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Tapping Tumor Physics Advances Lung Cancer Prognosis in CT Imaging

A new proof-of-principle study from Japan took advantage of lung tumors’ biophysical features to predict patient prognosis using 3-D CT imaging.

Vijay Shankar Balakrishnan, Contributor
Wednesday, April 11, 2018


Shutterstock/Suttha Burawonk


Computer tomography, or CT, has advanced cancer care by helping doctors visualize solid tumors. But in lung cancers, the tumors are often in different material states, and those states can determine the type and severity of malignancy they give rise to. This poses diagnostic challenges in imaging-based care.

Solid lung tumors are largely composed of tumor cells and blood vessels, while subsolid ones also have air pockets within the tumor, as well as fluid mucous within and around. Conventionally, radiologists record images along different planes of these tumors in 2-D and reconstruct them as 3-D images for diagnosis or a prognosis after a surgery. For subsolid tumors with varying opacity, airiness and fluidity, the conventional 2-D scans or 2-D to 3-D reconstructions mostly take only the solid parts of the tumor into account.

Now, a study from Japan has used a new approach to perform scans that also take the fluid and gas parts of the tumors into account. The researchers rendered images in 3-D by using a scanner with multiple detectors, allowing images to be taken from 64 different angles. Then, they used data from the scans to calculate the entire tumor volume.

Through a retrospective analysis of 246 subsolid lung tumor images obtained from a patient database, the researchers showed that their 3-D volumetric approach outperforms other approaches they compared it with in predicting cancer recurrence after surgery, making it a promising potential tool in lung cancer care.