Virtual Biomarkers and Simplified Metrics in the Modeling of Breast Cancer Neoadjuvant Therapy: A Proof-of-Concept Case Study Based on Diagnostic Imaging

Marino, Graziella; De Bonis, Maria Valeria; Mecca, Marisabel; Sichetti, Marzia; Cammarota, Aldo; Botte, Manuela; Dinardo, Giuseppina; Lancellotti, Maria Imma; Villonio, Antonio; Prudente, Antonella; Thodas, Alexios; Zifarone, Emanuela; Sanseverino, Francesca; Modano, Pasqualina; Schettini, Francesco; Rocca, Andrea; Generali, Daniele; Ruocco, Gianpaolo

doi:10.3390/medsci13040242

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Virtual Biomarkers and Simplified Metrics in the Modeling of Breast Cancer Neoadjuvant Therapy: A Proof-of-Concept Case Study Based on Diagnostic Imaging

by

Graziella Marino

¹

,

Maria Valeria De Bonis

²,

Marisabel Mecca

^3,*

,

Marzia Sichetti

³

,

Aldo Cammarota

⁴,

Manuela Botte

⁴,

Giuseppina Dinardo

⁴,

Maria Imma Lancellotti

⁴,

Antonio Villonio

⁴,

Antonella Prudente

⁵,

Alexios Thodas

¹

,

Emanuela Zifarone

⁶,

Francesca Sanseverino

⁷,

Pasqualina Modano

⁸,

Francesco Schettini

⁹

,

Andrea Rocca

¹⁰,

Daniele Generali

^10,11

and

Gianpaolo Ruocco

^2,12,*

¹

Breast Cancer Unit, Centro di Riferimento Oncologico della Basilicata (IRCCS-CROB), 85028 Rionero in Vulture, Italy

²

Initiatives for Bio-Material Behaviour (Ibmb), 85100 Potenza, Italy

³

Laboratory of Preclinical and Translational Research, Centro di Riferimento Oncologico della Basilicata (IRCCS-CROB), 85028 Rionero in Vulture, Italy

⁴

Diagnostic and Imaging Department, Centro di Riferimento Oncologico Della Basilicata (IRCCS-CROB), 85028 Rionero in Vulture, Italy

⁵

Medical Oncology Unit, Centro di Riferimento Oncologico Della Basilicata (IRCCS-CROB), 85028 Rionero in Vulture, Italy

⁶

Trial Office, Centro di Riferimento Oncologico della Basilicata (IRCCS-CROB), 85028 Rionero in Vulture, Italy

⁷

Unit of Gynecologic Oncology, Centro di Riferimento Oncologico della Basilicata (IRCCS-CROB), 85028 Rionero in Vulture, Italy

⁸

Emergency and Palliative Care Unit, Centro di Riferimento Oncologico della Basilicata (IRCCS-CROB), 85028 Rionero in Vulture, Italy

⁹

Medical Oncology Department, Hospital Clinic of Barcelona, 08036 Barcelona, Spain

¹⁰

Department of Medicine, Surgery and Health Sciences, University of Trieste, Cattinara Hospital, 34128 Trieste, Italy

¹¹

Multidisciplinary Unit of Breast Pathology and Translational Research, Cremona Hospital, 26100 Cremona, Italy

¹²

Modeling and Prototyping Laboratory, College of Engineering, University of Basilicata, 85100 Potenza, Italy

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Med. Sci. 2025, 13(4), 242; https://doi.org/10.3390/medsci13040242 (registering DOI)

Submission received: 3 October 2025 / Revised: 17 October 2025 / Accepted: 20 October 2025 / Published: 24 October 2025

(This article belongs to the Special Issue Feature Papers in Section “Cancer and Cancer-Related Research”)

