Background: Expression of PD-L1 is the most investigated predictor of benefit from immune checkpoint blockade in advanced NSCLC but little is known about the association of PD-L1 expression and clinicopathological parameters of patients with unresectable stage III NSCLC. Methods: National registry data was searched for medical records of consecutive inoperable stage III NSCLC patients treated with ChT and RT from January 2012 to December 2017. Totally 249 patients were identified that met inclusion criteria and of those 117 patients had sufficient tissue for PD-L1 immunohistochemical staining. Results: Eighty patients (68.4%) expressed PD-L1 of ≥ 1% and 29.9% of more than 50%. Median PFS was 15.9 months in PD-L1 negative patients and 16.1 months in patients with PD-L1 expression ≥ 1% (p = 0.696). Median OS in PD-L1 negative patients was 29.9 months compared to 28.5 months in patients with PD-L1 expression ≥ % (p = 0.888). There was no difference in median OS in patients with high PD-L1 expression (≥ 50%) with 29.8 months compared to 29.9 months in those with low (1–49%) or no PD-L1 expression (p = 0.694). We found that patients who received a total dose of 60 Gy or more had significantly better median OS (32 months vs. 17.5 months, p < 0.001) as well as patients with PS 0 (33.2 vs. 20.3 months, p = 0.005). Conclusions: In our patients PD-L1 expression had no prognostic value regarding PFS and OS. Patients with good performance status and those who received a total radiation dose of more than 60 Gy had significantly better mOS.

We investigated the feasibility of telecytology for rapid on-site evaluation (ROSE) of cytological specimens. During a six-month study, 385 consecutive adult patients underwent image-guided fine needle aspiration biopsies of the lung or transbronchial needle aspirations of mediastinal lymph nodes. Direct smears were immediately wet-fixed and a cytopathologist or a trained cytotechnologist evaluated specimen adequacy using a telecytology system. Specimens were reported as adequate or inadequate. A total of 238 specimens (62%) were diagnosed without rapid assessment. The percentage of non-representative samples in the group without rapid evaluation was 42%. In the group of 147 specimens (38%) which underwent rapid evaluation, 48 (33%) were reported as inadequate and 99 (67%) as adequate. Only 22% of final diagnoses were non-representative, including samples which were assessed as inadequate by rapid evaluation. In three patients of 32, where the obtained material was recognized as adequate, final diagnoses were non-representative. Rapid evaluation with the use of telecytology improves the diagnostic yield of guided fine-needle aspiration biopsies by decreasing the percentage of non-representative specimens.

Aim: This study evaluates the comparative effectiveness of pathologists versus artificial intelligence (AI) algorithms in scoring PD‐L1 expression in non‐small cell lung carcinoma (NSCLC). Immune‐checkpoint inhibitors have revolutionized NSCLC treatment, with PD‐L1 expression, measured as the tumour proportion score (TPS), serving as a critical predictive biomarker for therapeutic response.Methods and Results: In our analysis, 51 SP263‐stained NSCLC cases were scored by six pathologists using light microscopy and whole‐slide images (WSI), alongside evaluations by two commercially available software tools: uPath software (Roche) and the PD‐L1 Lung Cancer TME application (Visiopharm). The study examined intra‐ and interobserver agreement among pathologists at TPS cutoffs of 1% and 50%, revealing moderate interobserver agreement (Fleiss' kappa 0.558) for TPS <1% and almost perfect agreement (Fleiss' kappa 0.873) for TPS ≥50%. Intraobserver consistency was high, with Cohen's kappa ranging from 0.726 to 1.0. Comparisons between the AI algorithms and the median pathologist scores showed fair agreement for uPath (Fleiss' kappa 0.354) and substantial agreement for the Visiopharm application (Fleiss' kappa 0.672) at the 50% TPS cutoff.Conclusion: These results indicate that while there is strong interobserver concordance among pathologists at higher TPS levels, the performance of AI algorithms is less consistent. The study underscores the need for further refinement of AI tools to match the reliability of expert human evaluation, particularly in critical clinical decision‐making contexts.

