ArticleDiagnostics (Basel, Switzerland)2022
A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review.International journal of molecular sciences · 2022Pooled it
- Artificial Intelligence for Breast Cancer Molecular Subtype Prediction From Medical Imaging: A Systematic Review of Literature.Technology in cancer research & treatmentPooled it
- RiTex: Harmonization of Radiomic Features Based on Riemannian Geometry.Journal of imaging · 2026Article
- Monitoring Pharmacological Treatment of Breast Cancer with MRI.Current issues in molecular biology · 2025Review
- Multimodality Imaging Features of Papillary Renal Cell Carcinoma.Diagnostics (Basel, Switzerland) · 2025Article
- Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024Review
- The effect of feature normalization methods in radiomics.Insights into imaging · 2024Article
- Collinearity and Dimensionality Reduction in Radiomics: Effect of Preprocessing Parameters in Hypertrophic Cardiomyopathy Magnetic Resonance T1 and T2 Mapping.Bioengineering (Basel, Switzerland) · 2023Article
- Radiomics in Oncology III.Diagnostics (Basel, Switzerland) · 2023Article
- A Framework of Analysis to Facilitate the Harmonization of Multicenter Radiomic Features in Prostate Cancer.Journal of clinical medicine · 2022Article
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10 authors.
Funding
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Abstract
Radiomics is rapidly advancing in precision diagnostics and cancer treatment. However, there are several challenges that need to be addressed before translation to clinical use. This study presents an ad-hoc weighted statistical framework to explore radiomic biomarkers for a better characterization of the radiogenomic phenotypes in breast cancer. Thirty-six female patients with breast cancer were enrolled in this study. Radiomic features were extracted from MRI and PET imaging techniques for malignant and healthy lesions in each patient. To reduce within-subject bias, the ratio of radiomic features extracted from both lesions was calculated for each patient. Radiomic features were further normalized, comparing the z-score, quantile, and whitening normalization methods to reduce between-subjects bias. After feature reduction by Spearman's correlation, a methodological approach based on a principal component analysis (PCA) was applied. The results were compared and validated on twenty-seven patients to investigate the tumor grade, Ki-67 index, and molecular cancer subtypes using classification methods (LogitBoost, random forest, and linear discriminant analysis). The classification techniques achieved high area-under-the-curve values with one PC that was calculated by normalizing the radiomic features via the quantile method. This pilot study helped us to establish a robust framework of analysis to generate a combined radiomic signature, which may lead to more precise breast cancer prognosis.
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