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
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  surname: Bernatowicz
  fullname: Bernatowicz, Kinga
  email: kbernatowicz@vhio.net
  organization: Radiomics Group, Vall d’Hebron Institute of Oncology (VHIO)
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  givenname: Francesco
  surname: Grussu
  fullname: Grussu, Francesco
  organization: Radiomics Group, Vall d’Hebron Institute of Oncology (VHIO)
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  givenname: Marta
  surname: Ligero
  fullname: Ligero, Marta
  organization: Radiomics Group, Vall d’Hebron Institute of Oncology (VHIO)
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  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)
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  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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