با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
Oral Cancer
سرطان دهان-2020 Oral squamous cell carcinoma (OSCC), a distinct subtype of head and neck squamous
cell carcinoma, is typically human papillomavirus-negative and harbors TP53 loss-offunction
mutations.
OSCC is thought to begin with cancer initiating cells that are able to self-renew and
generate heterogeneous clones of neoplastic cells to comprise the tumor (ie, tumor
heterogeneity).
Carcinogenesis is a multistep process, which involves an accumulation of both genetic
and epigenetic alterations in oncogenes and/or tumor suppressor genes.
Metastasis is one of the major prognostic indicators in OSCC. Both epithelial-tomesenchymal
transition and interactions between OSCC cells and the tumor microenvironment
play significant roles in this complex process.
The integration of omics technologies, bioinformatics, and molecular biology uncovers
complex, clinically meaningful information that greatly improves our understanding of
the disease process. KEYWORDS : Oral cancer | Oral squamous cell carcinoma | Malignant transformation | Epigenetics | Omics technology | Big data | Personalized medicine | Precision medicine |
مقاله انگلیسی |
2 |
Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma
یادگیری ماشین برای پیش بینی متاستاز گره غشایی در کارسینوم سلول سنگفرشی اولیه دهان-2019 Objectives: To develop and validate an algorithm to predict occult nodal metastasis in clinically node negative
oral cavity squamous cell carcinoma (OCSCC) using machine learning. To compare algorithm performance to a
model based on tumor depth of invasion (DOI).
Materials and methods: Patients who underwent primary tumor extirpation and elective neck dissection from
2007 to 2013 for clinical T1-2N0 OCSCC were identified from the National Cancer Database (NCDB). Multiple
machine learning algorithms were developed to predict pathologic nodal metastasis using clinicopathologic data
from 782 patients. The algorithm was internally validated using test data from 654 patients in NCDB and was
then externally validated using data from 71 patients treated at a single academic institution. Performance was
measured using area under the receiver operating characteristic (ROC) curve (AUC). Machine learning and DOI
model performance were compared using Delong’s test for two correlated ROC curves.
Results: The best classification performance was achieved with a decision forest algorithm (AUC=0.840). When
applied to the single-institution data, the predictive performance of machine learning exceeded that of the DOI
model (AUC=0.657, p=0.007). Compared to the DOI model, machine learning reduced the number of neck
dissections recommended while simultaneously improving sensitivity and specificity.
Conclusion: Machine learning improves prediction of pathologic nodal metastasis in patients with clinical T1-
2N0 OCSCC compared to methods based on DOI. Improved predictive algorithms are needed to ensure that
patients with occult nodal disease are adequately treated while avoiding the cost and morbidity of neck dissection
in patients without pathologic nodal disease. Keywords: Oral cancer | Squamous cell carcinoma | Machine learning | Artificial intelligence |
مقاله انگلیسی |
3 |
Integrative data mining and meta-analysis to investigate the prognostic role of microRNA-200 family in various human malignant neoplasms: A consideration on heterogeneity
استخراج داده های یکپارچه و متاآنالیز به منظور بررسی نقش پیش آگهی خانواده microRNA-200 در نئوپلاسم های مختلف بدخیم انسانی: توجه به ناهمگونی-2019 Background: Existing meta-analysis have shown that the miR-200 family can be taken as a prognostic biomarker
for many tumors. However, great heterogeneity was shown in predicting overall survival (OS) and progressionfree
survival (PFS). Emerging studies indicate that the expression levels of members of the miR-200 family are
tissue-specific among various tumor tissues, which may be the main reason of the heterogeneity in predicting
survival prognosis of tumor patients with the miR-200 family as biomarkers. By further analysis of heterogeneity
of the miR-200 family as a biomarker for predicting survival prognosis of patients with different tumors, we
expected to provide an accurate basis for the clinical application of the miR-200 family to predict the prognosis
of patients with different tumors.
Methods: Eligible published studies were identified by searching the databases of PubMed, Embase and Web of
Science. The clinical data of patients in the studies were pooled, and pooled hazard ratios (HR) with 95%
confidence intervals (95% CI) were used to calculate the strength of this association. The expressions of miRNAs
were extracted from The Cancer Genome Atlas (TCGA). We presented the expressions of each member in miR-
200 family in 15 types of cancer by boxplot, and analyzed the correlation among the members of miR-200 family
by Spearman method. Different subgroup analyses were then performed based on the correlation among the
members of miR-200 family, and the publication bias was assessed using the funnel plot of the Egger bias
indicator test.
