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Identification of Precise 3D CT Radiomics for Habitat Computation by Machine Learning in Cancer.

Olivia Prior¹, Carlos Macarro¹, Víctor Navarro¹

¹From the Radiomics Group, Vall d'Hebron Institute of Oncology, Carrer de Natzaret 115-117, Barcelona 08035, Spain (O.P., C. Macarro, C. Monreal, M.L., A.G.R., F.G., K.B., R.P.L.); Oncology Data Science Group, Vall d'Hebron Institute of Oncology, Barcelona, Spain (V.N., R.D.); Molecular Pathology Group, Vall d'Hebron Institute of Oncology, Barcelona, Spain (G.S., S.S., P.N.); Department of Medical Oncology, Vall d'Hebron University Hospital, Barcelona, Spain (I.B., M.V., E.G., J.C.); Molecular Therapeutic Research Unit, Vall d'Hebron Institute of Oncology, Barcelona, Spain (I.B., M.V., E.G., J.C.); Department of Radiology, Vall d'Hebron University Hospital, Barcelona, Spain (M.E.); Biomakers and Clonal Dynamics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain (R.T.); Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland (A.T.B.); and National Pre-clinical Imaging Centre, Dublin, Ireland (A.T.B.).

Radiology. Artificial Intelligence

|January 31, 2024

Summary

This summary is machine-generated.

Precise radiomics features from CT scans identify stable tumor habitats for assessing cancer heterogeneity. This machine learning approach enhances tumor subtyping in lung and liver cancer, correlating with MRI and histology findings.

Keywords:

CT Diffusion-weighted Imaging Dynamic Contrast-enhanced MRI Liver Lung MRI Oncology Radiomics Unsupervised Learning

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Oncology
Medical Imaging
Machine Learning

Background:

Cancer heterogeneity assessment is crucial for treatment planning.
Radiomics analysis of CT images offers potential for non-invasive tumor characterization.
Current radiomics methods face challenges in repeatability and reproducibility.

Purpose of the Study:

To identify precise 3D radiomics features in CT images for stable, biologically meaningful tumor habitat computation.
To utilize machine learning for cancer heterogeneity assessment using these precise features.
To evaluate the stability and biological correlates of CT-derived tumor habitats.

Main Methods:

Retrospective analysis of 605 CT scans from 331 cancer patients (liver/lung lesions).
Computation of 3D radiomics features from original and perturbed CT images.
Identification of precise features using intraclass correlation coefficient (ICC) for repeatability and reproducibility (LCL of ICC ≥ 0.50).
Tumor habitats derived using Gaussian mixture models and precise features, assessed for stability with Dice Similarity Coefficient (DSC).

Main Results:

Excellent reproducibility of radiomics features against bin size (LCL of ICC: median [IQR], 0.929 [0.853-0.988]).
Twenty-six precise radiomics features were identified, varying between lung and liver lesions.
Habitats derived from precise features showed significantly higher stability (DSC: median [IQR], 0.601-0.651) compared to all features (DSC: median [IQR], 0.532-0.587).
CT habitats quantitatively and qualitatively correlated with multiparametric MRI and histology findings in a case study.

Conclusions:

Precise 3D radiomics features can be identified on CT images.
These features enable the computation of stable tumor habitats for cancer heterogeneity assessment.
This approach holds promise for improving non-invasive tumor characterization in oncology.