Predictive value of T2‑weighted magnetic resonance imaging for the prognosis of patients with mass‑type breast cancer with peritumoral edema

Liang, Ting; Hu, Bin; Du, Hongwu; Zhang, Yili

doi:10.3892/ol.2020.12177

Cited by 17 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This may be explained by the fact that the inner zone of the breast tumor is related to the most aggressive phenotype of the tumor, and is more informative with respect to the gene expression status 39 . On the other hand, marginal zones may be associated with tumor edema or necrotic regions, which contain the metabolically nonactive solid component of the tumor, 40 and hence may be less informative. Our findings suggest that image features extracted from intratumoral subregions that are related to dense tumor tissues, rather than the whole tumor, could be more important for predicting the Ki‐67 levels.…”

Section: Discussionmentioning

confidence: 99%

Intratumoral analysis of digital breast tomosynthesis for predicting the Ki‐67 level in breast cancer: A multi‐center radiomics study

Jiang

Chang

et al. 2021

Medical Physics

View full text Add to dashboard Cite

Purpose To non‐invasively evaluate the Ki‐67 level in digital breast tomosynthesis (DBT) images of breast cancer (BC) patients based on subregional radiomics. Methods A total of 266 patients who underwent DBT scans were consecutively enrolled at two centers, between September 2017 and September 2021. The whole tumor region was partitioned into various intratumoral subregions, based on individual‐ and population‐level clustering. Handcrafted radiomics and deep learning‐based features were extracted from the subregions and from the whole tumor region, and were selected by least absolute shrinkage and selection operator (LASSO) regression, yielding radiomics signatures (RSs). The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the developed RSs. Results Each breast tumor region was partitioned into an inner subregion (S1) and a marginal subregion (S2). The RSs derived from S1 always generated higher AUCs compared with those from S2 or from the whole tumor region (W), for the external validation cohort (AUCs, S1 vs. W, handcrafted RSs: 0.583 [95% CI, 0.429–0.727] vs. 0.559 [95% CI, 0.405–0.705], p‐value: 0.920; deep RSs: 0.670 [95% CI, 0.516–0.802] vs. 0.551 [95% CI, 0.397–0.698], p‐value: 0.776). The fusion RSs, combining handcrafted and deep learning‐based features derived from S1, yielded the highest AUCs of 0.820 (95% CI, 0.714–0.900) and 0.792 (95% CI, 0.647–0.897) for the internal and external validation cohorts, respectively. Conclusions The subregional radiomics approach can accurately predict the Ki‐67 level based on DBT data; thus, it may be used as a potential non‐invasive tool for preoperative treatment planning in BC.

show abstract

Section: Discussionmentioning

confidence: 99%

Intratumoral analysis of digital breast tomosynthesis for predicting the Ki‐67 level in breast cancer: A multi‐center radiomics study

Jiang

Chang

et al. 2021

Medical Physics

View full text Add to dashboard Cite

show abstract

“…The ROI in this study included the breast tumor itself and peritumoral edema. Previous studies (38)showed that the presence of perineural edema in breast cancer and axillary lymph node metastasis were closely related, and T2WI was the best sequence to show perineural edema.…”

Section: Discussionmentioning

confidence: 94%

Predicting of axillary lymph node metastasis in invasive breast cancer using multiparametric MRI dataset based on CNN model

