Effect of Non-surgical Periodontal Therapy on Levels of Inflammatory Markers in Gingival Crevicular Fluid and Serum of Patients With Breast Cancer and Periodontitis.

Purpose: studies have demonstrated the positive impact of non-surgical periodontal therapy (NSPT) on the control of local and systemic infection/inflammation in normosystemic and systemically compromised patients, represented by the improvement of periodontal clinical parameters and reduction in the levels of inflammatory markers in the gingival crevicular fluid (GCF), saliva and serum. This study aimed to evaluate periodontal clinical parameters and inflammatory mediators in GCF and serum, before and after NSPT, in patients with periodontitis and breast cancer, before chemotherapy. Methods: seventeen women with histopathological diagnosis of invasive ductal carcinoma and periodontitis were submitted to the evaluation of clinical periodontal parameters (plaque index – PI, bleeding on probing – BOP, probing depth – PD, clinical attachment level – CAL) and submitted to scaling and root planing (SRP), at an interval of 24 hours. At the beginning of the study (baseline), before NSPT, samples of tumor microenvironment fluid (TM), GCF and peripheral blood (serum) were collected for the determination of inflammatory markers IL-1β, TNF-α, TGF-β and IL-17, using the LUMINEX methodology. Seven days after SRP, new GCF and serum samples were obtained and analyzed.Results: TGF-β levels were significantly decreased in GCF and serum (p<0.05), while IL-17 concentrations were statistically reduced in GCF (p<0.05). Conclusion: NSPT decreased local and systemic inflammatory markers and may be an important tool in the multidisciplinary approach of women with breast cancer and periodontitis before chemotherapy.

A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses

We developed a machine learning model based on radiomics to predict the BI-RADS category of ultrasound-detected suspicious breast lesions and support medical decision-making towards short-interval follow-up versus tissue sampling. From a retrospective 2015–2019 series of ultrasound-guided core needle biopsies performed by four board-certified breast radiologists using six ultrasound systems from three vendors, we collected 821 images of 834 suspicious breast masses from 819 patients, 404 malignant and 430 benign according to histopathology. A balanced image set of biopsy-proven benign (n = 299) and malignant (n = 299) lesions was used for training and cross-validation of ensembles of machine learning algorithms supervised during learning by histopathological diagnosis as a reference standard. Based on a majority vote (over 80% of the votes to have a valid prediction of benign lesion), an ensemble of support vector machines showed an ability to reduce the biopsy rate of benign lesions by 15% to 18%, always keeping a sensitivity over 94%, when externally tested on 236 images from two image sets: (1) 123 lesions (51 malignant and 72 benign) obtained from two ultrasound systems used for training and from a different one, resulting in a positive predictive value (PPV) of 45.9% (95% confidence interval 36.3–55.7%) versus a radiologists’ PPV of 41.5% (p < 0.005), combined with a 98.0% sensitivity (89.6–99.9%); (2) 113 lesions (54 malignant and 59 benign) obtained from two ultrasound systems from vendors different from those used for training, resulting into a 50.5% PPV (40.4–60.6%) versus a radiologists’ PPV of 47.8% (p < 0.005), combined with a 94.4% sensitivity (84.6–98.8%). Errors in BI-RADS 3 category (i.e., assigned by the model as BI-RADS 4) were 0.8% and 2.7% in the Testing set I and II, respectively. The board-certified breast radiologist accepted the BI-RADS classes assigned by the model in 114 masses (92.7%) and modified the BI-RADS classes of 9 breast masses (7.3%). In six of nine cases, the model performed better than the radiologist did, since it assigned a BI-RADS 3 classification to histopathology-confirmed benign masses that were classified as BI-RADS 4 by the radiologist.

Percutaneous CT-Guided Biopsy of the Craniovertebral Junction: Safety, Diagnostic Yield, and Technical Notes

The craniovertebral junction defined as the occiput, the atlas, and the axis is a complex bony region that contains vital neural and vascular structures. We report the experience of a single academic institution regarding CT-guided biopsy of this skeletal region. We reviewed all of the CT-guided biopsies performed in our department, completed in the craniovertebral junction. We collected data in regard to biopsy procedures, patients’ vital statistics, and histopathological diagnosis. In total, 16 patients (8M and 8F; mean age 52; range 16–86 years old) were included in this series. In eight patients, the lesions were located in the atlas vertebra (8/16—50%), in six patients in the axis (37.5%), and in two patients in the occiput (12.5%). No complications were observed during or after the procedures. All of the procedures were technically successful. The biopsy was diagnostic in 13/16 patients (81.3%): four metastatic lesions (25%—three breast and one prostate cancers), four multiple myeloma bone lesions (25%), three aneurismal bone cysts (18.8%), one aggressive hemangioma (6.3%), and one pseudogout (6.3%). Moreover, in two-thirds (66.6%) of non-diagnostic histological reports, malignancies were excluded. CT-guided percutaneous biopsy is a safe tool and allows obtaining a histological diagnosis, in most cases, even in the most delicate site of the human skeleton—the craniovertebral junction.

