A web‐based prediction score for head and neck cancer referrals

Lau, Kimberley; Wilkinson, J.; Moorthy, Ram

doi:10.1111/coa.13098

Cited by 18 publications

(29 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…16,17 Persistent hoarseness and neck lump have been previously highlighted as the most common symptoms resulting in cancer diagnosis. [16][17][18][19] These symptoms were part of HaNC-RC since its first version. One of the new additions to the HaNC-RC v2 is the sore throat symptom.…”

Section: Comparison With Other Studiesmentioning

confidence: 99%

“…This is substantially higher than the ROC of the HaN-RC v.1 (77%) and the ROC of the Lau et al, model (79%). 16,18 Our study is the first to include patients from all types of clinic…”

Section: Comparison With Other Studiesmentioning

confidence: 99%

“…Although the AUC was high at 0.79, the sensitivity was low at 31% with high false negative figures in their external validation cohort. 18 Artificial intelligence methods have also been attempted for the development of HNC risk calculators with the variation logistic regression being suggested the most effective method. 19 (Table 1).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Head and neck cancer risk calculator (HaNC‐RC)—V.2. Adjustments and addition of symptoms and social history factors

Tikka

Kavanagh

Lowit

et al. 2020

Clinical Otolaryngology

105

View full text Add to dashboard Cite

Objectives: Head and neck cancer (HNC) diagnosis through the 2-week wait, urgent suspicion of cancer (USOC) pathway has failed to increase early cancer detection rates in the UK. A head and neck cancer risk calculator (HaNC-RC) has previously been designed to aid referral of high-risk patients to USOC clinics (predictive power: 77%). Our aim was to refine the HaNC-RC to increase its prediction potential. Design: Following sample size calculation, prospective data collection and statistical analysis of referral criteria and outcomes. Setting: Large tertiary care cancer centre in Scotland. Participants: 3531 new patients seen in routine, urgent and USOC head and neck (HaN) clinics. Main outcome measures: Data collected were as follows: demographics, social history, presenting symptoms and signs and HNC diagnosis. Univariate and multivariate regression analysis were performed to identify significant predictors of HNC. Internal validation was performed using 1000 sample bootstrapping to estimate model diagnostics included the area under the receiver operator curve (AUC), sensitivity and specificity. Results:The updated version of the risk calculator (HaNC-RC v.2) includes age, gender, unintentional weight loss, smoking, alcohol, positive and negative symptoms and signs of HNC. It has achieved an AUC of 88.6% with two recommended triage referral cut-offs to USOC (cut-off: 7.1%; sensitivity: 85%, specificity: 78.3%) or urgent clinics (cut-off: 2.2%; sensitivity: 97.1%; specificity of 52.9%). This could redistribute cancer detection through USOC clinics from the current 60.9%-85.2%, without affecting total numbers seen in each clinical setting. Conclusions:The use of the HaNC-RC v.2 has a significant potential in both identifying patients at high risk of HNC early thought USOC clinics but also improving health service delivery practices by reducing the number of inappropriately urgent referrals. | 381TIKKA eT Al.

show abstract

Section: Comparison With Other Studiesmentioning

confidence: 99%

“…This is substantially higher than the ROC of the HaN-RC v.1 (77%) and the ROC of the Lau et al, model (79%). 16,18 Our study is the first to include patients from all types of clinic…”

Section: Comparison With Other Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Head and neck cancer risk calculator (HaNC‐RC)—V.2. Adjustments and addition of symptoms and social history factors

Tikka

Kavanagh

Lowit

et al. 2020

Clinical Otolaryngology

105

View full text Add to dashboard Cite

show abstract

“…AI has also been used to support clinical diagnoses and treatments, decision-making, the Table 4. Continued prediction of prognoses [98][99][100]125,126], disease profiling, the construction of mass spectral databases [43,[127][128][129], the identification or prediction of disease progress [101,105,[107][108][109][110]130], and the confirmation of diagnoses and the utility of treatments [102][103][104]112,131]. Although many algorithms have been applied, some are not consistently reliable, and certain challenges remain.…”

