Abnormality detection in soft tissues: multivariate outlier framework based on multi-mechanical characterization using indentation.

Fast and accurate detection of abnormalities, such as tumor nodules, in soft tissue is a critical step toward effective cancer diagnosis. Clinical examples include the use of tactile feedback during digital rectal examination for prostate cancer screening and intraoperative tumor localization. However, the absence of robust mechanical characterization and detection methods limits the clinical applicability of these techniques. In this study, we investigate instrumented indentation as a tool for detecting tumor-mimicking nodules embedded within porcine liver tissue models. Multi-mechanical characterization, including hyperelasticity, viscoelasticity, and dynamic indentation, was performed to capture the mechanical response of the tissue at different points across its surface. A multivariate statistical outlier detection approach, based on Mahalanobis distance, was applied to assess the effectiveness of different mechanical metrics in identifying embedded nodules. The results demonstrate that this outlier detection framework reliably identifies stiff nodules within one to two standard deviations, offering a promising, clinically relevant method for soft tissue cancer detection.