Pleomorphic xanthoastrocytoma is a heterogeneous entity with pTERT mutations prognosticating shorter survival

Pleomorphic xanthoastrocytoma (PXA) in its classic manifestation exhibits distinct morphological features and is assigned to CNS WHO grade 2 or grade 3. Distinction from glioblastoma variants and lower grade glial and glioneuronal tumors is a common diagnostic challenge. We compared a morphologically defined set of PXA (histPXA) with an independent set, defined by DNA methylation analysis (mcPXA). HistPXA encompassed 144 tumors all subjected to DNA methylation array analysis. Sixty-two histPXA matched to the methylation class mcPXA. These were combined with the cases that showed the mcPXA signature but had received a histopathological diagnosis other than PXA. This cohort constituted a set of 220 mcPXA. Molecular and clinical parameters were analyzed in these groups. Morphological parameters were analyzed in a subset of tumors with FFPE tissue available. HistPXA revealed considerable heterogeneity in regard to methylation classes, with methylation classes glioblastoma and ganglioglioma being the most frequent mismatches. Similarly, the mcPXA cohort contained tumors of diverse histological diagnoses, with glioblastoma constituting the most frequent mismatch. Subsequent analyses demonstrated the presence of canonical pTERT mutations to be associated with unfavorable prognosis among mcPXA. Based on these data, we consider the tumor type PXA to be histologically more varied than previously assumed. Histological approach to diagnosis will predominantly identify cases with the established archetypical morphology. DNA methylation analysis includes additional tumors in the tumor class PXA that share similar DNA methylation profile but lack the typical morphology of a PXA. DNA methylation analysis also assist in separating other tumor types with morphologic overlap to PXA. Our data suggest the presence of canonical pTERT mutations as a robust indicator for poor prognosis in methylation class PXA.

The Effect of Broccoli Extract in Arsenic-Induced Experimental Poisoning on the Hematological, Biochemical, and Electrophoretic Parameters of the Liver and Kidney of Rats

Heavy metals such as arsenic contribute to environmental pollution that can lead to systemic effects in various body organs. Some medicinal plants such as broccoli have been shown to reduce the harmful effects of these heavy metals. The main aim of the present study is to evaluate the effects of broccoli extract on liver and kidney toxicity, considering hematological and biochemical changes. The experimental study was performed in 28 days on 32 male Wistar rats classified into four groups: the control group (C), a group receiving 5 mg/kg oral arsenic (AS), a group receiving 300 mg/kg broccoli (B), and a group receiving arsenic and broccoli combination (AS + B). Finally, blood samples were taken to evaluate the hematological and biochemical parameters of the liver and kidney, as well as serum proteins’ concentration. Liver and kidney tissue were fixed and stained by H&E and used for histopathological diagnosis. The results demonstrated a significant decrease in white blood cells (WBC), red blood cells (RBC), and hemoglobin (Hb) in the AS group compared to other groups. However, in the B group, a significant increase in RBC and WBC was observed compared to the AS and C groups ( P < 0.05). Moreover, RBC and WBC levels increased significantly in the AS + B group compared to the AS group ( P = 0.046). However, in the AS group, aspartate aminotransferase (AST), alanine aminotransferase (ALT), urea, and creatinine levels increased, while total protein, albumin, and globulin decreased. This can be a result of liver and kidney damage, which was observed in the AS group. Furthermore, the increase in the concentration of albumin and globulin in the AS + B group was higher than that in the AS group. Infiltration of inflammatory cells and necrosis of the liver and kidney tissue in the pathological evaluation of the AS group were significantly higher than other groups. There was an increase in superoxide dismutases (SOD), glutathione peroxidase (GPx), and total antioxidant capacity (TAC); however, a decrease in malondialdehyde (MDA) concentration was seen in the AS + B group compared to the AS group. It seems that broccoli is highly effective at reducing liver and kidney damage and improving the hematological and biochemical factors in arsenic poisoning conditions.

