AIMC Topic: Prognosis

Clear Filters Showing 3471 to 3480 of 3837 articles

Added prognostic value of 3D deep learning-derived features from preoperative MRI for adult-type diffuse gliomas.

Neuro-oncology
BACKGROUND: To investigate the prognostic value of spatial features from whole-brain MRI using a three-dimensional (3D) convolutional neural network for adult-type diffuse gliomas.

A nomogram for predicting prognosis in patients with transjugular intrahepatic portosystemic shunt creation based on deep learning-derived spleen volume-to-platelet ratio.

The British journal of radiology
OBJECTIVES: The objective of our study was to develop a nomogram to predict post-transjugular intrahepatic portosystemic shunt (TIPS) survival in patients with cirrhosis based on CT images.

Deep Learning-based Fibrosis Extent on Computed Tomography Predicts Outcome of Fibrosing Interstitial Lung Disease Independent of Visually Assessed Computed Tomography Pattern.

Annals of the American Thoracic Society
Radiologic pattern has been shown to predict survival in patients with fibrosing interstitial lung disease. The additional prognostic value of fibrosis extent by quantitative computed tomography (CT) is unknown. We hypothesized that fibrosis extent...

Deep learning-based radiomics allows for a more accurate assessment of sarcopenia as a prognostic factor in hepatocellular carcinoma.

Journal of Zhejiang University. Science. B
Hepatocellular carcinoma (HCC) is one of the most common malignancies and is a major cause of cancer-related mortalities worldwide (Forner et al., 2018; He et al., 2023). Sarcopenia is a syndrome characterized by an accelerated loss of skeletal muscl...

Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma.

GigaScience
BACKGROUND: Deep learning has revolutionized medical image analysis in cancer pathology, where it had a substantial clinical impact by supporting the diagnosis and prognostic rating of cancer. Among the first available digital resources in the field ...

Predicting TNFRSF4 expression and prognosis in head and neck squamous cell carcinoma tissue: a pathological image analysis approach.

Polish journal of pathology : official journal of the Polish Society of Pathologists
Head and neck squamous cell carcinoma (HNSCC) exhibits a poor 5-year survival rate. TNFRSF4 is gaining attention in tumor therapy. The objective of this study was to forecast the expression of TNFRSF4 in HNSCC tissue using analysis of pathological im...

[CLINICAL EVALUATION OF THERAPEUTIC EFFECT PREDICTORS IN PEMBROLIZUMAB FOR ADVANCED UROTHELIAL CANCER].

Nihon Hinyokika Gakkai zasshi. The japanese journal of urology
(Purpose) We performed a clinical retrospective study on the evaluation of pembrolizumab treatment results for advanced urothelial cancer in our hospital. (Materials and Methods) Twenty-seven patients diagnosed with advanced or metastatic urothelial ...

Machine Learning-Based Pathomics Model to Predict the Prognosis in Clear Cell Renal Cell Carcinoma.

Technology in cancer research & treatment
Clear cell renal cell carcinoma (ccRCC) is a highly lethal urinary malignancy with poor overall survival (OS) rates. Integrating computer vision and machine learning in pathomics analysis offers potential for enhancing classification, prognosis, and ...

Predicting High-Grade Patterns in Stage I Solid Lung Adenocarcinoma: A Study of 371 Patients Using Refined Radiomics and Deep Learning-Guided CatBoost Classifier.

Technology in cancer research & treatment
INTRODUCTION: This study aimed to devise a diagnostic algorithm, termed the Refined Radiomics and Deep Learning Features-Guided CatBoost Classifier (RRDLC-Classifier), and evaluate its efficacy in predicting pathological high-grade patterns in patien...

Prediction of T Stage of Rectal Cancer After Neoadjuvant Therapy by Multi-Parameter Magnetic Resonance Radiomics Based on Machine Learning Algorithms.

Technology in cancer research & treatment
INTRODUCTION: Since the response of patients with rectal cancer (RC) to neoadjuvant therapy is highly variable, there is an urgent need to develop accurate methods to predict the post-treatment T (pT) stage. The purpose of this study was to evaluate ...