Abstract 6188: Pathomics of prostate cancer incorporating histology and mass spectrometry imaging
Authors: Luna José Marcio , Nakhoul Hani , Weimholt Cody , Kim Eric H. , Song Sheng-Kwei , Angel Peggi M. , Drake Richard R. , Ippolito Joseph E. 
Published: March 2024

Abstract
Background: Recent evidence shows that interactions between tumor cells, immune cells, and extracellular matrix (ECM) proteins in the stroma play an important role in prostate cancer (PCa) progression. However, to date these factors are not accounted for in clinical evaluation of PCa patients. Our team has developed methods based on matrix-assisted laser desorption/ionization (MALDI) imaging to identify sets of ECM glycan and collagen molecules associated with PCa. In this study, we leverage computational imaging methods and pathomic textural features to assess the potential of MALDI imaging to guide the identification of regions in PCa hematoxylin and eosin stain (H&E) images as tumor or non-tumor.
Methods: We used a cohort of PCa patients (n=10), who have undergone radical prostatectomy, with grade groups GG3−GG5, and with available H&E and N-glycan MALDI images. We used five MALDI images per patient representing the distribution of N-glycan species at 1419, 2539, 2686, 1663, and 1809 m/z. For each MALDI image, an N-glycan mask was generated and co-registered with the corresponding H&E image via computational imaging methods (i.e., Otsu thresholding, largest connected components labeling, sequential affine and B-spline registration using mutual information). To quantify the overlap of each mask with tumor regions, we calculated Dice scores between the masks and the pathology annotation in H&E. To assess differences in imaging patterns of regions indicated by the different masks, we extracted a set of pathomic textural features (n=70) from the H&E image within each mask using PyRadiomics, and we performed Wilcoxon signed rank tests across all pathomic textural features for all paired combinations of the five masks.
Results: The mean Dice scores between the pathology tumor annotation and the N-glycan masks from 1419 (0.56 [0.44‒0.65]) and 2539 m/z (0.64 [0.59‒0.82]) was significantly higher (p&lt;0.008) than the Dice score of 1663 (0.24 [0.17‒0.36]), 1809 (0.31 [0.13‒0.45]), and 2686 m/z (0.32 [0.19‒0.37]). This suggests a large overlap between 1419 and 2539 m/z and tumor, and large overlap between 1663 and 1809 m/z and non-tumor regions. Consistent with these results, our pathomic textural analysis showed that 50 out of 70 features were significantly different (p&lt;0.05) between the masks derived from 1419 and 1663 m/z, 52 between 1419 and 1809 m/z, 62 between 2539 and 1663 m/z, and 57 between 2539 and 1809 m/z (p&lt;0.05). Moreover, there were only 23 significantly different features between 1419 and 2539 m/z (p&lt;0.05), and 0 features between 1663 and 1809 m/z. Further evaluation is needed for 2686 m/z for which a mean of 54 [45‒64] significantly different features (p&lt;0.05) from all other masks were found.
Conclusions: We provide preliminary evidence that MALDI imaging can elucidate tissue properties and guide pathomic textural analysis in H&E images, with potential applications in treatment guidance and prognostication of PCa.
Citation Format: José Marcio Luna, Hani Nakhoul, Cody Weimholt, Eric H. Kim, Sheng-Kwei Song, Peggi M. Angel, Richard R. Drake, Joseph E. Ippolito. Pathomics of prostate cancer incorporating histology and mass spectrometry imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6188.