<p>Precise cancer lesion analysis in medical imaging critically depends on the accurate definition of regions of interest (ROIs), which directly influence diagnostic and clinical outcomes. While peritumoral features are known to enhance lesion characterization, efficiently defining meaningful peritumoral ROIs remains a challenge. We propose an adaptive peritumoral area selection approach (APASA) that systematically identifies the most informative ROI surrounding a lesion, enabling the extraction of meaningful radiomic features for improved diagnostic performance. Unlike conventional heuristic or morphology-based methods, APASA leverages the minimum coverage graph algorithm, using the tumor ROI as a reference to construct a graph encompassing both the tumor and its peritumoral microenvironment. The effectiveness of the proposed approach was evaluated within AI-based frameworks for automated lesion differentiation in breast and thyroid cancers. Extensive experiments employing five widely used machine learning models demonstrated that APASA-selected peritumoral features consistently outperformed conventional morphological dilation. Performance improvements reached up to 30.75% in AUC and 29.00% in F1-score compared with the tumor ROI baseline. Moreover, the optimal model was found to vary depending on the ROI type, shape, and cancer type, offering new insights into the interaction between ROI selection and model choice. These results highlight APASA as a principled and efficient strategy for adaptive ROI definition in ultrasound-based cancer lesion analysis, demonstrating effectiveness across two ultrasound datasets, with potential extension to other imaging modalities and clinical settings pending further validation.</p>