External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12-07

AUTHORS

François Lucia, Dimitris Visvikis, Martin Vallières, Marie-Charlotte Desseroit, Omar Miranda, Philippe Robin, Pietro Andrea Bonaffini, Joanne Alfieri, Ingrid Masson, Augustin Mervoyer, Caroline Reinhold, Olivier Pradier, Mathieu Hatt, Ulrike Schick

ABSTRACT

PurposeThe aim of this study was to validate previously developed radiomics models relying on just two radiomics features from 18F-fluorodeoxyglucose positron emission tomography (PET) and magnetic resonance imaging (MRI) images for prediction of disease free survival (DFS) and locoregional control (LRC) in locally advanced cervical cancer (LACC).MethodsPatients with LACC receiving chemoradiotherapy were enrolled in two French and one Canadian center. Pre-treatment imaging was performed for each patient. Multicentric harmonization of the two radiomics features was performed with the ComBat method. The models for DFS (using the feature from apparent diffusion coefficient (ADC) MRI) and LRC (adding one PET feature to the DFS model) were tuned using one of the French cohorts (n = 112) and applied to the other French (n = 50) and the Canadian (n = 28) external validation cohorts.ResultsThe DFS model reached an accuracy of 90% (95% CI [79–98%]) (sensitivity 92–93%, specificity 87–89%) in both the French and the Canadian cohorts. The LRC model reached an accuracy of 98% (95% CI [90–99%]) (sensitivity 86%, specificity 100%) in the French cohort and 96% (95% CI [80–99%]) (sensitivity 83%, specificity 100%) in the Canadian cohort. Accuracy was significantly lower without ComBat harmonization (82–85% and 71–86% for DFS and LRC, respectively). The best prediction using standard clinical variables was 56–60% only.ConclusionsThe previously developed PET/MRI radiomics predictive models were successfully validated in two independent external cohorts. A proposed flowchart for improved management of patients based on these models should now be confirmed in future larger prospective studies. More... »

PAGES

864-877

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00259-018-4231-9

DOI

http://dx.doi.org/10.1007/s00259-018-4231-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1110448603

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30535746


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27 schema:description PurposeThe aim of this study was to validate previously developed radiomics models relying on just two radiomics features from 18F-fluorodeoxyglucose positron emission tomography (PET) and magnetic resonance imaging (MRI) images for prediction of disease free survival (DFS) and locoregional control (LRC) in locally advanced cervical cancer (LACC).MethodsPatients with LACC receiving chemoradiotherapy were enrolled in two French and one Canadian center. Pre-treatment imaging was performed for each patient. Multicentric harmonization of the two radiomics features was performed with the ComBat method. The models for DFS (using the feature from apparent diffusion coefficient (ADC) MRI) and LRC (adding one PET feature to the DFS model) were tuned using one of the French cohorts (n = 112) and applied to the other French (n = 50) and the Canadian (n = 28) external validation cohorts.ResultsThe DFS model reached an accuracy of 90% (95% CI [79–98%]) (sensitivity 92–93%, specificity 87–89%) in both the French and the Canadian cohorts. The LRC model reached an accuracy of 98% (95% CI [90–99%]) (sensitivity 86%, specificity 100%) in the French cohort and 96% (95% CI [80–99%]) (sensitivity 83%, specificity 100%) in the Canadian cohort. Accuracy was significantly lower without ComBat harmonization (82–85% and 71–86% for DFS and LRC, respectively). The best prediction using standard clinical variables was 56–60% only.ConclusionsThe previously developed PET/MRI radiomics predictive models were successfully validated in two independent external cohorts. A proposed flowchart for improved management of patients based on these models should now be confirmed in future larger prospective studies.
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33 schema:keywords Canadian Centre
34 Canadian cohort
35 ComBat harmonization
36 ConclusionsThe
37 DFS model
38 French
39 French cohort
40 Future large prospective studies
41 LACC
42 LRC model
43 MRI
44 MethodsPatients
45 PET/MRI
46 accuracy
47 advanced cervical cancer
48 aim
49 better prediction
50 cancer
51 cancer patients
52 center
53 cervical cancer
54 cervical cancer patients
55 chemoradiotherapy
56 clinical variables
57 cohort
58 combat methods
59 control
60 disease-free survival
61 emission tomography
62 external cohort
63 external validation
64 external validation cohort
65 features
66 flowchart
67 free survival
68 harmonization
69 images
70 imaging
71 imaging images
72 independent external cohort
73 large prospective studies
74 locoregional control
75 magnetic resonance imaging (MRI) images
76 management
77 management of patients
78 method
79 model
80 patients
81 positron emission tomography
82 pre-treatment imaging
83 prediction
84 prediction of recurrence
85 predictive model
86 prospective study
87 radiomic features
88 radiomics
89 radiomics model
90 recurrence
91 resonance imaging images
92 standard clinical variables
93 study
94 survival
95 tomography
96 validation
97 validation cohort
98 variables
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