The aims of our work are:
- To describe the Radiomics process;
- To discuss the Radiomics’ applicationin the management of lung cancer.
Tumour’s stage is the most important prognosticator used in the management of lung cancer,
anyway even in patient with the same stage a different clinical behaviour has been seen.
Indeed in addition to the anatomic extension of the lesion other factors related to the patient,
tumour histology or “habitat” determine prognosis.
Therefore multifactorial predictive models are mandatory to obtain the so-called “personalized medicine”.
Cancer tissue is represented by a continuous interaction between microenvironment and neoplastic cells,
which are continually forced in genetic mutation to generate...
Findings and procedure details
Radiomics workflow: steps and challenges
The Radiomics workflow includes multiple steps: data selection and image acquisition; image segmentation; features extraction (pre-processing steps); exploratory analysis/feature selection,
model building and validation [3-4].
Data Selection and Image acquisition
A Radiomics analysis begins with the choice of an imaging type,
the region/volume of interest (ROI/VOI) and a prediction target.
Medical images acquired for standard clinical diagnostics,
treatment planning and follow-up can be used,
with the possibility to obtain a big amount of data .
A principal drawback is that...
In the era of “personalized medicine”,
the challenge is to identify quantitative multimodal prognostic factors and models to recognize tumour and patient subpopulations that may or may not benefit from a specific treatment.
Radiomics is an expanding research field that non-invasively and widespread could be used in human tumour evaluation,
with promising results in the management of lung cancer.
However different technical aspects must be taken into account to ensure the quality of the data and to facilitate its use in clinical practice.
Resident doctor in Radiology
Department of Radiology - Catholic University of Sacred Heart - Policlinico “A.
Gemelli” Foundation – Rome,
Quantitative imaging in cancer evolution and ecology.
Imaging Heterogeneity in Lung Cancer: Techniques,
AJR Am J Roentgenol.
de Jong EEC,
van Timmeren J,
van Wijk Y,
van Soest J,
van Elmpt W,