Artificial intelligence for HPV status prediction based on disease-specific images in head and neck cancer: A systematic review and meta-analysis

HPV16 Phylogenetic Variants in Anogenital and Head and Neck Cancers: State of the Art and Perspectives

HPV16 is responsible for approximately 60% and 90% of global HPV-induced cervical and oropharyngeal cancers, respectively. HPV16 intratype variants have been identified by HPV genome sequencing and classified into four phylogenetic lineages (A-D). Our understanding of HPV16 variants mostly derives from epidemiological studies on cervical cancer (CC) in which HPV16 B, C, and D lineages (previously named "non-European" variants) were mainly associated with high-grade cervical lesions and cancer. Although a predominance of HPV16 lineage A (previously named "European variants") has been observed in head and neck squamous cell carcinoma (HNSCC), epidemiological and in vitro biological studies are still limited for this tumor site. Next Generation Sequencing (NGS) of the entire HPV genome has deepened our knowledge of the prevalence and distribution of HPV variants in CC and HNSCC. Research on cervical cancer has shown that certain HPV16 sublineages, such as D2, D3, A3, and A4, are associated with an increased risk of cervical cancer, and sublineages A4, D2, and D3 are linked to a higher risk of developing adenocarcinomas. Additionally, lineage C and sublineages D2 or D3 of HPV16 show an elevated risk of developing premalignant cervical lesions. However, it is still crucial to conduct large-scale studies on HPV16 variants in different HPV-related tumor sites to deeply evaluate their association with disease development and outcomes. This review discusses the current knowledge and updates on HPV16 phylogenetic variants distribution in HPV-driven anogenital and head and neck cancers.

Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis

BACKGROUND

Accurate diagnosis and early treatment are essential in the fight against lymphatic cancer. The application of artificial intelligence (AI) in the field of medical imaging shows great potential, but the diagnostic accuracy of lymphoma is unclear. This study was done to systematically review and meta-analyse researches concerning the diagnostic performance of AI in detecting lymphoma using medical imaging for the first time.

METHODS

Searches were conducted in Medline, Embase, IEEE and Cochrane up to December 2023. Data extraction and assessment of the included study quality were independently conducted by two investigators. Studies that reported the diagnostic performance of an AI model/s for the early detection of lymphoma using medical imaging were included in the systemic review. We extracted the binary diagnostic accuracy data to obtain the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022383386.

RESULTS

Thirty studies were included in the systematic review, sixteen of which were meta-analyzed with a pooled sensitivity of 87% (95%CI 83-91%), specificity of 94% (92-96%), and AUC of 97% (95-98%). Satisfactory diagnostic performance was observed in subgroup analyses based on algorithms types (machine learning versus deep learning, and whether transfer learning was applied), sample size (≤ 200 or >  200), clinicians versus AI models and geographical distribution of institutions (Asia versus non-Asia).

CONCLUSIONS

Even if possible overestimation and further studies with a better standards for application of AI algorithms in lymphoma detection are needed, we suggest the AI may be useful in lymphoma diagnosis.