Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using radiomics of pretreatment dynamic contrast-enhanced MRI

Yoshida, Kotaro; Kawashima, Hiroko; Kannon, Takayuki; Tajima, Atsushi; Ohno, Naoki; Terada, Kanako; Takamatsu, Atsushi; Adachi, H.; Ohno, Minoru; Miyati, Tosiaki; Ishikawa, Satoko; Ikeda, Hiroko; Gabata, Toshifumi

doi:10.1016/j.mri.2022.05.018

Cited by 12 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After the data are standardized, the distribution of the data is observed through t-SNE visualization. t-SNE is a commonly used dimensionality reduction algorithm for reducing highdimensional data to 2 or 3 dimensions, which is mainly used for exploratory data analysis and visualization of highdimensional data [44]. As can be seen from Figure 2, the data are standardized to have better divisibility.…”

Section: Standardized Processing Of Datamentioning

confidence: 99%

Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy

Chen,

Mei,

Zhou

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Breast cancer has replaced lung cancer as the number one cancer among women worldwide. In this paper, we take breast cancer as the research object, and pioneer a hybrid strategy to process the data, and combine the machine learning method to build a more accurate and efficient breast cancer auxiliary diagnosis model. First, the combined sampling method SMOTE-ENN is used to solve the problem of sample imbalance, and the data are standardized to make the data have better separability. Then, the features of the dataset are initially screened using the mutual information method, and further secondary feature selection is performed using the recursive feature elimination method based on the XGBoost algorithm. Thus, the feature dimensionality of the dataset is reduced and the generalization ability of the model is improved. Finally, five different machine learning models are used for classification prediction, the best combination of parameters for each model is found using a grid search method, and the final results of each model are derived using a 10-fold cross-validation method. The experiments are conducted using the Wisconsin Diagnostic Breast Cancer dataset (WDBC), and the results of the study find that after the data are processed by the hybrid strategy, the best prediction results are obtained using the RF model with 99.52% accuracy, which is better than the previous research methods.

show abstract

Section: Standardized Processing Of Datamentioning

confidence: 99%

Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy

Chen,

Mei,

Zhou

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…124 The current review extracted and organized the data in tabular form and summarized the application of MRI in breast cancer diagnosis (Table 3). 109,119,120,123,125–166…”

Section: Introductionmentioning

confidence: 99%

Machine learning and new insights for breast cancer diagnosis

Guo,

Zhang,

Yuan

et al. 2024

J Int Med Res

View full text Add to dashboard Cite

Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.

show abstract

“…Radiomics involves high-throughput computing to extract many quantitative features from medical imaging, allowing the prediction of the tumor phenotype through mathematic models built with selected radiomics features [2]. Prior studies have reported that DCE-MRI based texture analysis can be used to detect Ki-67 [3,4] and human epidermal growth factor receptor 2 (HER2) status [3], determine molecular subtypes [5,6], identify sentinel lymph node metastasis [7,8], and evaluate the response to neoadjuvant chemotherapy [9,10]. However, these studies only extracted intratumoral features from the first phase [4,5,7,11] or the peak phase [3,12] following enhancements.…”

Section: Introductionmentioning

confidence: 99%

Molecular subtypes of breast cancer identified by dynamically enhanced MRI radiomics: the delayed phase cannot be ignored

Huang,

Du,

Gao

et al. 2024

Insights Imaging

View full text Add to dashboard Cite

Objectives To compare the diagnostic performance of intratumoral and peritumoral features from different contrast phases of breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) by building radiomics models for differentiating molecular subtypes of breast cancer. Methods This retrospective study included 377 patients with pathologically confirmed breast cancer. Patients were divided into training set (n = 202), validation set (n = 87) and test set (n = 88). The intratumoral volume of interest (VOI) and peritumoral VOI were delineated on primary breast cancers at three different DCE-MRI contrast phases: early, peak, and delayed. Radiomics features were extracted from each phase. After feature standardization, the training set was filtered by variance analysis, correlation analysis, and least absolute shrinkage and selection (LASSO). Using the extracted features, a logistic regression model based on each tumor subtype (Luminal A, Luminal B, HER2-enriched, triple-negative) was established. Ten models based on intratumoral or/plus peritumoral features from three different phases were developed for each differentiation. Results Radiomics features extracted from delayed phase DCE-MRI demonstrated dominant diagnostic performance over features from other phases. However, the differences were not statistically significant. In the full fusion model for differentiating different molecular subtypes, the most frequently screened features were those from the delayed phase. According to the Shapley additive explanation (SHAP) method, the most important features were also identified from the delayed phase. Conclusions The intratumoral and peritumoral radiomics features from the delayed phase of DCE-MRI can provide additional information for preoperative molecular typing. The delayed phase of DCE-MRI cannot be ignored. Critical relevance statement Radiomics features extracted and radiomics models constructed from the delayed phase of DCE-MRI played a crucial role in molecular subtype classification, although no significant difference was observed in the test cohort. Key Points The molecular subtype of breast cancer provides a basis for setting treatment strategy and prognosis. The delayed-phase radiomics model outperformed that of early-/peak-phases, but no differently than other phases or combinations. Both intra- and peritumoral radiomics features offer valuable insights for molecular typing. Graphical Abstract

show abstract

Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using radiomics of pretreatment dynamic contrast-enhanced MRI

Cited by 12 publications

References 29 publications

Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy

Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy

Machine learning and new insights for breast cancer diagnosis

Molecular subtypes of breast cancer identified by dynamically enhanced MRI radiomics: the delayed phase cannot be ignored

Contact Info

Product

Resources

About