Embryonal rhabdomyosarcoma arising from the uterine corpus in a postmenopausal female: a surgical case challenging the genuine diagnosis on a cytology specimen

A 55-year-old postmenopausal female presented with genital bleeding and lower abdominal mass. An abdominal MRI revealed a heterogeneously enhanced, 15 × 10 cm mass, completely filling the lumen of the enlarged uterus. The cytologic analysis of the mass showed tumor cells in small clusters and as individual cells showing hyperchromatic round to oval nuclei, and pleomorphic and occasionally unipolar “tadpole”-shaped cytoplasm, in a background of severe necrosis and many degenerated squamous cells. We first interpreted it merely as atypical cells, possibly originated from sarcoma. A total abdominal hysterectomy and salpingo-oophorectomy were performed, and gross examination showed an exophytic polypoid mass with a whitish to white-grayish, necrotic appearance, protruding from the endometrial mucosa. Microscopically, the tumor was composed of a diffuse proliferation of highly atypical spindle-shaped cells, admixed with many characteristic rhabdomyoblasts having abundant densely eosinophilic cytoplasm with sometimes distinct cross-striations, coexisted with cellular primitive small blue round to oval cells foci. However, neither carcinoma nor additional heterologous sarcoma components were completely seen within our thorough investigation. Therefore, we finally made a diagnosis of embryonal rhabdomyosarcoma arising from the uterine corpus. We should be aware that owing to its characteristic features, cytopathologists might be able to determine a genuine diagnosis, based on multiple and adequate cytology samplings

シンキンコウソク ト セイカツ シュウカンビョウ

Acute myocardial infarction and angina pectoris are both called as ischemic coronary disease or coronary artery disease which is defined as a clinical or pathologic event caused by myocardial ischemia. Recent observations have highlighted the potential role of life associated disease in acute disease onset and prognosis of coronary artery disease. Plaque disruption and thrombosis frequently occurs at the site of a previously mild stenosis, suggesting that the transformation from a stable to unstable plaque occurs acutely. Many of the important risk factors for cardiovascular disease are modifiable by specific preventive measures. These included smoking, obesity, dyslipidemia, hypertension, diabetes and so on. To prevent acute onset of coronary artery disease, it is important to find these risk factor and treat them earlier. In addition to the medical treatment, however, improvement of inappropriate lifestyles is essential

心エコー図法による心不全の診断

The number of patients with heart failure is steadily increasing in Japan, and this situation is called the “heart failure pandemic”. Nowadays, echocardiography plays a center role in diagnosis of heart failure. It gives not only a definitive diagnosis of heart failure, but can also be used to determine its pathophysiology and the effect of treatment. Echocardiography can evaluate not only the morphology of the heart but also its function. The hemodynamic diagnosis of heart failure is made by demonstrating １） increased preload, ２） elevated left atrial pressure, and ３） decreased cardiac output. This article describes how to evaluate each of these including evaluation of left ventricular diastolic dysfunction. We also explain the clinical significance of preload stress echocardiography, which we are developing, in patients with heart failure. Although such echocardiographic diagnostic method is useful for understanding the condition of patients, it has become complicated, and it is difficult to make an accurate diagnosis unless a specialist in echocardiography. Recently, a new way of using ultrasound called “point-of-care ultrasonography（POCUS）” has been developed. This is an ultrasonography in which a physician who is not a specialist in ultrasonography can obtain information to be used as part of a physical examination and make on-site decisions. A diagnostic method for heart failure using POCUS is also described in this paper. In order to properly deal with heart failure, accurate evaluation of the pathology and selection of appropriate treatment, as well as picking up high-risk patients and initiating treatment early to prevent heart failure are essential. We would like to make widespread use of these echocardiographic techniques, which are useful for both understanding the pathology and determining the risk of heart failure, so that more patients can benefit

