AIMC Topic: Neoplasm Recurrence, Local
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Prediction of early recurrence in primary central nervous system lymphoma based on multimodal MRI-based radiomics: A preliminary study.
European journal of radiology
Apr 18, 2025
OBJECTIVES: To evaluate the role of multimodal magnetic resonance imaging radiomics features in predicting early recurrence of primary central nervous system lymphoma (PCNSL) and to investigate their correlation with patient prognosis.
Decoding Recurrence in Early-Stage and Locoregionally Advanced Non-Small Cell Lung Cancer: Insights From Electronic Health Records and Natural Language Processing.
JCO clinical cancer informatics
Apr 18, 2025
PURPOSE: Recurrences after curative resection in early-stage and locoregionally advanced non-small cell lung cancer (NSCLC) are common, necessitating a nuanced understanding of associated risk factors. This study aimed to establish a natural language...
Identification of Recurrence-associated Gene Signatures and Machine Learning-based Prediction in IDH-Wildtype Histological Glioblastoma.
Journal of molecular neuroscience : MN
Apr 14, 2025
Glioblastoma (GBM) is a highly aggressive brain tumor with frequent recurrence, yet the molecular mechanisms driving recurrence remain poorly understood. Identifying recurrence-associated genes may improve prognosis and treatment strategies. We appli...
Novel CT radiomics models for the postoperative prediction of early recurrence of resectable pancreatic adenocarcinoma: A single-center retrospective study in China.
Journal of applied clinical medical physics
Apr 11, 2025
PURPOSE: To assess the predictive capability of CT radiomics features for early recurrence (ER) of pancreatic ductal adenocarcinoma (PDAC).
A machine learning approach to risk-stratification of gastric cancer based on tumour-infiltrating immune cell profiles.
Annals of medicine
Apr 10, 2025
BACKGROUND: Gastric cancer (GC) is a highly heterogeneous disease, and the response of patients to clinical treatment varies substantially. There is no satisfactory strategy for predicting curative effects to date. We aimed to explore a new method fo...
Interpretable Machine Learning Radiomics Model Predicts 5-year Recurrence-Free Survival in Non-metastatic Clear Cell Renal Cell Carcinoma: A Multicenter and Retrospective Cohort Study.
Academic radiology
Apr 10, 2025
RATIONALE AND OBJECTIVES: To develop and validate a computed tomography (CT) radiomics-based interpretable machine learning (ML) model for predicting 5-year recurrence-free survival (RFS) in non-metastatic clear cell renal cell carcinoma (ccRCC).
Machine learning based radiomics approach for outcome prediction of meningioma - a systematic review.
F1000Research
Mar 25, 2025
INTRODUCTION: Meningioma is the most common brain tumor in adults. Magnetic resonance imaging (MRI) is the preferred imaging modality for assessing tumor outcomes. Radiomics, an advanced imaging technique, assesses tumor heterogeneity and identifies ...
Establishment of a deep-learning-assisted recurrent nasopharyngeal carcinoma detecting simultaneous tactic (DARNDEST) with high cost-effectiveness based on magnetic resonance images: a multicenter study in an endemic area.
Cancer imaging : the official publication of the International Cancer Imaging Society
Mar 24, 2025
BACKGROUND: To investigate the feasibility of detecting local recurrent nasopharyngeal carcinoma (rNPC) using unenhanced magnetic resonance images (MRI) and optimize a layered management strategy for follow-up with a deep learning model.
Discussion of a Simple Method to Generate Descriptive Images Using Predictive ResNet Model Weights and Feature Maps for Recurrent Cervix Cancer.
Tomography (Ann Arbor, Mich.)
Mar 20, 2025
BACKGROUND: Predictive models like Residual Neural Networks (ResNets) can use Magnetic Resonance Imaging (MRI) data to identify cervix tumors likely to recur after radiotherapy (RT) with high accuracy. However, there persists a lack of insight into m...
Denoised recurrence label-based deep learning for prediction of postoperative recurrence risk and sorafenib response in HCC.
BMC medicine
Mar 18, 2025
BACKGROUND: Pathological images of hepatocellular carcinoma (HCC) contain abundant tumor information that can be used to stratify patients. However, the links between histology images and the treatment response have not been fully unveiled.
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