Identification of CT-based non-invasive radiomic biomarkers for overall survival prediction in oral cavity squamous cell carcinoma

The role of molecular biomarkers in the diagnosis, prognosis, and treatment stratification of oral squamous cell carcinoma: A comprehensive review

One of the most common cancers targeting the area of the head and neck is oral squamous cell carcinoma (OSCC), carrying a heavy global health cost. With a high incidence of metastasis and recurrence, the outlook for OSCC remains dismal despite advancements in treatment. This has sparked an investigation into molecular biomarkers, which have the potential to improve early diagnosis, forecast patient outcomes, and direct therapeutic approaches. An extensive summary of the function of molecular biomarkers in OSCC diagnosis, prognosis, and medical care stratification is given in this article. Complex genetic mutations, epigenetic changes, and dysregulated signalling pathways are all part of the aetiology of OSCC. Tumor protein p53 (Tp53), Epidermal growth factor receptor (EGFR-targeted), Cyclin D1 (CCND1), and Human papilloma virus (HPV) status are examples of molecular biomarkers that have demonstrated potential in recognising disease at an early stage and identifying malignant changes. The non-invasive detection capabilities of diagnostic biomarkers such as salivary proteins, circulating tumour DNA (ctDNA), and microRNAs are being explored more and more because they may provide early intervention and better patient outcomes. Prognostically, tumour aggressiveness, recurrence risk, and overall survival have all been linked to biomarkers such as matrix metalloproteinases (MMPs), E-cadherin, and different cytokines. Furthermore, immune checkpoints such as cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and programmed death-ligand 1 (PD-L1) are becoming recognised as important markers of the tumour microenvironment's function in the course of the disease and its reaction to immunotherapy. The significance of biomarkers in personalised medicine has been further highlighted by the recognition of subgroups with elevated risk that might gain benefit from more aggressive treatment options thanks to the genetic profiling of OSCC. Predictive biomarkers are essential for therapy classification because they allow therapeutic regimens to be tailored. For example, (Kirsten rat sarcoma viral oncogene homologous) KRAS mutations and EGFR expression influence the effectiveness of targeted therapies, and the existence of specific epigenetic markers influences choices about radiation or chemotherapy. It is expected that the incorporation of multi-omics techniques, which integrate transcriptome, proteome, and genomic data, will improve these tactics and increase accuracy in OSCC treatment. Molecular indicators have the potential to significantly improve the medical treatment of ovarian cancer. Better patient outcomes will eventually result from earlier identification, more precise prognostication, and individualised therapy regimens made possible by advancements in biomarker research. For these biomarkers to be widely used, further research must be done on verifying them and incorporating them into standard clinical practice.

Identification of CT based radiomic biomarkers for progression free survival in head and neck squamous cell carcinoma

This study addresses the limited noninvasive tools for Head and Neck Squamous Cell Carcinoma (HNSCC) progression-free survival (PFS) prediction by identifying Computed Tomography (CT)-based biomarkers for predicting prognosis. A retrospective analysis was conducted on data from 203 HNSCC patients. An ensemble feature selection involving correlation analysis, univariate survival analysis, best-subset selection, and the LASSO-Cox algorithm was used to select functional features, which were then used to build final Cox Proportional Hazards models (CPH). Our CPH achieved a 0.69 concordance index in an external indepedent cohort of 77 patients. The model identified five CT-based radiomics features, Gradient ngtdm Contrast, Logσ=33D-FirstorderRootMeanSquared, Logσ=0.13D-glszm SmallAreaLowGrayLevelEmphasis, Exponential-gldm LargeDependenceHighGrayLevelEmphasis, and Gradient ngtdm Strength as survival biomarkers (p-value < 0.05). These findings contribute to our knowledge of how radiomics can be used to predict the outcome so that treatment plans can be tailored for people with HNSCC to improve their prognosis.

