Alexios Thodas

Phyllodes tumours (PTs) of the breast are rare fibroepithelial neoplasms with potentially aggressive behaviour, characterised by rapid growth, a significant risk of local recurrence, and occasional metastatic spread. Optimal management remains controversial, particularly regarding surgical margins, adjuvant radiotherapy, and the relevance of molecular markers in predicting tumour behaviour. A PRISMA 2020-guided qualitative systematic review was conducted of studies published between January 2000 and December 2024 in PubMed/MEDLINE, Scopus, and Web of Science. Eligible studies included malignant PTs of the breast and addressed at least one of the following domains: molecular pathology, surgical margins and local recurrence, adjuvant radiotherapy, or predictors of recurrence and metastasis. A clinical case of malignant PT treated at our institution is presented as an illustrative study. Thirty-four studies met the inclusion criteria. Evidence suggests that margin status, stromal proliferative activity, and selected molecular markers influence recurrence risk. Several retrospective studies suggest that adjuvant radiotherapy may improve local control in selected high-risk malignant PTs, although the evidence remains heterogeneous, retrospective, and potentially affected by treatment-selection bias, and no consistent survival benefit has been demonstrated. Molecular alterations, including MED12 mutations, TERT promoter mutations, TP53 alterations, and increased Ki-67 expression, have been associated with tumour progression and aggressive behaviour. A 44-year-old woman presented with a 2.4 cm left breast mass on radiological examination. Lumpectomy revealed a malignant PT with stromal hypercellularity, nuclear atypia, and a mitotic index of 20/10 HPF with close margins. Immunohistochemistry showed positivity for CD99, Bcl-2, and CD34 with a Ki-67 proliferation index of 20%. The patient underwent wide local re-excision followed by adjuvant radiotherapy (60 Gy), and at 24-month follow-up, the patient remained disease-free. Evidence synthesis highlights the importance of complete surgical excision, multidisciplinary management, and consideration of adjuvant radiotherapy in selected malignant PTs. Emerging molecular profiling may contribute to improved biological understanding and future risk stratification of malignant PTs, although its routine clinical utility remains to be validated in prospective studies.

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.