Morphological aspects of basal cell carcinoma vascularization.

Basal cell carcinoma (BCC) is a malignant skin cancer which commonly exhibits aberrant blood flow because of angiogenesis. Its invasiveness and lack of metastatic potential may be explained by the typical pattern of vascularization seen in BCCs, where blood vessels are absent in the tumor islands and prominent in the tumor's periphery. From clinical point of view, high-frequency ultrasound (HFUS) is a useful tool for the evaluation of the lateral and depth extension of these tumors; furthermore, by employing color Doppler, important data regarding the vascularization degree of BCCs is provided. Knowingly, the sonographic vascular pattern of cutaneous tumors can aid in improving diagnosis and treatment by differentiating between benign and malignant lesions, between various types of cutaneous malignancies and also between various types of BCC (e.g., low risk versus high risk). Our aim was to perform a review integrating all currently known vascular properties of BCC as a tumor entity.

A Late-Detected Paraganglioma in a Young Patient with Resistant Hypertension and Severe Aortic Regurgitation-A Case Report and Review of the Literature.

BACKGROUND: Paraganglioma is a rare neuroendocrine tumor derived from chromaffin cells. The overproduction of catecholamines accounts for the presenting symptoms and cardiovascular complications. The clinical presentation frequently overlaps with the associated cardiac diseases, delaying the diagnosis. Multimodality imaging and a multidisciplinary team are essential for the correct diagnosis and adequate clinical management. CASE SUMMARY: A 37-year-old woman with a personal medical history of long-standing arterial hypertension and radiofrequency ablation for atrioventricular nodal reentry tachycardia presented with progressive exertional dyspnea and elevated blood pressure values, despite a comprehensive pharmacological treatment with six antihypertensive drugs. The echocardiography showed a bicuspid aortic valve and severe aortic regurgitation. The computed tomography angiography revealed a retroperitoneal space-occupying solid lesion, with imaging characteristics suggestive of a paraganglioma. The multidisciplinary team concluded that tumor resection should be completed first, followed by an aortic valve replacement if necessary. The postoperative histopathology examination confirmed the diagnosis of paraganglioma. After the successful resection of the tumor, the patient was asymptomatic, and the intervention for aortic valve replacement was delayed. DISCUSSION: This was a rare case of a late-detected paraganglioma in a young patient with resistant hypertension overlapping the clinical presentation and management of severe aortic regurgitation. A multimodality imaging approach including transthoracic and transesophageal echocardiography, computed tomography, and magnetic resonance imaging had an emerging role in establishing the diagnosis and in guiding patient management and follow-up. The resection of paraganglioma was essential for the optimal timing of surgical correction for severe aortic regurgitation. We further reviewed various cardiovascular complications induced by pheochromocytomas and paragangliomas.

Effects of Gold Nanoparticles Phytoreduced with Rutin in an Early Rat Model of Diabetic Retinopathy and Cataracts.

Diabetic retinopathy (DR) and cataracts (CA) have an early onset in diabetes mellitus (DM) due to the redox imbalance and inflammation triggered by hyperglycaemia. Plant-based therapies are characterised by low tissue bioavailability. The study aimed to investigate the effect of gold nanoparticles phytoreduced with Rutin (AuNPsR), as a possible solution. Insulin, Rutin, and AuNPsR were administered to an early, six-week rat model of DR and CA. Oxidative stress (MDA, CAT, SOD) was assessed in serum and eye homogenates, and inflammatory cytokines (IL-1 beta, IL-6, TNF alpha) were quantified in ocular tissues. Eye fundus of retinal arterioles, transmission electron microscopy (TEM) of lenses, and histopathology of retinas were also performed. DM was linked to constricted retinal arterioles, reduced endogen antioxidants, and eye inflammation. Histologically, retinal wall thickness decreased. TEM showed increased lens opacity and fibre disorganisation. Rutin improved retinal arteriolar diameter, while reducing oxidative stress and inflammation. Retinas were moderately oedematous. Lens structure was preserved on TEM. Insulin restored retinal arteriolar diameter, while increasing MDA, and amplifying TEM lens opacity. The best outcomes were obtained for AuNPsR, as it improved fundus appearance of retinal arterioles, decreased MDA and increased antioxidant capacity. Retinal edema and disorganisation in lens fibres were still present.

Nodular and Micronodular Basal Cell Carcinoma Subtypes Are Different Tumors Based on Their Morphological Architecture and Their Interaction with the Surrounding Stroma.

Basal cell carcinoma (BCC) is the most frequent cancer of the skin and comprises low-risk and high-risk subtypes. We selected a low-risk subtype, namely, nodular (N), and a high-risk subtype, namely, micronodular (MN), with the aim to identify differences between them using a classical morphometric approach through a gray-level co-occurrence matrix and histogram analysis, as well as an approach based on deep learning semantic segmentation. From whole-slide images, pathologists selected 216 N and 201 MN BCC images. The two groups were then manually segmented and compared based on four morphological areas: center of the BCC islands (tumor, T), peripheral palisading of the BCC islands (touching tumor, TT), peritumoral cleft (PC) and surrounding stroma (S). We found that the TT pattern varied the least, while the PC pattern varied the most between the two subtypes. The combination of two distinct analysis approaches yielded fresh insights into the characterization of BCC, and thus, we were able to describe two different morphological patterns for the T component of the two subtypes.

Deep learning with transfer learning in pathology. Case study: classification of basal cell carcinoma.

