Dataset Information

MRI-based machine learning radiomics for prediction of HER2 expression status in breast invasive ductal carcinoma.

ABSTRACT:

Background

Human epidermal growth factor receptor 2 (HER2) is a tumor biomarker with significant prognostic and therapeutic implications for invasive ductal breast carcinoma (IDC).

Objective

This study aimed to explore the effectiveness of a multisequence magnetic resonance imaging (MRI)-based machine learning radiomics model in classifying the expression status of HER2, including HER2-positive, HER2-low, and HER2 completely negative (HER2-zero), among patients with IDC.

Methods

A total of 402 female patients with IDC confirmed through surgical pathology were enrolled and subsequently divided into a training group (n = 250, center I) and a validation group (n = 152, center II). Radiomics features were extracted from the preoperative MRI. A simulated annealing algorithm was used for key feature selection. Two classification tasks were performed: task 1, the classification of HER2-positive vs. HER2-negative (HER2-low and HER2-zero), and task 2, the classification of HER2-low vs. HER2-zero. Logistic regression, random forest (RF), and support vector machine were conducted to establish radiomics models. The performance of the models was evaluated using the area under the curve (AUC) of the operating characteristics (ROC).

Results

In total, 4506 radiomics features were extracted from multisequence MRI. A radiomics model for prediction of expression state of HER2 was successfully developed. Among the three classification algorithms, RF achieved the highest performance in classifying HER2-positive from HER2-negative and HER2-low from HER2-zero, with AUC values of 0.777 and 0.731, respectively.

Conclusions

Machine learning-based MRI radiomics may aid in the non-invasive prediction of the different expression status of HER2 in IDC.

SUBMITTER: Luo HJ

PROVIDER: S-EPMC11324846 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

MRI-based machine learning radiomics for prediction of HER2 expression status in breast invasive ductal carcinoma.

Luo Hong-Jian HJ Ren Jia-Liang JL Mei Guo Li L Liang Niu Jin J Song Xiao-Li XL

European journal of radiology open 20240719

<h4>Background</h4>Human epidermal growth factor receptor 2 (HER2) is a tumor biomarker with significant prognostic and therapeutic implications for invasive ductal breast carcinoma (IDC).<h4>Objective</h4>This study aimed to explore the effectiveness of a multisequence magnetic resonance imaging (MRI)-based machine learning radiomics model in classifying the expression status of HER2, including HER2-positive, HER2-low, and HER2 completely negative (HER2-zero), among patients with IDC.<h4>Method ...[more]

PMID: 39149534

Similar Datasets

Project description:PurposeThe aim of this study is to investigate radiomics features extracted from the optimal peritumoral region and the intratumoral area on the early phase of dynamic contrast-enhanced MRI (DCE-MRI) for predicting molecular subtypes of invasive ductal breast carcinoma (IDBC).MethodsA total of 422 IDBC patients with immunohistochemical and fluorescence in situ hybridization results from two hospitals (Center 1: 327 cases, Center 2: 95 cases) who underwent preoperative DCE-MRI were retrospectively enrolled. After image preprocessing, radiomic features were extracted from the intratumoral area and four peritumoral regions on DCE-MRI from two centers, and selected the optimal peritumoral region. Based on the intratumoral, peritumoral radiomics features, and clinical-radiological characteristics, five radiomics models were constructed through support vector machine (SVM) in multiple classification tasks related to molecular subtypes and visualized by nomogram. The performance of radiomics models was evaluated by receiver operating characteristic curves, confusion matrix, calibration curves, and decision curve analysis.ResultsA 6-mm peritumoral size was defined the optimal peritumoral region in classification tasks of hormone receptor (HR)-positive vs others, triple-negative breast cancer (TNBC) vs others, and HR-positive vs human epidermal growth factor receptor 2 (HER2)-enriched vs TNBC, and 8 mm was applied in HER2-enriched vs others. The combined clinical-radiological and radiomics models in three binary classification tasks (HR-positive vs others, HER2-enriched vs others, TNBC vs others) obtained optimal performance with AUCs of 0.838, 0.848, and 0.930 in the training cohort, respectively; 0.827, 0.813, and 0.879 in the internal test cohort, respectively; and 0.791, 0.707, and 0.852 in the external test cohort, respectively.ConclusionRadiomics features in the intratumoral and peritumoral regions of IDBC on DCE-MRI had a potential to predict the HR-positive, HER2-enriched, and TNBC molecular subtypes preoperatively.

Project description:BackgroundAccurate determination of human epidermal growth factor receptor 2 (HER2) is important for choosing optimal HER2 targeting treatment strategies. HER2-low is currently considered HER2-negative, but patients may be eligible to receive new anti-HER2 drug conjugates.PurposeTo use breast MRI BI-RADS features for classifying three HER2 levels, first to distinguish HER2-zero from HER2-low/positive (Task-1), and then to distinguish HER2-low from HER2-positive (Task-2).Study typeRetrospective.Population621 invasive ductal cancer, 245 HER2-zero, 191 HER2-low, and 185 HER2-positive. For Task-1, 488 cases for training and 133 for testing. For Task-2, 294 cases for training and 82 for testing.Field strength/sequence3.0 T; 3D T1-weighted DCE, short time inversion recovery T2, and single-shot EPI DWI.AssessmentPathological information and BI-RADS features were compared. Random Forest was used to select MRI features, and then four machine learning (ML) algorithms: decision tree (DT), support vector machine (SVM), k-nearest neighbors (k-NN), and artificial neural nets (ANN), were applied to build models.Statistical testsChi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant. For ML models, the generated probability was used to construct the ROC curves.ResultsPeritumoral edema, the presence of multiple lesions and non-mass enhancement (NME) showed significant differences. For distinguishing HER2-zero from non-zero (low + positive), multiple lesions, edema, margin, and tumor size were selected, and the k-NN model achieved the highest AUC of 0.86 in the training set and 0.79 in the testing set. For differentiating HER2-low from HER2-positive, multiple lesions, edema, and margin were selected, and the DT model achieved the highest AUC of 0.79 in the training set and 0.69 in the testing set.Data conclusionBI-RADS features read by radiologists from preoperative MRI can be analyzed using more sophisticated feature selection and ML algorithms to build models for the classification of HER2 status and identify HER2-low.Level of evidence: 4Technical efficacyStage 2.

