Iron deficiency anaemia – useful diagnostic tool for right sided colon cancers?

Rai, Sajal; Hemingway, D.

doi:10.1111/j.1463-1318.2005.00880.x

Cited by 16 publications

(17 citation statements)

References 5 publications

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“…Blood loss is present in 60% of CRC cases, and a daily loss of as little as 3 mL in the stool can cause iron deficiency anemia [15]. However, as only 18% of CRC cases had anemia more than a year before diagnosis [16], a significant proportion of the population is not anemic [17]. The fecal occult blood test detects only current bleeding, while in CRC, blood loss is commonly intermittent [10].…”

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confidence: 99%

Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data

Hornbrook

Goshen²,

Choman³

et al. 2017

Dig Dis Sci

100

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Background Machine learning tools identify patients with blood counts indicating greater likelihood of colorectal cancer and warranting colonoscopy referral. Aims To validate a machine learning colorectal cancer detection model on a US community-based insured adult population. Methods Eligible colorectal cancer cases (439 females, 461 males) with complete blood counts before diagnosis were identified from Kaiser Permanente Northwest Region's Tumor Registry. Control patients (n = 9108) were randomly selected from KPNW's population who had no cancers, received at C1 blood count, had continuous enrollment from 180 days prior to the blood count through 24 months after the count, and were aged 40-89. For each control, one blood count was randomly selected as the pseudo-colorectal cancer diagnosis date for matching to cases, and assigned a ''calendar year'' based on the count date. For each calendar year, 18 controls were randomly selected to match the general enrollment's 10-year age groups and lengths of continuous enrollment. Prediction performance was evaluated by area under the curve, specificity, and odds ratios. Results Area under the receiver operating characteristics curve for detecting colorectal cancer was 0.80 ± 0.01. At 99% specificity, the odds ratio for association of a high-risk detection score with colorectal cancer was 34.7 (95% CI 28.9-40.4). The detection model had the highest accuracy in identifying right-sided colorectal cancers. Conclusions ColonFlag Ò identifies individuals with tenfold higher risk of undiagnosed colorectal cancer at curable stages (0/I/II), flags colorectal tumors 180-360 days prior to usual clinical diagnosis, and is more accurate at identifying right-sided (compared to left-sided) colorectal cancers.

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mentioning

confidence: 99%

Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data

Hornbrook

Goshen²,

Choman³

et al. 2017

Dig Dis Sci

100

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“…However, wide variations of Hb levels among cancer patients, or even the general population, have been extensively reported, making it difficult to diagnose anemia solely based on a single measurement of Hb level at baseline. The newest updated NCCN guideline suggested that a drop of as little as 2 g/dL in Hb level, even in non-anemic patients with a high baseline Hb level, is an alarming indicator for the presence of anemia [3,24-26]. Nonetheless, as yet there has been no report systemically evaluating the role of post-diagnosis change of Hb level in predicting cancer patient survival.…”

Section: Discussionmentioning

confidence: 99%

Post-diagnosis hemoglobin change associates with overall survival of multiple malignancies – results from a 14-year hospital-based cohort of lung, breast, colorectal, and liver cancers

et al. 2013

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BackgroundAnemia refers to low hemoglobin (Hb) level and is a risk factor of cancer patient survival. The National Comprehensive Cancer Network recently suggested that post-diagnosis Hb change, regardless of baseline Hb level, indicates the potential presence of anemia. However, there is no epidemiological study evaluating whether Hb change has direct prognostic values for cancer patients at the population level.MethodsWe identified 6675 patients with a diagnosis of primary lung, breast, colorectal, or liver cancer who visited the Kimmel Cancer Center at the Thomas Jefferson University from 1998 to 2011. All patients had at least two Hb measurements within the first six months after diagnosis. We analyzed the main, dose-dependent, and time-dependent effects of Hb changes on patient survival.ResultsCompared to patients with a low Hb change (|∆Hb|≤2.6), those having a |∆Hb|>2.6 exhibited a significantly shorter survival (hazard ratio=1.40, 95% confidence interval 1.31-1.50, P=4.5 × 10-22, Plog rank=1.6 × 10-39). This association remained significant across the four cancer types. Bootstrap resampling validated these findings 100% of the time with P<0.01 in all patients and in patients of individual cancers. The association exhibited an apparent U-shape dose-dependent pattern. Time-dependent modeling demonstrated that the effect of Hb change on the survival of the overall patient population persisted for approximately 4.5 years after diagnosis.ConclusionPost-diagnosis Hb change associates with the survival of multiple cancers and may have clinical values in tailoring anti-anemia treatments. Because Hb level is frequently measured during cancer treatment, Hb changes may be a potentially important variable in building cancer prognosis models.

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“…Occurrence of anemia varies considerably between different types of cancers 3, 4. Despite high prevalence at cancer diagnosis, anemia as assessed through hemoglobin concentration has been found to function poorly as a diagnostic tool 8–10…”

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confidence: 99%

Pattern of declining hemoglobin concentration before cancer diagnosis

Edgren

Bagnardi

Bellocco

et al. 2010

Intl Journal of Cancer

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Although anemia is widely considered an early sign of malignant disease, little is known about the pattern of hemoglobin decline before diagnosis. As an approach to understanding the duration of the preclinical phase of different types of malignant diseases, we investigated prediagnostic hemoglobin concentration changes in a large cohort of blood donors. Using a nested case‐control design, we analyzed a population‐based cohort comprising 1.1 million Scandinavian blood donors with complete follow‐up through record linkage to population and cancer registers. A total of 16,375 cancer cases were identified, for whom we selected 161,995 controls. We used conditional logistic regression to estimate the risk of cancer in relation to hemoglobin concentration during the 5 years preceding the cancer diagnosis. Hemoglobin concentration decline began already 3 years before diagnosis of stomach cancer, multiple myeloma, and lymphatic leukemia; 2 years before diagnosis of small intestinal and colon cancer as well as of Hodgkin lymphoma. A decline was evident during the last year for non‐Hodgkin lymphoma and myeloid/monocytic leukemia, whereas no change was found for cancer of the esophagus, breast or prostate. In conclusion, in this study, we have demonstrated that the pattern of declining hemoglobin concentration before cancer diagnosis varies considerably between malignancies without being a suitable screening tool for any of them. For some malignancies, however, the long duration of hemoglobin decline before clinical diagnosis suggests a substantial lead‐time with systemic effects, during which earlier diagnosis should be achievable by emerging diagnostic tools.

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Iron deficiency anaemia – useful diagnostic tool for right sided colon cancers?

Abstract: Anaemia is a poor predictor of right-sided colon cancers and cannot be used as an effective investigative tool in symptomatic patients.

Cited by 16 publications

References 5 publications

Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data

Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data

Post-diagnosis hemoglobin change associates with overall survival of multiple malignancies – results from a 14-year hospital-based cohort of lung, breast, colorectal, and liver cancers

Pattern of declining hemoglobin concentration before cancer diagnosis

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