MRI-Based Radiomics Reveals Cannabinoid-Associated Tumor Phenotypes in a Murine Breast Cancer Model

INTRODUCTION AND AIM: THC full extract, exhibit diverse and potentially subtle effects on tumor biology. Radiomics enables high-throughput extraction of quantitative imaging features that capture intratumoral heterogeneity beyond gross tumor volume. The primary aim of this study was to evaluate the utility of MRI-based radiomics as a sensitive tool for detecting cannabinoid-associated tumor phenotypic modulation in a preclinical breast cancer model. METHODS: Orthotopic breast tumors were induced in mice using the 4T1 cell line. Animals received cannabinoid formulations in combination with chemotherapy according to a predefined protocol. Tumor burden was assessed at baseline and post-treatment using ultrasonography and whole-body MRI to calculate tumor doubling time. T1- and T2-weighted MRI datasets were segmented and analyzed using radiomics to extract morphometric and signal-based features. RESULTS: THC full extract. In contrast, radiomics identified distinct, compound-specific tumor phenotypes, including structural features consistent with reduced aggressiveness, in JWH-182-treated tumors, despite similar volumetric growth patterns. CONCLUSION: MRI-based radiomics sensitively captures cannabinoid-associated tumor phenotype alterations beyond volumetric assessment, supporting its value as a pharmaco-imaging tool for characterizing treatment-related tumor biology in preclinical oncology.

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Publication Details

Journal
Molecules
Published
2026-03-31
DOI
https://doi.org/10.3390/molecules31071154
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

MRI-Based Radiomics Reveals Cannabinoid-Associated Tumor Phenotypes in a Murine Breast Cancer Model

Debbie Anaby, Andrei Szilagyi, Cristina Mariana Urîtu, Kfir M. Cohen et al.
Molecules
Radiomics and Machine Learning in Medical Imaging
article

MRI-Based Radiomics Reveals Cannabinoid-Associated Tumor Phenotypes in a Murine Breast Cancer Model

Debbie Anaby, Andrei Szilagyi, Cristina Mariana Urîtu, Kfir M. Cohen, Wael Jalloul, Ioana Creangă-Murariu, Raluca-Maria Gogu, Teodora Alexa-Stratulat, Dragos-Constantin ANITĂ, Adriana-Elena Anita, Mitica Ciorpac, Radu-Andrei Baisan, Cosmin-Vasilica Pricope, Bogdan-Ionel Tamba
article en

Abstract

INTRODUCTION AND AIM: THC full extract, exhibit diverse and potentially subtle effects on tumor biology. Radiomics enables high-throughput extraction of quantitative imaging features that capture intratumoral heterogeneity beyond gross tumor volume. The primary aim of this study was to evaluate the utility of MRI-based radiomics as a sensitive tool for detecting cannabinoid-associated tumor phenotypic modulation in a preclinical breast cancer model. METHODS: Orthotopic breast tumors were induced in mice using the 4T1 cell line. Animals received cannabinoid formulations in combination with chemotherapy according to a predefined protocol. Tumor burden was assessed at baseline and post-treatment using ultrasonography and whole-body MRI to calculate tumor doubling time. T1- and T2-weighted MRI datasets were segmented and analyzed using radiomics to extract morphometric and signal-based features. RESULTS: THC full extract. In contrast, radiomics identified distinct, compound-specific tumor phenotypes, including structural features consistent with reduced aggressiveness, in JWH-182-treated tumors, despite similar volumetric growth patterns. CONCLUSION: MRI-based radiomics sensitively captures cannabinoid-associated tumor phenotype alterations beyond volumetric assessment, supporting its value as a pharmaco-imaging tool for characterizing treatment-related tumor biology in preclinical oncology.

MoleculesVol. 31(7)
Sheba Medical Center (IL), Grigore T. Popa University of Medicine and Pharmacy (RO), Institutul Regional de Oncologie (RO), "Ion Ionescu de la Brad" Iasi University of Life Sciences (RO), Spitalul Clinic Judeţean de Urgenţe "Sf. Spiridon" Iaşi (RO)
Good health and well-being
Openalex Percentile: Top 43%
Radiomics and Machine Learning in Medical Imaging
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