Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors

Intra- and peri-tumoral radiomics based on dynamic contrast-enhanced MRI for prediction of benign disease in BI-RADS 4 breast lesions: a multicentre study

BACKGROUND AND PURPOSE

The study aimed to create a radiomics model based on breast intra- and peri-tumoral regions in dynamic contrast-enhanced (DCE) MRI to distinguish benign from malignant breast lesions of Breast Imaging Reporting and Data System (BI-RADS) 4.

MATERIALS AND METHODS

A total of 516 patients from Hospital 1 were assigned to the training cohort. Then, 146 and 52 patients were enrolled from Hospital 2 and 3, respectively, as the internal and external test cohort. Seven classification models were built, using features extracted from the intra- and peri-tumoral regions. Diagnostic performance was evaluated by receiver operating characteristics (ROC) analysis and compared by the DeLong test. Subgroup analysis was performed after stratifying all lesions by enhancement pattern and the subdivision of BI-RADS 4.

RESULTS

The Comb2 model, built with features from peri-tumoral 2 mm and intra-tumoral region, demonstrated the best performance with AUCs of 0.828 and 0.844 in the internal and external test cohort, respectively. The Comb2 model was robust in both mass and non-mass enhancement (NME) lesions. At the three exploratory cutoff values on the ROC curve, the model identified 9.1% (sensitivity of C1 ≥ 98%), 27.3% (sensitivity of C2 ≥ 95%) and 36.4% (sensitivity of C3 ≥ 90%) of the benign lesions in the external test cohort. Applying the identified cutoff values in the external test cohort showed the potential to lower the number of unnecessary biopsies of benign lesions.

CONCLUSION

An MRI-based radiomics model built with features extracted from the intra-tumoral region combined with the peri-tumoral 2 mm showed the best potential to reduce false-positive diagnoses and may avoid unnecessary biopsies with a low underestimate risk.

A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI

PURPOSE

To explore a subregion-based RadioFusionOmics (RFO) model for discrimination between adult-type grade 4 astrocytoma and glioblastoma according to the 2021 WHO CNS5 classification.

METHODS

329 patients (40 grade 4 astrocytomas and 289 glioblastomas) with histologic diagnosis was retrospectively collected from our local institution and The Cancer Imaging Archive (TCIA). The volumes of interests (VOIs) were obtained from four multiparametric MRI sequences (T1WI, T1WI + C, T2WI, T2-FLAIR) using (1) manual segmentation of the non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE), and (2) K-means clustering of four habitats (H1: high T1WI + C, high T2-FLAIR; (2) H2: high T1WI + C, low T2-FLAIR; (3) H3: low T1WI + C, high T2-FLAIR; and (4) H4: low T1WI + C, low T2-FLAIR). The optimal VOI and best MRI sequence combination were determined. The performance of the RFO model was evaluated using the area under the precision-recall curve (AUPRC) and the best signatures were identified.

RESULTS

The two best VOIs were manual VOI3 (putative peritumoral edema) and clustering H34 (low T1WI + C, high T2-FLAIR (H3) combined with low T1WI + C and low T2-FLAIR (H4)). Features fused from four MRI sequences ([Formula: see text]) outperformed those from either a single sequence or other sequence combinations. The RFO model that was trained using fused features [Formula: see text] achieved the AUPRC of 0.972 (VOI3) and 0.976 (H34) in the primary cohort (p = 0.905), and 0.971 (VOI3) and 0.974 (H34) in the testing cohort (p = 0.402).

CONCLUSION

The performance of subregions defined by clustering was comparable to that of subregions that were manually defined. Fusion of features from the edematous subregions of multiple MRI sequences by the RFO model resulted in differentiation between grade 4 astrocytoma and glioblastoma.

Assessment of chemotherapy resistance changes in human colorectal cancer xenografts in rats based on MRI histogram features

PURPOSE

We investigated the value of magnetic resonance imaging (MRI) histogram features, a non-invasive method, in assessing the changes in chemoresistance of colorectal cancer xenografts in rats.

