Study demonstrates effectiveness of new, innovative machine learning technique to analyze the presence of rare circulating tumor cells (CTCs) in blood

Metastasis―the development of tumor growth at a secondary site―is responsible for the majority of cancer-related deaths. It occurs when the primary tumor site sheds cancerous cells which are then circulated through the body via blood vessels or lymph nodes. These become seeds for eventual tumor growth at a secondary location in the body.

Detection of these very rare cells, known as circulating tumor cells or CTCs, is important for early prognosis of serious disease as well as to monitor the effectiveness of treatment. Currently, there is only one method for CTC detection approved by the U.S. Food & Drug Administration (FDA), CellSearch, which is used to diagnose breastcolorectal and prostate cancer.

Results from a recent study―a collaboration between Lehigh University, Lehigh Valley Cancer Institute, and Pennsylvania State University―demonstrate the potential for a new method of detecting circulating tumor cells. Unlike existing methods, which rely on an expensive and time-consuming process that involves labelling antibodies with fluorescence, this technique uses a powerful label-free detection method. Developed by Yaling Liu, a faculty member in Lehigh’s Department of Bioengineering and in the Department of Mechanical Engineering and Mechanics, in collaboration with Xiaolei Huang, faculty member in Penn State’s College of Information Sciences and Technology, the technique applies a machine learning algorithm to bright field microscopy images of cells detected in patient blood samples containing white blood cells and CTCs.

The blood samples were drawn from participating patients undergoing treatment for stage 4 renal, or kidney, cancer at Lehigh Valley Hospital-Cedar Crest under the care of Dr. Suresh G. Nair, physician in chief at Lehigh Valley Cancer Institute. The model yielded a high rate of accuracy: 88.6% overall accuracy on patient blood and 97% on cultured cells. The results have been published in Nature Scientific Reports in an article called “Label-free detection of rare circulating tumor cells by image analysis and machine learning.” In addition to Liu, Huang, and Nair, authors include three Lehigh PhD students Shen WengYuyuan Zhou and Xiachen Qin

Nair says Liu’s innovative technique to isolate rare circulating cancer cells in a tube of blood―which can number as few as 15 cells in one billion―represents “a simpler, elegant and cost effective approach to monitoring patients on therapies such as immunotherapy and targeted therapy for cancer at the circulating cell level rather than scans such as CAT scans, which look for 100 million or more cells organized into a one centimeter tumor.”

“This study, though small, demonstrates that our method can achieve high accuracy on the identification of rare CTCs without the need for advanced devices or expert users, thus providing a faster and simpler way for counting and identifying CTCs,” says Liu. “With more data becoming available in the future, the machine learning model can be further improved and serve as an accurate and easy-to-use tool for CTC analysis.”

The method, he says, requires minimal data pre-processing and has an easy experimental setup. To arrive at the results, the team preprocessed the whole blood samples, capturing the bright field and fluorescent images of the cells. They trained a deep learning model with cropped single-cell in bright field images and used the corresponding fluorescent images as ground truth labels. They also trained and tested a model with cultured cell lines as a comparison. The group then did the testing and summarized the statistical results of the trained model. 

“We tuned the details of the model to reach better results until the outcome reached the state-of-the-art,” says Liu.

Read the full story in the Lehigh University News Center

Yaling Liu

Yaling Liu is a faculty member in the Department of Bioengineering and the Department of Mechanical Engineering and Mechanics in the P.C. Rossin College of Engineering and Applied Science.