AIMC Topic: Neoplasm Invasiveness
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Deep learning-based prediction of tumor aggressiveness in RCC using multiparametric MRI: a pilot study.
International urology and nephrology
Dec 13, 2024
OBJECTIVE: To investigate the value of multiparametric magnetic resonance imaging (MRI) as a non-invasive method to predict the aggressiveness of renal cell carcinoma (RCC) by developing a convolutional neural network (CNN) model and fusing it with c...
Personalized predictions of Glioblastoma infiltration: Mathematical models, Physics-Informed Neural Networks and multimodal scans.
Medical image analysis
Dec 12, 2024
Predicting the infiltration of Glioblastoma (GBM) from medical MRI scans is crucial for understanding tumor growth dynamics and designing personalized radiotherapy treatment plans. Mathematical models of GBM growth can complement the data in the pred...
The Value of Machine Learning-based Radiomics Model Characterized by PET Imaging with Ga-FAPI in Assessing Microvascular Invasion of Hepatocellular Carcinoma.
Academic radiology
Dec 7, 2024
RATIONALE AND OBJECTIVES: This study aimed to develop a radiomics model characterized by Ga-fibroblast activation protein inhibitors (FAPI) positron emission tomography (PET) imaging to predict microvascular invasion (MVI) of hepatocellular carcinoma...
Computed tomography-based fully automated artificial intelligence model to predict extrapancreatic perineural invasion in pancreatic ductal adenocarcinoma.
International journal of surgery (London, England)
Dec 1, 2024
BACKGROUND: Extrapancreatic perineural invasion (EPNI) increases the risk of postoperative recurrence in pancreatic ductal adenocarcinoma (PDAC). This study aimed to develop and validate a computed tomography (CT)-based, fully automated preoperative ...
MRI-based radiomic and machine learning for prediction of lymphovascular invasion status in breast cancer.
BMC medical imaging
Nov 27, 2024
OBJECTIVE: Lymphovascular invasion (LVI) is critical for the effective treatment and prognosis of breast cancer (BC). This study aimed to investigate the value of eight machine learning models based on MRI radiomic features for the preoperative predi...
A Predictive Model Integrating AI Recognition Technology and Biomarkers for Lung Nodule Assessment.
The Thoracic and cardiovascular surgeon
Nov 26, 2024
BACKGROUND: Lung cancer is the most prevalent and lethal cancer globally, necessitating accurate differentiation between benign and malignant pulmonary nodules to guide treatment decisions. This study aims to develop a predictive model that integrat...
Decision Support Techniques
Retrospective Studies
Tomography, X-Ray Computed
Female
Aged
Neoplasm Invasiveness
Biomarkers, Tumor
Predictive Value of Tests
Humans
Male
Artificial Intelligence
Middle Aged
Reproducibility of Results
Lung Neoplasms
Risk Factors
Multiple Pulmonary Nodules
Solitary Pulmonary Nodule
Automatic pituitary adenoma segmentation and identification of cavernous sinus invasion via multitask learning.
Clinical radiology
Nov 21, 2024
AIM: This study aimed to develop a multitask deep learning model for pituitary macroadenoma (PMA) segmentation and identification of cavernous sinus (CS) invasion.
Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form.
PloS one
Nov 21, 2024
BACKGROUND AND OBJECTIVE: Detecting patients at high risk of occurrence of an Invasive Disease Event after a first diagnosis of breast cancer, such as recurrence, distant metastasis, contralateral tumor and second tumor, could support clinical decisi...
Deep Learning Predicts Lymphovascular Invasion Status in Muscle Invasive Bladder Cancer Histopathology.
Annals of surgical oncology
Oct 29, 2024
BACKGROUND: Lymphovascular invasion (LVI) is linked to poor prognosis in patients with muscle-invasive bladder cancer (MIBC). Accurately identifying the LVI status in MIBC patients is crucial for effective risk stratification and precision treatment....
Decision Fusion Model for Predicting Microvascular Invasion in Hepatocellular Carcinoma Based on Multi-MR Habitat Imaging and Machine-Learning Classifiers.
Academic radiology
Oct 28, 2024
RATIONALE AND OBJECTIVES: Accurate prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for guiding treatment. This study evaluates and compares the performance of clinicoradiologic, traditional radiomics, deep-lear...
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