AIMC Topic: Neoplasm Grading

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Cone-beam computed tomography-based radiomics in prostate cancer: a mono-institutional study.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
PURPOSE: The purpose of the reported study was to investigate the value of cone-beam computed tomography (CBCT)-based radiomics for risk stratification and prediction of biochemical relapse in prostate cancer.

Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gle...

Soft Tissue Sarcoma: Preoperative MRI-Based Radiomics and Machine Learning May Be Accurate Predictors of Histopathologic Grade.

AJR. American journal of roentgenology
The purpose of this study was to assess the value of radiomics features for differentiating soft tissue sarcomas (STSs) of different histopathologic grades. The T1-weighted and fat-suppressed T2-weighted MR images of 70 STSs of varying grades (35 l...

Development and Validation of a Deep-learning Model to Assist With Renal Cell Carcinoma Histopathologic Interpretation.

Urology
OBJECTIVE: To develop and test the ability of a convolutional neural network (CNN) to accurately identify the presence of renal cell carcinoma (RCC) on histopathology specimens, as well as differentiate RCC histologic subtype and grade.

Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning.

Gut
OBJECTIVE: Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), hi...

Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review.

Oral oncology
This systematic review analyses and describes the application and diagnostic accuracy of Artificial Intelligence (AI) methods used for detection and grading of potentially malignant (pre-cancerous) and cancerous head and neck lesions using whole slid...

Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning.

Scientific reports
The Ki-67 index is an established prognostic factor in gastrointestinal neuroendocrine tumors (GI-NETs) and defines tumor grade. It is currently estimated by microscopically examining tumor tissue single-immunostained (SS) for Ki-67 and counting the ...