Tumour irradiation combined with vascular-targeted photodynamic therapy enhances antitumour effects in pre-clinical prostate cancer.

BACKGROUND: There is a need to improve the treatment of prostate cancer (PCa) and reduce treatment side effects. Vascular-targeted photodynamic therapy (VTP) is a focal therapy for low-risk low-volume localised PCa, which rapidly disrupts targeted tumour vessels. There is interest in expanding the use of VTP to higher-risk disease. Tumour vasculature is characterised by vessel immaturity, increased permeability, aberrant branching and inefficient flow. FRT alters the tumour microenvironment and promotes transient 'vascular normalisation'. We hypothesised that multimodality therapy combining fractionated radiotherapy (FRT) and VTP could improve PCa tumour control compared against monotherapy with FRT or VTP. METHODS: We investigated whether sequential delivery of FRT followed by VTP 7 days later improves flank TRAMP-C1 PCa tumour allograft control compared to monotherapy with FRT or VTP. RESULTS: FRT induced 'vascular normalisation' changes in PCa flank tumour allografts, improving vascular function as demonstrated using dynamic contrast-enhanced magnetic resonance imaging. FRT followed by VTP significantly delayed tumour growth in flank PCa allograft pre-clinical models, compared with monotherapy with FRT or VTP, and improved overall survival. CONCLUSION: Combining FRT and VTP may be a promising multimodal approach in PCa therapy. This provides proof-of-concept for this multimodality treatment to inform early phase clinical trials.

Artificial intelligence for advance requesting of immunohistochemistry in diagnostically uncertain prostate biopsies.

The use of immunohistochemistry in the reporting of prostate biopsies is an important adjunct when the diagnosis is not definite on haematoxylin and eosin (H&E) morphology alone. The process is however inherently inefficient with delays while waiting for pathologist review to make the request and duplicated effort reviewing a case more than once. In this study, we aimed to capture the workflow implications of immunohistochemistry requests and demonstrate a novel artificial intelligence tool to identify cases in which immunohistochemistry (IHC) is required and generate an automated request. We conducted audits of the workflow for prostate biopsies in order to understand the potential implications of automated immunohistochemistry requesting and collected prospective cases to train a deep neural network algorithm to detect tissue regions that presented ambiguous morphology on whole slide images. These ambiguous foci were selected on the basis of the pathologist requesting immunohistochemistry to aid diagnosis. A gradient boosted trees classifier was then used to make a slide-level prediction based on the outputs of the neural network prediction. The algorithm was trained on annotations of 219 immunohistochemistry-requested and 80 control images, and tested by threefold cross-validation. Validation was conducted on a separate validation dataset of 222 images. Non IHC-requested cases were diagnosed in 17.9 min on average, while IHC-requested cases took 33.4 min over multiple reporting sessions. We estimated 11 min could be saved on average per case by automated IHC requesting, by removing duplication of effort. The tool attained 99% accuracy and 0.99 Area Under the Curve (AUC) on the test data. In the validation, the average agreement with pathologists was 0.81, with a mean AUC of 0.80. We demonstrate the proof-of-principle that an AI tool making automated immunohistochemistry requests could create a significantly leaner workflow and result in pathologist time savings.