BACKGROUND: In 2022, our team launched the pioneering national proficiency testing (PT) scheme for the pathological diagnosis of breast cancer, rapidly establishing its credibility throughout China. Aiming to continuously monitor and improve the proficiency of Chinese pathologists in breast pathology, the second round of the PT scheme was initiated in 2023, which will expand the number of participating institutions, and will conduct a nationwide investigation into the interpretation of HER2 0, 1+, and 2+/FISH- categories in China.
METHODS: The methodology employed in the current round of PT scheme closely mirrors that of the preceding cycle in 2022, which is designed and implemented according to the \"Conformity assessment-General requirements for proficiency testing\"(GB/T27043-2012/ISO/IEC 17043:2010). More importantly, we utilized a statistics-based method to generate assigned values to enhance their robustness and credibility.
RESULTS: The final PT results, published on the website of the National Quality Control Center for Cancer ( http://117.133.40.88:3927 ), showed that all participants passed the testing. However, a few institutions demonstrated systemic biases in scoring HER2 0, 1+, and 2+/FISH- with accuracy levels below 59%, considered unsatisfactory. Especially, the concordance rate for HER2 0 cases was only 78.1%, indicating challenges in distinguishing HER2 0 from low HER2 expression. Meanwhile, areas for histologic type and grade interpretation improvement were also noted.
CONCLUSIONS: Our PT scheme demonstrated high proficiency in diagnosing breast cancer in China. But it also identified systemic biases in scoring HER2 0, 1+, and 2+/FISH- at some institutions. More importantly, our study highlighted challenges in the evaluation at the extreme lower end of the HER2 staining spectrum, a crucial area for further research. Meanwhile, it also revealed the need for improvements in interpreting histologic types and grades. These findings strengthened the importance of robust quality assurance mechanisms, like the nationwide PT scheme conducted in this study, to maintain high diagnostic standards and identify areas requiring further training and enhancement.

BACKGROUND: Genomic diagnostic testing is necessary to guide optimal treatment for non-small cell lung cancer (NSCLC) patients. The proportion of NSCLC patients whose treatment was selected based on genomic testing is still unknown in many countries or needs further improvement. This survey aimed to assess perception of genomic testing and targeted therapy for NSCLC in clinical pathologists and physicians across China.
METHODS: The web-based survey was conducted with 150 clinical pathologists and 450 physicians from oncology, respiratory and thoracic surgery departments from May to September 2020, across 135 cities in China. The participants had >5 years of clinical experience in genomic testing, diagnosis or treatment of NSCLC.
RESULTS: Clinical pathologists reported capability of epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), and ROS proto-oncogene 1 (ROS-1) testing as 95.3%, 94.7%, and 84.7%, respectively, but only 81.9%, 75.5%, and 65.6% of physicians believed that the pathology department of the hospital is capable of performing the testing. The proportions of sending out specimens for testing were 21.0% and 49.7% as reported from clinical pathologists and physicians, respectively. Testing for EGFR mutation was recommended by physicians most often, followed by ALK and ROS-1 rearrangement. As first-line treatment, among the newly diagnosed patients with EGFR mutation, 77% received tyrosine kinase inhibitors (TKIs) therapy (49% treated with gefitinib); among patients with ALK rearrangement, 71% received TKI (64% treated with crizotinib); among patients with ROS-1 fusion, 65% received TKI (88% treated with crizotinib).
CONCLUSIONS: The improvement of the non-tertiary hospital pathology departments\' detection capabilities and the physicians\' awareness are needed for enhancing the rate of genomic testing and targeted therapy in NSCLC patients in China.

