The prognostic role of the pre-treatment neutrophil to lymphocyte ratio (NLR) and tumor depth of invasion (DOI) in early-stage squamous cell carcinomas of the oral tongue

The Neutrophil-to-Lymphocyte Ratio as a Biomarker in Cutaneous Oncology: A Systematic Review of Evidence beyond Malignant Melanoma

With the ongoing progress of basic research along with the introduction of new pharmaceutical options spanning almost all therapeutic areas, the need for biomarkers that will be implemented into the personalized medical approach is higher than ever. Their use can be incorporated into clinical practice and can be applied to the classification of disorders and the evaluation of disease severity but also to the monitoring of the progress of therapeutic/pharmaceutical interventions. This systematic review collects the findings of hematologic biomarkers in various cutaneous malignancies, excluding malignant melanoma, to support their potential use in the prognosis but also in the assessment of therapeutic strategies for the specific category of skin disorders.

The Prognosis Performance of a Neutrophil- and Lymphocyte-Associated Gene Mutation Score in a Head and Neck Cancer Cohort

The treatment of head and neck squamous cell carcinomas (HNSCCs) is multimodal, and chemoradiotherapy (CRT) is a critical component. However, the availability of predictive or prognostic markers in patients with HNSCC is limited. Inflammation is a well-documented factor in cancer, and several parameters have been studied, with the neutrophil-to-lymphocyte ratio (NLR) being the most promising. The NLR is the most extensively researched clinical biomarker in various solid tumors, including HNSCC. In our study, we collected clinical and next-generation sequencing (NGS) data with targeted sequencing information from 107 patients with HNSCC who underwent CRT. The difference in the NLR between the good response group and the poor response group was significant, with more patients having a high NLR in the poor response group. We also examined the genetic alterations linked to the NLR and found a total of 41 associated genes across eight common pathways searched from the KEGG database. The overall mutation rate was low, and there was no significant mutation difference between the low- and high-NLR groups. Using a multivariate binomial generalized linear model, we identified three candidate genes (MAP2K2, MAP2K4, and ABL1) that showed significant results and were used to create a gene mutation score (GMS). Using the NLR-GMS category, we noticed that the high-NLR-GMS group had significantly shorter relapse-free survival compared to the intermediate- or low-NLR-GMS groups.

Support Tools in the Differential Diagnosis of Salivary Gland Tumors through Inflammatory Biomarkers and Radiomics Metrics: A Preliminary Study

BACKGROUND

The purpose of this study was to investigate how the systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR), and radiomic metrics (quantitative descriptors of image content) extracted from MRI sequences by machine learning increase the efficacy of proper presurgical differentiation between benign and malignant salivary gland tumors.

METHODS

A retrospective study of 117 patients with salivary gland tumors was conducted between January 2015 and November 2022. Univariate analyses with nonparametric tests and multivariate analyses with machine learning approaches were used.

RESULTS

Inflammatory biomarkers showed statistically significant differences (p < 0.05) in the Kruskal-Wallis test based on median values in discriminating Warthin tumors from pleomorphic adenoma and malignancies. The accuracy of NLR, PLR, SII, and SIRI was 0.88, 0.74, 0.76, and 0.83, respectively. Analysis of radiomic metrics to discriminate Warthin tumors from pleomorphic adenoma and malignancies showed statistically significant differences (p < 0.05) in nine radiomic features. The best multivariate analysis result was obtained from an SVM model with 86% accuracy, 68% sensitivity, and 91% specificity for six features.

CONCLUSIONS

Inflammatory biomarkers and radiomic features can comparably support a pre-surgical differential diagnosis.

Oral functional impairment may cause malnutrition following oral cancer treatment in a single-center cross-sectional study

Oral dysfunction and dysphagia after oral cancer treatment are linked to altered nutritional status. We aimed to identify specific oral functions related to nutritional status. We conducted a cross-sectional study from September 2019 to December 2021, recruited 75 participants (median age: 72.0 years), including 52 males and 23 females, collected background data, and evaluated oral function. The Mini Nutritional Assessment-Short Form (MNA-SF) scores were divided into three groups (normal nutritional status, at risk of malnutrition, and malnourished), and a multi-group comparison was conducted for each oral function measurement (microorganisms, oral dryness, occlusal force, tongue pressure, masticatory function, and Eating Assessment Tool [EAT-10]). The primary tumor site was the tongue in 31 patients (41.3%), gingiva in 30 (40.0%), and others in 14 (18.7%). Multiple comparisons revealed significant differences in occlusal force, tongue pressure, masticatory function, and EAT-10 levels, categorized as Type I (Transport type) and Type III (Occlusion type) postoperative oral dysfunctions, between each MNA-SF group. Multiple regression analysis showed a statistically significant association with MNA-SF in terms of masticatory function and EAT-10 levels, categorized as Type I. Type I and Type III are risk factors for malnutrition, confirming that different types of postoperative oral dysfunction require unique nutritional guidance.

Radiomics Metrics Combined with Clinical Data in the Surgical Management of Early-Stage (cT1-T2 N0) Tongue Squamous Cell Carcinomas: A Preliminary Study

Objective: To predict the risk of metastatic lymph nodes and the tumor grading related to oral tongue squamous cell carcinoma (OTSCC) through the combination of clinical data with radiomics metrics by computed tomography, and to develop a supportive approach in the management of the lymphatic cervical areas, with particular attention to the early stages (T1−T2). Between March 2016 and February 2020, patients with histologically confirmed OTSCC, treated by partial glossectomy and ipsilateral laterocervical lymphadenectomy and subjected to computed tomography (CT) before surgery, were identified by two centers: 81 patients (49 female and 32 male) with 58 years as the median age (range 19−86 years). Univariate analysis with non-parametric tests and multivariate analysis with machine learning approaches were used. Clinical, hematological parameters and radiological features extracted by CT were considered individually and in combination. All clinical parameters showed statistically significant differences (p < 0.05) for the Kruskal−Wallis test when discriminating both the tumor grading and the metastatic lymph nodes. DOI, PLR, SII, and SIRI showed an accuracy of 0.70 (ROC analysis) when identifying the tumor grading, while an accuracy ≥ 0.78 was shown by DOI, NLR, PLR, SII, and SIRI when discriminating metastatic lymph nodes. In the context of the analysis of radiomics metrics, the original_glszm_HighGrayLevelZoneEmphasis feature was selected for identifying the tumor grading (accuracy of 0.70), while the wavelet_HHH_glrlm_LowGrayLevelRunEmphasis predictor was selected for determining metastatic lymph nodes (accuracy of 0.96). Remarkable findings were also obtained when classifying patients with a machine learning approach. Radiomics features alone can predict tumor grading with an accuracy of 0.76 using a logistic regression model, while an accuracy of 0.82 can be obtained by running a CART algorithm through a combination of three clinical parameters (SIRI, DOI, and PLR) with a radiomics feature (wavelet_LLL_glszm_SizeZoneNonUniformityNormalized). In the context of predicting metastatic lymph nodes, an accuracy of 0.94 was obtained using 15 radiomics features in a logistic regression model, while both CART and CIDT achieved an asymptotic accuracy value of 1.00 using only one radiomics feature. Radiomics features and clinical parameters have an important role in identifying tumor grading and metastatic lymph nodes. Machine learning approaches can be used as an easy-to-use tool to stratify patients with early-stage OTSCC, based on the identification of metastatic and non-metastatic lymph nodes.