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Introduction to CTIIP

Most cancer diagnoses are made based on images. You have to see a tumor, or compare images of it over time, to determine its level of threat. Ultrasounds, MRIs, and X-rays are all common types of images that radiologists use to collect information about a patient and perhaps cause a doctor to recommend a biopsy. Once that section of the tumor is under the microscope, pathologists learn more about the tumor. To gather even more information, a doctor may run a genetic test and determine that the patient has a genetic anomaly. This genetic anomaly may have a precise match to an effective therapy thanks to animal research and other forms of precision medicine.

Each of these types of information is a different scale image. A large-scale image like an X-ray may be almost life-size. Genes, on the other hand, fit on a slide that is put under a microscope. If you were the patient, wouldn't you want your medical team to benefit from an integration of all of these images?

For example, breast cancer has biomarkers (progesterone status, etc.). One question to ask is "if the estrogen status is negative in humans, what does the pathology look like?" Then compare this to mice. Is the model we have a good model for the human condition?

Slides of cells, however, also tell us something about

Imaging-based cancer research is ushering in an integrative-biology revolution. It is now feasible to extract large sets of quantitative image features relevant to cancer prognosis or treatment across three complementary research domains: clinical imaging, pre-clinical imaging, and digital pathology. These high-dimensional image feature sets can be used to infer clinical phenotypes or correlate with gene–protein signatures. This type of analysis, however, requires large volumes of data.

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