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Abstract

Background: Neoadjuvant chemotherapy (NAC) is a standard preoperative intervention for early-stage breast cancer (BC). Dynamic contrast-enhanced magnetic resonance imaging (CE-MRI) has emerged as a critical tool for evaluating treatment response and pathological complete response (pCR) following NAC. Computational modeling offers a robust framework to simulate tumor growth dynamics and therapy response, leveraging patient-specific data to enhance predictive accuracy. Despite this potential, integrating imaging data with computational models for personalized treatment prediction remains underexplored. This case study presents a proof-of-concept prognostic tool that bridges oncology, radiology, and computational modeling by simulating BC behavior and predicting individualized NAC outcomes. Methods: CE-MRI scans, clinical assessments, and blood samples from three retrospective NAC patients were analyzed. Tumor growth was modeled using a system of partial differential equations (PDEs) within a reaction–diffusion mass transfer framework, incorporating patient-specific CE-MRI data. Tumor volumes measured pre- and post-treatment were compared with model predictions. A 20% error margin was applied to assess computational accuracy. Results: All cases were classified as true positive (TP), demonstrating the model’s capacity to predict tumor volume changes within the defined threshold, achieving 100% precision and sensitivity. Absolute differences between predicted and observed tumor volumes ranged from 0.07 to 0.33 cm³. Virtual biomarkers were employed to quantify novel metrics: the biological conversion coefficient ranged from 4 × 10⁻⁷ to 6 × 10⁻⁶ s^-1, while the pharmacodynamic efficiency coefficient ranged from 1 × 10⁻⁷ to 4 × 10⁻⁴ s^-1, reflecting intrinsic tumor biology and treatment effects, respectively. Conclusions: This approach demonstrates the feasibility of integrating CE-MRI and computational modeling to generate patient-specific treatment predictions. Preliminary model training on retrospective cohorts with matched BC subtypes and therapy regimens enabled accurate prediction of NAC outcomes. Future work will focus on model refinement, cohort expansion, and enhanced statistical validation to support broader clinical translation.

Keywords: breast cancer; neoadjuvant therapy; biomarker; reactive–diffusive modeling; multidimensional modeling; diagnostic imaging; computational prognosis

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MDPI and ACS Style

Marino, G.; De Bonis, M.V.; Mecca, M.; Sichetti, M.; Cammarota, A.; Botte, M.; Dinardo, G.; Lancellotti, M.I.; Villonio, A.; Prudente, A.; et al. Virtual Biomarkers and Simplified Metrics in the Modeling of Breast Cancer Neoadjuvant Therapy: A Proof-of-Concept Case Study Based on Diagnostic Imaging. Med. Sci. 2025, 13, 242. https://doi.org/10.3390/medsci13040242

AMA Style

Marino G, De Bonis MV, Mecca M, Sichetti M, Cammarota A, Botte M, Dinardo G, Lancellotti MI, Villonio A, Prudente A, et al. Virtual Biomarkers and Simplified Metrics in the Modeling of Breast Cancer Neoadjuvant Therapy: A Proof-of-Concept Case Study Based on Diagnostic Imaging. Medical Sciences. 2025; 13(4):242. https://doi.org/10.3390/medsci13040242

Chicago/Turabian Style

Marino, Graziella, Maria Valeria De Bonis, Marisabel Mecca, Marzia Sichetti, Aldo Cammarota, Manuela Botte, Giuseppina Dinardo, Maria Imma Lancellotti, Antonio Villonio, Antonella Prudente, and et al. 2025. "Virtual Biomarkers and Simplified Metrics in the Modeling of Breast Cancer Neoadjuvant Therapy: A Proof-of-Concept Case Study Based on Diagnostic Imaging" Medical Sciences 13, no. 4: 242. https://doi.org/10.3390/medsci13040242

APA Style

Marino, G., De Bonis, M. V., Mecca, M., Sichetti, M., Cammarota, A., Botte, M., Dinardo, G., Lancellotti, M. I., Villonio, A., Prudente, A., Thodas, A., Zifarone, E., Sanseverino, F., Modano, P., Schettini, F., Rocca, A., Generali, D., & Ruocco, G. (2025). Virtual Biomarkers and Simplified Metrics in the Modeling of Breast Cancer Neoadjuvant Therapy: A Proof-of-Concept Case Study Based on Diagnostic Imaging. Medical Sciences, 13(4), 242. https://doi.org/10.3390/medsci13040242

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Virtual Biomarkers and Simplified Metrics in the Modeling of Breast Cancer Neoadjuvant Therapy: A Proof-of-Concept Case Study Based on Diagnostic Imaging

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