Prospects and Challenges of Radiomics by Using Nononcologic Routine Chest CT.

Chest CT scans are one of the most common medical imaging procedures. The automatic extraction and quantification of imaging features may help in diagnosis, prognosis of, or treatment decision in cardiovascular, pulmonary, and metabolic diseases. However, an adequate sample size as a statistical necessity for radiomics studies is often difficult to achieve in prospective trials. By exploiting imaging data from clinical routine, a much larger amount of data could be used than in clinical trials. Still, there is only little literature on the implementation of radiomics in clinical routine chest CT scans. Reasons are heterogeneous CT scanning protocols and the resulting technical variability (eg, different slice thicknesses, reconstruction kernels or timings after contrast material administration) in routine CT imaging data. This review summarizes the recent state of the art of studies aiming to develop quantifiable imaging biomarkers at chest CT, such as for osteoporosis, chronic obstructive pulmonary disease, interstitial lung disease, and coronary artery disease. This review explains solutions to overcome heterogeneity in routine data such as the use of imaging repositories, the standardization of radiomic features, algorithmic approaches to improve feature stability, test-retest studies, and the evolution of deep learning for modeling radiomics features.

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2020 by the Radiological Society of North America, Inc.

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Disclosures of Conflicts of Interest: S.R. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: author is consultant for contextflow. Other relationships: disclosed no relevant relationships. J.H. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: author is consultant/receives speaker fees from Boehringer-Ingelheim. Other relationships: disclosed no relevant relationships. F.P. Activities related to the present article: institution receives grant from Boehringer-Ingelheim. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. H.M. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: author is advisory board member for Zebra Medical Vision and Contextvision; author employed by HSE-SO Valais; author receives royalties from Springer. Other relationships: disclosed no relevant relationships. H.P. Activities related to the present article: institution receives grant and support for travel to meetings from Boehringer-Ingelheim. Activities not related to the present article: author receives speaker fees from Boehringer-Ingelheim, Roche, Novartis, MSD, BMS, GSK, Chiesi, and AstraZeneca. Other relationships: disclosed no relevant relationships. G.L. Activities related to the present article: institution receives grant from Siemens Healthineers (research grant to department). Activities not related to the present article: cofounder and shareholder in contextflow; speaker fees from Roche and Siemens; research support from Siemens, Novartis, IBM, and NVIDIA. Other relationships: disclosed no relevant relationships.