Computer Vision-Radiomics & Pathognomics

The role of computer vision in extracting radiographic (radiomics) and histopathologic (pathognomics) features is an extension of molecular biomarkers that have been foundational to our understanding across the spectrum of head and neck disorders. Especially within head and neck cancers, machine learning and deep learning applications have yielded advances in the characterization of tumor features, nodal features, and various outcomes. This review aims to overview the landscape of radiomic and pathognomic applications, informing future work to address gaps. Novel methodologies will be needed to potentially engineer ways of integrating multidimensional data inputs to examine disease features to guide prognosis comprehensively and ultimately clinical management.

Imaging Modalities for Head and Neck Cancer: Present and Future

Several imaging modalities are utilized in the diagnosis, treatment, and surveillance of head and neck cancer. First-line imaging remains computed tomography (CT); however, MRI, PET with CT (PET/CT), and ultrasound are often used. In the last decade, several new imaging modalities have been developed that have the potential to improve early detection, modify treatment, decrease treatment morbidity, and augment surveillance. Among these, molecular imaging, lymph node mapping, and adjustments to endoscopic techniques are promising. The present review focuses on existing imaging, novel techniques, and the recent changes to imaging practices within the field.

Radiomic biomarkers of locoregional recurrence: prognostic insights from oral cavity squamous cell carcinoma preoperative CT scans

Introduction

This study aimed to identify CT-based imaging biomarkers for locoregional recurrence (LR) in Oral Cavity Squamous Cell Carcinoma (OSCC) patients.

Methods

Computed tomography scans were collected from 78 patients with OSCC who underwent surgical treatment at a single medical center. We extracted 1,092 radiomic features from gross tumor volume in each patient's pre-treatment CT. Clinical characteristics were also obtained, including race, sex, age, tobacco and alcohol use, tumor staging, and treatment modality. A feature selection algorithm was used to eliminate the most redundant features, followed by a selection of the best subset of the Logistic regression model (LRM). The best LRM model was determined based on the best prediction accuracy in terms of the area under Receiver operating characteristic curve. Finally, significant radiomic features in the final LRM model were identified as imaging biomarkers.

Results and discussion

Two radiomics biomarkers, Large Dependence Emphasis (LDE) of the Gray Level Dependence Matrix (GLDM) and Long Run Emphasis (LRE) of the Gray Level Run Length Matrix (GLRLM) of the 3D Laplacian of Gaussian (LoG σ=3), have demonstrated the capability to preoperatively distinguish patients with and without LR, exhibiting exceptional testing specificity (1.00) and sensitivity (0.82). The group with LRE > 2.99 showed a 3-year recurrence-free survival rate of 0.81, in contrast to 0.49 for the group with LRE ≤ 2.99. Similarly, the group with LDE > 120 showed a rate of 0.82, compared to 0.49 for the group with LDE ≤ 120. These biomarkers broaden our understanding of using radiomics to predict OSCC progression, enabling personalized treatment plans to enhance patient survival.

Multimodality radiomics for tumor prognosis in nasopharyngeal carcinoma

BACKGROUND

The prognosis of nasopharyngeal carcinoma (NPC) is challenging due to late-stage identification and frequently undetectable Epstein-Barr virus (EBV) DNA. Incorporating radiomic features, which quantify tumor characteristics from imaging, may enhance prognosis assessment.

PURPOSE

To investigate the predictive power of radiomic features on overall survival (OS), progression-free survival (PFS), and distant metastasis-free survival (DMFS) in NPC.

MATERIALS AND METHODS

A retrospective analysis of 183 NPC patients treated with chemoradiotherapy from 2010 to 2019 was conducted. All patients were followed for at least three years. The pretreatment CT images with contrast medium, MR images (T1W and T2W), as well as gross tumor volume (GTV) contours, were used to extract radiomic features using PyRadiomics v.2.0. Robust and efficient radiomic features were chosen using the intraclass correlation test and univariate Cox proportional hazard regression analysis. They were then combined with clinical data including age, gender, tumor stage, and EBV DNA level for prognostic evaluation using Cox proportional hazard regression models with recursive feature elimination (RFE) and were optimized using 20 repetitions of a five-fold cross-validation scheme.