Establishing basal cell carcinoma (BCC) subtype is sometimes challenging for pathologists. Deep-learning (DL) algorithms are an emerging approach in image classification due to their performance, accompanied by a new concept - transfer learning, which implies replacing the final layers of a trained network and retraining it for a new task, while keeping the weights from the imported layers. A DL convolution-based software, capable of classifying 10 subtypes of BCC, was designed. Transfer learning from three general-purpose image classification networks (AlexNet, GoogLeNet, and ResNet-18) was used. Three pathologists independently labeled 2249 patches. Ninety percent of data was used for training and 10% for testing on 100 independent training sequences. Each of the resulted networks independently labeled the whole dataset. Mean and standard deviation (SD) accuracy (ACC) [%]∕sensitivity (SN) [%]∕specificity (SP) [%]∕area under the curve (AUC) for all the networks was 82.53±2.63∕72.52±3.63∕97.94±0.3/0.99. The software was validated on another 50-image dataset, and its results are comparable with the result of three pathologists in terms of agreement. All networks had similar classification accuracies, which demonstrated that they reached a maximum classification rate on the dataset. The software shows promising results, and with further development can be successfully used on histological images, assisting pathologists' diagnosis and teaching.

High-frequency ultrasound: an essential non-invasive tool for the pre-therapeutic assessment of basal cell carcinoma.

AIM: While histology remains the "gold standard" for cutaneous tumoral pathology, high-frequency ultrasound (HFUS) was shown to play a significant role in the non-invasive, pre-therapeutic assessment of skin tumors. The aim of our study was to determine whether there is a significant correlation between the ultrasound (US) and histological measurements of basal cell carcinoma (BCC) tumor depth. MATERIALS AND METHODS: The present study retrospectively analyzed clinical, dermoscopy, HFUS and histological examinations of 90 patients (52 men and 38 women) with histologically confirmed BCC, with focus on tumor depth index (DI). RESULTS: On clinical examination, 54 lesions were nodular (32 presented ulcerations) and 36 superficial lesions. Dermoscopy showed suggestive signs of BCC, most frequently "in focus" arborising superficial vessels (n=81), blue-grey ovoid nests (n=48) and specks of brown pigment (n=7). HFUS revealed well-defined (n=88) or poorly defined (n=2) hypoechoic, vascularized lesions, with inhomogeneous structure (n=90) and characteristic hyperechoic dots (n=36). A strong correlation (Pearson's r=0.92) between the HFUS (mean measured US depth = 1.33 mm) and histological (mean measured histological depth = 1.47 mm) DI of the investigated skin lesions was found, although significant differences (p<0.001 - t-test for paired samples) between the two measurements were observed. CONCLUSIONS: HFUS provides reliable information about BCC depth of invasion that cannot be otherwise obtained prior to surgery. In this manner, it completes the preclinical evaluation and can have an impact on the choice of the optimal therapeutic method.

Automated Gleason grading of prostate cancer using transfer learning from general-purpose deep-learning networks.

Two deep-learning algorithms designed to classify images according to the Gleason grading system that used transfer learning from two well-known general-purpose image classification networks (AlexNet and GoogleNet) were trained on Hematoxylin-Eosin histopathology stained microscopy images with prostate cancer. The dataset consisted of 439 images asymmetrically distributed in four Gleason grading groups. Mean and standard deviation accuracy for AlexNet derivate network was of 61.17±7 and for GoogleNet derivate network was of 60.9±7.4. The similar results obtained by the two networks with very different architecture, together with the normal distribution of classification error for both algorithms show that we have reached a maximum classification rate on this dataset. Taking into consideration all the constraints, we conclude that the resulted networks could assist pathologists in this field, providing first or second opinions on Gleason grading, thus presenting an objective opinion in a grading system which has showed in time a great deal of interobserver variability.

The study of tumor architecture components in prostate adenocarcinoma using fractal dimension analysis.

AIM: The aim of the study is to evaluate the three main components of the tumor architecture in correlation with two different grading systems of prostate adenocarcinoma (PA) using the fractal dimension (FD) analysis. PATIENTS, MATERIALS AND METHODS: 433 fields with different patterns of PA selected from 83 patients with total prostatectomy according to Gleason and Srigley grading systems were selected. Four serial sections were cut and stained in order to assess the following parameters: tumor grading with Hematoxylin-Eosin (H-E), tumor cells architecture (GÖ) with Gömöri technique, tumor stroma architecture (TC) with Goldner's trichrome, and vascular network (VN) architecture with cluster of differentiation 34 (CD34) immunomarker. Images were binarized with variable user-defined empiric threshold for Goldner's trichrome staining and CD34 immunostaining and k-nearest neighbor approach for GÖ staining. The FD was computed for each binary image using a box-counting algorithm. The three computed values were used for clustering and classification, k-nearest neighbor proving to be a good choice with a classification rate, due to the irregular distribution of cases in different patterns. Values tending to "1" had the meaning of a more "Linear type" distribution and values tending to "2" had the meaning of a more "Area type" distribution. RESULTS: Tumor cells architecture had a more ordered smooth ascending trend towards "area-like" type of distribution (with FD>1.5) in Srigley system than in Gleason system. Tumor stroma architecture had almost the same type of distribution - between "linear-like" and "area-like" (FD~1.5) - in both grading systems. VN architecture had a more "linear-like" type of distribution (FD<1.5), with a descending trend towards high-grade patterns in both systems. Tumor cells architecture had a direct correlation with tumor stroma architecture and VN architecture (p-value of Pearson's test <0.001), while tumor stroma architecture and VN architecture proved no correlation (p-value of Pearson's test >0.05), irrespective of grading pattern. CONCLUSIONS: Tumor cell population is remodeling and adapting TC and VN in the same way its architectural disposal evolves. TC and VN develop independently of each other, the former towards "Area type" and the latter towards "Linear type" of architectural disposal as the degree of differentiation is decreasing. FD analysis proved that Srigley system is more accurate in grading PA than Gleason system.