CancerMate: Imaging-Informed Computational Modelling of Solid Tumour Response

CancerMate is a research and development platform created by iBMB Srls to investigate patient-specific solid tumour dynamics, so far tested to neoadjuvant therapies of breast cancer.

The platform combines diagnostic-imaging measurements, selected biological markers and mechanistic reaction-diffusion models. Its purpose is to explore how measurable tumour metrics may evolve during treatment and to support research into computational oncology and Virtual Human Twins.

Evidence to Date

CancerMate Mark I was retrospectively evaluated using data from 17 patients with early-stage triple-negative breast cancer who received three weeks of Lynparza. The study examined the model’s ability to estimate post-treatment metabolic activity, measured using PET/CT SUVmax, and reported a strong correlation between simulated and observed values (Schettini, F. et al. (2023). Computational reactive–diffusive modeling for stratification and prognosis determination of patients with breast cancer receiving Olaparib. Scientific Reports, 13(1), 11951).

These results provide proof of concept within the studied cohort. Wider independent cohorts and prospective evaluation are still required before the model can be considered for a defined clinical context of use.

Current Development

Ongoing work (Mark II, in collaboration with IRCCS Istituto Nazionale dei Tumori Foundation, Milan, Italy) extends the framework to HER2-positive breast cancer and sequential neoadjuvant therapy (Paclitaxel/Herceptin): in particular, the second therapy stage has been found depending on a Microbiota Diversity Index, determined by a personalized combination of Simpson’s Diversity Index, and Firmicutes and Bacteroidetes concentrations. Then, complete patient-specific workflows will connect diagnostic imaging, three-dimensional geometry and finite-element simulation (Mark III): browse here for a clip regarding a HER2+ patient, treated with EC+Paclitaxel/Herceptin.

Such predictive modeling approach may have transformative implications for breast cancer oncology: we are paving the way for an innovative strategy that harnesses the predictive power of oncology mathematics to optimize therapeutic interventions, ultimately advancing the frontier of personalized oncology care.

Research-Use Notice

CancerMate is presented as a research and technology-development platform. Its outputs do not constitute medical advice and should not be used independently to establish a prognosis, select treatments, or modify patient care, but only to support clinical decision-making.