lesions upgraded to invasive carcinoma (upgrade DCIS lesions) among lesions diagnosed as DCIS on pre-operative biopsy. MATERIALS AND METHODS: Eighty-six consecutive women with 86 DCIS lesions diagnosed by biopsy underwent UF-MRI including pre- and 18 post-contrast ultrafast scans (temporal resolution of 3 s/phase). The last phase of UF-MRI was used to perform 3D segmentation. The time point at 6 s after the aorta started to enhance was used to obtain subtracted images. From the 3D segmentation and subtracted images, enhancement, shape, and texture features were calculated and compared between low- and non-low-grade or upgrade DCIS lesions using univariate analysis. Feature selection by least absolute shrinkage and selection operator (LASSO) algorithm and k-fold cross-validation were performed to evaluate the diagnostic performance. RESULTS: Surgical specimens revealed 16 low-grade DCIS lesions, 37 non-low-grade lesions and 33 upgrade DCIS lesions. In univariate analysis, five shape and seven texture features were significantly different between low- and non-low-grade lesions or upgrade DCIS lesions, whereas enhancement features were not. The six features including surface/volume ratio, irregularity, diff variance, uniformity, sum average, and variance were selected using LASSO algorism and the mean area under the receiver operating characteristic curve for training and validation folds were 0.88 and 0.88, respectively. CONCLUSION: The model with shape and texture features of UF-MRI could effectively distinguish low- from non-low-grade or upgrade DCIS lesions.
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