Bronchiolar adenoma/ciliated muconodular papillary tumor mixed with adenocarcinoma in situ in the same tumor

Uchiyama, Suiha; Mizutani, Ken-ichi; Suzuki, Eriko; Yoshii, Naoko; Watanabe, Takuya; Tsuchida, Hiroyuki; Yobita, Shogo; Iguchi, Kensuke; Nakamura, Minori; Endo, Takumi; Takahashi, Seishiro; Tanahashi, Masayuki

doi:10.1111/1759-7714.14784

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…5E ). MUC1 and KRT7 are reported to be predefined BA markers, and they are positive in BA 41,42 . COL1A1 is a marker of fibroblasts and a key gene in the development and progression of lung cancer, which is associated with tumor cell migration, invasion and proliferation 43,44 .…”

Section: Resultsmentioning

confidence: 99%

stGCL: A versatile cross-modality fusion method based on multi-modal graph contrastive learning for spatial transcriptomics

Yu,

Zhang,

Zhang

et al. 2023

Preprint

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Advances in spatial transcriptomics (ST) technologies have provided unprecedented opportunities to depict transcriptomic and histological landscapes in the spatial context. Multi-modal ST data provide abundant and comprehensive information about cellular status, function, and organization. However, in dealing with the processing and analysis of spatial transcriptomics data, existing algorithms struggle to effectively fuse the multi-modal information contained within ST data. Here, we propose a graph contrastive learning-based cross-modality fusion model named stGCL for accurate and robust integrating gene expression, spatial information as well as histological profiles simultaneously. stGCL adopts a novel histology-based Vision Transformer (H-ViT) method to effectively encode histological features and combines multi-modal graph attention auto-encoder (GATE) with contrastive learning to fuse cross-modality features. In addition, stGCL introduces a pioneering spatial coordinate correcting and registering strategy for tissue slices integration, which can reduce batch effects and identify cross-sectional domains precisely. Compared with state-of-the-art methods on spatial transcriptomics data across platforms and resolutions, stGCL achieves a superior clustering performance and is more robust in unraveling spatial patterns of biological significance. Additionally, stGCL successfully reconstructed three-dimensional (3D) brain tissue structures by integrating vertical and horizontal slices respectively. Application of stGCL in human bronchiolar adenoma (BA) data reveals intratumor spatial heterogeneity and identifies candidate gene biomarkers. In summary, stGCL enables the fusion of various spatial modality data and is a powerful tool for analytical tasks such as spatial domain identification and multi-slice integration.

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Section: Resultsmentioning

confidence: 99%

stGCL: A versatile cross-modality fusion method based on multi-modal graph contrastive learning for spatial transcriptomics

Yu,

Zhang,

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…When strictly defined, classical examples of BA/CMPT seem to have a benign clinical course 1,3,4 . However, several reports now exist in the literature describing malignant transformation 7–9 . It is not clear if these reports represent collision tumours of BA/CMPT and adenocarcinoma, true malignant degeneration in BA/CMPT or adenocarcinoma closely morphologically mimicking CMPT.…”

Section: Pulmonary Adenomasmentioning

confidence: 99%

“…1,3,4 However, several reports now exist in the literature describing malignant transformation. [7][8][9] It is not clear if these reports represent collision tumours of BA/ CMPT and adenocarcinoma, true malignant degeneration in BA/CMPT or adenocarcinoma closely morphologically mimicking CMPT. Regardless, strict diagnostic criteria should be used for BA/CMPT, including clear demonstration of a basal cell layer throughout the lesion and absence of significant nuclear atypia, to avoid misclassifying an adenocarcinoma as BA/CMPT.…”

Section: Pulmonary Adenomas B R O N C H I O L a R A D E N O M A / C I...mentioning

confidence: 99%

What's new in benign lung tumours?

Boland

2023

Histopathology

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While lung cancer is one of the most common malignancies routinely encountered by pathologists, benign pulmonary neoplasms are quite rare. However, it is important for pathologists to be familiar with the typical diagnostic features of benign lung tumors to avoid confusing them with malignant morphological mimics. There have also been intriguing discoveries in the genetics of benign pulmonary neoplasms in the past decade. This review will cover several of the most common benign lung tumors, including the diagnostic categories of pulmonary adenomas, bronchial papillomas, and benign mesenchymal tumors, with discussion of the current classification, differential diagnosis, and current knowledge regarding genetic drivers.

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stHGC: a self-supervised graph representation learning for spatial domain recognition with hybrid graph and spatial regularization

Wang,

Dai,

Duan

et al. 2024

Briefings in Bioinformatics

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Advancements in spatial transcriptomics (ST) technology have enabled the analysis of gene expression while preserving cellular spatial information, greatly enhancing our understanding of cellular interactions within tissues. Accurate identification of spatial domains is crucial for comprehending tissue organization. However, the effective integration of spatial location and gene expression still faces significant challenges. To address this challenge, we propose a novel self-supervised graph representation learning framework named stHGC for identifying spatial domains. Firstly, a hybrid neighbor graph is constructed by integrating different similarity metrics to represent spatial proximity and high-dimensional gene expression features. Secondly, a self-supervised graph representation learning framework is introduced to learn the representation of spots in ST data. Within this framework, the graph attention mechanism is utilized to characterize relationships between adjacent spots, and the self-supervised method ensures distinct representations for non-neighboring spots. Lastly, a spatial regularization constraint is employed to enable the model to retain the structural information of spatial neighbors. Experimental results demonstrate that stHGC outperforms state-of-the-art methods in identifying spatial domains across ST datasets with different resolutions. Furthermore, stHGC has been proven to be beneficial for downstream tasks such as denoising and trajectory inference, showcasing its scalability in handling ST data.

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Bronchiolar adenoma/ciliated muconodular papillary tumor mixed with adenocarcinoma in situ in the same tumor

Cited by 5 publications

References 13 publications

stGCL: A versatile cross-modality fusion method based on multi-modal graph contrastive learning for spatial transcriptomics

stGCL: A versatile cross-modality fusion method based on multi-modal graph contrastive learning for spatial transcriptomics

What's new in benign lung tumours?

stHGC: a self-supervised graph representation learning for spatial domain recognition with hybrid graph and spatial regularization

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