Head and neck cancer etiology and architecture is quite diverse and complex, impeding the prediction whether a patient could respond to a particular cancer immunotherapy or combination treatment. A concomitantly arising caveat is obviously the translation from pre-clinical, cell based in vitro systems as well as syngeneic murine tumor models towards the heterogeneous architecture of the human tumor ecosystems. To bridge this gap, we have established and employed a patient-derived HNSCC (head and neck squamous cell carcinoma) slice culturing system to assess immunomodulatory effects as well as permissivity and oncolytic virus (OV) action. The heterogeneous contexture of the human tumor ecosystem including tumor cells, cancer-associated fibroblasts and immune cells was preserved in our HNSCC slice culturing approach. Importantly, the immune cell compartment remained to be functional and cytotoxic T-cells could be activated by immunostimulatory antibodies. In addition, we uncovered that a high proportion of the patient-derived HNSCC slice cultures were susceptible to the OV VSV-GP. More specifically, VSV-GP infects a broad spectrum of tumor-associated lineages including epithelial and stromal cells and can induce apoptosis. In sum, this human tumor ex vivo platform might complement pre-clinical studies to eventually propel cancer immune-related drug discovery and ease the translation to the clinics.
Locally-advanced head and neck squamous cell carcinoma (HNSCC) is mainly defined by the presence of pathologic cervical lymph nodes (LNs) with or without extracapsular spread (ECS). Current radiologic criteria to classify LNs as non-pathologic, pathologic, or pathologic with ECS are primarily shape-based. However, significantly more quantitative information is contained within imaging modalities. This quantitative information could be exploited for classification of LNs in patients with locally-advanced HNSCC by means of artificial intelligence (AI). Currently, various reviews exploring the role of AI in HNSCC are available. However, reviews specifically addressing the current role of AI to classify LN in HNSCC-patients are sparse. The present work systematically reviews original articles that specifically explore the role of AI to classify LNs in locally-advanced HNSCC applying Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and the Study Quality Assessment Tool of National Institute of Health (NIH). Between 2001 and 2022, out of 69 studies a total of 13 retrospective, mainly monocentric, studies were identified. The majority of the studies included patients with oropharyngeal and oral cavity (9 and 7 of 13 studies, respectively) HNSCC. Histopathologic findings were defined as reference in 9 of 13 studies. Machine learning was applied in 13 studies, 9 of them applying deep learning. The mean number of included patients was 75 (SD ± 72; range 10–258) and of LNs was 340 (SD ± 268; range 21–791). The mean diagnostic accuracy for the training sets was 86% (SD ± 14%; range: 43–99%) and for testing sets 86% (SD ± 5%; range 76–92%). Consequently, all of the identified studies concluded AI to be a potentially promising diagnostic support tool for LN-classification in HNSCC. However, adequately powered, prospective, and randomized control trials are urgently required to further assess AI’s role in LN-classification in locally-advanced HNSCC.
Objective: Repeated computed tomography (CT) is essential for diagnosis, surgical planning and follow-up in patients with middle and inner ear pathology. Dose reduction to “as low as diagnostically acceptable” (ALADA) is preferable but challenging. We aimed to compare the diagnostic quality of images of subtle temporal bone structures produced with low doses (LD) and reference protocols (RP). Methods: Two formalin-fixed human cadaver heads were scanned using a 64-slice CT scanner and cone-beam CT (CBCT). The protocols were: RP (120 kV, 250 mA, CTDIvol 83.72 mGy), LD1 (100 kV, 80 mA, CTDIvol 26.79 mGy), LD2 (100 kV, 35 mA, CTDIvol 7.66 mGy), LD3 (80 kV, 40 mA, CTDIvol 4.82 mGy), and CBCT standard protocol. Temporal bone structures were assessed using a 5-point scale. Results: A median score of ≥2 was achieved with protocols such as the tendons of m. tensor tympani (RP/LD1/LD2/CBCT) and m. stapedius (CBCT), the incudostapedial joint (RP/LD1/CBCT), the incudomalleolar joint (RP/LD1/LD2/CBCT), the stapes feet (RP/LD1/CBCT), the stapes head (RP/LD1/LD2/CBCT), the tympanic membrane (RP/LD1/LD2/CBCT), the lamina spiralis ossea (none), the chorda tympani (RP/LD1/CBCT), and the modiolus (RP/LD1/LD2/CBCT). Adaptive statistical iterative reconstructions did not show advantages over the filtered back projection. Conclusions: LD protocols using a CTDIvol of 7.66 mGy may be sufficient for the identification of temporal bone structures.
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