Peritumoral Radiomic Deviation on DCE-MRI Exceeds the Bilateral Normal-Appearing Parenchymal Distance in Breast Cancer: A Within-Patient Multi-Metric Study

Background: The imaging-defined peritumoral region may contain quantitative imaging information beyond that captured by the tumor core, but its radiomic deviation from normal-appearing breast parenchyma remains insufficiently characterized. This retrospective single-center study investigated whether peritumoral-to-normal radiomic distances exceed the within-patient bilateral normal-appearing parenchymal reference distance. Fifty-six patients with histopathologically confirmed breast cancer and preoperative bilateral dynamic contrast-enhanced MRI were included. Methods: Single-slice radiomic analysis was performed for four predefined regions: tumor core (T), peritumoral microenvironment (M), remote ipsilateral normal-appearing parenchyma (I), and contralateral normal-appearing parenchyma (C). After quality control, 101 radiomic features were standardized and analyzed using standardized Euclidean, Manhattan, cosine, and Ledoit–Wolf Mahalanobis distances. Peritumoral-to-ipsilateral (dMI) and peritumoral-to-contralateral (dMC) distances were compared with the within-patient bilateral normal-appearing parenchymal distance (dIC). Results: Across all four metrics, both dMI and dMC exceeded dIC, and all eight primary paired Wilcoxon comparisons remained significant after Benjamini–Hochberg correction (q≤1.07×10−6), with matched-pairs rank-biserial correlations ranging from 0.749 to 0.989. The principal pattern was broadly preserved across alternative Mahalanobis covariance formulations, reduced-dimensional PCA analyses, particularly when three or four principal components were retained, exclusion of all 13 retained shape features, separate radiomic feature-family analyses, and replacement of selected non-normalized non-uniformity features with normalized counterparts. Conclusions: These findings show that, in the present cohort, peritumoral-to-normal radiomic distances exceeded the within-patient bilateral normal-appearing parenchymal reference distance. Because the four analyzed regions differed in size, shape, and sampling geometry, the reported distances should be interpreted as region-level radiomic differences under the present ROI definitions rather than as geometry-independent tissue properties. Larger multi-institutional studies using full-volume three-dimensional radiomics and standardized imaging and preprocessing strategies are needed to assess reproducibility, clarify the biological correlates of this radiomic pattern, and evaluate its potential clinical relevance.

Authors

Institutions

Publication Details

Journal
Cancers
Published
2026-10-16
DOI
https://doi.org/10.3390/cancers18203347
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Peritumoral Radiomic Deviation on DCE-MRI Exceeds the Bilateral Normal-Appearing Parenchymal Distance in Breast Cancer: A Within-Patient Multi-Metric Study

Özgür Çakır, Kağan Gökçe, Zeynep Hilal Kilimci, Demet Doğan et al.
Cancers
Radiomics and Machine Learning in Medical Imaging
article

Peritumoral Radiomic Deviation on DCE-MRI Exceeds the Bilateral Normal-Appearing Parenchymal Distance in Breast Cancer: A Within-Patient Multi-Metric Study

Özgür Çakır, Kağan Gökçe, Zeynep Hilal Kilimci, Demet Doğan, Oğuzhan Urhan
article en

Abstract

Background: The imaging-defined peritumoral region may contain quantitative imaging information beyond that captured by the tumor core, but its radiomic deviation from normal-appearing breast parenchyma remains insufficiently characterized. This retrospective single-center study investigated whether peritumoral-to-normal radiomic distances exceed the within-patient bilateral normal-appearing parenchymal reference distance. Fifty-six patients with histopathologically confirmed breast cancer and preoperative bilateral dynamic contrast-enhanced MRI were included. Methods: Single-slice radiomic analysis was performed for four predefined regions: tumor core (T), peritumoral microenvironment (M), remote ipsilateral normal-appearing parenchyma (I), and contralateral normal-appearing parenchyma (C). After quality control, 101 radiomic features were standardized and analyzed using standardized Euclidean, Manhattan, cosine, and Ledoit–Wolf Mahalanobis distances. Peritumoral-to-ipsilateral (dMI) and peritumoral-to-contralateral (dMC) distances were compared with the within-patient bilateral normal-appearing parenchymal distance (dIC). Results: Across all four metrics, both dMI and dMC exceeded dIC, and all eight primary paired Wilcoxon comparisons remained significant after Benjamini–Hochberg correction (q≤1.07×10−6), with matched-pairs rank-biserial correlations ranging from 0.749 to 0.989. The principal pattern was broadly preserved across alternative Mahalanobis covariance formulations, reduced-dimensional PCA analyses, particularly when three or four principal components were retained, exclusion of all 13 retained shape features, separate radiomic feature-family analyses, and replacement of selected non-normalized non-uniformity features with normalized counterparts. Conclusions: These findings show that, in the present cohort, peritumoral-to-normal radiomic distances exceeded the within-patient bilateral normal-appearing parenchymal reference distance. Because the four analyzed regions differed in size, shape, and sampling geometry, the reported distances should be interpreted as region-level radiomic differences under the present ROI definitions rather than as geometry-independent tissue properties. Larger multi-institutional studies using full-volume three-dimensional radiomics and standardized imaging and preprocessing strategies are needed to assess reproducibility, clarify the biological correlates of this radiomic pattern, and evaluate its potential clinical relevance.

CancersVol. 18(20)
TUBITAK BILGEM (TR), Teknoloji Arastirma ve Gelistirme Endustriyel Urunler Bilisim Teknolojileri San Tic (TR), Kocaeli Üniversitesi (TR), Okan University (TR)
Good health and well-being
Openalex Percentile: Top 13%
Radiomics and Machine Learning in Medical Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.