Rationale and Objectives
Peritumoral features have been suggested to be useful in improving the prediction
performance of radiomic models. The aim of this study is to systematically investigate
the prediction performance improvement for sentinel lymph node (SLN) status in breast
cancer from peritumoral features in radiomic analysis by exploring the effect of peritumoral
region sizes.
Materials and Methods
This retrospective study was performed using dynamic contrast-enhanced MRI scans of
162 breast cancer patients. The effect of peritumoral features was evaluated in a
radiomics pipeline for predicting SLN metastasis in breast cancer. Peritumoral regions
were generated by dilating the tumor regions-of-interest (ROIs) manually annotated
by two expert radiologists, with thicknesses of 2 mm, 4 mm, 6 mm, and 8 mm. The prediction
models were established in the training set (∼67% of cases) using the radiomics pipeline
with and without peritumoral features derived from different peritumoral thicknesses.
The prediction performance was tested in an independent validation set (the remaining
∼33%).
Results
For this specific application, the accuracy in the validation set when using the two
radiologists’ ROIs could be both improved from 0.704 to 0.796 by incorporating peritumoral
features. The choice of the peritumoral size could affect the level of improvement.
Conclusion
This study systematically investigates the effect of peritumoral region sizes in radiomic
analysis for prediction performance improvement. The choice of the peritumoral size
is dependent on the ROI drawing and would affect the final prediction performance
of radiomic models, suggesting that peritumoral features should be optimized in future
radiomics studies.
Key Words
Abbreviations:
AUC (area under the receiver operating characteristic curve), DCE-MRI (dynamic contrast-enhanced MRI), LASSO (least absolute shrinkage selection operator), NPV (negative predictive value), ROC (receiver operating characteristic), ROI (region-of-interest), SER (signal enhancement ratio), SLN (sentinel lymph node), TE (echo time), TR (repetition time)To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Academic RadiologyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Radiomics: extracting more information from medical images using advanced feature analysis.Eur J Cancer. 2012; 48: 441-446
- Radiomics: images are more than pictures, they are data.Radiology. 2015; 278: 563-577
- Radiomics: the bridge between medical imaging and personalized medicine.Nat Rev Clin Oncol. 2017; 14: 749-762
- MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, Oncotype DX, and PAM50 gene assays.Radiology. 2016; 281: 382-391
- Radiomics signature on magnetic resonance imaging: association with disease-free survival in patients with invasive breast cancer.Clin Cancer Res. 2018; 24: 4705-4714
- Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography.J Magn Reson Imag. 2017; 46: 604-616
- Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set.NPJ Breast Cancer. 2016; 2: 16012
- An MRI-based radiomics classifier for preoperative prediction of Ki-67 status in breast cancer.Acad Radiol. 2018; 25: 1111-1117
- Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer.Eur Radiol. 2019; 29: 3820-3829
- Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI.Eur Radiol. 2018; 28: 582-591
- Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI.J Magn Reson Imag. 2019; 49: 131-140
- Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy.J Magn Reson Imag. 2016; 44: 1107-1115
- Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.Breast Cancer Res. 2017; 19: 57
- Association of peritumoral radiomics with tumor biology and pathologic response to preoperative targeted therapy for HER2 (ERBB2)–positive breast cancer.JAMA Network Open. 2019; 2 (e192561-e)
- Axillary lymph nodes and breast cancer. A review.Cancer. 1995; 76: 1491-1512
- Clinical significance of angiogenic factors in breast cancer.Prognostic variables in node-negative and node-positive breast cancer. Springer, Boston, MA1998: 249-263
- Prognostic significance of peritumoral lymphatic and blood vessel invasion in node-negative carcinoma of the breast.J Clin Oncol. 1990; 8: 1457-1465
- Tumor microvessel density, p53 expression, tumor size, and peritumoral lymphatic vessel invasion are relevant prognostic markers in node-negative breast carcinoma.J Clin Oncol. 1994; 12: 454-466
- Lymphangiogenesis and cancer.Genes Cancer. 2011; 2: 1146-1158
- Tumor-infiltrating lymphocytes in breast cancer: ready for prime time?.J Clin Oncol. 2015; 33: 1298-1299
- Why the stroma matters in breast cancer: insights into breast cancer patient outcomes through the examination of stromal biomarkers.Cell Adhesion Migration. 2012; 6: 249-260
- Peritumoral edema on MRI at initial diagnosis: an independent prognostic factor for glioblastoma?.Eur J Neurol. 2009; 16: 874-878
- Focal breast edema associated with malignancy on T2-weighted images of breast MRI: peritumoral edema, prepectoral edema, and subcutaneous edema.Breast Cancer. 2015; 22: 66-70
- Pretreatment MR imaging features of triple-negative breast cancer: association with response to neoadjuvant chemotherapy and recurrence-free survival.Radiology. 2016; 281: 392-400
- Correlation of radiomic features with PD-L1 expression in early stage non-small cell lung cancer (ES-NSCLC) to predict recurrence and overall survival (OS).Journal of Clinical Oncology. 2018; 36: e24247
- Perinodular and intranodular radiomic features on lung CT images distinguish adenocarcinomas from granulomas.Radiology. 2019; 290: 783-792
- Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC.PloS One. 2018; 13e0206108
- Peritumoral tissue on preoperative imaging reveals microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis.Abdominal Radiol. 2018; 43: 3324-3330
- A freeware for tumor heterogeneity characterization in PET, SPECT, CT, MRI and US to accelerate advances in radiomics.J Nucl Med. 2017; 58 (1316-)
- Textured image segmentation.University of Southern California, Los Angeles, California1980
- Co-occurrence of local anisotropic gradient orientations (CoLlAGe): a new radiomics descriptor.Sci Rep. 2016; 6: 37241
- ADASYN: adaptive synthetic sampling approach for imbalanced learning.in: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). IEEE, Hong Kong2008: 1322-1328
- Task-based assessment of a convolutional neural network for segmenting breast lesions for radiomic analysis.Magn Reson Med. 2019; 82: 786-795
Article info
Publication history
Published online: November 04, 2020
Accepted:
October 10,
2020
Received in revised form:
October 5,
2020
Received:
August 26,
2020
Identification
Copyright
© 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
