Jun Lu

To evaluate the prognostic utility of the FIGO 2023 staging system with/without molecular classification vs. FIGO 2009 in endometrial cancer. A total of 172 patients between 2015 and 2020 diagnosed with endometrial cancer in our center were included in this study. Molecular classification subtypes were classified using DNA sequencing and immunohistochemistry. The clinical characteristics and patients' prognosis were analyzed. Of the 172 patients, 10 patients were classified to the POLEmut, 30 patients to the MMRd group, 106 patients to the NSMP group, and 26 patients to the p53abn group. Stage migration from FIGO 2009 to FIGO 2023 occurred in 27.3% of the patients (47/172). Among the 47 patients, upstaging from stage I to stage II was observed in 43 patients. The transition from stage III to the early stage occurred in 2 patients, with downstaging from stage III to IA3. 9 patients were restaged as IAm disease with the FIGO 2023 m system. Downstaging to stage IAm was observed in 7 patients due to the presence of POLE mutation. In addition, 14 patients had stage IICm disease with the FIGO 2023 m. Eight patients were upstaged to stage IICm due to the presence of p53 abnormality, while 6 patients already exhibited stage IIC disease based on the FIGO 2023 classification. Patients with endometrial cancer with POLE-EDM had the best prognosis in terms of RFS and OS; those with MMRd and NSMP exhibited intermediate prognosis, with no significant difference between the two groups; and those with p53abn had the worst prognosis. Molecular classification is prognostically essential in endometrial carcinoma. The integrated FIGO 2023 m system appears to enhance risk stratification relative to FIGO 2009 and non-molecular FIGO 2023. Formal comparison of staging systems is needed to confirm this improvement.

Background: According to the international standard, identifying and delineating the various growth types of adenocarcinomas is essential to the grading of lung cancer, the world-leading cause of cancer-related death. However, the task is currently conducted by manual inspection of the whole-slide images (WSIs), which is time-consuming and depends on individual experience. The aim of this study is to develop a patch-based deep learning (DL) framework that accurately classifies and delineates lung adenocarcinoma (LUAD) growth patterns in WSIs, addressing the time-consuming and experience-dependent nature of manual inspection while achieving performance comparable to experienced pathologists. Methods: This study presents a patch-based DL framework for classifying and delineating normal lung tissue and seven LUAD growth patterns in WSIs. To boost model performance, we combine self-supervised learning with consistency regularization and pseudo-labeling, using both labeled and unlabeled data. Our pseudo-labeling strategy applies weak and strong data augmentations to generate high-confidence pseudo-labels for unlabeled patches, assigning them only when predictions exceed a confidence threshold to ensure reliability. Additionally, we introduce an online hard patch mining method to improve feature extraction for histologically similar patches that are challenging to classify during training. Results: The model was trained and evaluated on a dataset of 288 WSIs, encompassing seven types of LUADs and normal tissues, achieving a precision of 95.3%±5.5% and a recall of 96.8%±2.9%, with an overall Intersection over Union (IoU) of 87.5%±6.9%. When benchmarked against other state-of-the-art models in LUAD classification using a publicly available dataset, our model demonstrated superior performance. For example, compared to DiPalma et al., our method achieved improvements of 1.44%, 19.34%, 6.78%, and 19.16% across various metrics. It also outperformed Sheikh et al. by 0.99%, 1.04%, and 0.84% in respective metrics. Moreover, the model’s performance was found to be comparable to that of experienced pathologists, with the arithmetic mean of Cohen’s kappa scores across 10 sample tests being 0.96, and when aggregated at the WSI level, the Cohen’s kappa score reached 0.97. Furthermore, the model is highly efficient, capable of analyzing a WSI in under 1 minute. Conclusions: The experimental findings demonstrate that the proposed framework achieves high accuracy in classifying normal lung tissues and seven types of lung tumor growth patterns, with its performance across all evaluation metrics rivaling that of pathologists’ assessments. This model is capable of generating predictive probability maps at the level of full-slice lung images. These maps effectively visualize the location, distribution, and proportion of lesion areas, thereby facilitating pathologists in conducting further grading and staging tasks. Moreover, when the model’s classification results are integrated with pathologists’ diagnostic outcomes, the accuracy and reliability of the pathologists’ clinical interpretations can be significantly enhanced. Besides, Hematoxylin and eosin staining and standardization measures ensure the method’s generalizability across institutions or scanners.

