Trends and variation in urgent referrals for suspected cancer 2009/2010–2019/2020

Smith, Graham; Sansom, Nigel; Hemphill, Scott; Bradley, Stephen H; Shinkins, Bethany; Wheatstone, Pete; Hamilton, William; Neal, Richard D

doi:10.3399/bjgp22x718217

Cited by 12 publications

(11 citation statements)

References 16 publications

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“…We also need to fully understand the views of patients, clinicians, and commissioners on the acceptability and utility of the tests. We note that each 2WW pathway is distinct, with its own challenges and priorities, as well as differing prevalences of cancer (see, eg, Smith et al 17 —these issues will likely require detailed consideration by all the key stakeholders on a pathway-by-pathway basis.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of multivariable machine learning algorithms to predict risk of cancer in symptomatic patients referred urgently from primary care: a diagnostic accuracy study

et al. 2022

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ObjectivesTo develop and validate tests to assess the risk of any cancer for patients referred to the NHS Urgent Suspected Cancer (2-week wait, 2WW) clinical pathways.SettingPrimary and secondary care, one participating regional centre.ParticipantsRetrospective analysis of data from 371 799 consecutive 2WW referrals in the Leeds region from 2011 to 2019. The development cohort was composed of 224 669 consecutive patients with an urgent suspected cancer referral in Leeds between January 2011 and December 2016. The diagnostic algorithms developed were then externally validated on a similar consecutive sample of 147 130 patients (between January 2017 and December 2019). All such patients over the age of 18 with a minimum set of blood counts and biochemistry measurements available were included in the cohort.Primary and secondary outcome measuressensitivity, specificity, negative predictive value, positive predictive value, Receiver Operating Characteristic (ROC) curve Area Under Curve (AUC), calibration curvesResultsWe present results for two clinical use-cases. In use-case 1, the algorithms identify 20% of patients who do not have cancer and may not need an urgent 2WW referral. In use-case 2, they identify 90% of cancer cases with a high probability of cancer that could be prioritised for review.ConclusionsCombining a panel of widely available blood markers produces effective blood tests for cancer for NHS 2WW patients. The tests are affordable, and can be deployed rapidly to any NHS pathology laboratory with no additional hardware requirements.

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Section: Discussionmentioning

confidence: 99%

Development and validation of multivariable machine learning algorithms to predict risk of cancer in symptomatic patients referred urgently from primary care: a diagnostic accuracy study

et al. 2022

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“…The detection rate (which is the percentage of new cancer cases treated as a result of TWW referral) is 56% for head and neck cancer (separate from skin cancer). 16 The goal is to improve the detection rate, so patients are diagnosed quickly via the pathway and not by other routes such as emergency admissions. This comes at the expense of low conversion rates as broad criteria encourage a large burden of benign lesions to be referred in via the TWW pathway.…”

Section: Referral Processmentioning

confidence: 99%

Early detection and prevention of head and neck cancers

Glen

Botha

2022

Br Dent J

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Squamous cell carcinoma predominates the majority of head and neck cancers, with advanced disease associated with 50% survival at three years.Dentists have a unique opportunity to screen for other head and neck cancers and cutaneous malignancies in addition to standard oral cavity screening Primary care physicians should be aware of referral criteria for suspected head and neck cancers and, where there is concern, should refer to secondary care units without delay.

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“…Non-dentists have, however, a lack of expertise and experience in oral pathology. Some 95% of patients referred for suspected head and neck cancer in England are not diagnosed with a cancer 4…”

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confidence: 99%

Teledentistry may help in detecting oral cancers in current GP and dentist shortages

Bradley,

Deane,

O’Hara

et al. 2024

BMJ

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Trends and variation in urgent referrals for suspected cancer 2009/2010–2019/2020

Cited by 12 publications

References 16 publications

Development and validation of multivariable machine learning algorithms to predict risk of cancer in symptomatic patients referred urgently from primary care: a diagnostic accuracy study

Development and validation of multivariable machine learning algorithms to predict risk of cancer in symptomatic patients referred urgently from primary care: a diagnostic accuracy study

Early detection and prevention of head and neck cancers

Teledentistry may help in detecting oral cancers in current GP and dentist shortages

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