DCE-MRI Pharmacokinetic-Based Phenotyping of Invasive Ductal Carcinoma: A Radiomic Study for Prediction of Histological Outcomes

Monti, Serena; Aiello, Marco; Incoronato, Mariarosaria; Grimaldi, Anna; Moscarino, Michela; Mirabelli, Peppino; Ferbo, Umberto; Cavaliere, Carlo; Salvatore, Marco

doi:10.1155/2018/5076269

Cited by 45 publications

(47 citation statements)

References 75 publications

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“…Recently, several articles have outlined the potential clinical applicability of radiomics in the field of breast cancer for different purposes, e.g. diagnosis 10 , 11 , tumor response prediction 12 – 14 , prediction of molecular tumor subtype 15 , 16 , and prediction of axillary lymph node metastases 17 , 18 .…”

Section: Introductionmentioning

confidence: 99%

MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability

Granzier

Verbakel

Ibrahim

et al. 2020

Sci Rep

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Radiomics is an emerging field using the extraction of quantitative features from medical images for tissue characterization. While MRI-based radiomics is still at an early stage, it showed some promising results in studies focusing on breast cancer patients in improving diagnoses and therapy response assessment. Nevertheless, the use of radiomics raises a number of issues regarding feature quantification and robustness. Therefore, our study aim was to determine the robustness of radiomics features extracted by two commonly used radiomics software with respect to variability in manual breast tumor segmentation on MRI. A total of 129 histologically confirmed breast tumors were segmented manually in three dimensions on the first post-contrast T1-weighted MR exam by four observers: a dedicated breast radiologist, a resident, a Ph.D. candidate, and a medical student. Robust features were assessed using the intraclass correlation coefficient (ICC > 0.9). The inter-observer variability was evaluated by the volumetric Dice Similarity Coefficient (DSC). The mean DSC for all tumors was 0.81 (range 0.19-0.96), indicating a good spatial overlap of the segmentations based on observers of varying expertise. In total, 41.6% (552/1328) and 32.8% (273/833) of all RadiomiX and Pyradiomics features, respectively, were identified as robust and were independent of inter-observer manual segmentation variability. Radiomics is a technique that is used to extract large amounts of quantitative information from routine medical images that decode information about a region of interest (ROI). The majority of radiomics articles published concerns its application in the oncological field 1-4. Here, radiomics bears the advantage of non-invasively quantifying the underlying phenotype of the entire tumor for multiple lesions simultaneously, in contrast to tissue biopsy, which samples only a small part of a single (often heterogeneous) tumor 2,5. The ability to characterize the tumor and to establish links to the underlying biology 6 and ultimately clinical outcomes, allows a more patienttailored treatment 7 , enabling 'precision medicine' 8,9. Recently, several articles have outlined the potential clinical applicability of radiomics in the field of breast cancer for different purposes, e.g. diagnosis 10,11 , tumor response prediction 12-14 , prediction of molecular tumor subtype 15,16 , and prediction of axillary lymph node metastases 17,18. Although these results are promising, issues regarding features robustness as well as the comparability of results, including inter-observer segmentation variability, need to be addressed 19-24. In order to extract clinically useful information from medical images and to use features as clinical biomarkers, it is important that extracted

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Section: Introductionmentioning

confidence: 99%

MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability

Granzier

Verbakel

Ibrahim

et al. 2020

Sci Rep

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“…This technique is multi-parametric and allows for several observations, both morphological and based on tumor contrast kinetics. Several studies using this technique showed a correlation between molecular subtypes and qualitative and semi-quantitative tumor features [ 24 , 35 , 36 ] and between perfusion parameters [ 37 ] and semi-automatic features of the images [ 9 , 22 , 38 , 39 , 40 , 41 , 42 , 43 ] after fusion imaging [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

