ArticleAcademic radiology2022
Optimizing the Peritumoral Region Size in Radiomics Analysis for Sentinel Lymph Node Status Prediction in Breast Cancer.
Article in Academic radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 2 of them syntheses that pooled it.
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Who cites it
60 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prediction of sentinel lymph node status in patients with early breast cancer using breast imaging as an alternative to surgical staging-a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- Association of peritumoral region features assessed on breast MRI and prognosis of breast cancer: a systematic review and meta-analysis.European radiology · 2024Pooled it
- Development and validation of an ultrasound-based deep learning radiomics nomogram for risk assessment of lymph node metastasis in papillary thyroid microcarcinoma.Quantitative imaging in medicine and surgery · 2026Article
- Integrating intratumoral and peritumoral ultrasound radiomics with clinicopathological features to predict axillary lymph node metastasis in early breast cancer.Gland surgery · 2026Article
- Development and internal validation of mammography feature-based prognostic models for distant recurrence-free survival of invasive breast cancer in a screening cohort.NPJ breast cancer · 2026Article
- Value of intratumoral and peritumoral radiomics based on DCE-MRI in predicting HER2-low and HER2-zero in breast cancer.BMC medical imaging · 2026Article
- Innovative fusion models: elevating preoperative gross ETE prediction in thyroid cancer patients.Scientific reports · 2026Article
- Multiregional MRI-based deep learning radiomics to predict axillary response after neoadjuvant chemotherapy in breast cancer patients.Insights into imaging · 2026Article
- MRI-based intratumoral and peritumoral radiomics predicting neoadjuvant chemotherapy response in osteosarcoma.Frontiers in oncology · 2026Article
- The predictive value ofFrontiers in medicine · 2026Article
- Uncertainty-Aware Machine Learning for Predicting Axillary Lymph Node Metastasis Using Breast MRI.Breast cancer (Dove Medical Press) · 2026Article
- MRI-Based Intratumoral and Multi-Range Peritumoral Radiomics for Predicting Axillary Lymph Node Metastasis in Breast Cancer.International journal of general medicine · 2026Article
- Multi-parametric MRI radiomics predicts different HER2 expression in breast cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Radiogenomic MRI biomarkers for noninvasive prediction of GPC3 expression and tumor microenvironment in hepatocellular carcinoma.Journal of translational medicine · 2025Article
- Intratumoral and peritumoral radiomics based on 2D ultrasound imaging in breast cancer was used to determine the optimal peritumoral range for predicting KI-67 expression.Journal of ultrasound · 2025Article
- Development and validation of a prediction model for lymph node metastasis in thyroid cancer: integrating deep learning and radiomics features from intra- and peri-tumoral regions.Gland surgery · 2025Article
- Multi-modality radiomics diagnosis of breast cancer based on MRI, ultrasound and mammography.BMC medical imaging · 2025Article
- Development of radiomics-based models on mammograms with mass lesions to predict prognostically relevant characteristics of invasive breast cancer in a screening cohort.British journal of cancer · 2025Article
- Predictive value of tumoral and peritumoral radiomic features in neoadjuvant chemotherapy response for breast cancer: a retrospective study.La Radiologia medica · 2025Article
- Integrative radiomics analysis of peri-tumoral and habitat zones for predicting major pathological response to neoadjuvant immunotherapy and chemotherapy in non-small cell lung cancer.Translational lung cancer research · 2025Article
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9 authors.
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Abstract
RATIONALE AND
objectivesPeritumoral 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
methodsThis 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%).
resultsFor 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.
conclusionThis 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.
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