Breast Cancer: Early Prediction of Response to Neoadjuvant Chemotherapy Using Parametric Response Maps for MR Imaging

Cho, Nariya; Im, Seock‐Ah; Park, In-Ae; Lee, Kyung-Hun; Li, Mulan; Han, Wonshik; Noh, Dong‐Young; Moon, Woo Kyung

doi:10.1148/radiol.14131332

Cited by 83 publications

(43 citation statements)

References 31 publications

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“…In breast cancer MRI radiomics, the feature extraction process typically requires an annotation of the tumor by a radiologist making the tumor segmentation process either completely manual or semiautomatic. It is well established that radiologists vary in their annotation of medical images and particularly in annotation of tumors in breast MRIs .…”

Section: Introductionmentioning

confidence: 99%

Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter‐reader variability in annotating tumors

2018

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Section: Introductionmentioning

confidence: 99%

Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter‐reader variability in annotating tumors

2018

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“…Approximately 80 % of patients have been found to respond to NACT, but only 6–25 % of patients show complete pathological response (pCR) [1–3]. Therefore, functional imaging techniques have been investigated for the prediction of response early after initiating therapy.…”

Section: Introductionmentioning

confidence: 99%

Assessment of early treatment response to neoadjuvant chemotherapy in breast cancer using non-mono-exponential diffusion models: a feasibility study comparing the baseline and mid-treatment MRI examinations

et al. 2016

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ObjectivesTo assess the feasibility of the mono-exponential, bi-exponential and stretched-exponential models in evaluating response of breast tumours to neoadjuvant chemotherapy (NACT) at 3 T.MethodsThirty-six female patients (median age 53, range 32–75 years) with invasive breast cancer undergoing NACT were enrolled for diffusion-weighted MRI (DW-MRI) prior to the start of treatment. For assessment of early response, changes in parameters were evaluated on mid-treatment MRI in 22 patients. DW-MRI was performed using eight b values (0, 30, 60, 90, 120, 300, 600, 900 s/mm2). Apparent diffusion coefficient (ADC), tissue diffusion coefficient (D t), vascular fraction (ƒ), distributed diffusion coefficient (DDC) and alpha (α) parameters were derived. Then t tests compared the baseline and changes in parameters between response groups. Repeatability was assessed at inter- and intraobserver levels.ResultsAll patients underwent baseline MRI whereas 22 lesions were available at mid-treatment. At pretreatment, mean diffusion coefficients demonstrated significant differences between groups (p < 0.05). At mid-treatment, percentage increase in ADC and DDC showed significant differences between responders (49 % and 43 %) and non-responders (21 % and 32 %) (p = 0.03, p = 0.04). Overall, stretched-exponential parameters showed excellent repeatability.ConclusionDW-MRI is sensitive to baseline and early treatment changes in breast cancer using non-mono-exponential models, and the stretched-exponential model can potentially monitor such changes.Key points• Baseline diffusion coefficients demonstrated significant differences between complete pathological responders and non-responders. • Increase in ADC and DDC at mid-treatment can discriminate responders and non-responders. • The ƒ fraction at mid-treatment decreased in responders whereas increased in non-responders. • The mono- and stretched-exponential models showed excellent inter- and intrarater repeatability. • Treatment effects can potentially be assessed by non-mono-exponential diffusion models. Electronic supplementary materialThe online version of this article (doi:10.1007/s00330-016-4630-x) contains supplementary material, which is available to authorized users.

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“…K. Kuhl et al, 2005; Warner et al, 2001). More recently, two research directions, radiomics (Lambin et al, 2012) and radiogenomics (Mazurowski, 2015), focus on extracting a variety of algorithmic imaging features to assess the abnormalities in breast for improved diagnosis (Yang, Li, Zhang, Shao, & Zheng, 2015; Zheng et al, 2009), prediction of outcomes (Cho et al, 2014; Mahrooghy et al, 2015; Mazurowski et al, 2015), and correlation with tumor genomics (Ashraf et al, 2014; Grimm, Zhang, & Mazurowski, 2015; Mazurowski, Zhang, Grimm, Yoon, & Silber, 2014; Wan et al, 2016; Wang et al, 2015). …”

Section: Introductionmentioning

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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Breast Cancer: Early Prediction of Response to Neoadjuvant Chemotherapy Using Parametric Response Maps for MR Imaging

Abstract: PRM analysis of DCE MR images may enable the early identification of the pathologic response to NAC after the first cycle of chemotherapy, whereas pharmacokinetic parameters (K(trans), kep, and ve) do not.

Cited by 83 publications

References 31 publications

Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter‐reader variability in annotating tumors

Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter‐reader variability in annotating tumors

Assessment of early treatment response to neoadjuvant chemotherapy in breast cancer using non-mono-exponential diffusion models: a feasibility study comparing the baseline and mid-treatment MRI examinations

Effects of MRI scanner parameters on breast cancer radiomics

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