AI predicts side effects of testicular cancer treatment on kidneys
IANS Jun 29, 2020
Researchers, including one of Indian-origin, have developed an Artificial Intelligence (AI)-based model that can predict how chemotherapy can affect the kidneys of testicular cancer patients.
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For the high-risk patients, the model was able to correctly predict 67 percent of affected patients, while for the low-risk, the model correctly predicted 92 percent of the patients that did not develop nephrotoxicity, said the study published in the journal JNCI Cancer Spectrum. "Understanding how and where AI technologies can be applied in clinical care, is increasingly important also in the future of responsible AI," said study co-author Ramneek Gupta, Associate Professor at Technical University of Denmark.
Testicular cancer is the most common cancer in young men. The number of new cases is increasing worldwide. There is a relatively high survival rate, with 95 percent surviving after 10 years -- if detected in time and treated properly. However, the standard chemotherapy includes cisplatin which has a wide range of long-term side effects, one of which can be nephrotoxicity -- when a drug or toxin causes damage to kidneys. "In testicular cancer patients, cisplatin-based chemotherapy is essential to ensure a high cure rate. Unfortunately, treatment can cause side effects, including renal impairment. However, we are not able to pinpoint who ends up having side effects and who does not," said study co-author Jakob Lauritsen from Rigshospitalet, a hospital in Denmark.
The researchers therefore asked the question: How far can we go in predicting nephrotoxicity risk in these patients using machine learning? First, it required some patient data. "Using a cohort of testicular-cancer patients from Denmark- in collaboration with Rigshospitalet, we developed a machine learning predictive model to tackle this problem," said Sara Garcia from Technical University of Denmark. The high-quality of Danish patient records allowed the identification of the key patients.
The project saw the development of several analyses strategies of genomics and patient data, bringing forward the promise of artificial intelligence for integration of diverse data streams. A risk score for an individual to develop nephrotoxicity during chemotherapy was generated, and key genes likely at play were proposed. Patients were classified into high, low, and intermediate risk.
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