AIMC Topic: Neoplasm Grading

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An integrated breast cancer risk assessment and management model based on fuzzy cognitive maps.

Computer methods and programs in biomedicine
BACKGROUND: There is a growing demand for women to be classified into different risk groups of developing breast cancer (BC). The focus of the reported work is on the development of an integrated risk prediction model using a two-level fuzzy cognitiv...

Selective invocation of shape priors for deformable segmentation and morphologic classification of prostate cancer tissue microarrays.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are computationally expensive. There are instances in an image where no overlapping objects are present and applying thes...

Towards semantic-driven high-content image analysis: an operational instantiation for mitosis detection in digital histopathology.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
This study concerns a novel symbolic cognitive vision framework emerged from the Cognitive Microscopy (MICO(1)) initiative. MICO aims at supporting the evolution towards digital pathology, by studying cognitive clinical-compliant protocols involving ...

Resting state fMRI feature-based cerebral glioma grading by support vector machine.

International journal of computer assisted radiology and surgery
PURPOSEĀ : Tumor grading plays an essential role in the optimal selection of solid tumor treatment. Noninvasive methods are needed for clinical grading of tumors. This study aimed to extract parameters of resting state blood oxygenation level-dependen...

MRI-based radiomics for differentiating high-grade from low-grade clear cell renal cell carcinoma: a systematic review and meta-analysis.

Abdominal radiology (New York)
PURPOSE: High-grade clear cell renal cell carcinoma (ccRCC) is linked to lower survival rates and more aggressive disease progression. This study aims to assess the diagnostic performance of MRI-derived radiomics as a non-invasive approach for pre-op...

MRI Radiomics and Automated Habitat Analysis Enhance Machine Learning Prediction of Bone Metastasis and High-Grade Gleason Scores in Prostate Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: To explore the value of machine learning models based on MRI radiomics and automated habitat analysis in predicting bone metastasis and high-grade pathological Gleason scores in prostate cancer.

Multimodal MRI radiomics enhances epilepsy prediction in pediatric low-grade glioma patients.

Journal of neuro-oncology
BACKGROUND: Determining whether pediatric patients with low-grade gliomas (pLGGs) have tumor-related epilepsy (GAE) is a crucial aspect of preoperative evaluation. Therefore, we aim to propose an innovative, machine learning- and deep learning-based ...