WHO firstly published the classification of paediatric tumours, in which genetic tumour syndromes were introduced as a separate chapter, covering the clinicopathological features, molecular genetic alterations, and diagnostic criteria of various tumor susceptibility syndromes common in children. This article briefly introduces and interprets 5 hotspot genetic tumour syndromes (neurofibromatosis type 1, naevoid basal cell carcinoma syndrome, von Hippel-Lindau syndrome, familial adenomatous polyposis and xeroderma pigmentosum) based on relevant literature, in order to bring new perspectives and insights to pathologists and clinicians.
WHO首次出版了儿童肿瘤分类，遗传性肿瘤综合征作为其独立章节被介绍，内容涵盖儿童常见各种肿瘤易感综合征的临床病理特征、分子遗传学改变以及诊断标准等。本文结合相关文献，对其中儿童好发的5种热点综合征（神经纤维瘤病1型、痣样基底细胞癌综合征、von Hippel-Lindau综合征、家族性腺瘤性息肉病和着色性干皮病）做简要介绍和解读，以使临床及病理医师对儿童肿瘤易感综合征有更多认识和了解。.

OBJECTIVE: Given real-world limitations in programmed death-ligand 1 (PD-L1) testing, concordance studies between PD-L1 assays are needed. We undertook comparisons of PD-L1 assays (DAKO22C3, Ventana SP263, Ventana SP142, E1L3N) among observers in esophageal squamous cell carcinoma (ESCC) to provide information on the analytical and clinical comparability of four PD-L1 IHC assays.
METHODS: Paraffin embedded samples of 50 cases of esophageal squamous cell carcinoma were obtained, satined with all four PD-L1 assays. PD-L1 was evaluated by 68 pathologists from 19 different hospitals. PD-L1 expression was assessed for combined positive score (CPS).
RESULTS: The expression sensitivity of SP263 was the highest in ESCC, followed by 22C3, E1L3N and SP142. Taking CPS 10 as the critical value, inter-observer concordance for CPS scores among 68 physicians was assessed for the 22C3, SP263, SP142, and E1L3N assays, yielding values of 0.777, 0.790, 0.758, and 0.782, respectively. In the comparison between assays, the overall CPS scores concordance rates between 22C3 and SP263, SP142, and E1L3N were 0.896, 0.833, and 0.853, respectively. 22C3 and SP263 have high concordance, with OPA of 0.896, while E1L3N and SP142 have the highest concordance, with OPA of 0.908.
CONCLUSIONS: In ESCC, the concordance of PD-L1 evaluation among observers is good, and the immune cell score is still an important factor affecting the concordance of interpretation among observers. Cases near the specific threshold are still the difficult problem of interpretation. SP263 had the highest CPS score of the four assays. SP263 cannot identify all 22C3 positive cases, but had good concordance with 22C3.E1L3N and SP142 showed high concordance.

The percent global glomerulosclerosis is a key factor in determining the outcome of renal transfer surgery. At present, the rate is typically computed by pathologists, which is labour intensive and nonstandardized. With the development of Deep Learning (DL), DL-based segmentation models can be used to better identify and segment normal and sclerosed glomeruli. Based on this, we can better quantify percent global glomerulosclerosis to reduce the discard rate of donor kidneys. We used 51 whole slide images (WSIs) from different institutions that are publicly available on the internet. However, the number of sclerosed glomeruli is much smaller than that of normal glomeruli in different WSIs, which can reduce the effectiveness of Deep Learning. For better sclerosed glomerular identification and segmentation performance, we modified and trained a GAN (generative adversarial network)-based image inpainting model to obtain more synthetic sclerosed glomeruli. Our proposed inpainting method achieved an average SSIM (Structural Similarity) of 0.8086 and an average PSNR (Peak Signal-to-Noise Ratio) of 22.8943 dB in the area of generated sclerosed glomeruli. We obtained sclerosed glomerular segmentation performance improvement by adding synthetic sclerosed glomerular images and achieved the best Dice of glomerular segmentation in different test sets based on the modified Unet model.

