Predicting the presence of colon cancer in members of a health maintenance organisation by evaluating analytes from standard laboratory records

Goshen, Ran; Mizrahi, Barak; Akiva, Pini; Kinar, Yaron; Choman, Eran; Shalev, Varda; Sopik, Victoria; Kariv, Revital; Narod, Steven A.

doi:10.1038/bjc.2017.53

Cited by 18 publications

(32 citation statements)

References 24 publications

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“…Tables S11-S17 shows the results from analyses of these five components for colorectal cancer diagnosis. Goshen 2017 [28] report a statistically significant difference in mean levels of each of these components between those with and without a diagnosis within 6 months for males and females (t-test all p < 0.05), except lymphocytes for males (t-test p = 0.43). Huang 2019 [36] showed no difference in mean lymphocyte levels between cases and controls (p ≥ 0.05) or those with benign polyps (p ≥ 0.05) zero to six months before diagnosis.…”

Section: Differential White Blood Cell Countmentioning

confidence: 95%

“…Panagiotopoulou 2014 [49] suggest that the odds of diagnosis in three months do not differ between those with mean corpuscular volume < 80 fL and ≥ 80 fL when unadjusted (OR = 1.73, 95% CI = 0.96-3.1), but do when adjusted for other factors (OR = 2.2, 95% CI = 1.2-4.1). Goshen 2017 [28] reported a statistically significant difference in mean corpuscular volume between those with and without a future diagnosis (t-tests p < 0.0001), with cases having 3.67 fL lower on average. Ay 2015 [15] reported that, on average, mean corpuscular volume did not statistically significantly differ between those with a diagnosis and those with a benign colorectal polyp (t-test p ≥ 0.05).…”

Section: Mean Corpuscular Volumementioning

confidence: 97%

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The Full Blood Count Blood Test for Colorectal Cancer Detection: A Systematic Review, Meta-Analysis, and Critical Appraisal

Virdee

Marian

Mansouri

et al. 2020

Cancers

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Introduction: A full blood count (FBC) blood test includes 20 components. We systematically reviewed studies that assessed the association of the FBC and diagnosis of colorectal cancer to identify components as risk factors. We reviewed FBC-based prediction models for colorectal cancer risk. Methods: MEDLINE, EMBASE, CINAHL, and Web of Science were searched until 3 September 2019. We meta-analysed the mean difference in FBC components between those with and without a diagnosis and critically appraised the development and validation of FBC-based prediction models. Results: We included 53 eligible articles. Three of four meta-analysed components showed an association with diagnosis. In the remaining 16 with insufficient data for meta-analysis, three were associated with colorectal cancer. Thirteen FBC-based models were developed. Model performance was commonly assessed using the c-statistic (range 0.72–0.91) and calibration plots. Some models appeared to work well for early detection but good performance may be driven by early events. Conclusion: Red blood cells, haemoglobin, mean corpuscular volume, red blood cell distribution width, white blood cell count, and platelets are associated with diagnosis and could be used for referral. Existing FBC-based prediction models might not perform as well as expected and need further critical testing.

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Section: Differential White Blood Cell Countmentioning

confidence: 95%

Section: Mean Corpuscular Volumementioning

confidence: 97%

The Full Blood Count Blood Test for Colorectal Cancer Detection: A Systematic Review, Meta-Analysis, and Critical Appraisal

Virdee

Marian

Mansouri

et al. 2020

Cancers

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“…It is possible that the performance of future versions of ColonFlag could be improved using a broader array of standard laboratory tests[ 8 ] or by increasing the number of risk factors included in the algorithm. For example, smoking, body mass index and family history of colorectal cancer are commonly recorded in a patient’s electronic medical record.…”

Section: Discussionmentioning

confidence: 99%

“…We have described the development and validation of the ColonFlag score (previously known as MeScore), an algorithm that incorporates patient factors (age and gender) with complete blood count information (CBC) and which is used to predict the presence of colorectal cancer at the time of testing. [ 6 – 8 ] ColonFlag was developed using data from healthy Israelis (Maccabi Health Care Services, MHS) and colorectal cancer patients. Training of the model was done using the MHS database and the Israeli National Cancer Registry, which documents invasive colorectal cancer but does not document pre-cancerous lesions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of findings at screening colonoscopy using a machine learning algorithm based on complete blood counts (ColonFlag)