Radiomic Analysis in Contrast-Enhanced Spectral Mammography for Predicting Breast Cancer Histological Outcome

et al. 2020

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Contrast-Enhanced Spectral Mammography (CESM) is a recently introduced mammographic method with characteristics particularly suitable for breast cancer radiomic analysis. This work aims to evaluate radiomic features for predicting histological outcome and two cancer molecular subtypes, namely Human Epidermal growth factor Receptor 2 (HER2)-positive and triple-negative. From 52 patients, 68 lesions were identified and confirmed on histological examination. Radiomic analysis was performed on regions of interest (ROIs) selected from both low-energy (LE) and ReCombined (RC) CESM images. Fourteen statistical features were extracted from each ROI. Expression of estrogen receptor (ER) was significantly correlated with variation coefficient and variation range calculated on both LE and RC images; progesterone receptor (PR) with skewness index calculated on LE images; and Ki67 with variation coefficient, variation range, entropy and relative smoothness indices calculated on RC images. HER2 was significantly associated with relative smoothness calculated on LE images, and grading tumor with variation coefficient, entropy and relative smoothness calculated on RC images. Encouraging results for differentiation between ER+/ER−, PR+/PR−, HER2+/HER2−, Ki67+/Ki67−, High-Grade/Low-Grade and TN/NTN were obtained. Specifically, the highest performances were obtained for discriminating HER2+/HER2− (90.87%), ER+/ER− (83.79%) and Ki67+/Ki67− (84.80%). Our results suggest an interesting role for radiomics in CESM to predict histological outcomes and particular tumors’ molecular subtype.

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“…Further work is required to assess the combined information from DCE‐MRI, DW‐MRI and PET, together with biological/histological data, in order to conduct an advanced analysis in a radiogenomic framework …”

Section: Discussionmentioning

confidence: 99%

A multi‐parametric PET/MRI study of breast cancer: Evaluation of DCE‐MRI pharmacokinetic models and correlation with diffusion and functional parameters

et al. 2018

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The objective of this study is to compare perfusion parameters, as measured by the shutter speed and the Tofts models applied in DCE‐MRI, in malignant breast lesions. Also, to investigate the relationship between perfusion, morphological and functional parameters extracted by the simultaneous acquisition of positron emission tomography (PET) and MRI. 46 patients with histologically confirmed breast cancer were imaged with a 3 T hybrid PET/MRI system, at staging. 18F‐fluorodeoxyglucose (FDG) PET images were acquired simultaneously with MRI, which included DW‐ and DCE‐MRI. Standardized uptake value (SUV), apparent diffusion coefficient (ADC) and pharmacokinetic maps (from the Tofts and shutter speed models) were generated for each primary lesion and a reference, healthy tissue area. Wilcoxon and Spearman tests were used for the statistical analysis. MRI‐derived parameters (perfusion and diffusion) and PET SUVmean were significantly different between tumour and reference tissue (p < 0.05). Significant correlations were found between the two pharmacokinetic models (p < 0.01) and between SUV and pharmacokinetic parameters (Ktrans, kep, ve and τi). No correlation was observed between ADC and SUV, or between ADC and pharmacokinetic parameters. In conclusion, PET/MRI allows for the characterization of primary lesions in breast cancer. Simultaneous analysis of perfusion, morphological and functional markers reveals good agreement between different pharmacokinetic models. Perfusion parameters showed a good correlation with the SUV in breast lesions. In particular, the shutter speed τi was found to be significantly correlated to 18F‐FDG uptake (negative correlation), indicating an association with tumour metabolic mechanisms.

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DCE-MRI Pharmacokinetic-Based Phenotyping of Invasive Ductal Carcinoma: A Radiomic Study for Prediction of Histological Outcomes

Cited by 45 publications

References 75 publications

MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability

MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability

Radiomic Analysis in Contrast-Enhanced Spectral Mammography for Predicting Breast Cancer Histological Outcome

A multi‐parametric PET/MRI study of breast cancer: Evaluation of DCE‐MRI pharmacokinetic models and correlation with diffusion and functional parameters

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