Section: Discussionmentioning

confidence: 99%

“…In medical device-based analyses, AI is used to evaluate tissue and blood test results, as well as the outcomes of otorhinolaryngology-specific tests (e.g., polysomnography) [ 72 , 73 , 122 ] and audiometry [ 123 , 124 ]. AI has also been used to support clinical diagnoses and treatments, decision-making, the prediction of prognoses [ 98 - 100 , 125 , 126 ], disease profiling, the construction of mass spectral databases [ 43 , 127 - 129 ], the identification or prediction of disease progress [ 101 , 105 , 107 - 110 , 130 ], and the confirmation of diagnoses and the utility of treatments [ 102 - 104 , 112 , 131 ].…”

Section: Discussionmentioning

confidence: 99%

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

Tama

Kim

et al. 2020

Clin Exp Otorhinolaryngol

View full text Add to dashboard Cite

Objectives. To present an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, with respect to opportunities, research challenges, and research directions.Methods. We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articles; we excluded non-English publications and duplicates, which resulted in a total of 90 remaining studies. These 90 studies were divided into those analyzing medical images, voice, medical devices, and clinical diagnoses and treatments.Results. Most studies (42.22%, 38/90) used AI for image-based analysis, followed by clinical diagnosis and treatments (24 studies); each of the remaining two subcategories included 14 studies.Conclusion. Machine and deep learning have been extensively applied in the field of otorhinolaryngology. However, performance varies and research challenges remain.

show abstract

Risk prediction models for head and neck cancer: A rapid review

Smith

McMahon

Ross

et al. 2022

Laryngoscope Investig Oto

View full text Add to dashboard Cite

Background: Cancer risk assessment models are used to support prevention and early detection. However, few models have been developed for head and neck cancer (HNC).Methods: A rapid review of Embase and MEDLINE identified n = 3045 articles. Following dual screening, n = 14 studies were included. Quality appraisal using the PRO-BAST (risk of bias) instrument was conducted, and a narrative synthesis was performed to identify the best performing models in terms of risk factors and designs.Results: Six of the 14 models were assessed as "high" quality. Of these, three had high predictive performance achieving area under curve values over 0.8 (0.87-0.89).The common features of these models were their inclusion of predictors carefully tailored to the target population/anatomical subsite and development with external validation.Conclusions: Some existing models do possess the potential to identify and stratify those at risk of HNC but there is scope for improvement.head and neck cancer, review, risk, risk assessment, risk model | INTRODUCTIONHead and neck squamous cell carcinomas-generally defined as aerodigestive squamous cancers of the oral cavity, larynx, and pharynxare a growing challenge for healthcare systems across the world: they are the eighth most common cancer, accounting for an estimated 878,348 new cases and 444,347 deaths globally in 2020. 1,2 The risk profile of head and neck cancer (HNC) is also changing-with oropharyngeal cancer increasingly associated with human papillomavirus (HPV) infection, 3 and inequalities in the burden of HNC associated with socioeconomic status. 4 Overall HNC survival varies greatly by subsite and stage of diagnosis. Despite advancement in treatments, 5-year survival has seen no major improvements observed in recent decades. [5][6][7] Furthermore, marginal improvements in prognosis may be undercut by the overall increased disease burden, particularly due to the changing epidemiology of HPV-associated oropharyngeal cancer. 8,9 As with all cancers, prognosis is worse with advanced stage disease at presentation. Thus, a major challenge posed by HNC is its traditionally late presentation with over half of cases diagnosed at stage III or IV, when locally advanced or regional or distant metastases are present. [10][11][12][13][14] Given the twin challenges of increasing HNC incidence and poor survival associated with late-stage detection, further attention needs to be given to primary and secondary prevention strategies-utilizing the potential of head and neck risk prediction models to identify those

show abstract

A web‐based prediction score for head and neck cancer referrals

Cited by 18 publications

References 18 publications

Head and neck cancer risk calculator (HaNC‐RC)—V.2. Adjustments and addition of symptoms and social history factors

Head and neck cancer risk calculator (HaNC‐RC)—V.2. Adjustments and addition of symptoms and social history factors

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

Risk prediction models for head and neck cancer: A rapid review

Contact Info

Product

Resources

About