  The diagnosis of prostate cancer is challenging due to the heterogeneity of
its presentations, leading to the over diagnosis and treatment of
non-clinically important disease. Accurate diagnosis can directly benefit a
patient's quality of life and prognosis. Towards addressing this issue, we
present a learning model for the automatic identification of prostate cancer.
While many prostate cancer studies have adopted Raman spectroscopy approaches,
none have utilised the combination of Raman Chemical Imaging (RCI) and other
imaging modalities. This study uses multimodal images formed from stained
Digital Histopathology (DP) and unstained RCI. The approach was developed and
tested on a set of 178 clinical samples from 32 patients, containing a range of
non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples.
For each histological sample, there is a pathologist labelled DP - RCI image
pair. The hypothesis tested was whether multimodal image models can outperform
single modality baseline models in terms of diagnostic accuracy. Binary
non-cancer/cancer models and the more challenging G3/G4 differentiation were
investigated. Regarding G3/G4 classification, the multimodal approach achieved
a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model
showed a sensitivity and specificity of 54.1% and 84.7% respectively. The
multimodal approach demonstrated a statistically significant 12.7% AUC
advantage over the baseline with a value of 85.8% compared to 73.1%, also
outperforming models based solely on RCI and median Raman spectra. Feature
fusion of DP and RCI does not improve the more trivial task of tumour
identification but does deliver an observed advantage in G3/G4 discrimination.
Building on these promising findings, future work could include the acquisition
of larger datasets for enhanced model generalization.
