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Multiparametric MRI-based radiomics nomogram for preoperative prediction of lymphovascular invasion and clinical outcomes in patients with breast invasive ductal carcinoma

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Abstract

Objective

To develop a multiparametric MRI-based radiomics nomogram for predicting lymphovascular invasion (LVI) status and clinical outcomes in patients with breast invasive ductal carcinoma (IDC).

Methods

A total of 160 patients with pathologically confirmed breast IDC (training cohort: n = 112; validation cohort: n = 48) who underwent preoperative breast MRI were included. Imaging features were extracted from T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) maps, and contrast-enhanced T1-weighted imaging (cT1WI) sequences. A four-step procedure was applied for feature selection and radiomics signature building. Univariate and multivariate logistic regression analyses were conducted to identify the features associated with LVI, which were then incorporated into the radiomics nomogram. The performance of the nomogram was evaluated by its discrimination, calibration, and clinical usefulness. Kaplan–Meier survival curves based on the two radiomics models were used to estimate disease-free survival (DFS).

Results

The fusion radiomics signature of the T2WI, cT1WI, and ADC maps achieved a better predictive efficacy for LVI than either of them alone. The proposed radiomics nomogram, incorporating the fusion radiomics signature and MRI-reported peritumoral edema, showed satisfactory capabilities of calibration and discrimination in both training and validation datasets, with AUCs of 0.919 (95% CI: 0.871–0.967) and 0.863 (95% CI: 0.726–0.999), respectively. The radiomics signature and nomogram-defined high-risk groups had a shorter DFS than those in the low-risk groups (both p < 0.05). Higher Rad-scores were independently associated with a worse DFS in the whole cohort (p < 0.05).

Conclusions

The proposed nomogram, incorporating multiparametric MRI-based radiomics signature and MRI-reported peritumoral edema, achieved a satisfactory preoperative prediction of LVI and clinical outcomes in IDC patients.

Key Points

• The fusion radiomics signature of the T2WI, cT1WI, and ADC maps achieved a better predictive efficacy for LVI than either of them alone.

• The proposed nomogram achieved a favorable prediction of LVI in IDC patients with AUCs of 0.919 and 0.863 in the training and validation datasets, respectively.

• The radiomics model could classify patients into high- and low-risk groups with significant differences in DFS.

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Abbreviations

AIC:

Akaike information criterion

AVS:

Adjacent vessel sign

BCS:

Breast-conserving surgery

cT1WI:

Contrast-enhanced T1-weighted imaging

DCA:

Decision curve analysis

DFS:

Disease-free survival

ICC:

Interclass correlation coefficient

IDC:

Invasive ductal carcinoma

LASSO:

Least absolute shrinkage and selection operator

LVI:

Lymphovascular invasion

mrALN:

MRI-reported axillary lymph nodes

NME:

Nonmass enhancement

T2WI:

T2-weighted imaging

TIC:

Time-intensity curve

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Funding

This study was supported by the National Natural Science Foundation of China (No. 82171923, 82001789 and 81802479), the Applied Basic Research Programs of Shanxi Province (No.201801D121307 and 201801D221390), the Key Research and Development (R&D) Projects of Shanxi Province (No. 201803D31168), the Youth Project of Shanxi Provincial Health Commission (No. 2019058), and the Open Fund from Shanxi Medical University-Collaborative Innovation Center for Molecular Imaging of Precision Medicine (No. 2020-MS01).

Author information

Author notes

  1. Junjie Zhang and Guanghui Wang contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China

    Junjie Zhang, Zhao Yang, Dandan Li, Yanfen Cui & Xiaotang Yang

  2. Department of Breast Surgery, Guizhou Provincial People’s Hospital, Guiyang, 550002, China

    Guanghui Wang

  3. GE Healthcare China, Beijing, China

    Jialiang Ren

Authors
  1. Junjie Zhang
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  2. Guanghui Wang
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  4. Zhao Yang
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Corresponding authors

Correspondence to Yanfen Cui or Xiaotang Yang.

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The scientific guarantor of this publication is Xiaotang Yang.

Conflict of interest

One of the authors (JR) is an employee of GE Healthcare. The remaining authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

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• retrospective

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• performed at one institution

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Zhang, J., Wang, G., Ren, J. et al. Multiparametric MRI-based radiomics nomogram for preoperative prediction of lymphovascular invasion and clinical outcomes in patients with breast invasive ductal carcinoma. Eur Radiol 32, 4079–4089 (2022). https://doi.org/10.1007/s00330-021-08504-6

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