IEEE journal of biomedical and health informatics
40030195
The machine learning-based model is a promising paradigm for predicting invasive disease events (iDEs) in breast cancer. Feature selection (FS) is an essential preprocessing technique employed to identify the pertinent features for the prediction mod...
Cancer imaging : the official publication of the International Cancer Imaging Society
40022261
OBJECTIVE: Exploring the construction of a fusion model that combines radiomics and deep learning (DL) features is of great significance for the precise preoperative diagnosis of meningioma sinus invasion.
BACKGROUND: Microvascular invasion (MVI) is an important risk factor for early postoperative recurrence of hepatocellular carcinoma (HCC). Based on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance ...
To develop a deep learning (DL) model based on MRI to predict muscle-invasive bladder cancer (MIBC). A total of 559 patients, including 521 patients in our center and 38 patients in external centers were collected from 2012 to 2023 to construct the D...
BACKGROUNDS: Exploring the molecular features that drive breast cancer invasion and migration remains an important biological and clinical challenge. In recent years, the use of interpretable machine learning models has enhanced our understanding of ...
PURPOSE: We aimed to systematically assess the value of radiomics/machine learning (ML) models for diagnosing microvascular invasion (MVI) in patients with cholangiocarcinoma (CCA) using various radiologic modalities.
Cancer imaging : the official publication of the International Cancer Imaging Society
40075547
PURPOSE: This study aimed to select robust features against lung motion in a phantom study and use them as input to feature selection algorithms and machine learning classifiers in a clinical study to predict the lymphovascular invasion (LVI) of non-...
Hepatocellular carcinoma (HCC) is an exceedingly aggressive form of cancer that often carries a poor prognosis, especially when it is complicated by the presence of microvascular invasion (MVI). Identifying patients at high risk of MVI is crucial for...
BACKGROUND: Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer characterized by a high risk of lymph node metastasis (LNM). The study aimed to identify predictors of LNM and to develop a machine learning (ML)-based risk predi...
Muscle-Invasive Bladder Cancer (MIBC) is a more aggressive disease than non-muscle-invasive bladder cancer (NMIBC), with greater chances of metastasis. We sought to develop machine learning (ML) models to predict metastasis and prognosis in MIBC pati...