Researchers from Imperial College London have developed a groundbreaking, non-invasive method that utilises artificial intelligence (AI) to classify lung cancer types and accurately predict the likelihood of the cancer progressing.

 

This new approach, combining medical imaging with AI, promises to revolutionise the diagnosis and treatment of lung cancer, eliminating the need for physical tissue samples and improving patient outcomes. 

 

Called tissue-metabolomic-radiomic-CT (TMR-CT), this deep-learning assessment tool represents a significant step forward in oncology. By extracting information about the chemical makeup of lung tumours from medical scans, it provides a ‘virtual biopsy’ for cancer patients. This is critical in selecting the right treatment, as it allows doctors to classify the type of lung cancer a patient has and predict if the cancer is likely to progress, all without the need for invasive procedures. 

 

The study, published in the journal Precision Oncology, shows promising results in classifying lung cancer and predicting patient outcomes using the TMR-CT tool. The researchers hope to confirm their method in other groups of lung cancer patients, and even extend its use to diagnose and predict outcomes for brain, ovarian, and endometrial cancers.

 

Furthermore, the TMR-CT method could be incorporated as an algorithm as part of the software loaded onto commercial medical imaging scanners. This potential integration could transform diagnostic and treatment protocols, especially in countries with high lung cancer prevalence, providing a more accurate, efficient, and non-invasive option for cancer detection and prognosis.