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Application of machine-learning model to optimize colonic adenoma detection in India

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Abstract

Aims

There is limited data on the prevalence and risk factors of colonic adenoma from the Indian sub-continent. We aimed at developing a machine-learning model to optimize colonic adenoma detection in a prospective cohort.

Methods

All consecutive adult patients undergoing diagnostic colonoscopy were enrolled between October 2020 and November 2022. Patients with a high risk of colonic adenoma were excluded. The predictive model was developed using the gradient-boosting machine (GBM)-learning method. The GBM model was optimized further by adjusting the learning rate and the number of trees and 10-fold cross-validation.

Results

Total 10,320 patients (mean age 45.18 ± 14.82 years; 69% men) were included in the study. In the overall population, 1152 (11.2%) patients had at least one adenoma. In patients with age > 50 years, hospital-based adenoma prevalence was 19.5% (808/4144). The area under the receiver operating curve (AUC) (SD) of the logistic regression model was 72.55% (4.91), while the AUCs for deep learning, decision tree, random forest and gradient-boosted tree model were 76.25% (4.22%), 65.95% (4.01%), 79.38% (4.91%) and 84.76% (2.86%), respectively. After model optimization and cross-validation, the AUC of the gradient-boosted tree model has increased to 92.2% (1.1%).

Conclusions

Machine-learning models may predict colorectal adenoma more accurately than logistic regression. A machine-learning model may help optimize the use of colonoscopy to prevent colorectal cancers.

Trial registration

ClinicalTrials.gov (ID: NCT04512729).

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Authors and Affiliations

  1. Department of Medical Gastroenterology, Asian Institute of Gastroenterology, Hyderabad, 500 082, India

    Nitin Jagtap, Rakesh Kalapala, Hardik Rughwani, Aniruddha Pratap Singh, Pradev Inavolu, Mohan Ramchandani, Sundeep Lakhtakia, P. Manohar Reddy, Manu Tandan, Zaheer Nabi, Jahangeer Basha, Rajesh Gupta, Sana Fathima Memon & D. Nageshwar Reddy

  2. Department of Pathology, Asian Institute of Gastroenterology, Hyderabad, 500 082, India

    Anuradha Sekaran

  3. Department of Surgical Gastroenterology, Asian Institute of Gastroenterology, Hyderabad 500 082, India

    G. Venkat Rao

  4. The University of Kansas Medical Center, Kansas City, KS, USA

    Prateek Sharma

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  1. Nitin Jagtap
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Contributions

Concept: PS, NJ, SL, DNR; design: NJ, RK, MR; supervision: RG, MT, SL; resources: NJ, PI, APS, HR; materials: NJ, PI, APS, AS; data collection and/or processing: NJ, PI, APS, SFM; analysis and/or interpretation: NJ, PS, RK; literature search: NJ, RK; writing manuscript: NJ, RK; critical review: MT, GVR, PS; final approval: all authors.

Corresponding author

Correspondence to Nitin Jagtap.

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Conflict of interest

NJ, RK, HR, APS, PI, MR, SL, PMR, AS, MT, ZN, JB, RG, SFM, GVR, PS and DNR declare no competing interests.

Ethics statement

The study was performed conforming to the Helsinki Declaration of 1975, as revised in 2000 and 2008 concerning human and animal rights, and the authors followed the policy concerning informed consent as shown on Springer.com.

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Informed consent was taken from study participants.

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The authors are solely responsible for the data and the contents of the paper. In no way, the Honorary Editor-in-Chief, Editorial Board Members, the Indian Society of Gastroenterology or the printer/publishers are responsible for the results/findings and content of this article.

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Jagtap, N., Kalapala, R., Rughwani, H. et al. Application of machine-learning model to optimize colonic adenoma detection in India. Indian J Gastroenterol (2024). https://doi.org/10.1007/s12664-024-01530-4

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