Robust imaging habitat computation using voxel-wise radiomics features

Tumor heterogeneity has been postulated as a hallmark of treatment resistance and a cure constraint in cancer patients. Conventional quantitative medical imaging (radiomics) can be extended to computing voxel-wise features and aggregating tumor subregions with similar radiological phenotypes (imagin...

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Vydáno v:	Scientific reports Ročník 11; číslo 1; s. 20133 - 8
Hlavní autoři:	Bernatowicz, Kinga, Grussu, Francesco, Ligero, Marta, Garcia, Alonso, Delgado, Eric, Perez-Lopez, Raquel
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 11.10.2021 Nature Publishing Group Nature Portfolio
Témata:	639/166/985 692/4028/67/2321 Computed tomography Diagnostic Tests, Routine - methods Entropy Habitats Heterogeneity Humanities and Social Sciences Humans Image Processing, Computer-Assisted - methods Lung cancer Lung Neoplasms - diagnostic imaging Lung Neoplasms - pathology multidisciplinary Patients Phenotypes Precision medicine Radiomics Reproducibility of Results Science Science (multidisciplinary) Tomography, X-Ray Computed - instrumentation Tomography, X-Ray Computed - methods Treatment resistance Tumors
ISSN:	2045-2322, 2045-2322
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Abstract	Tumor heterogeneity has been postulated as a hallmark of treatment resistance and a cure constraint in cancer patients. Conventional quantitative medical imaging (radiomics) can be extended to computing voxel-wise features and aggregating tumor subregions with similar radiological phenotypes (imaging habitats) to elucidate the distribution of tumor heterogeneity within and among tumors. Despite the promising applications of imaging habitats, they may be affected by variability of radiomics features, preventing comparison and generalization of imaging habitats techniques. We performed a comprehensive repeatability and reproducibility analysis of voxel-wise radiomics features in more than 500 lung cancer patients with computed tomography (CT) images and demonstrated the effect of voxel-wise radiomics variability on imaging habitats computation in 30 lung cancer patients with test–retest images. Repeatable voxel-wise features characterized texture heterogeneity and were reproducible regardless of the applied feature extraction parameters. Imaging habitats computed using robust radiomics features were more stable than those computed using all features in test–retest CTs from the same patient. Nine voxel-wise radiomics features (joint energy, joint entropy, sum entropy, maximum probability, difference entropy, Imc1, Imc2, Idn and Idmn) were repeatable and reproducible. This supports their application for computing imaging habitats in lung tumors towards the discovery of previously unseen tumor heterogeneity and the development of novel non-invasive imaging biomarkers for precision medicine.
AbstractList	Tumor heterogeneity has been postulated as a hallmark of treatment resistance and a cure constraint in cancer patients. Conventional quantitative medical imaging (radiomics) can be extended to computing voxel-wise features and aggregating tumor subregions with similar radiological phenotypes (imaging habitats) to elucidate the distribution of tumor heterogeneity within and among tumors. Despite the promising applications of imaging habitats, they may be affected by variability of radiomics features, preventing comparison and generalization of imaging habitats techniques. We performed a comprehensive repeatability and reproducibility analysis of voxel-wise radiomics features in more than 500 lung cancer patients with computed tomography (CT) images and demonstrated the effect of voxel-wise radiomics variability on imaging habitats computation in 30 lung cancer patients with test–retest images. Repeatable voxel-wise features characterized texture heterogeneity and were reproducible regardless of the applied feature extraction parameters. Imaging habitats computed using robust radiomics features were more stable than those computed using all features in test–retest CTs from the same patient. Nine voxel-wise radiomics features (joint energy, joint entropy, sum entropy, maximum probability, difference entropy, Imc1, Imc2, Idn and Idmn) were repeatable and reproducible. This supports their application for computing imaging habitats in lung tumors towards the discovery of previously unseen tumor heterogeneity and the development of novel non-invasive imaging biomarkers for precision medicine. Abstract Tumor heterogeneity has been postulated as a hallmark of treatment resistance and a cure constraint in cancer patients. Conventional quantitative medical imaging (radiomics) can be extended to computing voxel-wise features and aggregating tumor subregions with similar radiological phenotypes (imaging habitats) to elucidate the distribution of tumor heterogeneity within and among tumors. Despite the promising applications of imaging habitats, they may be affected by variability of radiomics features, preventing comparison and generalization of imaging habitats techniques. We performed a comprehensive repeatability and reproducibility analysis of voxel-wise radiomics features in more than 500 lung cancer patients with computed tomography (CT) images and demonstrated the effect of voxel-wise radiomics variability on imaging habitats computation in 30 lung cancer patients with test–retest images. Repeatable voxel-wise features characterized texture heterogeneity and were reproducible regardless of the applied feature extraction parameters. Imaging habitats computed using robust radiomics features were more stable than those computed using all features in test–retest CTs from the same patient. Nine voxel-wise radiomics features (joint energy, joint