Prognostic and Predictive Biomarkers in Head and Neck Squamous Cell Carcinoma Treated with Radiotherapy—A Systematic Review

Schanne, Daniel H.; Koch, Alexander; Eliçin, Olgun; Giger, Roland; Medová, Michaela; Zimmer, Yitzhak; Aebersold, Daniel M.

doi:10.3390/biomedicines10123288

Cited by 5 publications

(2 citation statements)

References 52 publications

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“…Guidelines indicate that tumor size measurement and staging, histological grading, molecular patterns, and lymph node involvement or metastasis are essential prerequisites for a valid prognostic evaluation of the patient [14][15][16]. There are different therapeutic strategies and different prognoses associated with each tumor type [17][18][19]. This was further highlighted in the eighth UICC/AJCC staging system, where two different TNM staging systems are proposed depending on the expression of p16 [20,21].…”

Section: Introductionmentioning

confidence: 99%

Predicting Response to Exclusive Combined Radio-Chemotherapy in Naso-Oropharyngeal Cancer: The Role of Texture Analysis

Bicci,

Calamandrei,

Di Finizio

et al. 2024

Diagnostics

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The aim of this work is to identify MRI texture features able to predict the response to radio-chemotherapy (RT-CHT) in patients with naso-oropharyngeal carcinoma (NPC-OPC) before treatment in order to help clinical decision making. Textural features were derived from ADC maps and post-gadolinium T1-images on a single MRI machine for 37 patients with NPC-OPC. Patients were divided into two groups (responders/non-responders) according to results from MRI scans and 18F-FDG-PET/CT performed at follow-up 3–4 and 12 months after therapy and biopsy. Pre-RT-CHT lesions were segmented, and radiomic features were extracted. A non-parametric Mann–Whitney test was performed. A p-value < 0.05 was considered significant. Receiver operating characteristic curves and area-under-the-curve values were generated; a 95% confidence interval (CI) was reported. A radiomic model was constructed using the LASSO algorithm. After feature selection on MRI T1 post-contrast sequences, six features were statistically significant: gldm_DependenceEntropy and DependenceNonUniformity, glrlm_RunEntropy and RunLengthNonUniformity, and glszm_SizeZoneNonUniformity and ZoneEntropy, with significant cut-off values between responder and non-responder group. With the LASSO algorithm, the radiomic model showed an AUC of 0.89 and 95% CI: 0.78–0.99. In ADC, five features were selected with an AUC of 0.84 and 95% CI: 0.68–1. Texture analysis on post-gadolinium T1-images and ADC maps could potentially predict response to therapy in patients with NPC-OPC who will undergo exclusive treatment with RT-CHT, being, therefore, a useful tool in therapeutical–clinical decision making.

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Section: Introductionmentioning

confidence: 99%

Predicting Response to Exclusive Combined Radio-Chemotherapy in Naso-Oropharyngeal Cancer: The Role of Texture Analysis

Bicci,

Calamandrei,

Di Finizio

et al. 2024

Diagnostics

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“…The scarcity of biomarkers of HNSCC response to anticancer therapy reflects the lack of relevant prognostic models for HNSCC [12][13][14]. Robust preclinical models are required to address the mechanisms of treatment resistance and progression of HNSCC in order to update the therapeutic strategies [15,16].…”

Section: Introductionmentioning

confidence: 99%

In Vitro Models of Head and Neck Cancer: From Primitive to Most Advanced

Arutyunyan,

Jumaniyazova,

Makarov

et al. 2023

JPM

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For several decades now, researchers have been trying to answer the demand of clinical oncologists to create an ideal preclinical model of head and neck squamous cell carcinoma (HNSCC) that is accessible, reproducible, and relevant. Over the past years, the development of cellular technologies has naturally allowed us to move from primitive short-lived primary 2D cell cultures to complex patient-derived 3D models that reproduce the cellular composition, architecture, mutational, or viral load of native tumor tissue. Depending on the tasks and capabilities, a scientific laboratory can choose from several types of models: primary cell cultures, immortalized cell lines, spheroids or heterospheroids, tissue engineering models, bioprinted models, organoids, tumor explants, and histocultures. HNSCC in vitro models make it possible to screen agents with potential antitumor activity, study the contribution of the tumor microenvironment to its progression and metastasis, determine the prognostic significance of individual biomarkers (including using genetic engineering methods), study the effect of viral infection on the pathogenesis of the disease, and adjust treatment tactics for a specific patient or groups of patients. Promising experimental results have created a scientific basis for the registration of several clinical studies using HNSCC in vitro models.

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