Quantitative Diffusion-Weighted MR Imaging: Is There a Prognostic Role in Noninvasively Predicting the Histopathologic Type of Uveal Melanomas?
Pietro Valerio FotiCorrado InìGiuseppe BroggiRenato FarinaStefano PalmucciCorrado SpatolaMaria Chiara Lo GrecoEmanuele DavidRosario CaltabianoLidia PuzzoAndrea RussoAntonio LongoTeresio AvitabileAntonio BasilePublished in: Cancers (2023)
Histopathologically, uveal melanomas (UMs) can be classified as spindle cell, mixed cell and epithelioid cell type, with the latter having a more severe prognosis. The aim of our study was to assess the correlation between the apparent diffusion coefficient (ADC) and the histologic type of UMs in order to verify the role of diffusion-weighted magnetic resonance imaging (DWI) as a noninvasive prognostic marker. A total of 26 patients with UMs who had undergone MRI and subsequent primary enucleation were retrospectively selected. The ADC of the tumor was compared with the histologic type. The data were compared using both one-way analysis of variance (ANOVA) (assessing the three histologic types separately) and the independent t -test (dichotomizing histologic subtypes as epithelioid versus non-epithelioid). Histologic type was present as follows: the epithelioid cell was n = 4, and the spindle cell was n = 11, the mixed cell type was n = 11. The mean ADC was 1.06 ± 0.24 × 10 -3 mm 2 /s in the epithelioid cells, 0.98 ± 0.19 × 10 -3 mm 2 /s in the spindle cells and 0.96 ± 0.26 × 10 -3 mm 2 /s in the mixed cell type. No significant difference in the mean ADC value of the histopathologic subtypes was found, either when assessing the three histologic types separately ( p = 0.76) or after dichotomizing the histologic subtypes as epithelioid and non-epithelioid ( p = 0.82). DWI-ADC is not accurate enough to distinguish histologic types of UMs.
Keyphrases
- diffusion weighted
- contrast enhanced
- diffusion weighted imaging
- magnetic resonance imaging
- computed tomography
- magnetic resonance
- single cell
- cell therapy
- induced apoptosis
- cell cycle arrest
- machine learning
- high resolution
- signaling pathway
- mass spectrometry
- oxidative stress
- stem cells
- bone marrow
- cell proliferation
- artificial intelligence
- deep learning
- electronic health record