Background. Thyroid cancer, especially papillary carcinoma, metastasizes most often into cervical lymph nodes. Cervical ultrasound and ultrasound-guided fine-needle aspiration biopsy are the most sensitive modalities in detecting locoregional neck recurrence. Objective. The aim of this study was to illustrate the ultrasound spectrum of lymph node metastases from papillary thyroid carcinoma. Patients and methods. During 1998–2002 years due to suspicion of recurrence of thyroid cancer, 75 ultrasound-guided fine-needle aspiration biopsies of regional lymph nodes were performed. Ultrasound examination of 75 patients with thyroid cancer (56 women and 19 men; mean age of patients was 54.67±12.89 years) was performed. All biopsies were performed on nonpalpable lesions (lymph node short axis £1.5 cm). Results. A total of 75 ultrasound-guided fine-needle aspiration biopsies of regional lymph nodes under suspicion of malignancy were performed. Only 5 (6.7%) of the 75 lymph nodes were cystic with internal septation. Other 70 (93.3%) lymph nodes were solid. Cytopathological results of 75 ultrasound-guided fine-needle aspiration biopsies from regional cervical lymph nodes were noninformative in 4 (5.3%) cases, benign – 40 (53.4%), suspicion – 4 (5.3%), and malignant – 27 (36.0%) cases. Eighteen patients underwent surgery for regional lymph nodes. All cystic metastases were confirmed to be papillary thyroid carcinoma on pathologic examination. Conclusion. Ultrasound cannot exactly distinguish benign from malign lesions, but sonographic appearance can suggest malignancy and help in selection of the correct lymph nodes to aspirate with ultrasound-guided fine-needle aspiration biopsy. Cystic lymph node metastases may occur in papillary thyroid carcinoma. Cystic neck lesion patients with thyroid papillary carcinoma should always be verified with fine-needle aspiration biopsy.

Typically, prostate evaluation is done by using different imaging sequences of magnetic resonance imaging. Dynamic contrast enhancement, one of such scanning modalities, allow to spot higher vascular permeability and density caused by the malignant tissue. Authors of this paper investigate the ability to identify malignant prostate regions by the functional data analysis and standard machine learning techniques. The dynamic contrast enhanced images of the prostate are divided into the regions and based on those time-signal intensity curves are calculated. Two classification approaches: functional k-Nearest Neighbors and machine learning Support Vector Machine are used to model signal curve behavior on temporal variation matrix and timestamp based prostate region division of image data. Preliminary research shows that both functional data analysis and machine learning classification methods are able to identify highest saturation timestamp that gives best tissue classification results on timestamp based dynamic contrast enhanced region map obtained by Simple Linear Iterative Clustering algorithm. Cancer region classification results are better when the dynamic contrast enhanced images are subdivided into regions at each timestamp than when using a temporal variation matrix.

The primary objective of this study was to demonstrate the high accuracy of multiparametric magnetic resonance imaging and ultrasound fusion (mpMRI/US)-guided targeted prostate biopsy for the detection of clinically significant prostate cancer (PCa) and to show that adapted systematic biopsy (AdSB) does not provide additional benefit in detecting clinically significant prostate cancer (PCa). In total, 283 patients have been included in the study. All patients underwent the mpMRI/US biopsies, which have been performed with the “BioJet” fusion system (D&K Technologies, Barum, Germany) using the transperineal approach by a single interventional radiologist. Lesion-targeted and systematic biopsies have been done when 2–4 cores have been taken from each PI-RADS 3–5 lesion, followed by AdSB. This study demonstrated that targeted prostate biopsy is sufficient for safe and sensitive identification of clinically significant PCa in primary biopsy-naïve cases without the need to perform adapted systematic biopsy.

In this paper, a method for analyzing transversal plane images obtained by computer tomography (CT) scans is presented. A mathematical model that describes the ribs-bounded contour was created and the problem of approximation is solved by finding out the optimal parameters of the model in the least-squares sense. The paper discloses the problems that appear in building the proper model. Such a model would be useful in the registration of images independently on the patient position on the bed and of the radiocontrast agent injection. We consider the slices where ribs are visible because many important internal organs are located here: liver, heart, stomach, pancreas, lungs, etc. The model is flexible and describes the ribs-bounded contour independently on the patient age, sex and disease. The only exception is patients with the bone fracture. This makes the basis for the proper registration of slices.

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