Derivation and validation of a risk scoring model to predict advanced colorectal neoplasm in adults of all ages

Yang, Hyo‐Joon; Choi, Sungkyoung; Park, Soo-Kyung; Jung, Yoon Suk; Choi, Kyu Yong; Park, Taesung; Kim, Ji Yeon; Park, Dong Il

doi:10.1111/jgh.13711

Cited by 26 publications

(42 citation statements)

References 35 publications

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“…Some have attempted to develop predictors of one’s risk for advanced adenomas and advanced neoplasia before colonoscopy, with unexceptional results and a moderate discriminative capacity[93-95]. Another scoring system implemented Fecal Immunochemical Testing results (FIT)[96], and others merged risk factors with laboratory results[97-99]. Three studies enrolled subjects younger than 50 years of age[99-100].…”

Section: Antithesis: Why Not Screen Before the Age Of 50?mentioning

confidence: 99%

“…Another scoring system implemented Fecal Immunochemical Testing results (FIT)[96], and others merged risk factors with laboratory results[97-99]. Three studies enrolled subjects younger than 50 years of age[99-100]. The most commonly included risk factors include sex, age, family history, body mass index (BMI), smoking, alcohol, aspirin, physical activity, red meat and vegetable consumption, cardiovascular diseases and hypertension.…”

Section: Antithesis: Why Not Screen Before the Age Of 50?mentioning

confidence: 99%

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Colorectal cancer screening from 45 years of age: Thesis, antithesis and synthesis

Mannucci

Zuppardo

Rosati

et al. 2019

WJG

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Colorectal cancer incidence and mortality in patients younger than 50 years are increasing, but screening before the age of 50 is not offered in Europe. Advanced-stage diagnosis and mortality from colorectal cancer before 50 years of age are increasing. This is not a detection-bias effect; it is a real issue affecting the entire population. Three independent computational models indicate that screening from 45 years of age would yield a better balance of benefits and risks than the current start at 50 years of age. Experimental data support these predictions in a sex- and race-independent manner. Earlier screening is seemingly affordable, with minimal impediments to providing younger adults with colonoscopy. Indeed, the American Cancer Society has already started to recommend screening from 45 years of age in the United States. Implementing early screening is a societal and public health problem. The three independent computational models that suggested earlier screening were criticized for assuming perfect compliance. Guidelines and recommendations should be derived from well-collected and reproducible data, and not from mathematical predictions. In the era of personalized medicine, screening decisions might not be based solely on age, and sophisticated prediction software may better guide screening. Moreover, early screening might divert resources away from older individuals with greater biological risks. Finally, it is still unknown whether early colorectal cancer is part of a continuum of disease or a biologically distinct disease and, as such, it might not benefit from screening at all. The increase in early-onset colorectal cancer incidence and mortality demonstrates an obligation to take actions. Earlier screening would save lives, and starting at the age of 45 years may be a robust screening option.

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Section: Antithesis: Why Not Screen Before the Age Of 50?mentioning

confidence: 99%

Section: Antithesis: Why Not Screen Before the Age Of 50?mentioning

confidence: 99%

Colorectal cancer screening from 45 years of age: Thesis, antithesis and synthesis

Mannucci

Zuppardo

Rosati

et al. 2019

WJG

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“…In this study, using a dataset of more than 70,000 subjects involving 26 clinical parameters, the DNN model exhibited better performance in the prediction of ACRN in comparison with the conventional LR model. The value of AUC reached 0.76, which is higher than that of that of any other clinical prediction models or scores employed to predict ACRNs [5][6][7][8][9][10]. Importantly, this performance was achieved by the inclusion of 26 clinical and laboratory parameters, indicating the potential for the DNN to be expanded to include more data, even from other sources, such as transcriptomics and me-tabolomics information from blood, stool, tissue samples, or even imaging data.…”

Section: Discussionmentioning

confidence: 93%

“…www.kjim.org https://doi.org/10.3904/kjim.2020.020 ACRN was classified as colorectal carcinoma or advanced adenoma. Advanced adenoma was defined as any adenoma ≥ 1 cm in size, or one that has a villous component or high-grade dysplasia [10].…”

