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Article

A Novel Method for Colorectal Cancer Screening Based on Circulating Tumor Cells and Machine Learning

by 1, 1 and 2,*
1
Research Genetic Cancer Centre SA (RGCC), 53100 Florina, Greece
2
Research Genetic Cancer Centre International GmbH, 6300 Zug, Switzerland
*
Author to whom correspondence should be addressed.
Academic Editors: Leonidas P. Karakatsanis and Dimitrios S. Monos
Entropy 2021, 23(10), 1248; https://doi.org/10.3390/e23101248
Received: 12 August 2021 / Revised: 20 September 2021 / Accepted: 21 September 2021 / Published: 25 September 2021
Colorectal cancer is one of the most common types of cancer, and it can have a high mortality rate if left untreated or undiagnosed. The fact that CRC becomes symptomatic at advanced stages highlights the importance of early screening. The reference screening method for CRC is colonoscopy, an invasive, time-consuming procedure that requires sedation or anesthesia and is recommended from a certain age and above. The aim of this study was to build a machine learning classifier that can distinguish cancer from non-cancer samples. For this, circulating tumor cells were enumerated using flow cytometry. Their numbers were used as a training set for building an optimized SVM classifier that was subsequently used on a blind set. The SVM classifier’s accuracy on the blind samples was found to be 90.0%, sensitivity was 80.0%, specificity was 100.0%, precision was 100.0% and AUC was 0.98. Finally, in order to test the generalizability of our method, we also compared the performances of different classifiers developed by various machine learning models, using over-sampling datasets generated by the SMOTE algorithm. The results showed that SVM achieved the best performances according to the validation accuracy metric. Overall, our results demonstrate that CTCs enumerated by flow cytometry can provide significant information, which can be used in machine learning algorithms to successfully discriminate between healthy and colorectal cancer patients. The clinical significance of this method could be the development of a simple, fast, non-invasive cancer screening tool based on blood CTC enumeration by flow cytometry and machine learning algorithms. View Full-Text
Keywords: colorectal cancer; circulating tumor cells; flow cytometry; machine learning; SVM; SMOTE colorectal cancer; circulating tumor cells; flow cytometry; machine learning; SVM; SMOTE
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MDPI and ACS Style

Hatzidaki, E.; Iliopoulos, A.; Papasotiriou, I. A Novel Method for Colorectal Cancer Screening Based on Circulating Tumor Cells and Machine Learning. Entropy 2021, 23, 1248. https://doi.org/10.3390/e23101248

AMA Style

Hatzidaki E, Iliopoulos A, Papasotiriou I. A Novel Method for Colorectal Cancer Screening Based on Circulating Tumor Cells and Machine Learning. Entropy. 2021; 23(10):1248. https://doi.org/10.3390/e23101248

Chicago/Turabian Style

Hatzidaki, Eleana, Aggelos Iliopoulos, and Ioannis Papasotiriou. 2021. "A Novel Method for Colorectal Cancer Screening Based on Circulating Tumor Cells and Machine Learning" Entropy 23, no. 10: 1248. https://doi.org/10.3390/e23101248

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