Can Signal Enhancement Ratio (SER) Reduce the Number of Recommended Biopsies without Affecting Cancer Yield in Occult MRI-detected Lesions?

Arasu, Vignesh A.; Chen, Ryan C.Y.; Newitt, David; Chang, Chee-Jen; Tso, Hilda H.; Hylton, Nola M.; Joe, Bonnie N.

doi:10.1016/j.acra.2011.02.008

Cited by 28 publications

(32 citation statements)

References 23 publications

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“…These CEIPs can be divided into six MRI phenotypic categories describing (a) size, (b) shape, (c) morphology, (d) enhancement texture, (e) kinetic curve assessment, and (f) enhancement-variance kinetic features. 36,44–49 …”

Section: Methodsmentioning

confidence: 99%

“…The kinetic features include maximum contrast enhancement, time to peak, uptake rate, washout rate, curve shape index, and signal enhancement ratio. 19,46,48,49 Enhancement–variance kinetics tumor CEIPs characterize the time course of the spatial variance of the enhancement within a breast tumor, and those extracted features include maximum variance of enhancement, time to peak, variance increase rate, and variance decrease rate. 47 …”

Section: Methodsmentioning

confidence: 99%

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Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

et al. 2016

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Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsy-proven invasive breast cancers from National Cancer Institute’s multi-institutional TCGA/TCIA. Immunohistochemistry molecular classification was performed including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and for 84 cases, the molecular subtype (normal-like, luminal A, luminal B, HER2-enriched, and basal-like). Computerized quantitative image analysis included: three-dimensional lesion segmentation, phenotype extraction, and leave-one-case-out cross validation involving stepwise feature selection and linear discriminant analysis. The performance of the classifier model for molecular subtyping was evaluated using receiver operating characteristic analysis. The computer-extracted tumor phenotypes were able to distinguish between molecular prognostic indicators; area under the ROC curve values of 0.89, 0.69, 0.65, and 0.67 in the tasks of distinguishing between ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative versus others, respectively. Statistically significant associations between tumor phenotypes and receptor status were observed. More aggressive cancers are likely to be larger in size with more heterogeneity in their contrast enhancement. Even after controlling for tumor size, a statistically significant trend was observed within each size group (P = 0.04 for lesions ≤ 2 cm; P = 0.02 for lesions >2 to ≤5 cm) as with the entire data set (P-value = 0.006) for the relationship between enhancement texture (entropy) and molecular subtypes (normal-like, luminal A, luminal B, HER2-enriched, basal-like). In conclusion, computer-extracted image phenotypes show promise for high-throughput discrimination of breast cancer subtypes and may yield a quantitative predictive signature for advancing precision medicine.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

et al. 2016

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“…Seven parameters in the semiquantitative analysis were estimated: tumor volume, the LD, the initial area under the curve at 60 s (iAUC 60 ), the signal enhancement ratio (SER), SER total tumor volume, SER partial volume, and SER washout volume . SER was calculated according to reference :

SER = \frac{S_{1} - S_{0}}{S_{2} - S_{0}},

where S 1 was the peak signal, S 0 was the mean precontrast signal (first to third dynamic scans), and S 2 was the signal at the 25th dynamic scan. The SER total tumor volume was defined as the number of all enhancing voxels.…”

Section: Methodsmentioning

confidence: 99%

DCE‐MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: Pilot study findings

Arlinghaus

Ayers

et al. 2013

Magnetic Resonance in Med

103

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Purpose The purpose of this pilot study is to determine 1) if early changes in both semi-quantitative and quantitative DCE-MRI parameters, observed after the first cycle of neoadjuvant chemotherapy in breast cancer patients, show significant difference between responders and non-responders, and 2) if these parameters can be used as a prognostic indicator of the eventual response. Methods Twenty-eight patients were examined using DCE-MRI pre-, post-one cycle, and just prior to surgery. The semi-quantitative parameters included longest dimension, tumor volume, initial area under the curve (iAUC), and signal enhancement ratio (SER) related parameters, while quantitative parameters included Ktrans, ve, kep, vp, and τi estimated using the standard Tofts-Kety (TK), extended Tofts-Kety (ETK), and fast exchange regime (FXR) models. Results Our preliminary results indicated that the SER washout volume and kep were significantly different between pathologic complete responders from non-responders (P < 0.05) after a single cycle of chemotherapy. Receiver operator characteristic (ROC) analysis showed that the AUC of the SER washout volume was 0.75, and the AUCs of kep estimated by three models were 0.78, 0.76, and 0.73, respectively. Conclusion In summary, the SER washout volume and kep appear to predict breast cancer response after one cycle of neoadjuvant chemotherapy. This observation should be confirmed with additional prospective studies.

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“…Features of tumor enhancement include mean and volumetric features computed after grouping tumor voxels (Ashraf et al, 2014), and forming signal enhancement ratio (SER) and peak enhancement (PE) maps (Arasu et al, 2011). Similar features from FGT masks were also computed along with features related to background parenchymal enhancement (Wu et al, 2015) as FGT enhancement features.…”

Section: Methodsmentioning

confidence: 99%

Effects of MRI scanner parameters on breast cancer radiomics

Saha

Sahoo

et al. 2017

Expert Systems with Applications

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Purpose To assess the impact of varying magnetic resonance imaging (MRI) scanner parameters on the extraction of algorithmic features in breast MRI radiomics studies. Methods In this retrospective study, breast imaging data for 272 patients were analyzed with magnetic resonance (MR) images. From the MR images, we assembled and implemented 529 algorithmic features of breast tumors and fibrograndular tissue (FGT). We divided the features into 10 groups based on the type of data used for the feature extraction and the nature of the extracted information. Three scanner parameters were considered: scanner manufacturer, scanner magnetic field strength, and slice thickness. We assessed the impact of each of the scanner parameters on each of the feature by testing whether the feature values are systematically diverse for different values of these scanner parameters. A two-sample t-test has been used to establish whether the impact of a scanner parameter on values of a feature is significant and receiver operating characteristics have been used for to establish the extent of that effect. Results On average, higher proportion (69% FGT versus 20% tumor) of FGT related features were affected by the three scanner parameters. Of all feature groups and scanner parameters, the feature group related to the variation in FGT enhancement was found to be the most sensitive to the scanner manufacturer (AUC = 0.81 ± 0.14). Conclusions Features involving calculations from FGT are particularly sensitive to the scanner parameters.

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Can Signal Enhancement Ratio (SER) Reduce the Number of Recommended Biopsies without Affecting Cancer Yield in Occult MRI-detected Lesions?

Cited by 28 publications

References 23 publications

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set

DCE‐MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: Pilot study findings

Effects of MRI scanner parameters on breast cancer radiomics

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