AIMC Topic: Neoplasm Grading
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Deep learning model for grading carcinoma with Gini-based feature selection and linear production-inspired feature fusion.
Scientific reports
Jul 1, 2025
The most common types of kidneys and liver cancer are renal cell carcinoma (RCC) and hepatic cell carcinoma (HCC), respectively. Accurate grading of these carcinomas is essential for determining the most appropriate treatment strategies, including su...
Artificial Intelligence-Based Digital Histologic Classifier for Prostate Cancer Risk Stratification: Independent Blinded Validation in Patients Treated With Radical Prostatectomy.
JCO clinical cancer informatics
Jun 18, 2025
PURPOSE: Artificial intelligence (AI) tools that identify pathologic features from digitized whole-slide images (WSIs) of prostate cancer (CaP) generate data to predict outcomes. The objective of this study was to evaluate the clinical validity of an...
Predicting therapeutic clinical trial enrollment for adult patients with low- and high-grade glioma using supervised machine learning.
Science advances
Jun 4, 2025
Therapeutic clinical trial enrollment does not match glioma incidence across demographics. Traditional statistical methods have identified independent predictors of trial enrollment; however, our understanding of the interactions between these factor...
Molecular detection of hrHPV-induced high-grade squamous intraepithelial lesions of the cervix through a targeted RNA next generation sequencing assay.
Molecular medicine (Cambridge, Mass.)
May 30, 2025
BACKGROUND: Cervical cancer screening programs are increasingly relying on sensitive molecular approaches as primary tests to detect high-risk human papillomaviruses (hrHPV), the causative agents of cervix cancer. Although hrHPV infection is a pre-re...
MSFusion: A multi-source hybrid feature fusion network for accurate grading of invasive breast cancer using H&E-stained histopathological images.
Medical image analysis
May 23, 2025
Invasive breast cancer (IBC) is a prevalent malignant tumor in women, and precise grading plays a pivotal role in ensuring effective treatment and enhancing survival rates. However, accurately grading IBC presents a significant challenge due to its h...
Morphometric and radiomics analysis toward the prediction of epilepsy associated with supratentorial low-grade glioma in children.
Cancer imaging : the official publication of the International Cancer Imaging Society
May 19, 2025
OBJECTIVES: Understanding the impact of epilepsy on pediatric brain tumors is crucial to diagnostic precision and optimal treatment selection. This study investigated MRI radiomics features, tumor location, voxel-based morphometry (VBM) for gray matt...
Surgical and radiological outcomes of giant cell tumor of the bone: prognostic value of Campanacci grading and selective use of denosumab.
Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology
May 3, 2025
BACKGROUND: Advancements in diagnostic and therapeutic modalities for giant cell tumors of bone (GCTB) have introduced molecular and radiological tools that refine clinical decision-making. H3.3 G34W immunohistochemical staining has become a routine ...
Utilizing explainable machine learning for progression-free survival prediction in high-grade serous ovarian cancer: insights from a prospective cohort study.
International journal of surgery (London, England)
May 1, 2025
BACKGROUND: High-grade serous ovarian cancer (HGSOC) remains one of the most challenging gynecological malignancies, with over 70% of ovarian cancer patients ultimately experiencing disease progression. The current prognostic tools for progression-fr...
Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector.
Scientific reports
Apr 30, 2025
Despite being one of the most prevalent cancers, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection and treatment. Currently, several screening and diagnostic tests are required to be carried out in ord...
Tumor grade-titude: XGBoost radiomics paves the way for RCC classification.
European journal of radiology
Apr 29, 2025
This study aimed to develop and evaluate a non-invasive XGBoost-based machine learning model using radiomic features extracted from pre-treatment CT images to differentiate grade 4 renal cell carcinoma (RCC) from lower-grade tumours. A total of 102 R...
Kidney Neoplasms
Carcinoma, Renal Cell
Boosting Machine Learning Algorithms
Middle Aged
Contrast Media
Neoplasm Grading
Radiographic Image Interpretation, Computer-Assisted
Female
Adult
Reproducibility of Results
Humans
Sensitivity and Specificity
Aged
Machine Learning
Radiomics
Aged, 80 and over
Tomography, X-Ray Computed
Male
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