Results: Of 36 articles, including 15 tumor types and 4644 patients were included to perform meta-analysis. It
was found that miR-200 family members can be used as independent protective factors in patients with various
tumors but the miR-200 family has a higher heterogeneity in predicting prognosis: OS (HR=0.82, 95% CI:
0.66–1.03, I2=85%, P < 0.01) and PFS (HR=0.81, 95% CI: 0.57–1.16, I2=97%, P < 0.01). The data from
TCGA database were used to analyze the expression levels of the miR-200 family and the results showed that the
expression of miR-429 in different cancers is very different, and there are significant differences in expression
levels compared with other miR-200 family members; the expression levels of miR-200a and miR-200b in
various tumor tissues were similar to each other, respectively; miR-200c and miR-141 showed similar expression
levels in each of most types of cancer tissues except ovarian cancer (OC). The expression levels of members of the
miR-200 family in breast cancer (BRCA), cervical cancer (CESC), colon cancer (COAD), esophageal cancer
(ESCA), head and neck cancer (HNSC), lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are
relatively stable, but great variations can be found in the expression levels of miR-200 family members in
ovarian cancer (OC), liver cancer (LIHC), renal clear cell carcinoma (KIRC) and renal papillary cell carcinoma
(KIRP). Cluster analysis of expression of target genes of miR-200 family in different cancers yielded similar
results to the expression level of the miR-200 family. Subgroup analysis of OC, LIHC, GC and LUAD based on
expression levels and clustering results reduced or even eliminated the heterogeneity of miR-200 family
members in predicting patient outcomes.
Conclusions: Our results convincingly demonstrated that the miR-200 family could serve as a prognostic biomarker
for cancers mentioned above and has potential value in clinical practice. MiR-200 family as prognostic
biomarkers needs to be performed according to different tumor tissues and correlation between members in miR-
200 family. Keywords: miR-200 family | Meta-analysis | miRNAs | Cancer | Prognosis | Heterogeneity |
مقاله انگلیسی |
4 |
Analysis of Raman spectroscopy data with algorithms based on paraconsistent logic for characterization of skin cancer lesions
تجزیه و تحلیل داده های طیف سنجی رامان با الگوریتم های مبتنی بر منطق paraconsistent برای توصیف ضایعات سرطان پوست-2019 Analysis of the Raman data to obtain results in discrimination models is usually done with multivariate statistics
based on principal component analysis (PCA). In this work, we present a technique based on a non-classical logic
called paraconsistent logic (PL). The aim of this work is to use computational procedures capable of generating
efficient expert systems to discriminate cutaneous tissue samples obtained by Raman spectroscopy. First, a set of
algorithms originating from PL is presented, and then its application in discrimination analyses is described; the
discrimination analysis was conducted using a database of skin tissue samples obtained ex vivo by Raman
spectroscopy of spectrum range of 400–1800 cm−1 wavelengths. Data processing, pattern creation, and comparisons
were performed using the set of paraconsistent algorithms (SPA-PAL2v). The total number of samples
was divided into four histopathological groups, with 115 spectra of basal cell carcinoma (BCC), 21 spectra of
squamous cell carcinoma (SCC), 57 spectra of actinic keratosis (AK), and 30 normal skin (NO) spectra. An
arrangement type was created for this study, and the samples were randomly selected and analyzed, and the NO
group was compared with the group of non-melanoma cancer lesions (BCC+SCC) and the AK tumor lesion. Two
analyses were performed. The first (SPA-PAL2v) Mode 1 (no cross-validation) achieved 76% of hits, and the
second (SPA-PAL2v) Mode 2 (with cross-validation) achieved 75.78% of hits. These results were compared with
discrimination using PCA statistical methods (PCA/DA) and presented superior percentages of hits, which proves
the robustness of the SPA-PAL2v, confirming its potential for Raman spectrum data analysis. Keywords: Raman spectroscopy | Algorithms | Skin cancer | Paraconsistent annotated logic | Medical diagnosis |
مقاله انگلیسی |
5 |
Variations among 5 European countries for curative treatment of resectable oesophageal and gastric cancer: A survey from the EURECCA Upper GI Group (EUropean REgistration of Cancer CAre)
تغییرات میان 5 کشور اروپایی برای درمان قطعی سرطان مری و برداشت معده : بررسی از بالای EUREKA گروه GI (ثبت نام اروپایی سرطان مراقبت)-2016 Introduction: EURECCA (EUropean REgistration of Cancer CAre) is a network aiming to improve cancer care by auditing outcome. EUR- ECCA initiated an international survey to share and compare patient outcome for oesophagogastric cancer. The present study assessed how a uniform dataset could be introduced for oesophagogastric cancer in Europe.
Methods: Participating countries presented data using common data items describing patients’, disease, strategies, and outcome character- istics. Patients treated with curative surgery for squamous cell carcinoma (SCC) or adenocarcinoma (ACA) were included. Results: United Kingdom, the Netherlands, France, Spain and Ireland participated. There were differences in data source ranging from na- tional registries to large collaborative groups. 4668 oesophagogastric cancer cases over a 12 months period were included. The predominant histological type was ACA. Disease stage tended to be earlier in France and Ireland. In oesophageal and junctional cancers neoadjuvant chemoradiotherapy was preferred in the Netherlands and Ireland contrasting with chemotherapy in the UK and France. All countries used perioperative chemotherapy in gastric cancer but 1/3 of patients received this treatment. The mean R0 resection rate was 86% for oesophageal and junctional resections and 88% for gastric resections. Postoperative mortality varied from 1% to 7%. Conclusion: This European survey shown that implementing a uniform treatment and outcome data format of oesophagogastric cancer is feasible. It identified differences in disease presentation, treatment approaches and outcome, which need to be investigated, especially by increasing the number of participating countries. Future comparisons will facilitate developments in treatment for the benefit of patient outcomes.© 2015 Elsevier Ltd. All rights reserved. Keywords: EURECCA | Oesophageal cancer | Gastric cancer | International audit | Outcomes |
مقاله انگلیسی |