Dataset Information

Appearance Constrained Semi-Automatic Segmentation from DCE-MRI is Reproducible and Feasible for Breast Cancer Radiomics: A Feasibility Study.

ABSTRACT: We present a segmentation approach that combines GrowCut (GC) with cancer-specific multi-parametric Gaussian Mixture Model (GCGMM) to produce accurate and reproducible segmentations. We evaluated GCGMM using a retrospectively collected 75 invasive ductal carcinoma with ERPR+ HER2- (n?=?15), triple negative (TN) (n?=?9), and ER-HER2+ (n?=?57) cancers with variable presentation (mass and non-mass enhancement) and background parenchymal enhancement (mild and marked). Expert delineated manual contours were used to assess the segmentation performance using Dice coefficient (DSC), mean surface distance (mSD), Hausdorff distance, and volume ratio (VR). GCGMM segmentations were significantly more accurate than GrowCut (GC) and fuzzy c-means clustering (FCM). GCGMM's segmentations and the texture features computed from those segmentations were the most reproducible compared with manual delineations and other analyzed segmentation methods. Finally, random forest (RF) classifier trained with leave-one-out cross-validation using features extracted from GCGMM segmentation resulted in the best accuracy for ER-HER2+ vs. ERPR+/TN (GCGMM 0.95, expert 0.95, GC 0.90, FCM 0.92) and for ERPR?+?HER2- vs. TN (GCGMM 0.92, expert 0.91, GC 0.77, FCM 0.83).

SUBMITTER: Veeraraghavan H

PROVIDER: S-EPMC5859113 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Appearance Constrained Semi-Automatic Segmentation from DCE-MRI is Reproducible and Feasible for Breast Cancer Radiomics: A Feasibility Study.

Veeraraghavan Harini H Dashevsky Brittany Z BZ Onishi Natsuko N Sadinski Meredith M Morris Elizabeth E Deasy Joseph O JO Sutton Elizabeth J EJ

Scientific reports 20180319 1

We present a segmentation approach that combines GrowCut (GC) with cancer-specific multi-parametric Gaussian Mixture Model (GCGMM) to produce accurate and reproducible segmentations. We evaluated GCGMM using a retrospectively collected 75 invasive ductal carcinoma with ERPR+ HER2- (n = 15), triple negative (TN) (n = 9), and ER-HER2+ (n = 57) cancers with variable presentation (mass and non-mass enhancement) and background parenchymal enhancement (mild and marked). Expert delineated manual contou ...[more]

PMID: 29556054

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Project description:PurposeMetabolically active tumour volume (MATV) is a potential quantitative positron emission tomography (PET) imaging biomarker in melanoma. Accumulating data indicate that low MATV may predict increased chance of response to immunotherapy and overall survival. However, metastatic melanoma can present with numerous (small) tumour lesions, making manual tumour segmentation time-consuming. The aim of this study was to evaluate multiple semi-automatic segmentation workflows to determine reliability and reproducibility of MATV measurements in patients with metastatic melanoma.MethodsAn existing cohort of 64 adult patients with histologically proven metastatic melanoma was used in this study. 18F-FDG PET/CT diagnostic baseline images were acquired using a European Association of Nuclear Medicine (EANM) Research Limited-accredited Siemens Biograph mCT PET/CT system (Siemens Healthineers, Knoxville, USA). PET data were analysed using manual, gradient-based segmentation and five different semi-automatic methods: three direct PET image-derived delineations (41MAX, A50P and SUV40) and two based on a majority-vote approach (MV2 and MV3), without and with (suffix '+') manual lesion addition. Correlation between the different segmentation methods and their respective associations with overall survival was assessed.ResultsCorrelation between the MATVs derived by the manual segmentation and semi-automated tumour segmentations ranged from R2 = 0.41 for A50P to R2 = 0.85 for SUV40+ and MV2+, respectively. Manual MATV segmentation did not differ significantly from the semi-automatic methods SUV40 (∆MATV mean ± SD 0.08 ± 0.60 mL, P = 0.303), SUV40+ (∆MATV - 0.10 ± 0.51 mL, P = 0.126), MV2+ (∆MATV - 0.09 ± 0.62 mL, P = 0.252) and MV3+ (∆MATV - 0.03 ± 0.55 mL, P = 0.615). Log-rank tests showed statistically significant overall survival differences between above and below median MATV patients for all segmentation methods with areas under the ROC curves of 0.806 for manual segmentation and between 0.756 [41MAX] and 0.807 [MV3+] for semi-automatic segmentations.ConclusionsSimple and fast semi-automated FDG PET segmentation workflows yield accurate and reproducible MATV measurements that correlate well with manual segmentation in metastatic melanoma. The most readily applicable and user-friendly SUV40 method allows feasible MATV measurement in prospective multicentre studies required for validation of this potential PET imaging biomarker for clinical use.

Dataset Information

Appearance Constrained Semi-Automatic Segmentation from DCE-MRI is Reproducible and Feasible for Breast Cancer Radiomics: A Feasibility Study.

Publications

Appearance Constrained Semi-Automatic Segmentation from DCE-MRI is Reproducible and Feasible for Breast Cancer Radiomics: A Feasibility Study.

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