Mapping the Intratumoral and Peritumoral Microenvironment: Multilayered Shell ADC Analysis and Its Association with Multiparametric Biomarkers in Invasive Breast Cancer

Tomography · Published 2026-03-31 · DOI 10.3390/tomography12040047

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Abstract

<b>Objective:</b> This study aimed to investigate the associations between intratumoral and peritumoral apparent diffusion coefficient (ADC) measurements and multiparametric biological markers in invasive breast cancer using a novel peritumoral analysis approach. <b>Materials and Methods:</b> In this retrospective study, 68 patients underwent 1.5 T breast magnetic resonance imaging. Following volumetric tumor segmentation, the peritumoral environment was analyzed using a segmentation-based, improved multilayered concentric shell model at distances of 0–2, 2–5, and 5–10 mm. The ADC values were normalized to contralateral parenchyma (rADC), and the intratumoral-to-peritumoral ADC ratios were calculated. Parameters were correlated with molecular subtypes, axillary metastasis, lymphovascular invasion (LVI), histologic grade, and Ki-67 index. <b>Results</b>: Lower intratumoral ADC and lower intratumoral-to-peritumoral ADC ratios were significantly associated with higher histologic grade, increased Ki-67, and axillary metastasis (<i>p</i> < 0.05). The 0–2 mm shell, representing the immediate invasion front, demonstrated the strongest associations with lymphovascular invasion and nodal involvement, while distance-dependent attenuation of effect sizes was observed across more distal peritumoral layers. <b>Conclusions:</b> The segmentation-based and improved multilayered shell model effectively captures the distance-dependent biological gradient of the peritumoral microenvironment. The intratumoral-to-peritumoral ADC ratios within the immediate 2 mm zone may provide complementary information regarding imaging markers of tumor aggressiveness when interpreted alongside absolute measurements. These findings suggest a potential role for these parameters as complementary imaging markers in preoperative risk stratification within a multiparametric framework.

Abstract from DOAJ. Public domain (CC0 1.0).

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Publication details

Year
2026

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