Hilsden

Heitman

Mizrahi³

et al. 2018

PLoS ONE

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Adenomatous polyps are a common precursor lesion for colorectal cancer. ColonFlag is a machine- learning-based algorithm that uses basic patient information and complete blood cell counts (CBC) to identify individuals at elevated risk of colorectal cancer for intensified screening. The purpose of this study was to determine whether ColonFlag is also able to predict the presence of high risk adenomatous polyps at colonoscopy. This study was conducted at a large colon cancer screening center in Calgary, Alberta. The study population included asymptomatic individuals between the ages of 50 and 75 who underwent a screening colonoscopy between January 2013 and June 2015. All subjects had at least one CBC result within the year prior to colonoscopy. Based on age, sex, red blood cell parameters, inflammatory cells and platelets, the ColonFlag algorithm generated a score from 0 to 100. We compared the ability of the ColonFlag test result to discriminate between individuals who were found to have a high risk polyp and those with a normal colonoscopy. Among the 17,676 individuals who underwent a screening colonoscopy there were 1,014 found to have a high risk precancerous lesion (5.7%) and 60 were found to have colorectal cancer (0.3%). At a specificity of 95%, the odds ratio for a positive ColonFlag was 2.0 for those with an advanced precancerous lesion compared with those with a normal colonoscopy. The odds ratio did not vary according to patient subgroup, colorectal cancer location or stage. ColonFlag is a passive test that can use routine blood test results to help identify individuals at elevated risk for high risk precancerous polyps as well as frank colorectal cancer. These individuals may be targeted in an effort to achieve greater compliance with conventional screening tests.

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“…For example, anaemia in FBC data from UK primary care predicts the risk of colorectal cancer and iron deficiency is an independent risk factor 8. In the last few years, a number of individual studies have reported on the association between the components of the FBC, including haemoglobin, platelet count and red cell distribution width, and diagnosis of colorectal cancer 9–13. Furthermore, risk scores have recently been developed using methods that incorporate FBC data as predictors of colorectal cancer diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Components of the full blood count as risk factors for colorectal cancer detection: a systematic review protocol

et al. 2019

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IntroductionColorectal cancer is the fourth most common type of cancer and the second most common cause of cancer-related deaths in the UK. The full blood count (FBC) is a blood test that may play a role in early detection of the disease. Previous studies have aimed to identify how levels of individual components, such as haemoglobin, can be used to assist the diagnosis. We aim to systematically review studies to identify whether components of the FBC are risk factors for diagnosis of colorectal cancer, critically appraise the methods used to assess the association and assess performance of the components.Methods and analysisThe MEDLINE (via OVID), EMBASE (via OVID), CINAHL (via EBSCOhost) and Web of Science databases will be searched to identify studies reporting the association between the levels of at least one FBC component and the risk of a future diagnosis of colorectal cancer in undiagnosed individuals. Clincialtrials.gov and the WHO registry will be searched to identify relevant ongoing research. Search terms will include relevant Medical Subject Headings and Emtree headings, and free-text terms relating to FBC, colorectal cancer and diagnosis. No date or language restrictions will be applied. Two reviewers will independently identify the studies for inclusion and perform data extraction. Time intervals between the blood tests and diagnosis will form the subgroups for analysis.Ethics and disseminationThere is no direct patient involvement and only published articles will be reviewed; no ethical approval is required. Results from this review will set a foundation for intended future work on developing a new risk score for early detection of colorectal cancer, derived using FBC data. This systematic review will also provide guidance on the analysis of time to diagnosis. The model will be freely available to UK primary care practices.PROSPERO registration numberCRD42019134400.

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Predicting the presence of colon cancer in members of a health maintenance organisation by evaluating analytes from standard laboratory records

Cited by 18 publications

References 24 publications

The Full Blood Count Blood Test for Colorectal Cancer Detection: A Systematic Review, Meta-Analysis, and Critical Appraisal

The Full Blood Count Blood Test for Colorectal Cancer Detection: A Systematic Review, Meta-Analysis, and Critical Appraisal

Prediction of findings at screening colonoscopy using a machine learning algorithm based on complete blood counts (ColonFlag)

Components of the full blood count as risk factors for colorectal cancer detection: a systematic review protocol

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