Identifying patients with undetected colorectal cancer: an independent validation of QCancer (Colorectal)

Collins, Gary S.; Altman, Douglas G.

doi:10.1038/bjc.2012.266

Cited by 46 publications

(60 citation statements)

References 28 publications

(41 reference statements)

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“…It is also representative of the population where such a model is likely to be used. 10 It has been used successfully to develop and validate a range of prognostic models 12,13 and models designed to help earlier detection of individual cancers. [4][5][6][7][8][9] This article describes the derivation and validation of the algorithm in females.…”

Section: Resultsmentioning

confidence: 99%

“…Red flag symptoms include symptoms which may indicate cancer 4,5,7,8,10,14,15 such as abdominal distension, abdominal pain, appetite loss, heartburn, indigestion, dysphagia, haematemesis, rectal bleeding, haematuria, haemoptysis, neck lump, weight loss, night sweats, breast lump, breast pain, nipple discharge or breast skin changes, dyspareunia, inter-menstrual bleeding, post-menopausal bleeding, and post-coital bleeding. A first occurrence of venous thrombo-embolism was also included as a red flag event as this can herald a previously undiagnosed cancer and recent NICE guidance recommends patients with venous thrombo-embolism have a cancer screen.…”

Section: Symptomsmentioning

confidence: 99%

“…8 This approach has been successful in establishing a set of algorithms which are being validated on an external population by an independent team. 10 It is apparent that many of the general symptoms (for example, appetite, weight loss, anaemia, and abdominal pain), and some of the more specific symptoms (such as rectal bleeding), are predictive of multiple types of cancer. In addition, in clinical practice, patients generally consult with one or more symptoms rather than as a suspected case of a particular type of cancer.…”

mentioning

confidence: 99%

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Symptoms and risk factors to identify women with suspected cancer in primary care: derivation and validation of an algorithm

Hippisley‐Cox

Coupland²

2013

Br J Gen Pract

108

140

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Section: Resultsmentioning

confidence: 99%

Section: Symptomsmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Symptoms and risk factors to identify women with suspected cancer in primary care: derivation and validation of an algorithm

Hippisley‐Cox

Coupland²

2013

Br J Gen Pract

108

140

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“…8 This approach has been successful in establishing a set of algorithms which are being validated on an external population by an independent team. 10 It's apparent that many of the general symptoms (for example, appetite, weight loss, anaemia, abdominal pain), and some of the more specific symptoms (for example, rectal bleeding), are predictive of multiple types of cancer. In addition, in clinical practice, patients generally consult with one or more symptoms rather than as a suspected case of a particular type of cancer.…”

mentioning

confidence: 99%

Symptoms and risk factors to identify men with suspected cancer in primary care: derivation and validation of an algorithm

Hippisley‐Cox¹,

Coupland²

2013

Br J Gen Pract

132

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“…It is thus crucial to try and quantify such o ptimism and provide a more honest estimate of the performance and critical e valuation of the prediction model. Q Your recent UK-based study evaluated the performance of QCancer® (Colorectal) prediction model [1]. What were the main findings?…”

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confidence: 99%

Validating predictive models for colorectal cancer

Collins¹

2012

Colorect. Cancer

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InterviewsQ What is the current status of research regarding the development of models for predicting colorectal cancer?In the primary care setting there are very few well-designed and accurate p rediction models for identifying individuals with undiagnosed cancer that have also undergone evaluation by independent investigators. It is worth stressing how important it is that any prediction model should undergo evaluation by independent investigators. Prediction models always appear to work well on the data used to develop the model, and authors of such models will naturally tend to downplay such issues and be rather optimistic about their model. It is thus crucial to try and quantify such o ptimism and provide a more honest estimate of the performance and critical e valuation of the prediction model. Q Your recent UK-based study evaluated the performance of QCancer® (Colorectal) prediction model [1]. What were the main findings?QCancer (Colorectal) is a new prediction model for identifying individuals with undiagnosed colorectal cancer. The results from our study provided an independent evaluation of the performance of QCancer (Colorectal) on a large cohort of 2.1 million general practice patients. In particular, our results confirmed that QCancer (Colorectal) offered an improvement over using individual signs and symptoms in predicting whether a patient has u ndiagnosed colorectal cancer.Q The analysis on this study included 2.1 million patients. What are the challenges in working with such large amounts of data?Data sets of this magnitude are relatively uncommon and without a doubt the main challenge is that of the computational burden this places on the actual computing and calculating of the performance measures that describe the predictive a bility of the prediction model. ASK THE EXPERTSGary Collins*: Gary Collins is a senior medical statistician at the Centre for Statistics in Medicine, University of Oxford, UK. His main research interests are in the validation of clinical prediction models, and the methodological and design issues in conducting validation studies funded by the UK Medical Research Council. He is also interested in reporting of these studies and is leading a group of prediction modeling experts, clinicians and journal editors to produce a consensus-based guideline to improve the quality of studies reporting the development and validation of prediction models.

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Identifying patients with undetected colorectal cancer: an independent validation of QCancer (Colorectal)

Cited by 46 publications

References 28 publications

Symptoms and risk factors to identify women with suspected cancer in primary care: derivation and validation of an algorithm

Symptoms and risk factors to identify women with suspected cancer in primary care: derivation and validation of an algorithm

Symptoms and risk factors to identify men with suspected cancer in primary care: derivation and validation of an algorithm

Validating predictive models for colorectal cancer

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