Section: Datasetmentioning

confidence: 99%

“…The customization of the screening based on the risk of CRC among the average risk population may improve the screening efficiency and adherence [5]. Several risk-prediction algorithms based on logistic regression (LR) models have been developed to identify individuals at high risk of advanced colorectal neoplasia (ACRN), for which colonoscopy may be most suitable [5][6][7][8][9][10]. However, these models demonstrated limited performance with low sensitivity and high false-positive rates, which may be due to the limited amount of information used in the model, limited performance of the LR method, or both.…”

Section: Introductionmentioning

confidence: 99%

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Application of deep learning to predict advanced neoplasia using big clinical data in colorectal cancer screening of asymptomatic adults

Yang

Cho

Jang

et al. 2020

Korean J Intern Med

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Background/Aims: We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal neoplasia (ACRN) in asymptomatic adults, based on which colorectal cancer screening could be customized. Methods: We collected data on 26 clinical and laboratory parameters, including age, sex, smoking status, body mass index, complete blood count, blood chemistry, and tumor marker, from 70,336 first-time colonoscopy screening recipients. For reference, we used a logistic regression (LR) model with nine variables manually selected from the 26 variables. A deep neural network (DNN) model was developed using all 26 variables. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of the models were compared in a randomly split validation group. Results: In comparison with the LR model (AUC, 0.724; 95% confidence interval [CI], 0.684 to 0.765), the DNN model (AUC, 0.760; 95% CI, 0.724 to 0.795) demonstrated significantly improved performance with respect to the prediction of ACRN (p < 0.001). At a sensitivity of 90%, the specificity significantly increased with the application of the DNN model (41.0%) in comparison with the LR model (26.5%) (p < 0.001), indicating that the colonoscopy workload required to detect the same number of ACRNs could be reduced by 20%. Conclusions: The application of DNN to big clinical data could significantly improve the prediction of ACRNs in comparison with the LR model, potentially realizing further customization by utilizing large quantities and various types of biomedical information.

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A scoping review of risk-stratified bowel screening: current evidence, future directions

Cairns

Greenley

Bamidele

et al. 2022

Cancer Causes Control

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Purpose In this scoping review, we examined the international literature on risk-stratified bowel screening to develop recommendations for future research, practice and policy. Methods Six electronic databases were searched from inception to 18 October 2021: Medline, Embase, PsycINFO, CINAHL, Cochrane Database of Systematic Reviews and Cochrane Central Register of Controlled Trials. Forward and backwards citation searches were also undertaken. All relevant literature were included. Results After de-deduplication, 3,629 records remained. 3,416 were excluded at the title/abstract screening stage. A further 111 were excluded at full-text screening stage. In total, 102 unique studies were included. Results showed that risk-stratified bowel screening programmes can potentially improve diagnostic performance, but there is a lack of information on longer-term outcomes. Risk models do appear to show promise in refining existing risk stratification guidelines but most were not externally validated and less than half achieved good discriminatory power. Risk assessment tools in primary care have the potential for high levels of acceptability and uptake, and therefore, could form an important component of future risk-stratified bowel screening programmes, but sometimes the screening recommendations were not adhered to by the patient or healthcare provider. The review identified important knowledge gaps, most notably in the area of organisation of screening services due to few pilots, and what risk stratification might mean for inequalities. Conclusion We recommend that future research focuses on what organisational challenges risk-stratified bowel screening may face and a consideration of inequalities in any changes to organised bowel screening programmes.

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Derivation and validation of a risk scoring model to predict advanced colorectal neoplasm in adults of all ages

Abstract: Our scoring model based on both clinical and laboratory risk factors is useful for the prediction of ACN. This score may be used to include adults < 50 years in colonoscopy screening.

Cited by 26 publications

References 35 publications

Colorectal cancer screening from 45 years of age: Thesis, antithesis and synthesis

Colorectal cancer screening from 45 years of age: Thesis, antithesis and synthesis

Application of deep learning to predict advanced neoplasia using big clinical data in colorectal cancer screening of asymptomatic adults

A scoping review of risk-stratified bowel screening: current evidence, future directions

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