H. Lee Moffitt Cancer Center and Research Institute, Inc.

University of South Florida

Olya Stringfield

Robert J Gillies

Dmitry B Goldgof

Lawrence O Hall

Robert A Gatenby

Yoganand Balagurunathan

John Arrington

Natarajan Raghunand

Virtually every cancer patient is imaged with CT, PET or MRI. Importantly, such imaging reveals that tumors are complex and heterogeneous, often containing multiple habitats within them. Disclosed herein are methods for analyzing these images to infer cellular and molecular structure in each of these habitats. The methods can involve spatially superimposing two or more radiological images of the tumor sufficient to define regional habitat variations in two or more ecological dynamics in the tumor, and comparing the habitat variations to one or more controls to predict the severity of the tumor.

Radiologically identified tumor habitats

Disclosed are methods for quantitatively predicting the severity of a tumor in a subject. In some embodiments, the methods further comprise selecting a course of therapy for the subject. In some embodiments, the tumor comprises is non-small cell lung cancer.

Quantitative predictors of tumor severity

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Tampa, FL, United States of America

Olya Stringfield