Identifying the optimal strategy for screening of advanced colorectal neoplasia

Jung, Yoon Suk; Park, Chan Hyuk; Kim, Nam Hee; Park, Jung Ho; Park, Dong Il; Sohn, Chong Il

doi:10.1111/jgh.13634

Cited by 6 publications

(4 citation statements)

References 27 publications

(69 reference statements)

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“…Fifty-nine studies (n = 3 491 045) (published in 78 articles) that evaluated the accuracy of various screening tests were included. There were no new studies published since the prior review that would add to the understanding of screening sensitivity or specificity for colonoscopy, CT colonography, or flexible sigmoidoscopy.…”

Section: Resultsmentioning

confidence: 99%

Screening for Colorectal Cancer

Lin

Perdue

Henrikson

et al. 2021

JAMA

323

193

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Section: Resultsmentioning

confidence: 99%

Screening for Colorectal Cancer

Lin

Perdue

Henrikson

et al. 2021

JAMA

323

193

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“…An updated search confirmed an additional three eligible studies, resulting in a final sample of 13 studies included in the meta-analysis. [9][10][11][12][13][17][18][19][20][21][22][23][24][25][26][27] Study characteristics and quality assessment. Tables 1 and 2 summarize the characteristics and quality evaluation of the 13 included studies, all of which were conducted in the Asia-Pacific region.…”

Section: Resultsmentioning

confidence: 99%

“…A total of 10 eligible studies were identified in this initial search. An updated search confirmed an additional three eligible studies, resulting in a final sample of 13 studies included in the meta‐analysis 9–13,17–27 …”

Section: Resultsmentioning

confidence: 99%

Performance of the Asia–Pacific Colorectal Screening score in stratifying the risk of advanced colorectal neoplasia: A meta‐analysis and systematic review

Luu,

Trinh,

Tran

et al. 2024

J of Gastro and Hepatol

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Background and AimThis study systematically reviewed and meta‐analyzed the performance of the Asia–Pacific Colorectal Screening (APCS) score and its incorporation with the fecal immunochemical test (FIT) in stratifying the risk of advanced colorectal neoplasia (ACN).MethodsWe systematically searched for relevant articles in 12 electronic databases and registers on October 20, 2021, and updated the search to September 1, 2023. Random‐effect models were used to obtain the pooled performance statistics of the APCS score for ACN risk.ResultsFrom the 101 records screened, 13 eligible studies in the Asia–Pacific region involving 69 762 subjects who had undergone colonoscopy were enrolled. The pooled prevalences of ACN in the average‐risk (AR) tier (APCS 0–1), moderate‐risk (MR) tier (APCS 2–3), and high‐risk (HR) tier (APCS ≥ 4) groups were 0.9%, 3.1%, and 8.1%, respectively. Compared with the combined AR–MR group, the HR group was significantly associated with a higher ACN risk (pooled diagnostic odds ratio: 2.84, 95% confidence interval [CI]: 2.35–3.45, P < 0.001). The APCS score showed a sensitivity of 0.42 (95% CI: 0.40–0.44) and a specificity of 0.86 (95% CI: 0.85–0.86) for predicting the ACN risk, with a weighted area under the curve of 0.642 (95% CI: 0.610–0.657). The combination of the APCS score and FIT substantially improved ACN risk identification, demonstrating pooled diagnostic odds ratios of 4.02 (95% CI: 2.50–6.49) in the AR–MR groups and 5.44 (95% CI: 1.89–15.63) in the MR–HR groups.ConclusionsThe APCS score could effectively stratify the ACN risk in the Asia–Pacific population. Incorporating FIT further improves its performance in identifying high‐risk subjects who should be prioritized for colonoscopy screenings.

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“…In our screening program, some participants underwent a fecal immunochemical test (FIT) as well as colonoscopy [ 22 ]. A one-time stool sample was collected within 3 days before colonoscopy in a buffered sampling tube (Eiken Chemical Company, Tokyo, Japan) and sent to the laboratory on the day of the health examination.…”

Section: Methodsmentioning

confidence: 99%

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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Identifying the optimal strategy for screening of advanced colorectal neoplasia

Abstract: The performance in ACRN prediction differs among screening methods (sigmoidoscopy, FIT, and APCS). Among the various single or combined screening strategies, several specific strategies can be chosen depending on medical resources, financial budgets, and screenee preference.

Cited by 6 publications

References 27 publications

Screening for Colorectal Cancer

Screening for Colorectal Cancer

Performance of the Asia–Pacific Colorectal Screening score in stratifying the risk of advanced colorectal neoplasia: A meta‐analysis and systematic review

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

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