Staging of Endometrial Cancer Using Fusion T2-Weighted Images with Diffusion-Weighted Images: A Way to Avoid Gadolinium?

Endometrial cancer is the eighth most common cancer worldwide, and its prognosis depends on various factors, with myometrial invasion having a major impact on prognosis. Optimizing MRI protocols is essential, and it would be useful to improve the diagnostic accuracy without the need for other sequences. We conducted a retrospective, single-center study, which included a total of 87 patients with surgically confirmed primary endometrial cancer, and who had undergone a pre-operative pelvic MRI. All exams were read by an experienced radiologist dedicated to urogenital radiology, and the depth of myometrial invasion was evaluated using T2-Weighted Images (T2WI) and fused T2WI with Diffusion-Weighted Images (DWI). Both results were compared to histopathological evaluations. When comparing both sets of imaging (T2WI and fused T2WI-DWI images) in diagnosing myometrial invasion, the fused images had better accuracy, and this difference was statistically significant (p < 0.001). T2WI analysis correctly diagnosed 82.1% (70.6–88.7) of cases, compared to 92.1% correctly diagnosed cases with fused images (79.5–97.2). The addition of fused images to a standard MRI protocol improves the diagnostic accuracy of myometrial invasion depth, encouraging its use, since it does not require more acquisition time.

Clinical Implication of Individually Tailored Segmentation Method for Distorted Hypothalamus in Craniopharyngioma

ObjectiveSeveral attempts have been done to capture damaged hypothalamus (HT) using volumetric measurements to predict the development of hypothalamic obesity in patients with craniopharyngioma (CP). This study was to develop a novel method of HT volume measurement and examine the associations between postoperative HT volume and clinical parameters in patients with CP.MethodsWe included 78 patients with adult-onset CP who underwent surgical resection. Postoperative HT volume was measured using T1- and T2-weighted magnetic resonance imaging (MRI) with a slice thickness of 3 mm, and corrected for temporal lobe volume. We collected data on pre- and postoperative body weights, which were measured at the time of HT volume measurements.ResultsThe corrected postoperative HT volume measured using T1- and T2-weighted images was significantly correlated (r=0.51 [95% confidence interval (CI) 0.32 to 0.67], P&lt;0.01). However, HT volume was overestimated using T1-weighted images owing to obscured MR signal of the thalamus in patients with severe HT damage. Therefore, we used T2-weighted images to evaluate its clinical implications in 72 patients with available medical data. Postoperative HT volume was negatively associated with preoperative body weight and preoperative tumor volume (r=–0.25 [95% CI -0.45 to -0.04], P=0.04 and r=–0.26 [95% CI -0.40 to -0.15], P=0.03, respectively). In the subgroup analysis of CP patients who underwent primary surgery (n=56), pre- and postoperative body weights were negatively associated with HT volume (r=–0.30 [95% CI -0.53 to -0.03], P=0.03 and r=–0.29 [95% CI -0.53 to -0.02], P=0.03, respectively).ConclusionsAdult-onset CP patients showed negative associations between postoperative HT volume and preoperative/postoperative body weight using a new method of HT volume measurement based on T2-weighted images.

MR Imaging of Pancreatic Lesions

Aim: The aim of this work was to evaluate the role of MRI in differentiating between benign and malignant pancreatic lesions and its correlation with histopathological results as the reference standard. Patients and Methods: This MRI study included 30 patients, 17 females and 13 males with a mean age 50 years. Sixteen patients had malignant masses (14 patients were adenocarcinoma, one patient was lymphoma and one patient was metastasis) and 14 patients had benign masses (7 patients were pancreatic pseudocysts, two patients were pancreatic abscesses, three patients were simple cysts and two patients were focal pancreatitis). The main clinical symptom was abdominal pain and most of masses were located in the head of the pancreas. Results: In our study, 25 cases of the 30 patients showed increased intensity at T2-weighted images. Most of malignant cases showed low or equal intensity on T1- and high intensity on T2-weighted images compared to normal pancreatic parenchyma. In our study, DW-MRI was performed on all subjects at b-values of 500 and 1000 s/mm2. Benign pancreatic masses as pancreatic pseudocyst, simple cyst and abscess show low signal intensities on DWI, however malignant pancreatic masses as adenocarcinoma, lymphoma and metastasis show high signal intensities on DWI with a cut-off value of 1.5 x10-3 s/mm2 for the differentiation of benign from malignant pancreatic masses by b-value 1000 s/mm2 with the sensitivity, specificity, PPV, NPV& p value were 100%, 83.33%, 100%, 88.88% and <0.001 respectively. Conclusion: MRI plays an important role in the diagnosis of different pancreatic lesions and can assess the neoplastic pancreatic lesions with accurate detection of extension, nodal involvement and hepatic metastatic lesions. It also has a major role in differentiation between benign and malignant pancreatic lesions by the aids of DWI.

