AIMC Topic: Neoplasm Grading

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Hybrid Unified Deep Learning Network for Highly Precise Gleason Grading of Prostate Cancer.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Prostate cancer is one of the leading causes of death around the world. The manual Gleason grading of prostate cancer after histological analysis of stained tissue slides is rigorous, time-consuming and also suffers from subjectivity among experts. I...

Automating the Capture of Structured Pathology Data for Prostate Cancer Clinical Care and Research.

JCO clinical cancer informatics
PURPOSE: Cancer pathology findings are critical for many aspects of care but are often locked away as unstructured free text. Our objective was to develop a natural language processing (NLP) system to extract prostate pathology details from postopera...

A novel machine learning-derived decision tree including uPA/PAI-1 for breast cancer care.

Clinical chemistry and laboratory medicine
Background uPA and PAI-1 are breast cancer biomarkers that evaluate the benefit of chemotherapy (CT) for HER2-negative, estrogen receptor-positive, low or intermediate grade patients. Our objectives were to observe clinical routine use of uPA/PAI-1 a...

Artificial intelligence at the intersection of pathology and radiology in prostate cancer.

Diagnostic and interventional radiology (Ankara, Turkey)
Pathologic grading plays a key role in prostate cancer risk stratification and treatment selection, traditionally assessed from systemic core needle biopsies sampled throughout the prostate gland. Multiparametric magnetic resonance imaging (mpMRI) ha...

Prediction of ISUP grading of clear cell renal cell carcinoma using support vector machine model based on CT images.

Medicine
BACKGROUND: To explore whether radiomics combined with computed tomography (CT) images can be used to establish a model for differentiating high grade (International Society of Urological Pathology [ISUP] grade III-IV) from low-grade (ISUP I-II) clea...

Bone-Cancer Assessment and Destruction Pattern Analysis in Long-Bone X-ray Image.

Journal of digital imaging
Bone cancer originates from bone and rapidly spreads to the rest of the body affecting the patient. A quick and preliminary diagnosis of bone cancer begins with the analysis of bone X-ray or MRI image. Compared to MRI, an X-ray image provides a low-c...

Nomogram and Artificial Neural Network for Prognostic Performance on the Albumin-Bilirubin Grade for Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization.

Journal of vascular and interventional radiology : JVIR
PURPOSE: To construct the albumin-bilirubin (ALBI) grade and the Child-Turcotte-Pugh (CTP) score based on nomograms, as well as to develop an artificial neural network (ANN) to compare the prognostic performance of the 2 scores for hepatocellular car...

The Tumor Target Segmentation of Nasopharyngeal Cancer in CT Images Based on Deep Learning Methods.

Technology in cancer research & treatment
Radiotherapy is the main treatment strategy for nasopharyngeal carcinoma. A major factor affecting radiotherapy outcome is the accuracy of target delineation. Target delineation is time-consuming, and the results can vary depending on the experience ...

[An artificial neural network model for glioma grading using image information].

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences
To explore the feasibility and efficacy of artificial neural network for differentiating high-grade glioma and low-grade glioma using image information.
 Methods: A total of 130 glioma patients with confirmed pathological diagnosis were selected retr...

Automated Renal Cancer Grading Using Nuclear Pleomorphic Patterns.

JCO clinical cancer informatics
PURPOSE: Nuclear pleomorphic patterns are essential for Fuhrman grading of clear cell renal cell carcinoma (ccRCC). Manual observation of renal histopathologic slides may lead to subjective and inconsistent assessment between pathologists. An automat...