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Cohort-Scale Spatial Autocorrelation for Tumor Prediction in Mid-Infrared Pathology and Spatial Biomarker Discovery
Miriam F Rittel1,2, Nikolas Ebert3, Denis Abu Sammour1
1CeMOS Research and Transfer Center, Mass Spectrometry and Optical Spectroscopy, Technische Hochschule Mannheim, Mannheim, Germany.
Abstract:
Mid-infrared (MIR) imaging is an emerging label-free modality for classifying tissue types, including viable tumor in highly heterogeneous cancers, by assessing spatial differences in chemical composition. However, common data analysis neglects spatial vicinity and relies on time-consuming pathological insight for hotspot prediction of viable tumor areas and computational tissue type annotation. Here, we present a method that uses spatial autocorrelation on MIR projection images computed from data of selected wavenumbers found by random forest ranking for computational tissue type annotation: Interdependent data processing enabled high accuracy annotations, whereas referencing of sequentially added new samples to a hyperspectral tissue database ensured computational efficiency and scalability for larger cohorts. Applied to clinical colorectal cancer liver metastasis samples, the method matched manual pathology assessment in a double-blind study. As an option, MIR-based hotspots can be correlated with mass spectrometry imaging. This multimodal approach identified sphingomyelin isoforms as lipidomic tumor marker candidates by imaging parallel reaction monitoring-parallel accumulation serial fragmentation (iprm-PASEF) directly on tissue. Taken together, spatial autocorrelation analysis on MIR imaging data could improve automated accurate annotation of tissue morphologies of heterogeneous cancer specimens and support the discovery of spatial cancer biomarkers.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Scaling