entropy, sum entropy, maximum probability, difference entropy, Imc1, Imc2, Idn and Idmn) were repeatable and reproducible. This supports their application for computing imaging habitats in lung tumors towards the discovery of previously unseen tumor heterogeneity and the development of novel non-invasive imaging biomarkers for precision medicine. Tumor heterogeneity has been postulated as a hallmark of treatment resistance and a cure constraint in cancer patients. Conventional quantitative medical imaging (radiomics) can be extended to computing voxel-wise features and aggregating tumor subregions with similar radiological phenotypes (imaging habitats) to elucidate the distribution of tumor heterogeneity within and among tumors. Despite the promising applications of imaging habitats, they may be affected by variability of radiomics features, preventing comparison and generalization of imaging habitats techniques. We performed a comprehensive repeatability and reproducibility analysis of voxel-wise radiomics features in more than 500 lung cancer patients with computed tomography (CT) images and demonstrated the effect of voxel-wise radiomics variability on imaging habitats computation in 30 lung cancer patients with test-retest images. Repeatable voxel-wise features characterized texture heterogeneity and were reproducible regardless of the applied feature extraction parameters. Imaging habitats computed using robust radiomics features were more stable than those computed using all features in test-retest CTs from the same patient. Nine voxel-wise radiomics features (joint energy, joint entropy, sum entropy, maximum probability, difference entropy, Imc1, Imc2, Idn and Idmn) were repeatable and reproducible. This supports their application for computing imaging habitats in lung tumors towards the discovery of previously unseen tumor heterogeneity and the development of novel non-invasive imaging biomarkers for precision medicine.Tumor heterogeneity has been postulated as a hallmark of treatment resistance and a cure constraint in cancer patients. Conventional quantitative medical imaging (radiomics) can be extended to computing voxel-wise features and aggregating tumor subregions with similar radiological phenotypes (imaging habitats) to elucidate the distribution of tumor heterogeneity within and among tumors. Despite the promising applications of imaging habitats, they may be affected by variability of radiomics features, preventing comparison and generalization of imaging habitats techniques. We performed a comprehensive repeatability and reproducibility analysis of voxel-wise radiomics features in more than 500 lung cancer patients with computed tomography (CT) images and demonstrated the effect of voxel-wise radiomics variability on imaging habitats computation in 30 lung cancer patients with test-retest images. Repeatable voxel-wise features characterized texture heterogeneity and were reproducible regardless of the applied feature extraction parameters. Imaging habitats computed using robust radiomics features were more stable than those computed using all features in test-retest CTs from the same patient. Nine voxel-wise radiomics features (joint energy, joint entropy, sum entropy, maximum probability, difference entropy, Imc1, Imc2, Idn and Idmn) were repeatable and reproducible. This supports their application for computing imaging habitats in lung tumors towards the discovery of previously unseen tumor heterogeneity and the development of novel non-invasive imaging biomarkers for precision medicine.
ArticleNumber	20133
Author	Bernatowicz, Kinga Grussu, Francesco Garcia, Alonso Ligero, Marta Perez-Lopez, Raquel Delgado, Eric
Author_xml	– sequence: 1 givenname: Kinga surname: Bernatowicz fullname: Bernatowicz, Kinga email: kbernatowicz@vhio.net organization: Radiomics Group, Vall d’Hebron Institute of Oncology (VHIO) – sequence: 2 givenname: Francesco surname: Grussu fullname: Grussu, Francesco organization: Radiomics Group, Vall d’Hebron Institute of Oncology (VHIO) – sequence: 3 givenname: Marta surname: Ligero fullname: Ligero, Marta organization: Radiomics Group, Vall d’Hebron Institute of Oncology (VHIO) – sequence: 4 givenname: Alonso surname: Garcia fullname: Garcia, Alonso organization: Radiomics Group, Vall d’Hebron Institute of Oncology (VHIO) – sequence: 5 givenname: Eric surname: Delgado fullname: Delgado, Eric organization: Radiomics Group, Vall d’Hebron Institute of Oncology (VHIO) – sequence: 6 givenname: Raquel surname: Perez-Lopez fullname: Perez-Lopez, Raquel organization: Radiomics Group, Vall d’Hebron Institute of Oncology (VHIO), Radiology Department, Vall d’Hebron University Hospital
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Snippet	Tumor heterogeneity has been postulated as a hallmark of treatment resistance and a cure constraint in cancer patients. Conventional quantitative medical... Abstract Tumor heterogeneity has been postulated as a hallmark of treatment resistance and a cure constraint in cancer patients. Conventional quantitative...
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SubjectTerms	639/166/985 692/4028/67/2321 Computed tomography Diagnostic Tests, Routine - methods Entropy Habitats Heterogeneity Humanities and Social Sciences Humans Image Processing, Computer-Assisted - methods Lung cancer Lung Neoplasms - diagnostic imaging Lung Neoplasms - pathology multidisciplinary Patients Phenotypes Precision medicine Radiomics Reproducibility of Results Science Science (multidisciplinary) Tomography, X-Ray Computed - instrumentation Tomography, X-Ray Computed - methods Treatment resistance Tumors
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Title	Robust imaging habitat computation using voxel-wise radiomics features
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