Malignant pleural mesothelioma (MPM) is a devastating malignancy with limited therapeutic options. Fibroblast growth factor receptors (FGFR) and their ligands were shown to contribute to MPM aggressiveness and it was suggested that subgroups of MPM patients could benefit from FGFR-targeted inhibitors. In the current investigation, we determined the expression of all four FGFRs (FGFR1–FGFR4) by immunohistochemistry in tissue samples from 94 MPM patients. From 13 of these patients, we were able to establish stable cell lines, which were subjected to FGFR1–4 staining, transcript analysis by quantitative RT-PCR, and treatment with the FGFR inhibitor infigratinib. While FGFR1 and FGFR2 were widely expressed in MPM tissue and cell lines, FGFR3 and FGFR4 showed more restricted expression. FGFR1 and FGFR2 showed no correlation with clinicopathologic data or patient survival, but presence of FGFR3 in 42% and of FGFR4 in 7% of patients correlated with shorter overall survival. Immunostaining in cell lines was more homogenous than in the corresponding tissue samples. Neither transcript nor protein expression of FGFR1–4 correlated with response to infigratinib treatment in MPM cell lines. We conclude that FGFR3 and FGFR4, but not FGFR1 or FGFR2, have prognostic significance in MPM and that FGFR expression is not sufficient to predict FGFR inhibitor response in MPM cell lines.

Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome. Subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. Here, we propose a machine learning framework for tumour prognosis assessment based on a quantitative, automated and repeatable evaluation of the spatial distribution of cells immunohistochemically positive for the proliferation marker Ki-67, performed on the entire extent of high-resolution whole slide images. Combining features from the fields of graph theory, fractality analysis, stochastic geometry and information theory, we describe the topology of replicating cells and predict prognosis in a histology-independent way. We demonstrate how our approach outperforms the well-recognised prognostic role of Ki-67 Labelling Index on a multi-centre dataset comprising the most controversial lung NENs. Moreover, we show that our system identifies arrangement patterns in the cells positive for Ki-67 that appear independently of tumour subtyping. Strikingly, the subset of these features whose presence is also independent of the value of the Labelling Index and the density of Ki-67-positive cells prove to be especially relevant in discerning prognostic classes. These findings disclose a possible path for the future of grading and classification of NENs.

Background: Hypoxia correlates with poor prognosis in several cancer types, including lung cancer. Prolyl hydroxylase domain proteins (PHDs) play a role in cell oxygen sensing, negatively regulating the hypoxia-inducible factor (HIF) pathway. Our study aim was to evaluate PHD1, PHD2 and PHD3 mRNA expression levels in primary tumours and normal lungs of non-small-cell lung cancer (NSCLC) patients and to correlate it with selected regulators of HIF signalling, with clinicopathological characteristics and overall survival (OS). Methods: Tumour tissue samples were obtained from 60 patients with surgically resected NSCLC who were treated with radical surgery. In 22 out of 60 cases, matching morphologically normal lung tissue was obtained. PHD1, PHD2 and PHD3 mRNA expressions were measured using RT-qPCR. Results: The PHD1 and PHD2 mRNA levels in primary tumours were significantly decreased compared to those in normal lungs (both p < 0.0001). PHD1 and PHD2 expression in tumours was positively correlated (r_s = 0.82; p < 0.0001) and correlated well with HIF pathway downstream genes HIF1A, PKM2 and PDK1. Decreased PHD1 and PHD2 were associated with larger tumour size, higher tumour stage (PHD1 only) and squamous cell carcinoma. Patients with low PHD1 and patients with low PHD2 expression had shorter OS than patients with high PHD1 (p = 0.02) and PHD2 expression (p = 0.01). PHD1 showed borderline independent prognostic values in multivariate analysis (p = 0.06). In contrast, we found no associations between PHD3 expression and any of the observed parameters. Conclusions: Our results show that reduced expression of PHD1 and PHD2 is associated with the development and progression of NSCLC. PHD1 could be further assessed as a prognostic marker in NSCLC.