Artificial Intelligence Predicts Treatment Success for Melanoma Patients from Early CT Scans

Artificial intelligence (AI) is poised to revolutionize the field of radiology as a tool to improve disease detection, diagnosis, and clinical care. The technology has the potential to assist clinicians by uncovering hidden information within imaging scans invisible to even the well-trained eye. Now, researchers have demonstrated that applying AI to standard-of-care imaging can help predict how well immunotherapy will work for patients with melanoma.

Researchers at Columbia University (New York, NY, USA) have developed a machine learning algorithm that analyzes a patient’s computed tomography (CT) scans and creates a biomarker – known as a radiomic signature – that correlates with patient outcome. The signature used specific features of the tumor to determine with high accuracy whether a given individual’s disease would respond well to immunotherapy, remain stable, or continue to progress. The goal of immunotherapy, which has become a primary treatment for melanoma, is to stimulate a patient’s own immune system to fight cancer.

Currently, clinicians rely almost entirely on tumor size to estimate the benefit of a therapy. Patients receive a baseline CT scan and then subsequent follow-up scans after treatment has begun. If the tumor shrinks, the treatment seems to be working, while growth implies that the patient’s disease is getting worse. But this is not necessarily the case with immunotherapy, and studies have shown that tumor size and growth does not always correlate with overall survival.

Biologically, tumors may evolve throughout the course of a patient’s disease in ways that are more complex than a measure of size alone can reflect. As an example of this, the researchers found that their machine learning algorithm worked best when it took not only tumor volume and growth into account, but also tumor spatial heterogeneity, or the non-uniform distribution of cancer cells across disease sites, and texture, which looks at the variation of pixel intensities across the tumor CT image.

The researchers validated the algorithm on data from 287 patients with advanced melanoma who were administered the immunotherapy drug pembrolizumab. The radiomic signature, which used CT images obtained at baseline and three-month follow-up was able to estimate overall survival at six months with a high degree of accuracy. In fact, it outperformed the standard method based on tumor diameter, known as Response Evaluation Criteria in Solid Tumors 1.1 (RECIST 1.1), which is commonly used in clinical trials to assess treatment efficacy.

The researchers now aim to expand the project to a variety of different tumor types - such as lung cancer, colon cancer, renal cancer, and prostate cancer - as well as other treatments beyond immunotherapy. The researchers wanted to start with a novel therapy and chose melanoma because of the recent, rapid adoption of immunotherapy for the disease.

“We hope to take a patient early on who looks like they are not doing well on a given therapy because of their signature and enhance, change, or add another drug to the therapy,” said Lawrence H. Schwartz, MD, the James Picker Professor and chair of the Department of Radiology at Columbia University Vagelos College of Physicians and Surgeons (VP&S).

“The field of radiology and imaging in general has never been more exciting with this artificial intelligence revolution,” added Dr. Schwartz. “We’ve always looked at advances in terms of new machines, new tracers, and things like this. But this gives us an opportunity to optimize the information that we have from all of our imaging modalities to speed diagnosis, to become more accurate and precise, and give patients more effective treatments.”

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