Foto: Jesper Scheel

Predicting side effects after testicular cancer

Tuesday 01 Dec 20
by Tom Nervil


Sara Garcia
PhD student
DTU Health Tech


Ramneek Gupta
Groupleader, Associate Professor
DTU Health Tech
+45 45 25 24 22

In a collaboration between Rigshospitalet and DTU, researchers have developed a method for predicting side effects from treatment for testicular cancer.

Testicular cancer is the most frequently occurring cancer type in young men, approximately half of whom will be treated with chemotherapy at some point. Unfortunately, the treatment may result in side effects such as reduced kidney function later in life. In a collaboration with Rigshospitalet (Copenhagen University Hospital), researchers from DTU Health Tech have developed a method for predicting who are at risk of developing impaired kidney function.

To begin with, this requires a large volume of patient data.

“Using a cohort of testicular cancer patients and machine learning, we’ve been able to develop a prediction model in collaboration with Rigshospitalet,” says Sara Garcia, PhD student at DTU Health Tech.

The high quality of Danish patient medical records made it possible to obtain precise information about side effects and treatment data. Through a technology partnership with the analysis platform DTU Multi-Assay Core (DMAC) and the life science enterprise YouDoBio, the researchers then obtained patient DNA with saliva kits, sent to the patients’ homes by regular mail. 

By feeding the large data volume into a computer and using machine learning, the researchers developed the prediction model.

The patients were classified at high, medium, and low risk of developing kidney impairment. It has turned out that the model is very precise in its predictions in relation to low-risk patients.

For high-risk patients, the model was able to predict the development of kidney impairment in 67 per cent of the cases, while it made a correct prediction of the patients who did not develop impaired kidney function in 92 per cent of the cases for the low-risk group.

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