Yue W, et al. Korean J Radiol. 2025 Oct;26(10):973-985. https://doi.org/10.3348/kjr.2025.0633

The formation of kidney stones is the best-known disease of the urinary tract and constitutes an enormous health care burden among the working age population. The prevalence of kidney stones has ri...

Background.: T cell–mediated rejection (TCMR) is a severe issue after renal transplantation, but research on its T cell–receptor (TCR) repertoire is lacking. This study intended to elucidate the TCR repertoire landscape in TCMR and hence identify novel potential targets. Methods.: A total of 12 multiomics data sets were collected. The TRUST4 algorithm was used to construct and analyze the TCR repertoire in renal allografts with TCMR and stable renal function. Then, novel TCR-related key genes were identified through various criteria and literature research. In bulk transcriptome, cell line, single-cell transcriptome data sets, multiple immune cell infiltration algorithms, and gene set enrichment analysis were used to analyze potential mechanisms of the identified key gene. Twenty-three pathological sections were collected for immunofluorescence staining in the clinical cohort. Finally, the diagnostic and prognostic values of ANXA2R were evaluated in multiple renal transplant data sets. Results.: Allografts with TCMR showed significantly increased clonotype and specific clonal expansion. ANXA2R was found to be a novel key gene for TCMR and showed strong positive connections with the TCR complex and lymphocyte cells, especially CD8⁺ T cells. Immunofluorescence staining confirmed the existence of ANXA2R⁺ CD8⁺ T cells, with their percentage significantly elevated in TCMR compared with stable renal function. Finally, both mRNA and protein levels of ANXA2R showed promising diagnostic and prognostic value for renal transplant recipients. Conclusions.: ANXA2R, identified as a novel TCR-related gene, had critical roles in clinicopathology, diagnosis, and prognosis in renal transplantation, which offered promising potential therapeutic targets.

Aims: In recent years, patients with programmed cell death-Ligand 1 (PD-L1)-positive oesophageal squamous cell carcinoma (OSCC) have been able to benefit from immunotherapy. However, method for improving the treatment efficacy of PD-L1-positive patients is a problem that needs further consideration. Studies on the relationship between human epidermal growth factor receptor 2 (HER2) and PD-L1 expression have recently been reported in certain cancers, but the relationship between PD-L1 and HER2 expression in OSCC is still unclear. Methods: A total of 263 patients with OSCC were included in the study. PD-L1 protein expression and HER2 protein expression were analysed by immunohistochemistry (IHC), and fluorescence in situ hybridisation (FISH) was performed to assess HER2 gene amplification. The significance of differences between HER2 status, PD-L1 status and clinicopathological parameters was assessed. The relationship between PD-L1 status and HER2 status was examined. Results: Of the 263 OSCC cases, the PD-L1-positive expression rates were 39.2% and 77.2% in OSCC for Tumour Proportion Score (TPS) and Combined Positive Score (CPS), respectively, and PD-L1 expression was associated with the degree of tumour differentiation. The HER2 expression was positive in 24% (63/263) of cases based on IHC and FISH. HER2 expression was not significantly associated with clinicopathological characteristics. PD-L1 TPS expression and CPS expression were significantly positively correlated with HER2 expression in OSCC. Conclusions: PD-L1 expression was significantly positively correlated with HER2 expression in OSCC. The results provide valuable insight for the future application of HER2-targeted therapy combined with immunotherapy in OSCC.