NI-12 The ratio of T1-Weighted to T2-Weighted Signal Intensity and IDH mutation in glioma

Introduction: Prediction of IDH mutation status for Lower-grade glioma (LrGG) is clinically significant. The purpose of this study is to test the hypothesis that the T1-weighted image/T2-weighted image ratio (rT1/T2), an imaging surrogate developed for myelin integrity, is a useful MRI biomarker for predicting the IDH mutation status of LrGG. Methods: Twenty-five LrGG patients (IDHwt: 8, IDHmt: 17) at Asahikawa Medical University Hospital (AMUH) were used as an exploratory cohort. Twenty-nine LrGG patients (IDHwt: 13, IDHmt: 16) from Osaka International Cancer Institute (OICI) and 103 patients from the Cancer Imaging Archive (TCIA) / Cancer Genome Atlas (TCGA) dataset (IDHwt: 19, IDHmt: 84) were used as validation cohorts. rT1/T2 images were calculated from T1- and T2-weighted images using a recommended signal correction. The region-of-interest was defined on T2-weighted images, and the relationship between the mean rT1/T2 (mrT1/T2) and the IDH mutation status was investigated. Results: The mrT1/T2 was able to significantly predict the IDH mutation status for the AMUH exploratory cohort (AUC = 0.75, p = 0.048). The ideal cut-off for detecting mutant IDH was mrT1/T2 &lt; 0.666 ~ 0.677, with a sensitivity of 58.8% and a specificity of 87.5%. This result was further validated by the OICI validation cohort (AUC = 0.75, p = 0.023) with a sensitivity of 56.3% and a specificity of 69.2%. On the other hand, the sensitivity was 42.9% and the specificity was 68.4 % for the TCIA validation cohort (AUC = 0.63, p = 0.068). Conclusion: Our results supported the hypothesis that mrT1/T2 could be a useful image surrogate to predict the IDH mutation status of LrGG using two domestic cohorts. The decline of the accuracy for the TCIA cohort should be further investigated.

Using Deep Learning to Detect Spinal Cord Diseases on Thoracolumbar Magnetic Resonance Images of Dogs

Deep Learning based Convolutional Neural Networks (CNNs) are the state-of-the-art machine learning technique with medical image data. They have the ability to process large amounts of data and learn image features directly from the raw data. Based on their training, these networks are ultimately able to classify unknown data and make predictions. Magnetic resonance imaging (MRI) is the imaging modality of choice for many spinal cord disorders. Proper interpretation requires time and expertise from radiologists, so there is great interest in using artificial intelligence to more quickly interpret and diagnose medical imaging data. In this study, a CNN was trained and tested using thoracolumbar MR images from 500 dogs. T1- and T2-weighted MR images in sagittal and transverse planes were used. The network was trained with unremarkable images as well as with images showing the following spinal cord pathologies: intervertebral disc extrusion (IVDE), intervertebral disc protrusion (IVDP), fibrocartilaginous embolism (FCE)/acute non-compressive nucleus pulposus extrusion (ANNPE), syringomyelia and neoplasia. 2,693 MR images from 375 dogs were used for network training. The network was tested using 7,695 MR images from 125 dogs. The network performed best in detecting IVDPs on sagittal T1-weighted images, with a sensitivity of 100% and specificity of 95.1%. The network also performed very well in detecting IVDEs, especially on sagittal T2-weighted images, with a sensitivity of 90.8% and specificity of 98.98%. The network detected FCEs and ANNPEs with a sensitivity of 62.22% and a specificity of 97.90% on sagittal T2-weighted images and with a sensitivity of 91% and a specificity of 90% on transverse T2-weighted images. In detecting neoplasms and syringomyelia, the CNN did not perform well because of insufficient training data or because the network had problems differentiating different hyperintensities on T2-weighted images and thus made incorrect predictions. This study has shown that it is possible to train a CNN in terms of recognizing and differentiating various spinal cord pathologies on canine MR images. CNNs therefore have great potential to act as a “second eye” for imagers in the future, providing a faster focus on the altered image area and thus increasing workflow in radiology.