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.

Plane wave compounding is a useful mode for ultrasound imaging because it can make a good compromise between imaging quality and frame rate. It is also useful for broad view ultrasound imaging. Traditional coherent plane wave compounding coherently sums the echo data of different steered transmitting waves as the output. The data correlation information of different emissions is not considered. Therefore, some adaptive techniques can be introduced into the compounding procedure. In this paper, we propose a Joint Transmitting-Receiving (JTR) adaptive beamforming scheme for plane wave compounding. Unlike traditional adaptive beamformers, the proposed beamforming scheme is designed for the 2-D data set obtained from multiple plane wave firings. It calculates both the transmitting aperture weights and the receiving aperture weights and then combines them into a 2-D adaptive weight function for compounding. Experiments are conducted on both simulated and phantom data. Results show that the proposed scheme has better performance on both point targets and cysts than the existing plane wave compounding approach. Because of the adaptive process in both apertures for compounding, an improved resolution is observed in both simulation and phantom studies. When the eigenanalysis is introduced, a contrast enhancement is achieved. For the simulated cyst, a contrast ratio (CR) improvement of 48% is achieved compared with the traditional plane wave compounding. For the phantom cyst, this improvement is 213.8%. The proposed scheme also has good robustness against sound velocity errors. Therefore, it is effective in enhancing the coherent plane wave compounding quality.