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Article

Machine Learning Models Cannot Replace Screening Colonoscopy for the Prediction of Advanced Colorectal Adenoma

1
Department of Internal Medicine, General Hospital Oberndorf, Teaching Hospital of the Paracelsus Medical University Salzburg, 5020 Salzburg, Austria
2
Division of Gastroenterology and Hepatology, Department of Internal Medicine III, Medical University of Vienna, 1090 Vienna, Austria
3
Second Department of Medicine, Paracelsus Medical University Salzburg, 5020 Salzburg, Austria
4
Fondazione Bruno Kessler Research Institute, 38123 Trento, Italy
5
First Department of Medicine, Paracelsus Medical University Salzburg, 5020 Salzburg, Austria
*
Authors to whom correspondence should be addressed.
Authors contributed equally.
Academic Editor: Shang-Ming Zhou
J. Pers. Med. 2021, 11(10), 981; https://doi.org/10.3390/jpm11100981
Received: 23 August 2021 / Revised: 24 September 2021 / Accepted: 24 September 2021 / Published: 29 September 2021
(This article belongs to the Section Epidemiology)
Screening for colorectal cancer (CRC) continues to rely on colonoscopy and/or fecal occult blood testing since other (non-invasive) risk-stratification systems have not yet been implemented into European guidelines. In this study, we evaluate the potential of machine learning (ML) methods to predict advanced adenomas (AAs) in 5862 individuals participating in a screening program for colorectal cancer. Adenomas were diagnosed histologically with an AA being ≥ 1 cm in size or with high-grade dysplasia/villous features being present. Logistic regression (LR) and extreme gradient boosting (XGBoost) algorithms were evaluated for AA prediction. The mean age was 58.7 ± 9.7 years with 2811 males (48.0%), 1404 (24.0%) of whom suffered from obesity (BMI ≥ 30 kg/m²), 871 (14.9%) from diabetes, and 2095 (39.1%) from metabolic syndrome. An adenoma was detected in 1884 (32.1%), as well as AAs in 437 (7.5%). Modelling 36 laboratory parameters, eight clinical parameters, and data on eight food types/dietary patterns, moderate accuracy in predicting AAs with XGBoost and LR (AUC-ROC of 0.65–0.68) could be achieved. Limiting variables to established risk factors for AAs did not significantly improve performance. Moreover, subgroup analyses in subjects without genetic predispositions, in individuals aged 45–80 years, or in gender-specific analyses showed similar results. In conclusion, ML based on point-prevalence laboratory and clinical information does not accurately predict AAs. View Full-Text
Keywords: machine learning; artificial intelligence; colorectal adenoma; colorectal cancer; advanced adenoma; screening machine learning; artificial intelligence; colorectal adenoma; colorectal cancer; advanced adenoma; screening
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MDPI and ACS Style

Semmler, G.; Wernly, S.; Wernly, B.; Mamandipoor, B.; Bachmayer, S.; Semmler, L.; Aigner, E.; Datz, C.; Osmani, V. Machine Learning Models Cannot Replace Screening Colonoscopy for the Prediction of Advanced Colorectal Adenoma. J. Pers. Med. 2021, 11, 981. https://doi.org/10.3390/jpm11100981

AMA Style

Semmler G, Wernly S, Wernly B, Mamandipoor B, Bachmayer S, Semmler L, Aigner E, Datz C, Osmani V. Machine Learning Models Cannot Replace Screening Colonoscopy for the Prediction of Advanced Colorectal Adenoma. Journal of Personalized Medicine. 2021; 11(10):981. https://doi.org/10.3390/jpm11100981

Chicago/Turabian Style

Semmler, Georg, Sarah Wernly, Bernhard Wernly, Behrooz Mamandipoor, Sebastian Bachmayer, Lorenz Semmler, Elmar Aigner, Christian Datz, and Venet Osmani. 2021. "Machine Learning Models Cannot Replace Screening Colonoscopy for the Prediction of Advanced Colorectal Adenoma" Journal of Personalized Medicine 11, no. 10: 981. https://doi.org/10.3390/jpm11100981

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