The head and neck region is particularly challenging
Now, however, artificial intelligence can also automatically detect the tumor. As Wesarg explains, “Our software tool localizes and labels the tumor in the computer tomography images, presents it in 3D and analyzes the corresponding image data.” The system is based on neural networks and was trained with data in which the tumor was labeled manually. It then used this data to generate a model. Additional data is added from the head and neck atlas, such as the information that the larynx is completely healthy-looking, so the system doesn’t need to look for the tumor there. The results of the head and neck atlas thereby provide a preselection.
How is brightness distributed within the tumor? Is there anything that isn’t noticeable when a human looks at it? The tool uses various descriptive parameters to answer these questions. In total, with the appropriate software, more than a hundred parameters of this kind can be extracted from the images of a head and neck tumor.
Faster, cheaper and gentler than a biopsy
Initial results show that, with this approach, CT images can even provide information that once could only be obtained through a surgical procedure followed by laboratory analysis of the extracted tumor tissue. “So it’s conceivable, for instance, that a correlation could be found between the intensity pattern within the tumor region and a cell abnormality detected in the lab. With enough patients, it could one day be possible to infer – with statistical certainty – pathological cell changes on the basis of the appearance of the tumor in the image data.” Thus, so the theory goes, it will soon be possible to use artificial intelligence to draw conclusions regarding tissue markers, obviating the need for an actual biopsy. This is easier not only on patients but also on health insurance companies’ budgets. On top of that, the results are available much more quickly than they would be for a biopsy with a lab analysis of the extracted tissue.
Parts of this technology are already being used in initial test runs at the HNO clinic of the University Hospital of Düsseldorf. The doctors there are using the technology to retroactively analyze patient data and review cohort assignments. In the months ahead, this test is expected to reveal how the AI findings correlate with empirical knowledge, thus marking the first step toward cohorting – and onward toward treatment tailored to the individual patient.
The long-term aim is to personalize medical care – to identify the therapy with the highest probability of success for each patient. To achieve this, the algorithms developed for the head and neck region could also be extended to other types of cancer. For this, however, the algorithm needs to have the relevant information as to which structures it should look for in the image data. This is because tumors in the head and neck region have different markers than, say, lung tumors.
In collaboration with MedCom GmbH in Darmstadt, a Fraunhofer IGD spin-off, the researchers also want to begin this process as early as the initial diagnosis. In the BMBF ECHOMICS project, they are using artificial intelligence to analyze ultrasound images of the lymph nodes in a process analogous to a biopsy. This is because a permanent enlargement of the lymph nodes may indicate the presence of a tumor in the body. This would enable doctors to detect tumors much sooner than is currently possible, thus facilitating swifter treatment and improving the chances of success.