Purpose To evaluate the associations among mathematical modeling with the use of magnetic resonance (MR) imaging-based texture features and deep myometrial invasion (DMI), lymphovascular space invasion (LVSI), and histologic high-grade endometrial carcinoma. Materials and Methods Institutional review board approval was obtained for this retrospective study. This study included 137 women with endometrial carcinomas measuring greater than 1 cm in maximal diameter who underwent 1.5-T MR imaging before hysterectomy between January 2011 and December 2015. Texture analysis was performed with commercial research software with manual delineation of a region of interest around the tumor on MR images (T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced images and apparent diffusion coefficient maps). Areas under the receiver operating characteristic curve and diagnostic performance of random forest models determined by using a subset of the most relevant texture features were estimated and compared with those of independent and blinded visual assessments by three subspecialty radiologists. Results A total of 180 texture features were extracted and ultimately limited to 11 features for DMI, 12 for LVSI, and 16 for high-grade tumor for random forest modeling. With random forest models, areas under the receiver operating characteristic curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were estimated at 0.84, 79.3%, 82.3%, 81.0%, 76.7%, and 84.4% for DMI; 0.80, 80.9%, 72.5%, 76.6%, 74.3%, and 79.4% for LVSI; and 0.83, 81.0%, 76.8%, 78.1%, 60.7%, and 90.1% for high-grade tumor, respectively. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of visual assessment for DMI were 84.5%, 82.3%, 83.2%, 77.7%, and 87.8% (reader 3). Conclusion The mathematical models that incorporated MR imaging-based texture features were associated with the presence of DMI, LVSI, and high-grade tumor and achieved equivalent accuracy to that of subspecialty radiologists for assessment of DMI in endometrial cancers larger than 1 cm. However, these preliminary results must be interpreted with caution until they are validated with an independent data set, because the small sample size relative to the number of features extracted may have resulted in overfitting of the models. RSNA, 2017 Online supplemental material is available for this article.

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Purpose To evaluate whether features from texture analysis of breast cancers were associated with pathologic complete response (pCR) after neoadjuvant chemotherapy and to explore the association between texture features and tumor subtypes at pretreatment magnetic resonance (MR) imaging. Materials and Methods Institutional review board approval was obtained. This retrospective study included 85 patients with 85 breast cancers who underwent breast MR imaging before neoadjuvant chemotherapy between April 10, 2008, and March 12, 2015. Two-dimensional texture analysis was performed by using software at T2-weighted MR imaging and contrast material-enhanced T1-weighted MR imaging. Quantitative parameters were compared between patients with pCR and those with non-pCR and between patients with triple-negative breast cancer and those with non-triple-negative cancer. Multiple logistic regression analysis was used to determine independent parameters. Results Eighteen tumors (22%) were triple-negative breast cancers. pCR was achieved in 30 of the 85 tumors (35%). At univariate analysis, mean pixel intensity with spatial scaling factor (SSF) of 2 and 4 on T2-weighted images and kurtosis on contrast-enhanced T1-weighted images showed a significant difference between triple-negative breast cancer and non-triple-negative breast cancer (P = .009, .003, and .001, respectively). Kurtosis (SSF, 2) on T2-weighted images showed a significant difference between pCR and non-pCR (P = .015). At multiple logistic regression, kurtosis on T2-weighted images was independently associated with pCR in non-triple-negative breast cancer (P = .033). A multivariate model incorporating T2-weighted and contrast-enhanced T1-weighted kurtosis showed good performance for the identification of triple-negative breast cancer (area under the receiver operating characteristic curve, 0.834). Conclusion At pretreatment MR imaging, kurtosis appears to be associated with pCR to neoadjuvant chemotherapy in non-triple-negative breast cancer and may be a promising biomarker for the identification of triple-negative breast cancer. RSNA, 2017.

).q RSNA, 2015 Purpose:To prospectively evaluate the capability of amide proton transfer-weighted chemical exchange saturation transfer magnetic resonance (MR) imaging for characterization of thoracic lesions. Materials and Methods:The institutional review board approved this study, and written informed consent was obtained from 21 patients (13 men and eight women; mean age, 72 years) prior to enrollment. Each patient underwent chemical exchange saturation transfer MR imaging by using respiratorysynchronized half-Fourier fast spin-echo imaging after a series of magnetization transfer pulses. Next, a magnetization transfer ratio asymmetry at 3.5 ppm map was computationally generated. Pathology examinations resulted in a diagnosis of 13 malignant and eight benign thoracic lesions. The malignant lesions were further diagnosed as being nine lung cancers, comprising six adenocarcinomas, three squamous cell carcinomas, and four other thoracic malignancies. The Student t test was used to evaluate the capability of magnetization transfer ratio asymmetry (at 3.5 ppm), as assessed by means of region of interest measurements, for differentiating benign and malignant lesions, lung cancers and other thoracic lesions, and adenocarcinomas and squamous cell carcinomas. Results:Magnetization transfer ratio asymmetry (at 3.5 ppm) was significantly higher for malignant tumors (mean 6 standard deviation, 3.56% 6 3.01) than for benign lesions (0.33% 6 0.38, P = .008). It was also significantly higher for other thoracic malignancies (6.71% 6 3.46) than for lung cancer (2.16% 6 1.41, P = .005) and for adenocarcinoma (2.88% 6 1.13) than for squamous cell carcinoma (0.71% 6 0.17, P = .02). Conclusion:Amide proton transfer-weighted chemical exchange saturation transfer MR imaging allows characterization of thoracic lesions.q RSNA, 2015

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cDWI2000 appears to be more effective than mDWI1000, and at least as effective as mDWI2000 for PCa diagnosis.

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Purpose To determine, with histopathological findings of radical prostatectomy as reference, whether diffusion‐weighted imaging (DWI) using b = 2000 s/mm2 for 3‐T magnetic resonance imaging (MRI) is superior to the use of b = 1000 s/mm2 for prostate cancer detection. Materials and Methods This study evaluated 73 patients with biopsy‐proven prostate cancer. All patients underwent preoperative 3‐T MRI using T2‐weighted imaging (T2WI) and DWI (b = 0, 1000, 2000 s/mm2). The following three sets of images were evaluated separately by two radiologists: protocol A (T2WI alone), protocol B (T2WI and DWI with b = 1000 s/mm2), and protocol C (T2WI and DWI with b = 2000 s/mm2). For estimation of diagnostic capability, area under the receiver operating characteristic (ROC) curve (AUC) was calculated. Results A total of 341 cancer foci were found in the prostectomy specimens of 73 patients. Reader 1 rated AUCs of the three sets as; A 0.66, B 0.77, C 0.80. ROC analysis showed significant differences among the three protocols (A vs. B vs. C: P < 0.0001, A vs. B: P < 0.0001, B vs. C: P < 0.0001). Conclusion The use of b = 2000 s/mm2 for DWI with 3‐T MRI is diagnostically superior to that of b = 1000 s/mm2 for prostate cancer detection. J. Magn. Reson. Imaging 2013;38:154–160. © 2012 Wiley Periodicals, Inc.