Differential Gene Expression of Tumors Undergoing Lepidic-Acinar Transition in Lung Adenocarcinoma DOI Creative Commons
Ethan N. Okoshi, Shiro Fujita, Kris Lami

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 20, 2024

ABSTRACT Lung adenocarcinoma is the most frequent subtype of thoracic malignancy, which itself largest contributor to cancer mortality. The lepidic a non-invasive tumor morphology, whereas acinar represents one invasive morphologies. This study investigates transition from an in context lung adenocarcinoma. Patients with pathologically confirmed mixed tumors consented analysis RNA-seq data extracted each area separately. included 17 patients found exhibit lepidic-acinar transition. 87 genes were be differentially expressed between and subtypes, 44 significantly upregulated samples, 43 samples. Gene ontology showed that many related immune response. Immune deconvolution there was higher proportion M1 macrophages total B cells areas. Immunohistochemistry mainly localized tertiary lymphoid structures area. first investigate molecular features transitional tumors. Immunological dynamics are presumed involved this subtype. Further research should conducted elucidate progression disease

Language: Английский

Deep learning radiopathomics predicts targeted therapy sensitivity in EGFR-mutant lung adenocarcinoma DOI Creative Commons
Taotao Yang,

Xianqi Wang,

Yuan Jin

et al.

Journal of Translational Medicine, Journal Year: 2025, Volume and Issue: 23(1)

Published: April 29, 2025

Language: Английский

Citations

0
Progression to invasive carcinoma: cellular activities and immune-related pathways define the lepidic and acinar subtypes of lung adenocarcinoma DOI Creative Commons
Ethan N. Okoshi, Shiro Fujita, Kris Lami

et al.

Pathology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

0
Toward Optimizing the Impact of Digital Pathology and Augmented Intelligence on Issues of Diagnosis, Grading, Staging and Classification DOI Creative Commons
Lewis Hassell, Marika L. Forsythe, Ami Bhalodia

et al.

Modern Pathology, Journal Year: 2025, Volume and Issue: unknown, P. 100765 - 100765

Published: April 1, 2025

Language: Английский

Citations

0
The Grading System for Lung Adenocarcinoma: Brief Review of its Prognostic Performance and Future Directions DOI
José G. Mantilla, André L. Moreira

Advances in Anatomic Pathology, Journal Year: 2024, Volume and Issue: 31(5), P. 283 - 288

Published: April 26, 2024

Histologic grading of tumors is associated with prognosis in many organs. In the lung, most recent system proposed by International association for Study Lung Cancer (IASLC) and adopted World Health Organization (WHO) incorporates predominant histologic pattern, as well presence high-grade architectural patterns (solid, micropapillary, complex glandular pattern) proportions >20% tumor surface. This has shown improved prognostic ability when compared prior based on pattern alone, across different patient populations. Interobserver agreement moderate to excellent, depending study. IASLC/WHO been correlate molecular alterations PD-L1 expression cells. Recent studies interrogating gene correlation grade microenvironment that can further stratify risk recurrence. The use machine learning algorithms nonmucinous adenocarcinoma under this accuracy comparable expert pulmonary pathologists. Future directions include evaluation context adjuvant neoadjuvant therapies, development better indicators mucinous adenocarcinoma.

Language: Английский

Citations

2
Artificial Intelligence and Lung Pathology DOI

Emanuel Caranfil,

Kris Lami, Wataru Uegami

et al.

Advances in Anatomic Pathology, Journal Year: 2024, Volume and Issue: 31(5), P. 344 - 351

Published: May 23, 2024

This manuscript provides a comprehensive overview of the application artificial intelligence (AI) in lung pathology, particularly diagnosis cancer. It discusses various AI models designed to support pathologists and clinicians. supporting are standardize diagnosis, score PD-L1 status, tumor cellularity count, indicating explainability for pathologic judgements. Several predict outcomes beyond clinical like patients’ survival molecular alterations. The emphasizes potential enhance accuracy efficiency while also addressing challenges future directions integrating into practice.

Language: Английский

Citations

2
Advancing Automatic Gastritis Diagnosis DOI
Mengke Ma, Xixi Zeng, Linhao Qu

et al.

American Journal Of Pathology, Journal Year: 2024, Volume and Issue: 194(8), P. 1538 - 1549

Published: May 17, 2024

Language: Английский

Citations

1
Deep Learning Classification and Quantification of Pejorative and Nonpejorative Architectures in Resected Hepatocellular Carcinoma from Digital Histopathologic Images DOI
Astrid Laurent-Bellue, Aymen Sadraoui,

Laura Claude

et al.

American Journal Of Pathology, Journal Year: 2024, Volume and Issue: 194(9), P. 1684 - 1700

Published: June 13, 2024

Language: Английский

Citations

1
Global bibliometric mapping of the research trends in artificial intelligence-based digital pathology for lung cancer over the past two decades DOI Creative Commons

Dandan Xiong,

Rong‐Quan He, Zhi‐Guang Huang

et al.

Digital Health, Journal Year: 2024, Volume and Issue: 10

Published: Jan. 1, 2024

Background and Objective The rapid development of computer technology has led to a revolutionary transformation in artificial intelligence (AI)-assisted healthcare. integration whole-slide imaging with AI algorithms facilitated the digital pathology for lung cancer (LC). However, there is lack comprehensive scientometric analysis this field. Methods A bibliometric was conducted on 197 publications related LC from 502 institutions across 39 countries, published 97 academic journals Web Science Core Collection between 2004 2023. Results Our identified United States China as primary research nations field LC. it important note that current primarily consists independent studies among emphasizing necessity strengthening collaboration data sharing nations. focus challenge lie enhancing accuracy classification prediction through improved deep learning algorithms. multi-omics presents promising future direction. Additionally, researchers are increasingly exploring application immunotherapy patients. Conclusions In conclusion, study provides knowledge framework LC, highlighting trends, hotspots, gaps It also theoretical basis clinical decision-making

Language: Английский

Citations

1
Establishment of artificial intelligence model for precise histological subtyping of lung adenocarcinoma and its application to quantitative and spatial analysis DOI

Eisuke Miura,

Katsura Emoto, Tokiya Abe

et al.

Japanese Journal of Clinical Oncology, Journal Year: 2024, Volume and Issue: 54(9), P. 1009 - 1023

Published: May 17, 2024

The histological subtype of lung adenocarcinoma is a major prognostic factor. We developed new artificial intelligence model to classify images into seven subtypes and adopted the for whole-slide investigate relationship between distribution clinicopathological factors.

Language: Английский

Citations

0
Differential Gene Expression of Tumors Undergoing Lepidic-Acinar Transition in Lung Adenocarcinoma DOI Creative Commons
Ethan N. Okoshi, Shiro Fujita, Kris Lami

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 20, 2024

ABSTRACT Lung adenocarcinoma is the most frequent subtype of thoracic malignancy, which itself largest contributor to cancer mortality. The lepidic a non-invasive tumor morphology, whereas acinar represents one invasive morphologies. This study investigates transition from an in context lung adenocarcinoma. Patients with pathologically confirmed mixed tumors consented analysis RNA-seq data extracted each area separately. included 17 patients found exhibit lepidic-acinar transition. 87 genes were be differentially expressed between and subtypes, 44 significantly upregulated samples, 43 samples. Gene ontology showed that many related immune response. Immune deconvolution there was higher proportion M1 macrophages total B cells areas. Immunohistochemistry mainly localized tertiary lymphoid structures area. first investigate molecular features transitional tumors. Immunological dynamics are presumed involved this subtype. Further research should conducted elucidate progression disease

Language: Английский

Citations

0