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Competing Risk Nomograms:
1. Mell LK, Shen H, Nguyen-Tân PF, Rosenthal DI, Zakeri K, Vitzthum LK et al. Nomogram to Predict the Benefit of Intensive Treatment for Locoregionally Advanced Head and Neck Cancer. Clin Cancer Res. 2019 Dec 1;25(23):7078-7088. PUBMED
2. Zakeri K, Rotolo F, Lacas B, Vitzthum LK et al. Predictor of effectiveness of treatment intensification on overall survival in head and neck cancer (HNC). Ann Oncol. 2018 Oct;29(8):375-376. ANN ONCOL
3. Vitzthum LK, Park H, Zakeri K, Bryant AK et al. Selection of Head and Neck Cancer Patients for Intensive Therapy. Int J Radiat Oncol Biol Phys. 2020 Jan 1;106(1):157-166. PUBMED
4. Experimental, pending validation
Generalized Competing Event(GCE) Model:
Carmona R, Gulaya S, Murphy JD, Rose BS, Wu J et al. Validated competing event model for the stage I-II endometrial cancer population. Int J Radiat Oncol Biol Phys. 2014 Jul 15;89(4):888-98. PUBMED
- The program on this page will generate risk scores to predict the proportional risk attributable to cancer (omega score) for patients with newly diagnosed, non-metastatic head and neck cancer undergoing primary radiation therapy.
- There are several possible scores because several data sets with different variables have been used to generate and validate different risk score algorithms
- The risk scores based on Radiation Therapy Oncology Group (RTOG) and Meta-Analysis of of Radiotherapy in squamous cell Carcinomas of Head and neck (MARCH) data have been validated as predictive algorithms. Patients with an omega score ≥ 0.80 are more likely to benefit from intensive treatment.
- Intensive treatment may consist of using concurrent, adjuvant, or induction chemotherapy, and/or accelerated or altered radiation fractionation.
- The risk score based on the Veterans Affairs (VINCI) data set has been validated as a prognostic algorithm, meaning it is accurate for forecasting patient's proportional risk attributable to cancer (omega). This algorithm takes into account comorbidity and some additional information that the RTOG and MARCH algorithms do not.
-The composite omega score takes into account all information used to create different risk scores. Studies are ongoing to test and validate this algorithm.
- Enter the patient's information in the form below.
- If all algorithms agree that the omega score is high (≥ 0.80), the bars will highlight in green. This indicates a strong likelihood the individual will benefit from intensive therapy.
- If all algorithms agree that the omega score is low (< 0.80), the bars will highlight in red. This indicates a strong likelihood the individual will not benefit from intensive therapy.
- If the algorithms disagree about whether the omega score is high or low, the bars will highlight in yellow. This indicates uncertainty about the likelihood the patient will benefit from intensive therapy
- These scores should be used as an evidence-based guide to assist physicians and patients make informed medical decisions. These scores should not be used to replace individual clinical decision making.
- Omega refers to a patient's proportional risk that is attributable to cancer. It is a number on the scale from 0-1.
- Patients with very high value (close to 1) have a high risk of cancer progression relative to dying from other causes. These patients are more likely to benefit from intensive cancer therapy.
- Patients with a very low value (close to 0) have a high risk of dying from other causes relative to cancer progression. These patients are less likely to benefit from intensive cancer therapy, and may benefit from alternative strategies directed toward their competing health risks.
- Comorbidity refers to existing health problems unrelated to cancer.
- For example, a patient with head and neck cancer may also have underlying heart and lung disease from previous smoking
- Co-morbid conditions affect patients' tolerance to and likelihood to benefit from intensive cancer therapies
- Competing risks refer to patients' simultaneous risk for adverse outcomes in addition to their risk for cancer recurrence
- Such outcomes might include severe side effects from therapy or mortality from causes unrelated to cancer
- Omega values quantify the degree of competing risks present in a population of patients
- Omega scores predict the omega value for individual patients