The invisible evil twin of an adrenal adenoma / Aimi Fadilah M … [et al.]

Primary aldosteronism (PA) causes a persistently elevated blood pressure (BP) due to excessive release of the hormone aldosterone from the adrenal glands. Classically, it is called Conn’s syndrome and is described as the triad of hypertension and hypokalemia with the presence of unilateral adrenal adenoma. It can be cured with surgical resection of the aldosterone-secreting adenoma leading to resolution of hypertension, hypokalemia and increased cardiovascular risk associated with hyperaldosteronism. We present a case of a man with previous ischemic heart disease (IHD) who presented with resistant hypertension. Investigations for secondary causes of hypertension revealed an elevated aldosterone level and saline suppression test confirmed the diagnosis of PA. Radiological examination revealed a left adrenal adenoma and a normal right adrenal gland. However, adrenal venous sampling showed lateralization of aldosterone secretion towards the right. He subsequently underwent a laparoscopic right adrenalectomy which improved his BP control promptly. This case highlights the importance of recognizing the need to investigate for secondary causes of hypertension. It also underscores the importance of dynamic tests, which may not be easily accessible to most clinicians but should pursue, to allow a definitive diagnosis and effective treatment

Fusion of B‐mode and shear wave elastography ultrasound features for automated detection of axillary lymph node metastasis in breast carcinoma

Abstract: In this study, we evaluate and compare the diagnostic performance of ultrasound for non‐invasive axillary lymph node (ALN) metastasis detection. The study was based on fusing shear wave elastography (SWE) and B‐mode ultrasonography (USG) images. These images were subjected to pre‐processing and feature extraction, based on bi‐dimensional empirical mode decomposition and higher order spectra methods. The resulting nonlinear features were ranked according to their p‐value, which was established with Student's t‐test. The ranked features were used to train and test six classification algorithms with 10‐fold cross‐validation. Initially, we considered B‐mode USG images in isolation. A probabilistic neural network (PNN) classifier was able to discriminate positive from negative cases with an accuracy of 74.77% using 15 features. Subsequently, only SWE images were used and as before, the PNN classifier delivered the best result with an accuracy of 87.85% based on 47 features. Finally, we combined SWE and B‐mode USG images. Again, the PNN classifier delivered the best result with an accuracy of 89.72% based on 71 features. These three tests indicate that SWE images contain more diagnostically relevant information when compared with B‐mode USG. Furthermore, there is scope in fusing SWE and B‐mode USG to improve non‐invasive ALN metastasis detection

Magnetic resonance imaging features of invasive breast cancer association with the tumour stromal ratio.

ObjectiveTo assess the association between breast cancer tumour stroma and magnetic resonance imaging (MRI) features.Materials and methodsA total of 84 patients with treatment-naïve invasive breast cancer were enrolled into this retrospective study. The tumour stroma ratio (TSR) was estimated from the amount of tumour stroma in the pathology specimen of the breast tumour. The MRI images of the patients were analysed based on Breast Imaging Reporting and Data Systems (ACR-BIRADS) for qualitative features which include T2- weighted, diffusion-weighted images (DWI) and dynamic contrast-enhanced (DCE) for kinetic features. The mean signal intensity (SI) of Short Tau Inversion Recovery (STIR), with the ratio of STIR of the lesion and pectoralis muscle (L/M ratio) and apparent diffusion coefficient (ADC) value, were measured for the quantitative features. Correlation tests were performed to assess the relationship between TSR and MRI features.ResultsThere was a significant correlation between the margin of mass, enhancement pattern, and STIR signal intensity of breast cancer and TSR. There were 54.76% (n = 46) in the low stromal group and 45.24% (n = 38) in the high stromal group. A significant association were seen between the margin of the mass and TSR (p = 0.034) between the L/M ratio (p ConclusionBreast cancer with high stroma had spiculated margins, lower STIR signal intensity, and a heterogeneous pattern of enhancement. Hence, in this preliminary study, certain MRI features showed a potential to predict TSR

Diagnostic Efficacy of Synthesized 2D Digital Breast Tomosynthesis in Multi-ethnic Malaysian Population

Synthesized 2D images can be reconstructed from tomosynthesis images in breast imaging. This study aims to investigate the diagnostic efficacy of synthesized 2D images (C-View) in comparison to full field digital mammography (FFDM) when used with digital breast tomosynthesis (DBT) in multi-ethnic Malaysian population. FFDM and C-View images (n = 380) were independently evaluated by three readers through Breast Imaging Reporting and Data System (BI-RADS) categorisation, breast density and lesion characterisation. Statistical analysis was done comparing sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of C-View + DBT with FFDM + DBT as standard of reference. Very good interreader agreement in BI-RADS category and density assessment between C-View + DBT and FFDM + DBT, with high sensitivity, specificity, PPV and NPV of C-View + DBT when compared with FFDM + DBT. There was comparable PPV between C-View + DBT and FFDM + DBT, with histopathology as gold standard. High level of interreader agreement in BI-RADS category and density assessment for FFDM + DBT and C-View + DBT. There was good agreement between FFDM + DBT and C-View + DBT in mass characterization, and almost perfect agreement in calcification and asymmetric density. 52.2% lower radiation dose incurred when using C-View + DBT. Hence, synthesized 2D images are comparable to FFDM with reduction in radiation dose within the limits of Malaysian multi-ethnic population. © 2019, The Author(s)