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Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly and delayed decision-making in routine oncology care and treatment. As cancer is expected to rank as the leading cause of death and the single most important barrier to increasing life expectancy in the 21st century, there is a major emphasis on precision medicine, particularly individualisation of treatment through better prediction of patient outcome. Over the past few years, both surgical and pathology specialties have suffered cutbacks and a low uptake of pathology specialists means a solution is required to enable high-throughput screening and personalised treatment in this area to alleviate bottlenecks. Digital imaging in pathology has undergone an exponential period of growth. Deep-learning (DL) platforms for hematoxylin and eosin (H&E) image analysis, with preliminary artificial intelligence (AI)-based grading capabilities of specimens, can evaluate image characteristics which may not be visually apparent to a pathologist and offer new possibilities for better modelling of disease appearance and possibly improve the prediction of disease stage and patient outcome. Although digital pathology and AI are still emerging areas, they are the critical components for advancing personalised medicine. Integration of transcriptomic analysis, clinical information and AI-based image analysis is yet an uncultivated field by which healthcare professionals can make improved treatment decisions in cancer. This short review describes the potential application of integrative AI in offering better detection, quantification, classification, prognosis and prediction of breast and prostate cancer and also highlights the utilisation of machine learning systems in biomarker evaluation.

Aims Neurofibromin 1 (NF1) as a tumour suppressor gene can give rise to several transcripts by an alternative splicing event, generated at least for CELF cofactors. At present, the NF1 isoforms and CELF splicing transcripts in sporadic breast cancer are unknown. The aim of the authors was to detect NF1 gene expression, the NF1 isoform ratio and the CELF transcripts present in sporadic breast cancer. Methods Neurofibromin and RAS expression were analysed on tissue microarrays containing sporadic breast cancer (n=22), benign lesions (n=18, including six fibroadenomas, six fibrocystic changes and six ductal hyperplasias) and normal breast tissue (n=6) by immunohistochemistry assay. NF1 and CELF 3–6 RNA expression was performed by end point reverse transcription-PCR in the breast samples. Results NF1 and RAS expression in breast tissues showed no differential expression by immunohistochemistry results. Interestingly, the authors observed a shift transition in the isoform transcripts, from type II in normal breast tissue to type I isoform in breast carcinomas. CELF cofactor expression failed to be related with the shift transition of NF1 in breast tissues. Conclusions These data suggest that there is a tendency for an NF1 expression shift transition from type II to type I isoform, which could comprise a significant event in the development and progression of sporadic breast cancer. This shift transition may not be related with CELF cofactors.