New referrals
A substantial part of outpatient care in head and neck surgical oncology
work involves assessment of patients referred in with symptoms
suspicious of HNC. Apart from a few malignancies (basal cell carcinomas,
low-grade salivary gland tumours and well-differentiated thyroid cancer
in young patients), most HNCs progress within a matter of months, and a
delay in management can have an adverse effect on the prognosis. In
2000, the Department of Health in the UK developed national guidelines
for referral of suspected HNC from primary care, updated by the National
Institute for Health and Care Excellence (NICE) in 2005 and 2015.
Systematic reviews have shown that the pooled detection rate of cancer
in this population is between 8.8% 8 and 11.1%9. Thus, the vast majority of referrals may be safely
triaged to a deferred assessment.
In 2016, our group used information from a cohort of 4715 patients
referred in for suspected HNC to generate a 13-symptom inventory which,
when combined with age, could generate a personalised risk of cancer
with a high diagnostic efficacy (area under the curve (AUC) 0.77)10. Sensitivity analysis identified the 8% risk
probability cut off to offer optimal performance for the calculator.
This risk calculator was subsequently validated in an external cohort of
1998 patients, where it performed well compared to the inception cohort,
with an AUC of 0.81 11.
After further refinements using additional information to the symptom
inventory in a new cohort (n= 3531), we published the Head and Neck
Cancer Risk Calculator (HaNC-RC)-V.2, which demonstrated increased
diagnostic efficacy with an area under the curve of 0.89, and a
sensitivity of 85% 12. The model performed optimally
at a probability cut off of 7.1%, where the negative predictive value
(NPV) was 98.6%. Thus, when the symptom inventory is applied to a
patient who has been referred for suspected cancer and the calculator
indicates a <7% risk probability for cancer diagnosis, the
chance of missing cancer if the patient is not seen by a face-to-face
conventional consultation is 1.4%. In all these instances, a logistic
regression model was used to define personalised probability risk for
each patient. When a variety of machine learning algorithms were used on
a similar cohort of 5082 patients referred for suspected HNC, logistic
regression, the technique used to create the calculator, offered one of
the highest true negative rates, an essential characteristic of a test
to triage patients out during resource constrained times13.