Project description:BackgroundMost studies of molecular subtype prediction in breast cancer were mainly based on two-dimensional MRI images, the predictive value of three-dimensional volumetric features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting breast cancer molecular subtypes has not been thoroughly investigated. This study aimed to look into the role of features derived from DCE-MRI and how they could be combined with clinical data to predict invasive ductal breast cancer molecular subtypes.MethodsFrom January 2019 to December 2021, 190 Chinese women with invasive ductal breast cancer were studied (32 triple-negative, 59 HER2-enriched, and 99 luminal lesions) in this institutional review board-approved retrospective cohort study. The image processing software extracted 1130 quantitative radiomic features from the segmented lesion area, including shape-based, first-order statistical, texture, and wavelet features. Three binary classifications of the subtypes were performed: triple-negative vs. non-triple-negative, HER2-overexpressed vs. non-HER2-overexpressed, and luminal (A + B) vs. non-luminal. For the classification, five machine learning methods (random forest, logistic regression, support vector machine, naïve Bayes, and eXtreme Gradient Boosting) were employed. The classifiers were chosen using the least absolute shrinkage and selection operator method. The area evaluated classification performance under the receiver operating characteristic curve, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean.ResultsEXtreme Gradient Boosting model showed the best performance in luminal and non-luminal groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.8282, 0.7524, 0.6542, 0.6964, 0.6086, 0.3458, 0.8524 and 0.7016, respectively. Meanwhile, the random forest model showed the best performance in HER2-overexpressed and non-HER2-overexpressed groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.8054, 0.2941, 0.9744, 0.7679, 0.4348, 0.0256, 0.8333 and 0.5353, respectively. Furthermore, eXtreme Gradient Boosting model showed the best performance in the triple-negative and non-triple-negative groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.9031, 0.9362, 0.4444, 0.8571, 0.9167, 0.5556, 0.8980 and 0.6450.ConclusionClinical data and three-dimension imaging features from DCE-MRI were identified as potential biomarkers for distinguishing between three molecular subtypes of invasive ductal carcinomas breast cancer. In the future, more extensive studies will be required to evaluate the findings.

Project description:ObjectivesTo explore the feasibility of predicting overall survival (OS) of patients with midline glioma using multi-parameter magnetic resonance imaging (MRI) features.MethodsData of 84 patients with midline gliomas were retrospectively collected, including 40 patients with OS > 12 months (28 cases were adults, 14 cases were H3 K27M-mutation) and 44 patients with OS < 12 months (29 cases were adults, 31 cases were H3 K27M-mutation). Features were extracted from the largest slice of tumors, which were manually segmented on T2-weighted (T2w), T2 fluid-attenuated inversion recovery (T2 FLAIR), and contrast-enhanced T1-weighted (T1c) images. Data were randomly divided into training (70%) and test cohorts (30%) and normalized and standardized using Z-scores. Feature dimensionality reduction was performed using the variance method and maximum relevance and minimum redundancy (mRMR) algorithm. We used the logistic regression algorithm to construct three models for T2w, T2 FLAIR, and T1c images as well as one combined model. The test cohort was used to evaluate the models, and receiver operating characteristic (ROC) curves, areas under the curve (AUCs), sensitivity, specificity, and accuracy were calculated. The nomogram of the combined model was built and evaluated using a calibration curve. Decision curve analysis (DCA) was used to evaluate the clinical application value of the four models.ResultsA total of 1,316 features were extracted from T2w, T2 FLAIR, and T1c images, respectively. And then the best non-redundant features were selected from the extracted features using the variance method and mRMR. Finally, five features were extracted each from T2w, T2 FLAIR, and T1c images, and 12 features were extracted for the combined model. Four models were established using the optimal features. In the test cohort, the combined model performed the best out of all models. The AUCs of the T2w, T2 FLAIR, T1c, and combined models were 0.73, 0.78, 0.74, and 0.87, respectively, and accuracies were 0.72, 0.76, 0.72, and 0.84, respectively. The ROC curves and DCA showed that the combined model had the highest efficiency and most favorable clinical benefits.ConclusionThe combined radiomics model based on multi-parameter MRI features provided a reliable non-invasive method for the prognostic prediction of midline gliomas.

Dataset Information

MRI-based machine learning radiomics for prediction of HER2 expression status in breast invasive ductal carcinoma.

Background

Objective

Methods

Results

Conclusions

Publications

MRI-based machine learning radiomics for prediction of HER2 expression status in breast invasive ductal carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets