Accumulating literature has explored how prognostically significant the prognostic nutritional index (PNI) was for the oral carcinoma population, but with inconsistent findings. Therefore, we retrieved the most recent data and carried out this meta-analysis to comprehensively analyze the prognostic performance of pretreatment PNI in oral cancer. The electronic databases of PubMed, Embase, China National Knowledge Infrastructure (CNKI), Cochrane Library and Web of Science were fully retrieved. PNI’s prognostic value for survival outcomes in oral carcinoma was assessed by estimating pooled hazard ratios (HRs) plus 95% confidence intervals (CIs). We examined the correlation of PNI with clinicopathological traits of oral carcinoma by utilizing the pooled odds ratios (ORs) plus 95% CIs. According to the pooled results of the present meta-analysis, which enrolled 10 studies involving 3,130 patients, for oral carcinoma suffers whose PNI was low, their disease-free survival (DFS) (HR=1.92, 95%CI=1.53-2.42, p<0.001) and overall survival (OS) (HR=2.44, 95%CI=1.45-4.12, p=0.001) would be inferior. Nonetheless, cancer-specific survival (CSS) was not linked significantly to PNI for the oral carcinoma population (HR=1.89, 95%CI=0.61-5.84, p=0.267). Significant associations of low PNI with TNM stages III-IV (OR=2.16, 95%CI=1.60-2.91, p<0.001) and age ≥ 65 years (OR=2.29, 95%CI=1.76-2.98, p<0.001) were found. As suggested by the present meta-analysis, a low PNI was linked to inferior DFS and OS among oral carcinoma patients. Oral cancer patients with low PNI may have high-risk of tumor progression. PNI could be served as a promising and effective index to predict prognosis in patients with oral cancer.

/pdf/prognostic-and-clinicopathological-significance-of-1lwovpvp.pdf

Prognostic and clinicopathological significance of prognostic nutritional index (PNI) in patients with oral cancer: a meta-analysis

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer type, has often an aggressive course and is poorly responsive to current therapeutic approaches, so that 5-year survival rates for patients diagnosed with advanced disease is lower than 50%. The Epidermal Growth Factor Receptor (EGFR) has emerged as an established oncogene in HNSCC. Indeed, although HNSCCs are a heterogeneous group of cancers which differ for histological, molecular and clinical features, EGFR is overexpressed or mutated in a percentage of cases up to about 90%. Moreover, aberrant expression of the other members of the ErbB receptor family, ErbB2, ErbB3 and ErbB4, has also been reported in variable proportions of HNSCCs. Therefore, an increased expression/activity of one or multiple ErbB receptors is found in the vast majority of patients with HNSCC. While aberrant ErbB signaling has long been known to play a critical role in tumor growth, angiogenesis, invasion, metastatization and resistance to therapy, more recent evidence has revealed its impact on other features of cancer cells’ biology, such as the ability to evade antitumor immunity. In this paper we will review recent findings on how ErbB receptors expression and activity, including that associated with non-canonical signaling mechanisms, impacts on prognosis and therapy of HNSCC.

https://www.frontiersin.org/articles/10.3389/fmed.2023.1066021/pdf

Recent findings on the impact of ErbB receptors status on prognosis and therapy of head and neck squamous cell carcinoma

A disintegrin and metalloproteinase domain‐containing protein 10 (ADAM‐10) involves in the tumour progression, but the impacts of single‐nucleotide polymorphism (SNP) of ADAM‐10 on oral squamous cell carcinoma (OSCC) remain unclear. The aim of this study was to investigate the influence of SNP of ADAM‐10 on the clinical features of OSCC in male Taiwanese. Five loci of ADAM‐10 SNPs including rs653765 (C/T), rs2305421 (A/G), rs514049 (A/C), rs383902 (T/C) and rs2054096 (A/T) were genotyped by TaqMan allelic discrimination in 1138 OSCC patients and 1199 non‐OSCC individuals. The ADAM‐10 SNP rs2305421 GG (AOR: 1.399, 95% CI: 1.045–1.874, p = 0.024) and G allele (AOR: 1.170, 95% CI: 1.012–1.351, p = 0.034) illustrated a significantly higher genotypic frequencies in the OSCC group compared to the distribution of the ADAM‐10 SNP rs2305421 AA wild type. In the subgroup analysis, the ADAM‐10 SNP rs383902 TC+CC was significantly correlated to tumour size larger than T2 in betel quid chewer (AOR: 1.375, 95% CI: 1.010–1.872, p = 0.043), while the ADAM‐10 SNP rs653765 CT+TT was significantly associated with tumour size larger than T2 in cigarette smoker (AOR: 1.346, 95% CI: 1.023–1.772, p = 0.034). The results from The Cancer Genome Atlas revealed highest ADAM‐10 mRNA level in T2 stage of current smokers with head and neck squamous cell carcinoma (HNSCC). In conclusions, the ADAM‐10 SNP rs2305421 G allele is associated with the presence of OSCC, and the ADAM‐10 SNP rs383902 TC+CC and ADAM‐10 SNP rs653765 CT+TT correlates to large tumour size in specific conditions.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jcmm.17728

Potential impact of ADAM‐10 genetic variants with the clinical features of oral squamous cell carcinoma

Stereotactic body radiation therapy (SBRT) is a precise and conformal radiation therapy (RT) that aims to deliver a high dose of radiation to the tumor whilst sparing surrounding normal tissue, making it an attractive option for head and neck cancer (HNC) patients who are not suitable for the traditional long course of RT with comprehensive RT target volume. Definitive SBRT for HNC has been investigated in different settings, including early stage glottis cancer, and as an alternative to brachytherapy boost after external beam RT. It is also used as a primary treatment option for elderly or medically unfit patients. More recently, an SBRT combination with immunotherapy in the neoadjuvant setting for HNC showed promising results. Salvage or adjuvant SBRT for HNC can be used in appropriately selected cases. Future studies are warranted to determine the optimum dose and fractionation schedules in any of these indications.

The Evolving Role of Stereotactic Body Radiation Therapy for Head and Neck Cancer: Where Do We Stand?

Head and neck squamous cell carcinoma (HNSCC) constitutes a significant global cancer burden, given its high prevalence and associated mortality. Despite substantial progress in survival rates due to the enhanced multidisciplinary approach to treatment, these methods often lead to severe tissue damage, compromised function, and potential toxicity. Thus, there is an imperative need for novel, effective, and minimally damaging treatment modalities. Neoadjuvant treatment, an emerging therapeutic strategy, is designed to reduce tumor size and curtail distant metastasis prior to definitive intervention. Currently, neoadjuvant chemotherapy (NACT) has optimized the treatment approach for a subset of HNSCC patients, yet it has not produced a noticeable enhancement in overall survival (OS). In the contemporary cancer therapeutics landscape, immunotherapy is gaining traction at an accelerated pace. Notably, neoadjuvant immunotherapy (NAIT) has shown promising radiological and pathological responses, coupled with encouraging efficacy in several clinical trials. This potentially paves the way for a myriad of possibilities in treatment de-escalation of HNSCC, which warrants further exploration. This paper reviews the existing strategies and efficacies of neoadjuvant immune checkpoint inhibitors (ICIs), along with potential de-escalation strategies. Furthermore, the challenges encountered in the context of the de-escalation strategies of NAIT are explored. The aim is to inform future research directions that strive to improve the quality of life (QoL) for patients battling HNSCC. 

Less Is More: Exploring Neoadjuvant Immunotherapy as a De-escalation Strategy in Head and Neck Squamous Cell Carcinoma Treatment

Neoadjuvant immunoradiotherapy in patients with locally advanced oral cavity squamous cell carcinoma: a retrospective study

Abstract Objective We aimed to develop a 5-year overall survival prediction model for patients with oral tongue squamous cell carcinoma based on machine learning methods. Subjects and methods The data were obtained from electronic medical records of 224 OTSCC patients at the PLA General Hospital. A five-year overall survival prediction model was constructed using logistic regression, Support Vector Machines, Decision Tree, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine. Model performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic curve. The output of the optimal model was explained using the Python package (SHapley Additive exPlanations, SHAP). Results After passing through the grid search and secondary modeling, the Light Gradient Boosting Machine was the best prediction model (AUC = 0.860). As explained by SHapley Additive exPlanations, N-stage, age, systemic inflammation response index, positive lymph nodes, plasma fibrinogen, lymphocyte-to-monocyte ratio, neutrophil percentage, and T-stage could perform a 5-year overall survival prediction for OTSCC. The 5-year survival rate was 42%. Conclusion The Light Gradient Boosting Machine prediction model predicted 5-year overall survival in OTSCC patients, and this predictive tool has potential prognostic implications for patients with OTSCC. 

