AIMC Topic: Adenocarcinoma of Lung

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Machine-learning and combined analysis of single-cell and bulk-RNA sequencing identified a DC gene signature to predict prognosis and immunotherapy response for patients with lung adenocarcinoma.

Journal of cancer research and clinical oncology
BACKGROUND: Innate immune effectors, dendritic cells (DCs), influence cancer prognosis and immunotherapy significantly. As such, dendritic cells are important in killing tumors and influencing tumor microenvironment, whereas their roles in lung adeno...

Deep learning-based diagnosis of histopathological patterns for invasive non-mucinous lung adenocarcinoma using semantic segmentation.

BMJ open
OBJECTIVES: The application of artificial intelligence (AI) to the field of pathology has facilitated the development of digital pathology, hence, making AI-assisted diagnosis possible. Due to the variety of lung cancers and the subjectivity of manua...

Development of a novel combined nomogram integrating deep-learning-assisted CT texture and clinical-radiological features to predict the invasiveness of clinical stage IA part-solid lung adenocarcinoma: a multicentre study.

Clinical radiology
AIM: To develop a novel combined nomogram based on deep-learning-assisted computed tomography (CT) texture (DL-TA) and clinical-radiological features for the preoperative prediction of invasiveness in patients with clinical stage IA lung adenocarcino...

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma.

European radiology
OBJECTIVES: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements.

Comparison and fusion prediction model for lung adenocarcinoma with micropapillary and solid pattern using clinicoradiographic, radiomics and deep learning features.

Scientific reports
To investigate whether the combination scheme of deep learning score (DL-score) and radiomics can improve preoperative diagnosis in the presence of micropapillary/solid (MPP/SOL) patterns in lung adenocarcinoma (ADC). A retrospective cohort of 514 co...

Deep learning-based classification and spatial prognosis risk score on whole-slide images of lung adenocarcinoma.

Histopathology
AIMS: Classification of histological patterns in lung adenocarcinoma (LUAD) is critical for clinical decision-making, especially in the early stage. However, the inter- and intraobserver subjectivity of pathologists make the quantification of histolo...

The diagnostic and prognostic value of radiomics and deep learning technologies for patients with solid pulmonary nodules in chest CT images.

BMC cancer
BACKGROUND: Solid pulmonary nodules are different from subsolid nodules and the diagnosis is much more challenging. We intended to evaluate the diagnostic and prognostic value of radiomics and deep learning technologies for solid pulmonary nodules.

Measuring pure ground-glass nodules on computed tomography: assessing agreement between a commercially available deep learning algorithm and radiologists' readings.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Deep learning algorithms (DLAs) could enable automatic measurements of solid portions of mixed ground-glass nodules (mGGNs) in agreement with the invasive component sizes measured during pathologic examinations. However, the measurement o...

Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Identification of novel non-invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle-bas...