lifequeue head and neck cancer diagnosis

Head and Neck Cancer ClassificationLifeQueue Ltd Jonny Edwards (CTO) and Jim Moor (Medical Director)

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The Head and Neck Cancer referral problem

LifeQueue

9 out of 10 referrals do not require further investigation

Set to rise to 9.7 out of 10 under new cancer guidelines

Context

Costs money for both primary and secondary care

Causes anxiety in the referred patients

Problem statement

Can we estimate whether a referral has Head and Neck cancer using simple symptom data?

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Challenges deep-dive

Challenge 1

Choose an appropriate ML/Statistical Classification Method

We picked a selection (9) using scikit-learn/bayes (python open source libraries)

Challenge 2

Acquire Symptom Data

Freeman Hospital Newcastle (thanks to Vin Paleri) gave us 4715 active cases with 397 positive diagnoses (2007-2010). Around 30 symptoms were coded (these may be time varying)

Challenge 3

Use data to remove or prioritise patients

Queuing is another talk

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Classifiers 101“Computer automated methods using prior patient ‘data’ (symptoms and diagnosis) to assess whether a new patient has a specific diagnosis given a set of symptoms”

AI and Deep Learning are the buzzwords, but we used Logistic Regression with some twists

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HighlightResult

26% of referrals can be removed from the referral process with a

NonC/Canc rate of 1/1000 (people)

Approach NonC/NonC

NonC/Canc

Canc/NonC

Canc/Canc

Logistic Regression (LR)

91.8+-2

7.6+-1

0.3+-0.5

0.2+-0.4

Variational LR (p=0.08)

25.8+-8.9

0.1+-0.16

66.4+-8.9

7.7+-1.4

Class weighted LR (C=100)

28.6+-8.4

0.2+-0.2

63.6+-8.2

7.6+-1.4

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● Test set %mean/std of 10 test experiments● 8% is all the cancer diagnoses

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This is an effort to visualise the decision space using a distance transform

MDS in scikit-learn Black dots are cancerThe axis are notional

Actually ● 14% diagnosis rate● CCGs that feed the Freeman

would save £75000 per year @£100 per referral

● And 26% would have not have gone through the anxiety

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● Nationally at 10% 100,000 cases

● Saving 25,000 referrals £2.5m or £7.5m under new (3%) targets

● BOE: If all cancers included 750,000 referrals which is £75m or £225m at (3%)

ANctNationallyy

Remember this is a conservative classifier!

Impact Problemss● Other cancer site? We are

looking for more data esp. vague symptoms

● Rethink the delivery of Cancer pathways

● Breaching? We have queue technology to help meet targets

● Small data● Getting data is a problem● Head and Neck is an

unfashionable cancer ● Where does this work sit?● Trusts are busy with other

more pressing problems

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Diagnosis Data is GoldNHS: Why not do this for every diagnosis?

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Thanks!Questions? Might be in our NCRI poster or draft BMJ paper

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