Dataset Information

Tumor-stromal ratio (TSR) of invasive breast cancer: correlation with multi-parametric breast MRI findings.

ABSTRACT: OBJECTIVE:To correlate the tumor-stromal ratio (TSR) of invasive breast cancer and MRI findings. METHODS:This study was approved by our institutional review board. 126 consecutive patients with surgically proven invasive breast cancer were included. All patients underwent MRI exams including short-tau inversion-recovery (STIR) T 2 weighted imaging, diffusion-weighted imaging (DWI) and post-contrast dynamic imaging. The mean signal intensity (SI) and apparent diffusion coefficient (ADC) value of each lesion were measured. To objectively evaluate the STIR images, the ratio of the SI of the lesion to the muscle (L/M ratio) was also measured. Percentages of MRI kinetic parameters obtained from dynamic images were also measured. The TSR was defined as the percentage of the stromal component, and categorized into high-stroma (> 50%) and low-stroma (< 50%) groups. Intergroup differences in the SI, L/M ratio, ADC value and percentages of kinetic parameters were examined. RESULTS:The SI and L/M ratio of the high-stroma group were significantly lower than those of the low-stromal group (208.64 vs 331.86 for SI, 5.69 vs 9.31 for L/M ratio) (p < 0.001). The high-stroma group had significantly lower percentages of a washout pattern (25% vs 34.7 %) (p = 0.012) and significantly higher percentages of a persistent pattern (36.92% vs 28.26 %) (p = 0.044). There were no significant correlations between the TSR and ADC value. CONCLUSION:STIR and dynamic sequence of breast MRI reflects the stromal component of invasive breast cancer. ADVANCES IN KNOWLEDGE:This is the first study to correlate TSR and MRI findings. STIR and post-contrast dynamic study correlated with the stromal component of breast cancer.

SUBMITTER: Yamaguchi K

PROVIDER: S-EPMC6580921 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Tumor-stromal ratio (TSR) of invasive breast cancer: correlation with multi-parametric breast MRI findings.

Yamaguchi Ken K Hara Yukiko Y Kitano Isao I Hamamoto Takahiro T Kiyomatsu Kazumitsu K Yamasaki Fumio F Egashira Ryoko R Nakazono Takahiko T Irie Hiroyuki H

The British journal of radiology 20190315 1097

<h4>Objective</h4>To correlate the tumor-stromal ratio (TSR) of invasive breast cancer and MRI findings.<h4>Methods</h4>This study was approved by our institutional review board. 126 consecutive patients with surgically proven invasive breast cancer were included. All patients underwent MRI exams including short-tau inversion-recovery (STIR) <i>T</i> <sub>2</sub> weighted imaging, diffusion-weighted imaging (DWI) and post-contrast dynamic imaging. The mean signal intensity (SI) and apparent diff ...[more]

PMID: 30835501

Similar Datasets

Project description:With the rapid growth and increasing use of brain MRI, there is an interest in automated image classification to aid human interpretation and improve workflow. We aimed to train a deep convolutional neural network and assess its performance in identifying abnormal brain MRIs and critical intracranial findings including acute infarction, acute hemorrhage and mass effect. A total of 13,215 clinical brain MRI studies were categorized to training (74%), validation (9%), internal testing (8%) and external testing (8%) datasets. Up to eight contrasts were included from each brain MRI and each image volume was reformatted to common resolution to accommodate for differences between scanners. Following reviewing the radiology reports, three neuroradiologists assigned each study to abnormal vs normal, and identified three critical findings including acute infarction, acute hemorrhage, and mass effect. A deep convolutional neural network was constructed by a combination of localization feature extraction (LFE) modules and global classifiers to identify the presence of 4 variables in brain MRIs including abnormal, acute infarction, acute hemorrhage and mass effect. Training, validation and testing sets were randomly defined on a patient basis. Training was performed on 9845 studies using balanced sampling to address class imbalance. Receiver operating characteristic (ROC) analysis was performed. The ROC analysis of our models for 1050 studies within our internal test data showed AUC/sensitivity/specificity of 0.91/83%/86% for normal versus abnormal brain MRI, 0.95/92%/88% for acute infarction, 0.90/89%/81% for acute hemorrhage, and 0.93/93%/85% for mass effect. For 1072 studies within our external test data, it showed AUC/sensitivity/specificity of 0.88/80%/80% for normal versus abnormal brain MRI, 0.97/90%/97% for acute infarction, 0.83/72%/88% for acute hemorrhage, and 0.87/79%/81% for mass effect. Our proposed deep convolutional network can accurately identify abnormal and critical intracranial findings on individual brain MRIs, while addressing the fact that some MR contrasts might not be available in individual studies.

Project description:ObjectiveTo investigate whether radiomics features extracted from multi-parametric MRI combining machine learning approach can predict molecular subtype and androgen receptor (AR) expression of breast cancer in a non-invasive way.Materials and methodsPatients diagnosed with clinical T2-4 stage breast cancer from March 2016 to July 2020 were retrospectively enrolled. The molecular subtypes and AR expression in pre-treatment biopsy specimens were assessed. A total of 4,198 radiomics features were extracted from the pre-biopsy multi-parametric MRI (including dynamic contrast-enhancement T1-weighted images, fat-suppressed T2-weighted images, and apparent diffusion coefficient map) of each patient. We applied several feature selection strategies including the least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), the maximum relevance minimum redundancy (mRMR), Boruta and Pearson correlation analysis, to select the most optimal features. We then built 120 diagnostic models using distinct classification algorithms and feature sets divided by MRI sequences and selection strategies to predict molecular subtype and AR expression of breast cancer in the testing dataset of leave-one-out cross-validation (LOOCV). The performances of binary classification models were assessed via the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). And the performances of multiclass classification models were assessed via AUC, overall accuracy, precision, recall rate, and F1-score.ResultsA total of 162 patients (mean age, 46.91 ± 10.08 years) were enrolled in this study; 30 were low-AR expression and 132 were high-AR expression. HR+/HER2- cancers were diagnosed in 56 cases (34.6%), HER2+ cancers in 81 cases (50.0%), and TNBC in 25 patients (15.4%). There was no significant difference in clinicopathologic characteristics between low-AR and high-AR groups (P > 0.05), except the menopausal status, ER, PR, HER2, and Ki-67 index (P = 0.043, <0.001, <0.001, 0.015, and 0.006, respectively). No significant difference in clinicopathologic characteristics was observed among three molecular subtypes except the AR status and Ki-67 (P = <0.001 and 0.012, respectively). The Multilayer Perceptron (MLP) showed the best performance in discriminating AR expression, with an AUC of 0.907 and an accuracy of 85.8% in the testing dataset. The highest performances were obtained for discriminating TNBC vs. non-TNBC (AUC: 0.965, accuracy: 92.6%), HER2+ vs. HER2- (AUC: 0.840, accuracy: 79.0%), and HR+/HER2- vs. others (AUC: 0.860, accuracy: 82.1%) using MLP as well. The micro-AUC of MLP multiclass classification model was 0.896, and the overall accuracy was 0.735.ConclusionsMulti-parametric MRI-based radiomics combining with machine learning approaches provide a promising method to predict the molecular subtype and AR expression of breast cancer non-invasively.

Dataset Information

Tumor-stromal ratio (TSR) of invasive breast cancer: correlation with multi-parametric breast MRI findings.

Publications

Tumor-stromal ratio (TSR) of invasive breast cancer: correlation with multi-parametric breast MRI findings.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets