Marie-Judith Saint Martin1, Fanny Orlhac2, Pia Akl2,3,4, Fahad Khalid2, Christophe Nioche2, Irène Buvat2, Caroline Malhaire2,4, Frédérique Frouin2. 1. Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France. marie-judith-astrid.saint-martin@u-psud.fr. 2. Inserm, Institut Curie,Université Paris-Saclay, Laboratoire d'Imagerie Translationnelle en Oncologie (LITO), Centre de Recherche de l'Institut Curie, Bât 101B rue Henri Becquerel, 91401, Orsay, France. 3. HCL, Radiologie du Groupement Hospitalier Est, Hôpital Femme Mère Enfant, Unité Fonctionnelle: Imagerie de la Femme, 3 Quai des Célestins, 69002, Lyon, France. 4. Institut Curie, Service de Radiodiagnostic, 26 rue d'Ulm, 75005, Paris, France.
Abstract
OBJECTIVE: Quantitative analysis in MRI is challenging due to variabilities in intensity distributions across patients, acquisitions and scanners and suffers from bias field inhomogeneity. Radiomic studies are impacted by these effects that affect radiomic feature values. This paper describes a dedicated pipeline to increase reproducibility in breast MRI radiomic studies. MATERIALS AND METHODS: T1, T2, and T1-DCE MR images of two breast phantoms were acquired using two scanners and three dual breast coils. Images were retrospectively corrected for bias field inhomogeneity and further normalised using Z score or histogram matching. Extracted radiomic features were harmonised between coils by the ComBat method. The whole pipeline was assessed qualitatively and quantitatively using statistical comparisons on two series of radiomic feature values computed in the gel mimicking the normal breast tissue or in dense lesions. RESULTS: Intra and inter-acquisition variabilities were strongly reduced by the standardisation pipeline. Harmonisation by ComBat lowered the percentage of radiomic features significantly different between the three coils from 87% after bias field correction and MR normalisation to 3% in the gel, while preserving or improving performance of lesion classification in the phantoms. DISCUSSION: A dedicated standardisation pipeline was developed to reduce variabilities in breast MRI, which paves the way for robust multi-scanner radiomic studies but needs to be assessed on patient data.
OBJECTIVE: Quantitative analysis in MRI is challenging due to variabilities in intensity distributions across patients, acquisitions and scanners and suffers from bias field inhomogeneity. Radiomic studies are impacted by these effects that affect radiomic feature values. This paper describes a dedicated pipeline to increase reproducibility in breast MRI radiomic studies. MATERIALS AND METHODS: T1, T2, and T1-DCE MR images of two breast phantoms were acquired using two scanners and three dual breast coils. Images were retrospectively corrected for bias field inhomogeneity and further normalised using Z score or histogram matching. Extracted radiomic features were harmonised between coils by the ComBat method. The whole pipeline was assessed qualitatively and quantitatively using statistical comparisons on two series of radiomic feature values computed in the gel mimicking the normal breast tissue or in dense lesions. RESULTS: Intra and inter-acquisition variabilities were strongly reduced by the standardisation pipeline. Harmonisation by ComBat lowered the percentage of radiomic features significantly different between the three coils from 87% after bias field correction and MR normalisation to 3% in the gel, while preserving or improving performance of lesion classification in the phantoms. DISCUSSION: A dedicated standardisation pipeline was developed to reduce variabilities in breast MRI, which paves the way for robust multi-scanner radiomic studies but needs to be assessed on patient data.
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Authors: Yang Nan; Javier Del Ser; Simon Walsh; Carola Schönlieb; Michael Roberts; Ian Selby; Kit Howard; John Owen; Jon Neville; Julien Guiot; Benoit Ernst; Ana Pastor; Angel Alberich-Bayarri; Marion I Menzel; Sean Walsh; Wim Vos; Nina Flerin; Jean-Paul Charbonnier; Eva van Rikxoort; Avishek Chatterjee; Henry Woodruff; Philippe Lambin; Leonor Cerdá-Alberich; Luis Martí-Bonmatí; Francisco Herrera; Guang Yang Journal: Inf Fusion Date: 2022-06 Impact factor: 17.564
Authors: R W Y Granzier; A Ibrahim; S Primakov; S A Keek; I Halilaj; A Zwanenburg; S M E Engelen; M B I Lobbes; P Lambin; H C Woodruff; M L Smidt Journal: J Magn Reson Imaging Date: 2021-12-22 Impact factor: 5.119