Details

Autor(en) / Beteiligte

• Hirsch, Jan‐Michaél
• Sandy, Ronak
• Hasséus, Bengt
• Lindblad, Joakim

Titel

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

Ist Teil von

• Journal of oral pathology & medicine, 2023-10, Vol.52 (9), p.826-833

Ort / Verlag

Copenhagen: Wiley Subscription Services, Inc

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Wiley-Blackwell Full Collection

Beschreibungen/Notizen

• Abstract 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.