GLS following high-dose chemotherapy

Background Cardiac amyloidosis (CA) is a secondary form of cardiomyopathy where abnormal accumulation of amyloid protein in the myocardial interstitium causes cardiac hypertrophy and myocardial fibrosis. If primary CA advances to heart failure, most patients do not survive for very long after the diagnosis. Case summary A 40-year-old man was admitted to our hospital for dyspnoea, progressive anaemia, and decreased appetite. He has diagnosed with amyloid light-chain (AL) amyloidosis. Although BD treatment (bortezomib + dexamethasone) and medical treatment were started, there was no sign of improvement. Then, high-dose chemotherapy followed by autologous peripheral blood stem cell transplantation (auto-PBSCT) was initiated. Pretreatment echocardiography revealed typical findings of CA, such as ventricular wall thickening, valvular thickening, diastolic dysfunction, and pericardial effusion. Global longitudinal strain (GLS) was significantly reduced, and bull's-eye mapping showed typical apical sparing. After auto-PBSCT, GLS gradually improved and was almost normal after 2 years. Other echocardiographic parameters, functional status, and laboratory data also showed that there was significant regression of CA. Discussion Although the prognosis in primary CA is extremely poor, we achieved long-term survival in a patient with effective high-dose chemotherapy and auto-PBSCT. Global longitudinal strain may be a useful marker of prognosis, regression, and recovery

Clinically Feasible and Accurate View Classification of Echocardiographic Images Using Deep Learning

A proper echocardiographic study requires several video clips recorded from different acquisition angles for observation of the complex cardiac anatomy. However, these video clips are not necessarily labeled in a database. Identification of the acquired view becomes the first step of analyzing an echocardiogram. Currently, there is no consensus whether the mislabeled samples can be used to create a feasible clinical prediction model of ejection fraction (EF). The aim of this study was to test two types of input methods for the classification of images, and to test the accuracy of the prediction model for EF in a learning database containing mislabeled images that were not checked by observers. We enrolled 340 patients with five standard views (long axis, short axis, 3-chamber view, 4-chamber view and 2-chamber view) and 10 images in a cycle, used for training a convolutional neural network to classify views (total 17,000 labeled images). All DICOM images were rigidly registered and rescaled into a reference image to fit the size of echocardiographic images. We employed 5-fold cross validation to examine model performance. We tested models trained by two types of data, averaged images and 10 selected images. Our best model (from 10 selected images) classified video views with 98.1% overall test accuracy in the independent cohort. In our view classification model, 1.9% of the images were mislabeled. To determine if this 98.1% accuracy was acceptable for creating the clinical prediction model using echocardiographic data, we tested the prediction model for EF using learning data with a 1.9% error rate. The accuracy of the prediction model for EF was warranted, even with training data containing 1.9% mislabeled images. The CNN algorithm can classify images into five standard views in a clinical setting. Our results suggest that this approach may provide a clinically feasible accuracy level of view classification for the analysis of echocardiographic data

Deep Learning for Echocardiography

Objectives: The aim of this study was to evaluate whether a deep convolutional neural network (DCNN) could detect regional wall motion abnormalities (RWMAs) and differentiate groups of coronary infarction territories from conventional 2-dimensional echocardiographic images compared with cardiologist/sonographer or resident readers. Background: An effective intervention for reduction of misreading of RWMAs is needed. We hypothesized that a DCNN trained with echocardiographic images may provide improved detection of RWMAs in the clinical setting. Methods: A total of 300 patients with history of myocardial infarction were enrolled. In this cohort, 100 each had infarctions of the left anterior descending branch (LAD), left circumflex branch (LCX), and right coronary artery (RCA). The age-matched 100 control patients with normal wall motion were selected from our database. Each case contained cardiac ultrasound images from short axis views at end-diastolic, mid-systolic and end-systolic phases. After 100 steps of training, diagnostic accuracies were calculated on the test set. We independently trained 10 versions of the same model, and performed ensemble predictions with them. Results: For detection of the presence of wall motion abnormality, the area under the receiver-operating characteristic curve (AUC) by deep learning algorithm was similar to that by cardiologist/sonographer readers (0.99 vs. 0.98, p =0.15), and significantly higher than the AUC by resident readers (0.99 vs. 0.90, p =0.002). For detection of territories of wall motion abnormality, the AUC by the deep learning algorithm was similar to the AUC by cardiologist/sonographer readers (0.97 vs. 0.95, p =0.61) and significantly higher than the AUC by resident readers (0.97 vs. 0.83, p =0.003). In a validation group from an independent site (n=40), the AUC by the DL algorithm was 0.90. Conclusions: Our results support the possibility of DCNN use for automated diagnosis of RWMAs in the field of echocardiography