Intratumoral heterogeneity of the distribution of kinetic parameters in breast cancer: comparison based on the molecular subtypes of invasive breast cancer

Yamaguchi, Ken; Abé, Hiroyuki; Newstead, Gillian M.; Egashira, Ryoko; Nakazono, Takahiko; Imaizumi, Takeshi; Irié, Hiroyuki

doi:10.1007/s12282-013-0512-0

Cited by 57 publications

(64 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There are several studies 19,20,24,29,30 in the literature that have related breast MRI kinetics to cancer subtype. Bhooshan et al 19,20 studied 353 MRI cases and correlated breast MRI radiomics features to non-invasive and invasive cancer types.…”

Section: Discussionmentioning

confidence: 99%

“…Mazurowski et al 24 performed radiogenomic analysis on a subset of 48 cases from TCIA to determine whether enhancement dynamics could predict the luminal B subtype. A study by Yamaguchi et al 29 investigated the connection between kinetic curve pattern and molecular subtypes. Blaschke et al 30 reported that HER2-enriched tumors have a larger volume of rapid early uptake compared with other molecular types.…”

Section: Discussionmentioning

confidence: 99%

“…Ashraf et al 28 found that intrinsic imaging phenotypes correlated with risk of recurrence score defined by Oncotype DX. Yamaguchi et al 29 studied the relationship between heterogeneous kinetic curve pattern and molecular subtype. Blaschke et al 30 reported that HER2-positive tumors have a greater portion of their tumor with rapid uptake compared with other molecular subtypes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2016

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2016

View full text Add to dashboard Cite

show abstract

“…These findings contribute to a range of recently elucidated quantitative markers that together define imaging phenotypes that have shown promise in the identification of additional cancer subtypes, lesion invasiveness, grade, and lymph node status, with associated treatment and prognostic implications. [5][6][7][8][9][10][11][18][19][20] Classification of breast cancer by molecular subtype furthermore has clinical value, as current treatment guidelines are determined by tumor subtype. 16 HER2 expression by cancers in particular needs to be determined, as anti-HER2 therapy has well documented efficacy.…”

Section: Discussionmentioning

confidence: 99%

MRI phenotype of breast cancer: Kinetic assessment for molecular subtypes

Blaschke

Abé

2015

Magnetic Resonance Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

“…The election imaging method for Breast Cancer (BC) was MRI (78%) [91,97,103,104,105,106,107,108,109,110,111,112,113,114,115,116,120,121]. About 78% of the BC published papers were correlation studies [91,103,104,105,106,107,108,109,110,111,112,114,117,118,119,120,122,123] and the most used genomic data was IHC results (65%) [103,104,105,107,108,111,112,113,114,115,116,117,118,119,120].…”

Section: Discussionmentioning

confidence: 99%

Radiogenomic Analysis of Oncological Data: A Technical Survey

Incoronato

Aiello

Infante

et al. 2017

IJMS

114

106

View full text Add to dashboard Cite

In the last few years, biomedical research has been boosted by the technological development of analytical instrumentation generating a large volume of data. Such information has increased in complexity from basic (i.e., blood samples) to extensive sets encompassing many aspects of a subject phenotype, and now rapidly extending into genetic and, more recently, radiomic information. Radiogenomics integrates both aspects, investigating the relationship between imaging features and gene expression. From a methodological point of view, radiogenomics takes advantage of non-conventional data analysis techniques that reveal meaningful information for decision-support in cancer diagnosis and treatment. This survey is aimed to review the state-of-the-art techniques employed in radiomics and genomics with special focus on analysis methods based on molecular and multimodal probes. The impact of single and combined techniques will be discussed in light of their suitability in correlation and predictive studies of specific oncologic diseases.

show abstract

Intratumoral heterogeneity of the distribution of kinetic parameters in breast cancer: comparison based on the molecular subtypes of invasive breast cancer

Cited by 57 publications

References 31 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

MRI phenotype of breast cancer: Kinetic assessment for molecular subtypes

Radiogenomic Analysis of Oncological Data: A Technical Survey

Contact Info

Product

Resources

About