New Prognostic Frontiers for Lung Neuroendocrine Tumors

Purpose Well-differentiated lung neuroendocrine tumors (Lu-NET) are classified as typical (TC) and atypical (AC) carcinoids, based on mitotic counts and necrosis. However, prognostic factors, other than tumor node metastasis (TNM) stage and the histopathological diagnosis, are still lacking. The current study is aimed to identify potential prognostic factors to better stratify lung NET, thus, improving patients’ treatment strategy and follow-up. Methods A multicentric retrospective study, including 300 Lung NET, all surgically removed, from Italian and Spanish Institutions. Results Median age 61 years (13-86), 37.7% were males, 25.0% were AC, 42.0% were located in the lung left parenchyma, 80.3% presented a TNM stage I-II. Mitotic count was ≥2 per 10 high power field (HPF) in 24.7%, necrosis in 13.0%. Median overall survival (OS) was 46.1 months (0.6-323), median progression free survival (PFS) was 36.0 months (0.3-323). Female sex correlated with a more indolent disease (T1; N0; lower Ki67; lower mitotic count and the absence of necrosis). Left-sided primary tumors were associated with higher mitotic count and necrosis. At Cox multivariate regression model, age, left-sided tumors and nodal (N) positive status resulted independent negative prognostic factors for OS and PFS. Conclusions This study confirms the prognostic relevance of TNM stage and diagnosis to stratify LuNET. However, the current analysis suggests a wider spectrum of clinical and pathological prognostic factors, including age and primary tumor’s location. These parameters could help clinicians to personalize the management of Lu-NET.

Lung and Colon Cancer Classification Using Medical Imaging : A Feature Engineering Approach

Lung and colon cancers are the most common causes of death. Their simultaneous occurrence is uncommon, however, in the absence of early diagnosis, the metastasis of cancer cells is very high between these two organs. Currently, histopathological diagnosis and appropriate treatment are the only possibility to improve the chances of survival and reduce cancer mortality. Using artificial intelligence in the histopathological diagnosis of colon and lung cancer can provide significant help to specialists in identifying cases of colon and lung cancers with less effort, time and cost. The objective of this study is to set up a computer-aided diagnostic system that can accurately classify five types of colon and lung tissues (two classes for colon cancer and three classes for lung cancer) by analyzing their histopathological images. Using machine learning, features engineering and image processing techniques, the five models XGBoost, SVM, RF, LDA and MLP were used to perform the classification of histopathological images of lung and colon cancers that were acquired from the LC25000 dataset. The main advantage of using machine learning models is that they allow for better interpretability of the classification model since they are based on feature engineering; however, deep learning models are black box networks whose working is very difficult to understand due to the complex network design. The acquired experimental results show that machine learning models give satisfactory results and are very precise in identifying classes of lung and colon cancer subtypes. The XGBoost model gave the best performance with an accuracy of 99% and a F1-score of 98.8%. The implementation and the development of this model will help healthcare specialists identify types of colon and lung cancers. The code will be available upon request.

Spectrum of Oral Cavity Lesions and its Clinico-Histopathological Correlation

Introduction: Oral cavity lesions comprise a wide spectrum of diseases that varies from non-neoplastic to neoplastic. The clinical evaluation alone is insufficient for proper diagnosis in most cases. So, histopathological examination is the gold standard method for diagnosis and management of patients accordingly. Objective: The present study was done to evaluate the histopathological spectrum of oral cavity lesions and compare them in relation to age, sex, site, clinical features, risk factors, and clinical diagnoses. Methods: This prospective cross-sectional study enrolled 127 cases of oral biopsies which were received at the Department of Pathology, Tribhuvan University and Teaching Hospital, Kathmandu Nepal, from May 2018 to April 2019 for histopathological examination. Specimens were fixed in 10% formalin and subjected for tissue processing and Hematoxylin and Eosin stained sections. Data entry and analysis were done by using SPSS 24 version where frequency and percentile were calculated. Results: Total cases were 127 with slight female predilection and the age group of 50-60 years (mean age of 44.24 years) were commonly affected. The tongue being the most common site, frequently lesions presented as swelling. Most of the lesions were non-neoplastic comprising 45% whereas malignant lesions comprised 23.6%. Smoking increased the risk of malignancy by 2 fold. The most common benign lesions were squamous papilloma & fibroepithelial polyp whereas the malignant lesion was squamous cell carcinoma. Sixty percent of clinical diagnoses didn’t show correlation. Conclusions: Oral cavity lesions have a wide spectrum of distribution in age, sex, site, and clinical presentation. Initially, oral lesions may present with subtle symptoms which may cause underdiagnosis. Thus, histopathological diagnosis is a must to rule out malignancy. Keywords: Clinical presentation; correlation; oral cavity; risk factors.