METHODS

A total of 50 tumor-bearing mice with colorectal cancer were randomly divided into two groups: control group and 5-fluorouracil (5-FU) group. The MRI histogram characteristics and the expression levels of p53 protein and MRP1 were obtained at 24 h, 48 h, 72 h, 120 h, and 168 h after treatment.

RESULTS

Sixty highly repeatable MRI histogram features were obtained. There were 16 MRI histogram parameters and MRP1 resistance protein differences between groups. At 24 h after treatment, the MRI histogram texture parameters of T2-weighted imaging (T2WI) images (10%, 90%, median, energy, and RootMeanSquared) and D images (10% and Range) were positively correlated with MRP1 (r = 0.925, p = 0.005). At 48 h after treatment, histogram texture parameters of apparent diffusion coefficient (ADC) images (Energy) were positively correlated with the presence of MRP1 resistance protein (r = 0.900, p = 0.037). There was no statistically significant difference between MRI histogram features and p53 protein expression level.

CONCLUSIONS

MRI histogram texture parameters based on T2WI, D, and ADC maps can help to predict the change of 5-FU resistance in colorectal cancer in the early stage and provide important reference significance for clinical treatment.

Dynamic contrast-enhanced magnetic resonance imaging radiomics analysis based on intratumoral subregions for predicting luminal and nonluminal breast cancer

Background

Breast cancer is a heterogeneous disease with different morphological and biological characteristics. The molecular subtypes of breast cancer are closely related to the treatment and prognosis of patients. In order to predict the luminal type of breast cancer in a noninvasive manner, our study developed and validated a radiomics nomogram combining clinical factors with a radiomics score based on the features of the intratumoral subregion to distinguish between luminal and nonluminal breast cancer.

Methods

From January 2018 to January 2020, 153 women with clinically and pathologically diagnosed breast cancer with an average age of 50.08 years were retrospectively analyzed. Using a semiautomatic segmentation method, the whole tumor was divided into 3 subregions on the basis of the time required for the contrast agent to reach its peak; 540 features were extracted from 3 subregions and the whole tumor region. Subsequently, 2 machine learning classifiers were developed. The least absolute shrinkage and selection operator method was used for feature selection and radiomics score (Rad-score) construction. Moreover, multivariable logistic regression analysis was applied to select independent factors from the Rad-score and clinical factors to establish a prediction model in the form of a nomogram. The performance of the nomogram was evaluated through calibration, discrimination, and clinical usefulness.

Results

The prediction performance of texture features from the rapid subregion was the best in the 3 intratumoral subregions, and the area under the receiver operating characteristic curve (AUC) values in the training and validation cohort were 0.805 (95% CI: 0.719-0.892) and 0.737 (95% CI: 0.581-0.893), respectively. The Rad-score, consisting of 5 features from the rapid subregion, was associated with the luminal type of breast cancer (P=0.001 and P=0.035 in the training and validation cohorts, respectively). The predictors included in the personalized prediction nomogram included Rad-score, human epidermal growth factor receptor 2 (HER2) status, and tumor histological grade. The nomogram showed good discrimination, with an area under the receiver operating characteristic curve in the training and validation cohorts of 0.830 (95% CI: 0.746-0.896) and 0.879 (95% CI: 0.748-0.957), respectively. The calibration curve of the 2 cohorts and decision curve analysis demonstrated that the nomogram had good calibration and clinical usefulness.

Conclusions

We proposed a nomogram model that combined clinical factors and Rad-score, which showed good performance in predicting the luminal type of breast cancer.

