Sporadic breast, ovarian, or uterine cancers as risk factors for colorectal cancer

Denlinger, Crystal S.; Weinberg, David S.

doi:10.1007/s11888-009-0004-y

Cited by 3 publications

(3 citation statements)

References 42 publications

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“…Sporadic ovarian carcinoma is the most common type of this malignancy [37]. A review by Denlinger et al [38] reported sporadic cases of ovarian cancer as a significant risk factor for colorectal cancer, which is in line with our findings.…”

Section: Discussionsupporting

confidence: 92%

Risk of secondary malignancies in patients with ovarian cancer: a systematic review and meta-analysis

2021

EJGO

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This study was performed to systematically assess the risk of secondary malignancies in patients with ovarian cancer. Methods: A systematic search was conducted in PubMed, Web of Science, and Scopus databases up to September 2019. In this study, the overall standardized incidence ratio (SIR) was calculated applying fixed/random-e fects models. Sixteen cohort studies, including 122715 ovarian cancer patients with 4458 secondary malignancies were included. Results: Combined SIRs showed an increased risk of secondary malignancies prevalence (SIR = 1.81, 95% CI 1.58--2.03).

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Section: Discussionsupporting

confidence: 92%

Risk of secondary malignancies in patients with ovarian cancer: a systematic review and meta-analysis

2021

EJGO

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“…In our study, the higher the risk of incidents of breast, endometrium, pancreas, stomach, gallbladder, and colorectal have been observed. In a review paper by Denlinger et al (Denlinger and Weinberg, 2009) in which he looks at ovarian cancer as a risk factor for colorectal cancer, he concluded that the occurrence of ovarian cancer will indeed higher the risk of colorectal cancer and that finding is in agreement on what we have found in this study.…”

Section: Discussionsupporting

confidence: 92%

The Risk of Secondary Malignancies in Patients with Ovarian Cancer: A Systematic Review and Meta-Analysis

Heydari

Rismantab

Alizadeh‐Navaei

et al. 2020

Preprint

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This study was performed to systematically asses the risk of secondary malignancies in patients with ovarian cancer. We conducted a systematic search in PubMed, Web of Science, and Scopus databases up to September 2019 to find target studies. In this study, the overall SIR has been calculated with fixed/random-effects models. Sixteen cohort studies including 122715 ovarian cancer patients with 4458 secondary malignancies have been included in this meta-analysis. Combined SIRs showed an increased risk of secondary malignancies prevalence (SIR: 1.81, 95%CI 1.58-2.03). The most prevalence diagnosed malignancies were as follows: breast cancer (1.34, 95%CI 1.5-1.18), intestine (2.36, 95%CI 1.11-3.61), colorectal (1.73, 95%CI 1.44-2.02), pancreatic (1.42, 95%CI 1.13-1.71), cervical cancer (11.57, 95%CI 6.94-16.21), renal (1.43, 95%CI 1.11-1.74), bladder (2.13, 95%CI 1.77-2.50), leukemia (3.33, 95%CI 2.23-4.43), connective tissue (2.61, 95%CI 1.56-3.66), and thyroid (1.59, 95%CI 1.13-2.04). In regards to the results, various malignancies have a greater prevalence in patients with ovarian cancer in comparison to the general public. Corpus cancer, leukemia cancer, endometrium cancer, connective tissue malignancy, and bladder cancer are among the high risks in these patients and need to be considered for them. Hence, the survival rate of the patients can be increased through prevention and early diagnosis.

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“…For example, uterine cancer was identified as a driver of EOCRC, suggesting a potential genetic association between these malignancies in younger patients. 31 Research also demonstrates that the incidence rate of second primary cancers among survivors is significantly higher than cancer in the general population, and survivors experience notable morbidity and mortality from their cancer treatment. 32 Additionally, the use of CT scans for other medical reasons could contribute to the incidental identification of EOCRC cases.…”

Section: Discussionmentioning

confidence: 99%

Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data

Sun,

Mobley,

Quillen

et al. 2024

Preprint

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Background: Colorectal cancer (CRC) is now the leading cause of cancer-related deaths among young Americans. Our study aims to predict early-onset CRC (EOCRC) using machine learning (ML) and structured electronic health record (EHR) data for individuals under the screening age of 45. Methods: We identified a cohort of patients under 45 from the OneFlorida+ Clinical Research Consortium. Given the distinct pathology of colon cancer (CC) and rectal cancer (RC), we created separate prediction models for each cancer type with various ML algorithms. We assessed multiple prediction time windows (0, 1, 3, and 5 years) and ensured robustness through propensity score matching (PSM) to account for confounding variables. Model performance was assessed using established metrics. Additionally, we employed the Shapley Additive exPlanations (SHAP) to identify risk factors for EOCRC. Results: Our study yielded results, with Area Under the Curve (AUC) scores of 0.811, 0.748, 0.689, and 0.686 for CC prediction, and 0.829, 0.771, 0.727, and 0.721 for RC prediction at 0, 1, 3, and 5 years, respectively. Notably, predictors included immune and digestive system disorders, along with secondary cancers and underweight, prevalent in both CC and RC groups. Blood diseases emerged as prominent indicators of CC. Conclusion: This study highlights the potential of ML techniques in leveraging EHR data to predict EOCRC, offering valuable insights for potential early diagnosis in patients who are below the recommended screening age.

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Sporadic breast, ovarian, or uterine cancers as risk factors for colorectal cancer

Cited by 3 publications

References 42 publications

Risk of secondary malignancies in patients with ovarian cancer: a systematic review and meta-analysis

Risk of secondary malignancies in patients with ovarian cancer: a systematic review and meta-analysis

The Risk of Secondary Malignancies in Patients with Ovarian Cancer: A Systematic Review and Meta-Analysis

Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data

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