Intra‐ and Peritumoral Based Radiomics for Assessment of Lymphovascular Invasion in Invasive Breast Cancer

Jiang, Wenyan; Meng, Raymond D.; Cheng, Yuan; Wang, Haotian; Han, Tingting; Qu, Ningsong; Yu, Tao; Hou, Yang; Xu, Shisan

doi:10.1002/jmri.28776

Cited by 14 publications

(4 citation statements)

References 35 publications

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“…However, this study had some limitations. First, it focused solely on analyzing intra-tumoral radiomic features while disregarding peri-tumoral radiomic features, which have been shown to hold significance in predicting LVI [ 25 ]. To enhance the comprehensiveness of future research, peritumoral radiomic signs should be included in future studies.…”

Section: Discussionmentioning

confidence: 99%

Leveraging multimodal MRI-based radiomics analysis with diverse machine learning models to evaluate lymphovascular invasion in clinically node-negative breast cancer

Jiang,

Zeng,

Zuo

et al. 2024

Heliyon

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Section: Discussionmentioning

confidence: 99%

Leveraging multimodal MRI-based radiomics analysis with diverse machine learning models to evaluate lymphovascular invasion in clinically node-negative breast cancer

Jiang,

Zeng,

Zuo

et al. 2024

Heliyon

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“…The radiomics features which reflected the changes of peritumoral environment can thus be used as the indicators for the density of infiltrating lymphocytes around the tumor (20)(21)(22). As researchers found the defects in the radiomics research that only extracted features from primary tumor, the role of peritumoral region attracted more attention due to its association with tumor infiltration, vascular proliferation and lymphovascular invasion (12,(23)(24)(25)(26). The increase of peritumoral interstitial fibrosis is related to the high invasiveness of the tumor (27), and the presence of peritumoral lymphovascular invasion (LVI) is closely associated with higher distant metastasis rate and mortality (28).…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of benign and malignant nodules with a radiomics model integrating features from nodules and mammary regions on DCE-MRI

Fan,

Sun,

et al. 2024

Front. Oncol.

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ObjectivesTo establish a radiomics model for distinguishing between the benign and malignant mammary gland nodules via combining the features from nodule and mammary regions on DCE-MRIMethodsIn this retrospective study, a total of 103 cases with mammary gland nodules (malignant/benign = 80/23) underwent DCE-MRI, and was confirmed by biopsy pathology. Features were extracted from both nodule region and mammary region on DCE-MRI. Three SVM classifiers were built for diagnosis of benign and malignant nodules as follows: the model with the features only from nodule region (N model), with the features only from mammary region (M model) and the model combining the features from nodule region and mammary region (NM model). The performance of models was evaluated with the area under the curve of receiver operating characteristic (AUC).ResultsOne radiomic features is selected from nodule region and 3 radiomic features is selected from mammary region. Compared with N or M model, NM model exhibited the best performance with an AUC of 0.756.ConclusionsCompared with the model only using the features from nodule or mammary region, the radiomics-based model combining the features from nodule and mammary region outperformed in the diagnosis of benign and malignant nodules.

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“…Previous studies have indicated that tumor features on quantitative radiomics can be used to predict LVI. Specifically, in patients with breast cancer, magnetic resonance imaging (MRI)-based radiomics is a promising method for the accurate prediction of LVI status [6][7][8][9] ; however, it has limitations. The reproducibility and accuracy of lesion segmentation are susceptible to manual errors and may be influenced by the expertise of the radiologists.…”

Section: Introductionmentioning

confidence: 99%

Prior Clinico‐Radiological Features Informed Multi‐Modal MR Images Convolution Neural Network: A novel deep learning framework for prediction of lymphovascular invasion in breast cancer

Zheng,

Jian,

et al. 2024

Cancer Medicine

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BackgroundCurrent methods utilizing preoperative magnetic resonance imaging (MRI)‐based radiomics for assessing lymphovascular invasion (LVI) in patients with early‐stage breast cancer lack precision, limiting the options for surgical planning.PurposeThis study aimed to develop a sophisticated deep learning framework called “Prior Clinico‐Radiological Features Informed Multi‐Modal MR Images Convolutional Neural Network (PCMM‐Net)” to improve the accuracy of LVI prediction in breast cancer. By incorporating multiparameter MRI and prior clinical knowledge, PCMM‐Net should enhance the precision of LVI assessment.MethodsA total of 341 patients with breast cancer were randomly divided into training and validation groups at a ratio of 7:3. Imaging features were extracted from T1‐weighted, T2‐weighted, and contrast‐enhanced T1‐weighted MRI sequences. Stepwise univariate and multivariate logistic regression were employed to establish a clinico‐radiological model for LVI prediction. The radiomics model was built using redundancy and the least absolute shrinkage and selection operator. Then, two deep learning frameworks were developed: the Multi‐Modal MR Images Convolutional Neural Network (MM‐Net), which does not consider prior radiological features, and PCMM‐Net, which incorporates multiparameter MRI and prior clinical knowledge. Receiver operating characteristic curves were used, and the corresponding areas under the curves (AUCs) were calculated for evaluation.ResultsPCMM‐Net achieved the highest AUC of 0.843. The clinico‐radiological features displayed the lowest AUC value of 0.743, followed by MM‐Net with an AUC of 0.774, and radiomics with an AUC of 0.795.ConclusionsThis study introduces PCMM‐Net, an innovative deep learning framework that integrates prior clinico‐radiological features for accurate LVI prediction in breast cancer. PCMM‐Net demonstrates excellent diagnostic performance and facilitates the application of precision medicine.

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Intra‐ and Peritumoral Based Radiomics for Assessment of Lymphovascular Invasion in Invasive Breast Cancer

Cited by 14 publications

References 35 publications

Leveraging multimodal MRI-based radiomics analysis with diverse machine learning models to evaluate lymphovascular invasion in clinically node-negative breast cancer

Leveraging multimodal MRI-based radiomics analysis with diverse machine learning models to evaluate lymphovascular invasion in clinically node-negative breast cancer

Diagnosis of benign and malignant nodules with a radiomics model integrating features from nodules and mammary regions on DCE-MRI

Prior Clinico‐Radiological Features Informed Multi‐Modal MR Images Convolution Neural Network: A novel deep learning framework for prediction of lymphovascular invasion in breast cancer

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