Overexpression of PD-L1 can be a predictive marker for anti-PD-1 therapeutic efficacy in classic Hodgkin lymphoma (CHL); however, harmonization of different IHC assays remains to be accomplished, and interpretations of PD-L1 immunostaining results remain controversial in CHL. In this study, we sought to optimize the PD-L1 immunohistochemistry (IHC) assay in CHL. All tests were performed on a tumour tissue microarray established from 54 CHL cases. Three IHC antibodies (405.9A11, SP142, 22C3) for detecting PD-L1 expression were compared semi quantitatively with the RNAscope assay (No. 310035, ACD), and the difference in the expression in background immune cells (ICs) between assays and the associations of expression levels with densities of TILs/TAMs were also analysed. 405.9A11 demonstrated best specificity in HRS cells and best sensitivity in ICs. Positive expression of PD-L1 was more frequent in ICs (85.2%) than in HRS cells (48.1%). Different subgroups of background ICs, including tumour-associated macrophages (TAMs), were assessed and scored for CD4, CD8, FOXP3, and CD163 expression. PD-L1 expression on ICs was the factor most associated with the density of TAMs. 405.9A11 provided the most convincing PD-L1 expression results. Pathologists should report PD-L1 expression in a combined manner, including both the status of HRS cells and the percentage of PD-L1-positive ICs.

暂无摘要。

Biomarker tests in lung cancer have been traditionally ordered by the treating oncologist upon confirmation of an appropriate pathological diagnosis. The delay this introduces prolongs yet further what is already a complex, multi-stage, pre-treatment pathway and delays the start of first-line systemic treatment, which is crucially informed by the results of such analysis. Reflex testing, in which the responsibility for testing for an agreed range of biomarkers lies with the pathologist, has been shown to standardise and expedite the process. Twelve experts discussed the rationale and considerations for implementing reflex testing as standard clinical practice.

OBJECTIVE: The classification of primary lung adenocarcinoma is complex and varied. Different subtypes of lung adenocarcinoma have different treatment methods and different prognosis. In this study, we collected 11 datasets comprising subtypes of lung cancer and proposed FL-STNet model to provide the assistance for improving clinical problems of pathologic classification in primary adenocarcinoma of lung.
METHODS: Samples were collected from 360 patients diagnosed with lung adenocarcinoma and other subtypes of lung diseases. In addition, an auxiliary diagnosis algorithm based on Swin-Transformer, which used Focal Loss for function in training, was developed. Meanwhile, the diagnostic accuracy of the Swin-Transformer was compared to pathologists.
RESULTS: The Swin-Transformer captures not only information in the overall tissue structure but also the local tissue details in the images of lung cancer pathology. Furthermore, training FL-STNet with the Focal Loss function can further balance the difference in the amount of data between different subtypes, improving recognition accuracy. The average classification accuracy, F1, and AUC of the proposed FL-STNet reached 85.71%, 86.57%, and 0.9903. The average accuracy of the FL-STNet was higher by 17% and 34%, respectively, than in the senior pathologist and junior pathologist group.
CONCLUSIONS: The first deep learning based on an 11-category classifier was developed for classifying lung adenocarcinoma subtypes based on WSI histopathology. Aiming at the deficiencies of the current CNN and Vit, FL-STNet model is proposed in this study by introducing Focal Loss and combining the advantages of Swin-Transformer model.

Breast cancer is the leading cause of cancer-related mortality in women worldwide. Despite the rapid developments in diagnostic techniques and medical sciences, pathologic diagnosis is still recognized as the gold standard for disease diagnose. Pathologic diagnosis is a time-consuming task performed for pathologists, needing profound professional knowledge and long-term accumulated diagnostic experience. Therefore, the development of automatic and precise histopathological image classification is essential for medical diagnosis. In this study, an improved VGG network was used to classify the breast cancer histopathological image from intraoperative rapid frozen sections. We adopt a transformed loss function by adding a penalty to cross-entropy in our training stage, which improved the accuracy on test data by 4.39%. Laplacian-4 was used for the enhancement of images, which contributes to the improvement of the accuracy. The accuracy of the proposed model on training data and test data reached 88.70% and 82.27%, respectively, which outperforms the original model by 9.39% of accuracy in test data. The process time was less than 0.25 s per image on average. Meanwhile, the heat maps of predictions were given to show the evidential regions in histopathological images, which could drive improvements in the accuracy, speed, and clinical value of pathological diagnoses. In addition to helping with the actual diagnosis, this technology may be a benefit to pathologists, surgeons, and patients. It might prove to be a helpful tool for pathologists in the future.