Prediction of 5-year overall survival of tongue cancer based machine learning

OBJECTIVES
We assessed the potential prognostic significance of the preoperative systemic inflammation index, platelet-to-lymphocyte ratio, and neutrophil-to-lymphocyte ratio in patients who underwent surgical resection.


SUBJECTS AND METHODS
This retrospective study included 224 patients with clinico-pathologically confirmed squamous carcinoma of the tongue who underwent surgery between August 2009 and December 2017. The optimal cut-off values for the indices were determined by receiver operating characteristic curves. Correlations between the indices and clinicopathological variables were determined by Pearson chi-square or Fisher exact tests. The Kaplan-Meier test was used to compare overall survival between groups (high and low values); the log-rank or Breslow test was used to assess differences in survival. Univariate and multivariate Cox regression models were used to analyse predictive values of the indices as independent indicators of overall survival. Bilateral P values of <0.05 were considered statistically significant.


RESULTS
Significant association was found between the indices and sex, tissue grade, tumour location, and lymph nodes metastases (p<0.05). On Kaplan-Meier analysis, patients with lower values of the indices had longer overall survival (p<0.05). Univariate and multivariate Cox regression models identified age, lymph node metastases, and neutrophil-to-lymphocyte ratio as independent predictors of overall survival.


CONCLUSION
The studied indices have potential prognostic significance in patients with squamous tongue cancer.

Predictive value of systemic inflammatory response-related indices for survival in tongue cancer.

This study aimed to build a home use deep learning segmentation model to identify the scope of caries lesions. A total of 494 caries photographs of molars and premolars collected via endoscopy were selected. Subsequently, these photographs were labeled by physicians and underwent segmentation training by using DeepLabv3+, and then verification and evaluation were performed. The mean accuracy was 0.993, the sensitivity was 0.661, the specificity was 0.997, the Dice coefficient was 0.685, and the intersection over union (IoU) was 0.529. Therefore, the present deep learning segmentation model can identify and segment the scope of caries.

[A deep learning segmentation model for detecting caries in molar teeth].

Background Caries are common, especially in economically undeveloped countries with limited access to medical resources. Sometimes patient cannot even realize that they have oral problems until they feel obvious pain. Deep convolutional neural networks (CNNs) have been widely adopted for medical image analysis and management and have yielded some progress in stomatology while the endoscopes are cheap and easily used in daily life for families or other non-medical situations. Therefore, we created a deep learning model to detect and recognize caries using endoscopic images. Methods We used 194 images of non-caries and 1,059 images of permanent molar and premolar caries to build a classification and a segmentation model in patients of endoscope images from the Department of Stomatology of People’s Liberation Army General Hospital (PLAGH). A classification model combined with an end-to-end semantic segmentation model, DeepLabv3+ was used for segmenting the caries, then we evaluated with a 5-fold cross-validation protocol whereby each fold was used once. Results In the classification model, the mean area under the curve (AUC) [90% confidence interval (CI)] was 0.9897 (0.9821–0.9956) (P<0.01) In the segmentation model, the mean accuracy was 0.9843 (0.9820–0.9871), the recall was 0.6996 (0.6810–0.7194), the specificity was 0.9943 (0.9937–0.9954), the Dice coefficient was 0.7099 (0.6948–0.7343), and the intersection over union (IoU) was 0.5779 (0.5646–0.6006). Conclusions We used a deep learning model to monitor caries and encourage their early diagnosis and treatment.

/pdf/a-deep-learning-model-using-convolutional-neural-networks-3f7buvdi.pdf

A deep learning model using convolutional neural networks for caries detection and recognition with endoscopes

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

N. Jin

Author Tools

Chat about Author