AIMC Topic: Neoplasm Staging

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Deep learning-based virtual cytokeratin staining of gastric carcinomas to measure tumor-stroma ratio.

Scientific reports
The tumor-stroma ratio (TSR) determined by pathologists is subject to intra- and inter-observer variability. We aimed to develop a computational quantification method of TSR using deep learning-based virtual cytokeratin staining algorithms. Patients ...

An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients.

European journal of nuclear medicine and molecular imaging
PURPOSE: The identification of pathological mediastinal lymph nodes is an important step in the staging of lung cancer, with the presence of metastases significantly affecting survival rates. Nodes are currently identified by a physician, but this pr...

Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer.

International journal of molecular sciences
Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for patients with non-small-cell lung cancer (NSCLC). We proposed a machin...

The use of a next-generation sequencing-derived machine-learning risk-prediction model (OncoCast-MPM) for malignant pleural mesothelioma: a retrospective study.

The Lancet. Digital health
BACKGROUND: Current risk stratification for patients with malignant pleural mesothelioma based on disease stage and histology is inadequate. For some individuals with early-stage epithelioid tumours, a good prognosis by current guidelines can progres...

The impact of site-specific digital histology signatures on deep learning model accuracy and bias.

Nature communications
The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and dri...

Artificial intelligence-based radiomics models in endometrial cancer: A systematic review.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
BACKGROUND: Radiological preoperative assessment of endometrial cancer (EC) is in some cases not precise enough and its performances improvement could lead to a clinical benefit. Radiomics is a recent field of application of artificial intelligence (...

The predictive power of artificial intelligence on mediastinal lymphnode metastasis.

General thoracic and cardiovascular surgery
OBJECTIVE: The aim of this study was to create the preoperative predictive model on mediastinal lymph-node metastasis based on artificial intelligence in surgically resected lung adenocarcinoma.

A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods.

Annals of nuclear medicine
OBJECTIVE: This study evaluates the ability of several machine learning (ML) algorithms, developed using volumetric and texture data extracted from baseline F-FDG PET/CT studies performed initial staging of patient with esophageal cancer (EC), to pre...