A paradigm shift in the prevention and diagnosis of oral squamous cell carcinoma.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is a widespread disease with only 50%-60% 5-year survival. Individuals with potentially malignant precursor lesions are at high risk. METHODS: Survival could be increased by effective, affordable, and simple screening methods, along with a shift from incisional tissue biopsies to non-invasive brush biopsies for cytology diagnosis, which are easy to perform in primary care. Along with the explainable, fast, and objective artificial intelligence characterisation of cells through deep learning, an easy-to-use, rapid, and cost-effective methodology for finding high-risk lesions is achievable. The collection of cytology samples offers the further opportunity of explorative genomic analysis. RESULTS: Our prospective multicentre study of patients with leukoplakia yields a vast number of oral keratinocytes. In addition to cytopathological analysis, whole-slide imaging and the training of deep neural networks, samples are analysed according to a single-cell RNA sequencing protocol, enabling mapping of the entire keratinocyte transcriptome. Mapping the changes in the genetic profile, based on mRNA expression, facilitates the identification of biomarkers that predict cancer transformation. CONCLUSION: This position paper highlights non-invasive methods for identifying patients with oral mucosal lesions at risk of malignant transformation. Reliable non-invasive methods for screening at-risk individuals bring the early diagnosis of OSCC within reach. The use of biomarkers to decide on a targeted therapy is most likely to improve the outcome. With the large-scale collection of samples following patients over time, combined with genomic analysis and modern machine-learning-based approaches for finding patterns in data, this path holds great promise.

Orbital volume and shape in Treacher Collins syndrome.

Orbito-palpebral reconstruction is a challenge in Treacher Collins syndrome (TCS). This study investigates orbital phenotypes in TCS using cephalometry and orbital shape analysis. Eighteen TCS and 52 control patients were included in this study, using the Dr Warehouse database. Orbital cephalometry was based on 20 landmarks, 10 planes, 16 angles, and 22 distances. Orbits were segmented. Registration-based, age-specific mean models were generated using semi-automatic segmentation, and aligned and compared using color-coded distance maps - mean absolute distance (MAD), Hausdorff distance (HD), and Dice similarity coefficient (DSC). Symmetry was assessed by mirroring and DSC computing. Central orbital depth (COD) and medial orbital depth (MOD) allowed 100% of orbits to be classified. COD and lateral orbital depth (LOD) were different from the controls. Average MAD between TCS and controls was ≤1.5 mm, while for HD it was >1.5 mm, and for DSC <1. TCS orbits were more asymmetrical than controls, and orbital volumes were smaller when age was considered as a confounding factor, and had a trend for normalization with age. This report emphasizes the importance of combining different morphometric approaches in the phenotype characterization of non-trivial structures such as the orbit, and supports composite skeletal and soft-tissue strategies for the management of the peri-orbital region.