Immune checkpoint inhibitors for blocking the programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) axis are now available for squamous cell carcinoma of the head and neck (HNSCC) in relapsing and/or metastatic settings. In this work, we compared the resulting combined positive score (CPS) of PD-L1 using alternative methods adopted in routine clinical practice and determined the level of diagnostic agreement and inter-observer reliability in this setting. The study applied 5 different protocols on 40 tissue microarrays from HNSCC. The error rate of the individual protocols ranged from a minimum of 7% to a maximum of 21%, the sensitivity from 79% to 96%, and the specificity from 50% to 100%. In the intermediate group (1 ≤ CPS < 20), the majority of errors consisted of an underestimation of PD-L1 expression. In strong expressors, 5 out of 14 samples (36%) were correctly evaluated by all the protocols, but no protocol was able to correctly identify all the “strong expressors”. The overall inter-observer agreement in PD-L1 CPS reached 87%. The inter-observer reliability was moderate, with an ICC of 0.774 (95% CI (0.651; 0.871)). In conclusion, our study showed moderate interobserver reliability among different protocols. In order to improve the performances, adequate specific training to evaluate PD-L1 by CPS in the HNSCC setting should be coordinated.

The present study applies for the first time as Matrix-Assisted Laser Desorption/Ionization (MALDI) Mass Spectrometry Imaging (MSI) on real thyroid Fine Needle Aspirations (FNAs) to test its possible complementary role in routine cytology in the diagnosis of thyroid nodules. The primary aim is to evaluate the potential employment of MALDI-MSI in cytopathology, using challenging samples such as needle washes. Firstly, we designed a statistical model based on the analysis of Regions of Interest (ROIs), according to the morphological triage performed by the pathologist. Successively, the capability of the model to predict the classification of the FNAs was validated in a different group of patients on ROI and pixel-by-pixel approach. Results are very promising and highlight the possibility to introduce MALDI-MSI as a complementary tool for the diagnostic characterization of thyroid nodules.