Small cell lung cancer (SCLC) is a highly aggressive subtype of lung cancer characterized by rapid tumor growth and early metastasis. Accurate prediction of prognosis and therapeutic response is crucial for optimizing treatment strategies and improving patient outcomes. In this study, we conducted a deep-learning analysis of Hematoxylin and Eosin (H&E) stained histopathological images using contrastive clustering and identified 50 intricate histomorphological phenotype clusters (HPCs) as pathomic features. We identified two of 50 HPCs with significant prognostic value and then integrated them into a pathomics signature (PathoSig) using the Cox regression model. PathoSig showed significant risk stratification for overall survival and disease-free survival and successfully identified patients who may benefit from postoperative or preoperative chemoradiotherapy. The predictive power of PathoSig was validated in independent multicenter cohorts. Furthermore, PathoSig can provide comprehensive prognostic information beyond the current TNM staging system and molecular subtyping. Overall, our study highlights the significant potential of utilizing histopathology images-based deep learning in improving prognostic predictions and evaluating therapeutic response in SCLC. PathoSig represents an effective tool that aids clinicians in making informed decisions and selecting personalized treatment strategies for SCLC patients.

To compare fluorescence in situ hybridization (FISH), immunohistochemistry (IHC) and quantitative real-time reverse transcription-PCR (qRT-PCR) assays for detection of ROS1 fusion in a large number of ROS1-positive lung adenocatcinoma (ADC) patients. Using IHC analysis, sixty lung ADCs including 16 cases with ROS1 protein expression and 44 cases without ROS1 expression were selected for this study. The ROS1 fusion status was examined by FISH and qRT-PCR assay. Among 60 cases, 16 (26.7%), 13 (21.7%) and 20 (33.3%) cases were ROS1 positive revealed by IHC, FISH and qRT-PCR, respectively. Using FISH as a standard method for ROS1 fusion detection, the sensitivity and specificity of IHC were 100% and 93.6%, respectively. Three IHC-positive cases, which showed FISH negative, were demonstrated with ROS1 fusion by qRT-PCR analysis. The sensitivity and specificity of qRT-PCR for detection for ROS1 fusion were 100% and 85.1%, respectively. The total concordance rate between IHC and qRT-PCR were 93.3%. IHC is a reliable and rapid screening tool in routine pathologic laboratories for the identification of suitable candidates for ROS1-targeted therapy. Some ROS1 IHC-positive but FISH-negative cases did harbor the translocation events and may benefit from crizotinib.

Background: The study was conducted to investigate the clinicopathological features and prevalence of ROS1 gene fusion in Chinese patients with non‐small cell lung cancer (NSCLC).Methods: The presence of ROS1 fusion was assessed by quantitative real‐time PCR. Associations between ROS1 fusion and clinical characteristics were analyzed.Results: In total, 6066 patients with pathologically confirmed NSCLC and ROS1 fusion test results were enrolled. The average age was 60.89 ± 10.60 years and fusion was detected in 157 (2.59%) patients. Fusion frequency was significantly correlated with age, gender, smoking status (all P < 0.001), pathology type (P = 0.017), and lymph node metastasis stage (P = 0.027). ROS1 fusion‐positive patients were significantly younger (55.68 ± 11.34 vs. negative 61.02 ± 10.44 years; P < 0.01). Fusion frequency was higher in women (3.71% vs. men 1.81%), never‐smokers (3.33% vs. smokers 1.21%), and patients with adenocarcinoma (2.77% vs. squamous lung cancer 0.93%) and at advanced node stages (1.31%, 1.40%, 2.07%, and 3.23% for N0, N1, N2, and N3, respectively). No significant correlation between ROS1 fusion status and pathological stage was found in subgroups classified by pathological, tumor, or metastasis stage (P > 0.05). Age, smoking status, and lymph node stage were statistically significantly correlated with ROS1 fusion frequency (all P < 0.05); gender and pathology type were not significantly correlated with ROS1 fusion status after adjusting for smoking status.Conclusion: An overall ROS1 fusion frequency of 2.59% was confirmed in this study. ROS1 fusion was more prevalent among younger patients, never‐smokers, and those at advanced node stages.

Immune checkpoint blockade targeting PD-1/PD-L1 has shown efficacy in several types of cancers. However, the correlation between PD-L1/PD-1 expression and the specific clinicopathological features in papillary thyroid carcinoma (PTC) has not been investigated. We examined the immunohistochemical expression of PD-L1, PD-1, and BRAF V600E on whole-tissue sections from 126 cases of primary PTC more than 1 cm in size. The correlation between the PD-L1/PD-1 expression and the clinicopathological features was evaluated. PD-L1 was positively expressed in 53.2% PTCs, and its expression was positively correlated with rich tumor-infiltrating lymphocytes (TILs), background chronic lymphocytic thyroiditis (CLT), female gender, absence of psammoma bodies, and PD-1 expression. Among these parameters, rich TILs, female gender, and absence of psammoma bodies were independent factors affecting PD-L1 expression on the multivariate logistic regression analysis. PD-1 expression was detected in the TILs and was positively correlated with rich TILs, background CLT, and absence of stromal calcification. Lack of stromal calcification was an independent factor affecting PD-1 expression. Neither PD-L1 nor PD-1 expression showed significant correlation with BRAF V600E expression. Our results show that the distinctive pathological features of PTCs, including TILs, background CLT, female gender, psammoma bodies, and stromal calcification, are useful parameters for predicting PD-L1 or PD-1 expression.