Virtual biopsy in abdominal pathology: where do we stand?

Abstract

In recent years, researchers have explored new ways to obtain information from pathological tissues, also exploring non-invasive techniques, such as virtual biopsy (VB). VB can be defined as a test that provides promising outcomes compared to traditional biopsy by extracting quantitative information from radiological images not accessible through traditional visual inspection. Data are processed in such a way that they can be correlated with the patient's phenotypic expression, or with molecular patterns and mutations, creating a bridge between traditional radiology, pathology, genomics, and artificial intelligence (AI). Radiomics is the backbone of VB, since it allows the extraction and selection of features from radiological images, feeding them into AI models in order to derive lesions' pathological characteristics and molecular status. Presently, the output of VB provides only a gross approximation of the findings of tissue biopsy. However, in the future, with the improvement of imaging resolution and processing techniques, VB could partially substitute the classical surgical or percutaneous biopsy, with the advantage of being non-invasive, comprehensive, accounting for lesion heterogeneity, and low cost. In this review, we investigate the concept of VB in abdominal pathology, focusing on its pipeline development and potential benefits.

Figure 1.

Scheme of the proposed virtual biopsy pathway. The patient undergoes the MRI imaging process, then the virtual biopsy will be performed instead of a traditional tissue biopsy. Finally, according to the results, the radiologist will write the clinical report processed image.

Figure 2.

The VB steps are divided as following: (a) data collection, where radiological images, clinical and genetic data are acquired; (b) data hormonisation, where all data are normalised according different methods; (c) ROI segmentation: the radiological images are segmented according to the area of interest. The development of VB signature can be performed using Machine Learning, which includes the following steps: (d) features extraction, (e) features selection and (f) model development; if Deep Learning is used, the only necessary steps will be (g) model development. ROI, region of interest; VB, Virtual Biopsy.