Radiomics characterization of tissues in an animal brain tumor model imaged using dynamic contrast enhanced (DCE) MRI

Here, we investigate radiomics-based characterization of tumor vascular and microenvironmental properties in an orthotopic rat brain tumor model measured using dynamic-contrast-enhanced (DCE) MRI. Thirty-two immune compromised-RNU rats implanted with human U-251N cancer cells were imaged using DCE-MRI (7Tesla, Dual-Gradient-Echo). The aim was to perform pharmacokinetic analysis using a nested model (NM) selection technique to classify brain regions according to vasculature properties considered as the source of truth. A two-dimensional convolutional-based radiomics analysis was performed on the raw-DCE-MRI of the rat brains to generate dynamic radiomics maps. The raw-DCE-MRI and respective radiomics maps were used to build 28 unsupervised Kohonen self-organizing-maps (K-SOMs). A Silhouette-Coefficient (SC), k-fold Nested-Cross-Validation (k-fold-NCV), and feature engineering analyses were performed on the K-SOMs' feature spaces to quantify the distinction power of radiomics features compared to raw-DCE-MRI for classification of different Nested Models. Results showed that eight radiomics features outperformed respective raw-DCE-MRI in prediction of the three nested models. The average percent difference in SCs between radiomics features and raw-DCE-MRI was: 29.875% ± 12.922%, p < 0.001. This work establishes an important first step toward spatiotemporal characterization of brain regions using radiomics signatures, which is fundamental toward staging of tumors and evaluation of tumor response to different treatments.

Fractal Dimension Analysis of Pixel Dynamic Contrast Enhanced-Magnetic Resonance Imaging Pharmacokinetic Parameters for Discrimination of Benign and Malignant Breast Lesions

PURPOSE

Breast cancer is the most frequent cancer in women worldwide. However, its diagnosis mostly depends on visual examination of radiologic images, leading to an overdiagnosis with substantial costs. Therefore, a quantitative approach such as dynamic contrast enhanced (DCE)-magnetic resonance imaging (MRI) through pharmacokinetic (PK) modeling is required for reliable analysis. As PK parameters lack information on parameter heterogeneity, texture-based analysis is required to quantify PK parameter heterogeneity. Therefore, this study focused on determining the usefulness of fractal dimension (FD) as a potential imaging biomarker of tumor heterogeneity for discriminating benign and malignant breast lesions.

METHODS

Parametric maps for PK parameters, extravasation rate of contrast agent from blood plasma to extravascular extracellular space (K^trans) and volume fraction of extravascular extracellular space (v_e), were generated for the regions of interest (ROIs) under the standard model using 18 lesions. Then, tumor ROI and pixel DCE-MRI time-course data were analyzed to extract pixel values of K^trans and v_e. For each ROI, FD values of K^trans and v_e were computed using the blanket method.

RESULTS

The FD values of K^trans for benign and malignant lesions varied from 2.96 to 3.49 and from 2.37 to 3.16, respectively, whereas FD values of v_e for benign and malignant lesions varied from 3.01 to 5.15 and 2.42 to 3.44, respectively. There were significant differences in FD values derived from K^trans parametric maps (P = .0053) and v_e parametric maps (P = .0271) between benign and malignant lesions according to the statistical analysis.

CONCLUSION

Incorporating texture heterogeneity changes in breast lesions captured by FD with quantitative DCE-MRI parameters generated under the standard model is a potential marker for prediction of malignant lesions.

Machine learning for multi-parametric breast MRI: radiomics-based approaches for lesion classification

In the artificial intelligence era, machine learning (ML) techniques have gained more and more importance in the advanced analysis of medical images in several fields of modern medicine. Radiomics extracts a huge number of medical imaging features revealing key components of tumor phenotype that can be linked to genomic pathways. The multi-dimensional nature of radiomics requires highly accurate and reliable machine-learning methods to create predictive models for classification or therapy response assessment.

Multi-parametric breast magnetic resonance imaging (MRI) is routinely used for dense breast imaging as well for screening in high-risk patients and has shown its potential to improve clinical diagnosis of breast cancer. For this reason, the application of ML techniques to breast MRI, in particular to multi-parametric imaging, is rapidly expanding and enhancing both diagnostic and prognostic power. In this review we will focus on the recent literature related to the use of ML in multi-parametric breast MRI for tumor classification and differentiation of molecular subtypes. Indeed, at present, different models and approaches have been employed for this task, requiring a detailed description of the advantages and drawbacks of each technique and a general overview of their performances.