RESULTS

Integrating radiomics with clinical data significantly enhanced the predictive power, yielding a C-index of 0.788 ± 0.066 to 0.848 ± 0.079 for the combined model versus 0.745 ± 0.082 to 0.766 ± 0.083 for clinical data alone (p<0.05). Multimodality radiomics combined with clinical data offered the highest performance. Despite the absence of EBV DNA, radiomics integration significantly improved survival predictions (C-index ranging from 0.770 ± 0.070 to 0.831 ± 0.083 in combined model versus 0.727 ± 0.084 to 0.734 ± 0.088 in clinical model, p<0.05).

CONCLUSIONS

The combination of multimodality radiomic features from CT and MR images could offer superior predictive performance for OS, PFS, and DMFS compared to relying on conventional clinical data alone.

Radiomic Biomarkers of Locoregional Recurrence: Prognostic Insights from Oral Cavity Squamous Cell Carcinoma preoperative CT scans

This study aimed to identify CT-based imaging biomarkers for locoregional recurrence (LR) in Oral Cavity Squamous Cell Carcinoma (OSCC) patients. Our study involved a retrospective review of 78 patients with OSCC who underwent surgical treatment at a single medical center. An approach involving feature selection and statistical model diagnostics was utilized to identify biomarkers. Two radiomics biomarkers, Large Dependence Emphasis (LDE) of the Gray Level Dependence Matrix (GLDM) and Long Run Emphasis (LRE) of the Gray Level Run Length Matrix (GLRLM) of the 3D Laplacian of Gaussian (LoG σ = 3), have demonstrated the capability to preoperatively distinguish patients with and without LR, exhibiting exceptional testing specificity (1.00) and sensitivity (0.82). The group with LRE > 2.99 showed a 3-year recurrence-free survival rate of 0.81, in contrast to 0.49 for the group with LRE ≤ 2.99. Similarly, the group with LDE > 120 showed a rate of 0.82, compared to 0.49 for the group with LDE ≤ 120. These biomarkers broaden our understanding of using radiomics to predict OSCC progression, enabling personalized treatment plans to enhance patient survival.

Leukotriene B4 receptor knockdown affects PI3K/AKT/mTOR signaling and apoptotic responses in colorectal cancer

Colorectal cancer (CRC) presents a landscape of intricate molecular dynamics. In this study, we focused on the role of the leukotriene B4 receptor (LTB4R) in CRC, exploring its significance in the disease's progression and potential therapeutic approaches. Using bioinformatics analysis of the GSE164191 and the Cancer Genome Atlas-colorectal adenocarcinoma (TCGA-COAD) datasets, we identified LTB4R as a hub gene influencing CRC prognosis. Subsequently, we examined the relationship between LTB4R expression, apoptosis, and the phosphoinositide 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) signaling pathway through cellular and mice experiments. Our findings revealed that LTB4R is highly expressed in CRC samples and is pivotal for determining prognosis. In vitro experiments demonstrated that silencing LTB4R significantly impeded CRC cell viability, migration, invasion, and colony formation. Correspondingly, in vivo tests indicated that LTB4R knockdown led to markedly slower tumor growth in mice models. Further in-depth investigation revealed that LTB4R knockdown significantly amplified the apoptosis in CRC cells and upregulated the expression of apoptosis-related proteins, such as caspase-3 and caspase-9, while diminishing p53 expression. Interestingly, silencing LTB4R also resulted in a significant downregulation of the PI3K/AKT/mTOR signaling pathway. Moreover, pretreatment with the PI3K activator 740Y-P only partially attenuated the effects of LTB4R knockdown on CRC cell behavior, emphasizing LTB4R's dominant influence in CRC cell dynamics and signaling pathways. LTB4R stands out as a critical factor in CRC progression, profoundly affecting cellular behavior, apoptotic responses, and the PI3K/AKT/mTOR signaling pathway. These findings not only shed light on LTB4R's role in CRC but also establish it as a potential diagnostic biomarker and a promising target for therapeutic intervention.