AIMC Topic: Adenocarcinoma

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A tree-based machine learning model to approach morphologic assessment of malignant salivary gland tumors.

Annals of diagnostic pathology
Malignant salivary gland tumors represent a challenge for pathologists due to their low frequency and morphologic overlap. In recent years machine learning techniques have been applied to the field of pathology to improve diagnostic performance. In t...

Characterizing impact of positive lymph node number in endometrial cancer using machine-learning: A better prognostic indicator than FIGO staging?

Gynecologic oncology
BACKGROUND: Number of involved lymph nodes (LNs) is a crucial stratification factor in staging of numerous disease sites, but has not been incorporated for endometrial cancer. We evaluated whether number of involved LNs provide improved prognostic va...

A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma.

European journal of radiology
OBJECTIVE: To develop and validate a deep learning nomogram (DLN) model constructed from non-contrast computed tomography (CT) images for discriminating minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) in patients with subso...

Identification of clinical trait-related small RNA biomarkers with weighted gene co-expression network analysis for personalized medicine in endocervical adenocarcinoma.

Aging
Endocervical adenocarcinoma (EAC) is an aggressive type of endocervical cancer. At present, molecular research on EAC mainly focuses on the genome and mRNA transcriptome, the investigation of small RNAs in EAC has not been fully described. Here, we s...

Highly accurate diagnosis of lung adenocarcinoma and squamous cell carcinoma tissues by deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Intraoperative detection of the marginal tissues is the last and most important step to complete the resection of adenocarcinoma and squamous cell carcinoma. However, the current intraoperative diagnosis is time-consuming and requires numerous steps ...

Simultaneous Recognition of Atrophic Gastritis and Intestinal Metaplasia on White Light Endoscopic Images Based on Convolutional Neural Networks: A Multicenter Study.

Clinical and translational gastroenterology
INTRODUCTION: Patients with atrophic gastritis (AG) or gastric intestinal metaplasia (GIM) have elevated risk of gastric adenocarcinoma. Endoscopic screening and surveillance have been implemented in high incidence countries. The study aimed to evalu...

Multi-institutional development and external validation of machine learning-based models to predict relapse risk of pancreatic ductal adenocarcinoma after radical resection.

Journal of translational medicine
BACKGROUND: Surgical resection is the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC) and the survival of patients after radical resection is closely related to relapse. We aimed to develop models to predict the risk o...

Label-free classification of dead and live colonic adenocarcinoma cells based on 2D light scattering and deep learning analysis.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
The measurement of cell viability plays an essential role in the area of cell biology. At present, the common methods for cell viability assay mainly on the responses of cells to different dyes. However, the additional steps of cell staining will con...