Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis

Abstract

Both cancer and fibrosis are diseases involving dysregulation of cell signaling pathways resulting in an altered cellular microenvironment which ultimately leads to progression of the condition. The two disease entities share common molecular pathophysiology and recent research has illuminated the how each promotes the other. Multiple imaging techniques have been developed to aid in the early and accurate diagnosis of each disease, and given the commonalities between the pathophysiology of the conditions, advances in imaging one disease have opened new avenues to study the other. Here, we detail the most up-to-date advances in imaging techniques for each disease and how they have crossed over to improve detection and monitoring of the other. We explore techniques in positron emission tomography (PET), magnetic resonance imaging (MRI), second generation harmonic Imaging (SGHI), ultrasound (US), radiomics, and artificial intelligence (AI). A new diagnostic imaging tool in PET/computed tomography (CT) is the use of radiolabeled fibroblast activation protein inhibitor (FAPI). SGHI uses high-frequency sound waves to penetrate deeper into the tissue, providing a more detailed view of the tumor microenvironment. Artificial intelligence with the aid of advanced deep learning (DL) algorithms has been highly effective in training computer systems to diagnose and classify neoplastic lesions in multiple organs. Ultimately, advancing imaging techniques in cancer and fibrosis can lead to significantly more timely and accurate diagnoses of both diseases resulting in better patient outcomes.

Keywords: Cancer; Diagnosis; Fibrosis; Imaging techniques; Tumor microenvironment.

Fig. 1

Normal cells are surrounded by blood vessels, collagen fibers, fibroblasts, and other extracellular matrix components. However, during tumor development, CAFs and TGF-β cause ECM alteration, leading to the formation of a stiff fibrotic layer around tumoral cells. This microenvironment facilitates the growth, invasiveness, and treatment resistance of tumoral cells

Fig. 2

69-year-old man with worsening dyspnea. Maximum intensity projection (MIP), axial CT image and fused axial FDG PET/CT demonstrating heterogeneous FDG uptake corresponding to reticular and linear opacities and areas of honeycombing corresponding to pulmonary fibrosis with focal intense left perihilar FDG uptake corresponding to mass on CT, subsequently biopsied and consistent with adenocarcinoma. Multiple additional FDG avid vertebral lesions were consistent with metastases and CT occult on prior conventional imaging. Low level FDG uptake in the periphery correlates with areas of fibrosis on CT

Fig. 3

72-year-old man with pulmonary fibrosis and dyspnea on exertion. Axial CT image and fused axial FDG PET/CT demonstrating areas of bilateral heterogeneous FDG uptake, corresponding to honeycombing and reticular opacities

Fig. 4

Relationship between artificial intelligence, machine learning, and deep learning