Researchers have identified two biomarkers that may predict the effectiveness of radiation treatments for patients with head and neck cancer, according to a study in the European Journal of Cancer (3 February 2014, Vol. 50:3, pp. 570-581).
An international team of researchers followed two groups of patients with head and neck squamous cell carcinoma (HNSCC). In the first group of 38 patients, researchers screened 18,000 genes and identified five distinct markers. The second group was larger with 86 patients and confirmed the findings, particularly two biomarkers. The two markers were good at predicting patients' poor response to radiation therapy.
Radiation therapy is commonly used to treat patients with HNSCC; however, it is not always well-tolerated. It can take two months and result in extensive side effects, noted lead researcher Dr. Jan Akervall, PhD, of the Head and Neck Cancer Multidisciplinary Clinic at Beaumont Hospital in Royal Oak, Michigan, in the U.S., and colleauges from University Hospital in Lund, Sweden, and the Van Andel Institute in Grand Rapids, Michigan.
Patients' biomarkers can shed light on whether their tumors will respond to radiation. If indications are that a tumor will not, physicians can look at other treatment options, saving time, possible risk for complications, and expense, Akervall said.
Biomarker studies can provide a bridge between emerging molecular information and clinical treatment, according to the study authors. Biomarkers may also lead to personalized treatment, in contrast to current protocol-based medicine.
The biological properties of tumors, as measured in the patient's pretreatment biopsies, may lead to the ability to predict the response to radiation therapy and concurrent chemoradiation, thus allowing for tailored patient-specific treatment strategies, the study authors concluded.
According to the U.S. National Cancer Institute, most cancers of the head and neck usually begin in the squamous cells that line the moist surfaces of the mouth, nose, and throat. Risk factors identified with HNSCC include tobacco and alcohol use, as well as infection with cancer-causing types of human papillomavirus (HPV).
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