Zhang

Liu²,

Ren³

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

PurposeTo develop a multiparametric MRI model for predicting axillary lymph node metastasis in invasive breast cancer.MethodsClinical data and T2WI, DWI, and DCE-MRI images of 252 patients with invasive breast cancer were retrospectively analyzed and divided into the axillary lymph node metastasis (ALNM) group and non-ALNM group using biopsy results as a reference standard. The regions of interest (ROI) in T2WI, DWI, and DCE-MRI images were segmented using MATLAB software, and the ROI was unified into 224 × 224 sizes, followed by image normalization as input to T2WI, DWI, and DCE-MRI models, all of which were based on ResNet 50 networks. The idea of a weighted voting method in ensemble learning was employed, and then T2WI, DWI, and DCE-MRI models were used as the base models to construct a multiparametric MRI model. The entire dataset was randomly divided into training sets and testing sets (the training set 202 cases, including 78 ALNM, 124 non-ALNM; the testing set 50 cases, including 20 ALNM, 30 non-ALNM). Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of models were calculated. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the diagnostic performance of each model for axillary lymph node metastasis, and the DeLong test was performed, P< 0.05 statistically significant.ResultsFor the assessment of axillary lymph node status in invasive breast cancer on the test set, multiparametric MRI models yielded an AUC of 0.913 (95% CI, 0.799-0.974); T2WI-based model yielded an AUC of 0.908 (95% CI, 0.792-0.971); DWI-based model achieved an AUC of 0.702 (95% CI, 0.556-0.823); and the AUC of the DCE-MRI-based model was 0.572 (95% CI, 0.424-0.711). The improvement in the diagnostic performance of the multiparametric MRI model compared with the DWI and DCE-MRI-based models were significant (P< 0.01 for both). However, the increase was not meaningful compared with the T2WI-based model (P = 0.917).ConclusionMultiparametric MRI image analysis based on an ensemble CNN model with deep learning is of practical application and extension for preoperative prediction of axillary lymph node metastasis in invasive breast cancer.

show abstract

“…ADC was applied to distinguish ductal carcinoma in situ (DCIS, orange) from invasive carcinoma (red). Note the presence of a benign lesion (a fibroadenoma), also correctly characterised by the ADC map (green), adjacent to the DCIS numerous authors [86][87][88]. In particular, advanced techniques such as high-resolution diffusion-weighted imaging and radiomics showed promising potential to analyse peritumoural tissue, and they may further support the clinical impact ofP2-bMRI [89][90][91].…”

Section: Associated Nonvascular Findings: Oedemamentioning

confidence: 90%

“…Authors identified perifocal oedema as an independent biomarker of disease recurrence (hazard ratio 2.48) potentially improving the prognostication of disease recurrence by conventional biomarkers [ 39 ]. Meanwhile, the prognostic value of the semantic criterion “oedema” has been verified by numerous authors [ 86 – 88 ]. In particular, advanced techniques such as high-resolution diffusion-weighted imaging and radiomics showed promising potential to analyse peritumoural tissue, and they may further support the clinical impact ofP2-bMRI [ 89 – 91 ].…”

Section: P2-bmri: Semantic Criteriamentioning

confidence: 97%

The potential of predictive and prognostic breast MRI (P2-bMRI)

et al. 2022

View full text Add to dashboard Cite

Magnetic resonance imaging (MRI) is an important part of breast cancer diagnosis and multimodal workup. It provides unsurpassed soft tissue contrast to analyse the underlying pathophysiology, and it is adopted for a variety of clinical indications. Predictive and prognostic breast MRI (P2-bMRI) is an emerging application next to these indications. The general objective of P2-bMRI is to provide predictive and/or prognostic biomarkers in order to support personalisation of breast cancer treatment. We believe P2-bMRI has a great clinical potential, thanks to the in vivo examination of the whole tumour and of the surrounding tissue, establishing a link between pathophysiology and response to therapy (prediction) as well as patient outcome (prognostication). The tools used for P2-bMRI cover a wide spectrum: standard and advanced multiparametric pulse sequences; structured reporting criteria (for instance BI-RADS descriptors); artificial intelligence methods, including machine learning (with emphasis on radiomics data analysis); and deep learning that have shown compelling potential for this purpose. P2-bMRI reuses the imaging data of examinations performed in the current practice. Accordingly, P2-bMRI could optimise clinical workflow, enabling cost savings and ultimately improving personalisation of treatment. This review introduces the concept of P2-bMRI, focusing on the clinical application of P2-bMRI by using semantic criteria.

show abstract

Predictive value of T2‑weighted magnetic resonance imaging for the prognosis of patients with mass‑type breast cancer with peritumoral edema

Cited by 17 publications

References 31 publications

Intratumoral analysis of digital breast tomosynthesis for predicting the Ki‐67 level in breast cancer: A multi‐center radiomics study

Intratumoral analysis of digital breast tomosynthesis for predicting the Ki‐67 level in breast cancer: A multi‐center radiomics study

Predicting of axillary lymph node metastasis in invasive breast cancer using multiparametric MRI dataset based on CNN model

The potential of predictive and prognostic breast MRI (P2-bMRI)

Contact Info

Product

Resources

About