Differentiating tumor specimens by polarimetric speckle pattern analysis

Definite diagnosis of brain neoplasms has been a challenge of neurosurgery practice especially when open surgery is not feasible to obtain adequate tissue samples for pathological investigation (e.g., for deep-seated tumors or tumors of eloquent areas undergoing biopsies or recurrent cases in patients that are candidates for palliative care). This complexity is exacerbated due to tumor heterogeneity, particularly when relying on limited samples obtained through stereotactic biopsies, which increases the risk of misdiagnosis. Addressing this critical issue, we propose a novel, label-free diagnostic approach by combining the strengths of polarimetry and speckle pattern analysis. This research introduces an innovative method that integrates Mueller matrix polarimetry with dynamic laser speckle analysis, offering a promising avenue for precise differentiation of biomedical samples and tissues. We validate the effectiveness of our technique by calculating the Mueller matrix images of the two various subtypes of brain neoplasms, followed by speckle pattern analysis through both numerical and graphical statistical post-processing. The results indicate considerable discrimination between the tissues, highlighting the potential of this methodology as an easily implementable non-invasive quantitative tool for analyzing extensive cancerous specimens.