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. 2022 Jul;10(13):749.
doi: 10.21037/atm-22-2844.

Development of the prediction model based on clinical-imaging omics: molecular typing and sentinel lymph node metastasis of breast cancer

Xian Wang  1   2 , Xueyang Wang  2   3 , Yanjun Zhang  1 , Dekang Zhang  3 , Zhou Song  1 , Qingyu Meng  1 , Yunjian Li  4 , Chunxi Wang  1
Affiliations

Development of the prediction model based on clinical-imaging omics: molecular typing and sentinel lymph node metastasis of breast cancer

Xian Wang et al. Ann Transl Med. 2022 Jul.

Abstract

Background: Imageology uses high-throughput and automatic computing methods to transform medical image data into quantitative data with feature space, and then makes accurate quantitative analysis, extracts features and builds models, which can intuitively observe the overall features of lesions and the surrounding tissues, and provide rich invisible information. At present, the research on the imaging features of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) to predict the molecular typing value has achieved results, but the imaging model based on DWI and DCE-magnetic resonance imaging (MRI) is not enough to predict the molecular subtypes, and the prediction value of the prediction model based on the three-dimensional volume of interest of the lesion to the four molecular subtypes of breast cancer has not been fully studied.

Methods: The clinical data of 202 breast cancer patients at our hospital from October 2020 to November 2021 were collected. All patients were examined with multimodal MRI before surgery. Base on immunohistochemical recombinant Ki-67 protein (Ki-67), estrogen receptor (ER), human epidermal growth factor receptor-2 (HER-2) and progesterone receptor (PR) results, the tumors were divided into four types According to the results of the sentinel lymph node (SLN) biopsies, the patients were divided into SLN (+) and SLN (-) groups. 3-dimensional (3D) Slicer software was used to outline the region of interest (ROI), and AMni-Kinetics software was used for feature extraction. The imaging characteristics were screened using least absolute shrinkage and selection operator (LASSO)-Logistic regression model using R statistical software, and the receiver operating characteristic (ROC) curve was drawn using "pROC" software package to evaluate the prediction efficiency of the model.

Results: The most efficacious model at predicting SLN (+) in breast cancer patients with different molecular subtypes and SLN metastasis was the model based on the imageological characteristics of fat inhibition, and T2-weighted imaging (T2WI), T1-weighted imaging + C (T1WI-C), and DWI combined sequences at the tumor + 2 mm periphery. AUC (sensitivity, specificity) of the validation group were 0.831 (0.856, 0.891), 0.832 (0.660, 0.877), 0.801 (0.772, 0.765), 0.904 (0.769, 0.873), and 0.819 (0.810, 0.500) respectively when the tumor was 2 mm around the tumor.

Conclusions: The imaging features extracted from multi-parameter DWI, T1WI+C, and T2WI in breast cancer have certain value at predicting different molecular types and SLNs of breast cancer.

Keywords: Clinical imaging; breast cancer; molecular typing; sentinel lymph nodes (SLNs).

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-2844/coif). The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
Artificial sketch of ROI and 3D view of breast tumor (A), artificial sketch of ROI and 3D view of breast tumor + peritumoral (2 mm) (B). ROI, region of interest; 3D, 3-dimensional. The arrows indicate tumor body.
Figure 2
Figure 2
Sentinel lymph node metastasis prediction model during tumor (A) and tumor + peritumoral (2 mm) (B) of ROC curves. AUC, area under the ROC curve; ROC, receiver operator characteristic.
See this image and copyright information in PMC

References

    1. Pekmezi D, Fontaine K, Rogers LQ, et al. Adapting MultiPLe behavior Interventions that eFfectively Improve (AMPLIFI) cancer survivor health: program project protocols for remote lifestyle intervention and assessment in 3 inter-related randomized controlled trials among survivors of obesity-related cancers. BMC Cancer 2022;22:471. 10.1186/s12885-022-09519-y - DOI - PMC - PubMed
    1. Luo C, Lu L, Zhang W, et al. The Value of Shear Wave Elastography in the Diagnosis of Breast Cancer Axillary Lymph Node Metastasis and Its Correlation With Molecular Classification of Breast Masses. Front Oncol 2022;12:846568. 10.3389/fonc.2022.846568 - DOI - PMC - PubMed
    1. Serrano Del Moral Á, Pérez Viejo E, Castaño Pascual Á, et al. Usefulness of histological superstudy of sentinel lymph nodes detected with radioisotopes in colon cancer. Rev Esp Med Nucl Imagen Mol (Engl Ed) 2021;40:358-66. 10.1016/j.remn.2020.12.003 - DOI - PubMed
    1. Omilian AR, Sheng H, Hong CC, et al. Multiplexed digital spatial profiling of invasive breast tumors from Black and White women. Mol Oncol 2022;16:54-68. 10.1002/1878-0261.13017 - DOI - PMC - PubMed
    1. Li Z, Zhong Q, Zhang L, et al. Computed Tomography-Based Radiomics Model to Preoperatively Predict Microsatellite Instability Status in Colorectal Cancer: A Multicenter Study. Front Oncol 2021;11:666786. 10.3389/fonc.2021.666786 - DOI - PMC - PubMed