Sensitivity of radiomic features to inter-observer variability and image pre-processing in Apparent Diffusion Coefficient (ADC) maps of cervix cancer patients.


Journal

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
ISSN: 1879-0887
Titre abrégé: Radiother Oncol
Pays: Ireland
ID NLM: 8407192

Informations de publication

Date de publication:
02 2020
Historique:
received: 24 06 2019
revised: 30 07 2019
accepted: 07 08 2019
pubmed: 4 9 2019
medline: 15 4 2021
entrez: 4 9 2019
Statut: ppublish

Résumé

The aims of this study are to evaluate the stability of radiomic features from Apparent Diffusion Coefficient (ADC) maps of cervical cancer with respect to: (1) reproducibility in inter-observer delineation, and (2) image pre-processing (normalization/quantization) prior to feature extraction. Two observers manually delineated the tumor on ADC maps derived from pre-treatment diffusion-weighted Magnetic Resonance imaging of 81 patients with FIGO stage IB-IVA cervical cancer. First-order, shape, and texture features were extracted from the original and filtered images considering 5 different normalizations (four taken from the available literature, and one based on urine ADC) and two different quantization techniques (fixed-bin widths from 0.05 to 25, and fixed-bin count). Stability of radiomic features was assessed using intraclass correlation coefficient (ICC): poor (ICC < 0.75); good (0.75 ≤ ICC ≤ 0.89), and excellent (ICC ≥ 0.90). Dependencies of the features with tumor volume were assessed using Spearman's correlation coefficient (ρ). The approach using urine-normalized values together with a smaller bin width (0.05) was the most reproducible (428/552, 78% features with ICC ≥ 0.75); the fixed-bin count approach was the least (215/552, 39% with ICC ≥ 0.75). Without normalization, using a fixed bin width of 25, 348/552 (63%) of features had an ICC ≥ 0.75. Overall, 26% (range 25-30%) of the features were volume-dependent (ρ ≥ 0.6). None of the volume-independent shape features were found to be reproducible. Applying normalization prior to features extraction increases the reproducibility of ADC-based radiomics features. When normalization is applied, a fixed-bin width approach with smaller widths is suggested.

Identifiants

pubmed: 31477335
pii: S0167-8140(19)33047-6
doi: 10.1016/j.radonc.2019.08.008
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

88-94

Informations de copyright

Copyright © 2019 The Author(s). Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Alberto Traverso (A)

MAASTRO Clinic and School for Oncology and Development Biology (GROW), Maastricht University Medical Centre, The Netherlands; Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada.

Michal Kazmierski (M)

MAASTRO Clinic and School for Oncology and Development Biology (GROW), Maastricht University Medical Centre, The Netherlands.

Mattea L Welch (ML)

Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada.

Jessica Weiss (J)

Department of Biostatistics, Princess Margaret Cancer Center, Toronto, Canada.

Sandra Fiset (S)

Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada.

Warren D Foltz (WD)

Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Canada.

Adam Gladwish (A)

Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Canada.

Andre Dekker (A)

MAASTRO Clinic and School for Oncology and Development Biology (GROW), Maastricht University Medical Centre, The Netherlands.

David Jaffray (D)

Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Canada.

Leonard Wee (L)

MAASTRO Clinic and School for Oncology and Development Biology (GROW), Maastricht University Medical Centre, The Netherlands.

Kathy Han (K)

Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Canada. Electronic address: kathy.han@rmp.uhn.on.ca.

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Classifications MeSH