The diagnostic and prognostic evaluation of follicular thyroid nodules is complex, and conventional 2D slice histology suing H&E staining is inherently limited by sampling constraints. The reliance on morphological features like capsular and vascular invasion can sometimes lead to misdiagnosis, either underestimating or overestimating the severity of the condition. Consequently, unexpected tumor recurrence and overtreatment represent significant clinical challenges. To mitigate these limitations and enable precision medicine and diagnostics, emerging diagnostic approaches are exploring advanced imaging modalities. Whereas for the 3D imaging and ex-vivo evaluation of human tissues in biomedical and pathology applications of conventional clinical CT fails to produce sufficient contrast and visibility, X-ray phase-contrast micro-CT based 3D virtual histology enables non-destructive and comprehensive sampling of the entire tumor capsule in the formalin-fixed paraffin-embedded (FFPE) blocks. It brings clear potential clinical benefits as was shown by our research [1,2] furthermore it requires no further sample preparation of the FFPE blocks and preserves them for further analysis thus could be seamlessly integrated in the clinical workflow. These aspects render 3D digital histopathology by X-ray phase-contrast micro-CT to an extremely interesting tool to revolutionize research in tumor diagnostic for precision medicine. Our research extends to complementing and combining 3D X-ray histopathology with machine learning and data science methods as is depicted in the graphical abstract.