Approaching virtual osteoid volume estimation and in-depth tissue characterization in patients with tumor-induced osteomalacia.

Abstract

Tumor-induced osteomalacia (TIO) poses a significant diagnostic challenge, leading to increased disease duration and patient burden also by missing clinical suspicion. Today, diagnosis of osteomalacia relies on invasive iliac crest biopsy, if needed. Therefore, a noninvasive method would be beneficial for patients with severe osteomalacia, such as TIO, to inform their clinical management and address specific needs, like estimating the regeneration capacity at high osteoid volumes (OVs) or the potential of a hungry bone syndrome after tumor removal. Furthermore, given the lack of comprehensive histological characterization of TIO, there is a need for additional tissue characterization. Therefore, our assessment encompassed iliac crest biopsies that were examined using quantitative electron backscattered microscopy, Raman spectroscopy, micro-computed tomography, and histology to analyze the biopsy tissue. Our clinical assessment encompassed DXA and high-resolution peripheral quantitative computed tomography (HR-pQCT) alongside with biochemical analyses and clinical evaluations. Combining imaging and clinical data, we established a model to predict the OV. We compared 9 TIO patients with 10 osteoporosis (OPO) patients and 10 healthy controls. Histological analyses confirmed a pronounced OV in TIO patients (OPO: 1.20% ± 1.23% vs TIO: 23.55% ± 12.23%, P < .0005), and spectroscopy revealed lower phosphate levels in TIO biopsies. By combining HR-pQCT and laboratory diagnostics, we developed a linear regression model to noninvasively predict the OV revealing significantly higher modeled OV/BVmodel values of 24.46% ± 14.22% for TIO compared to the control group (5.952% ± 3.44%, P ≤ .001). By combining laboratory diagnostics, namely, ALP and Tt.BMDRadius measured by HR-pQCT, we achieved the calculation of the virtual osteoid volume to bone volume ratio (OV/BVmodel) with a significant correlation to histology as well as reliable identification of TIO patients compared to OPO and control. This novel approach is potentially helpful for predicting OV by noninvasive techniques in diagnostic procedures and improving the clinical management of TIO.