AI Medical Compendium Topic:
Neoplasms

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From cancer big data to treatment: Artificial intelligence in cancer research.

The journal of gene medicine
In recent years, developing the idea of "cancer big data" has emerged as a result of the significant expansion of various fields such as clinical research, genomics, proteomics and public health records. Advances in omics technologies are making a si...

A survey on cancer detection via convolutional neural networks: Current challenges and future directions.

Neural networks : the official journal of the International Neural Network Society
Cancer is a condition in which abnormal cells uncontrollably split and damage the body tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical images play an indispensable role in detecting various cancers; however,...

Prediction of cancer recurrence based on compact graphs of whole slide images.

Computers in biology and medicine
Cancer recurrence is one of the primary causes of patient mortality following treatment, indicating increased aggressiveness of cancer cells and difficulties in achieving a cure. A critical step to improve patients' survival is accurately predicting ...

Transitioning from Da Vinci Si to Xi: assessing surgical outcomes at a high-volume robotic center.

World journal of urology
PURPOSE: In the emerging field of robotics, only few studies investigated the transition between different robotic platforms in terms of surgical outcomes. We aimed at assessing surgical outcomes of patients receiving robot-assisted radical prostatec...

Current Strengths and Weaknesses of ChatGPT as a Resource for Radiation Oncology Patients and Providers.

International journal of radiation oncology, biology, physics
PURPOSE: Chat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence program that uses natural language processing to generate conversational-style responses to questions or inputs, is increasingly being used by both patients and he...

Open science practices need substantial improvement in prognostic model studies in oncology using machine learning.

Journal of clinical epidemiology
OBJECTIVE: To describe the frequency of open science practices in a contemporary sample of studies developing prognostic models using machine learning methods in the field of oncology.

Workflow for Evaluating Normalization Tools for Omics Data Using Supervised and Unsupervised Machine Learning.

Journal of the American Society for Mass Spectrometry
To achieve high quality omics results, systematic variability in mass spectrometry (MS) data must be adequately addressed. Effective data normalization is essential for minimizing this variability. The abundance of approaches and the data-dependent n...

Percutaneous liver interventions with robotic systems: a systematic review of available clinical solutions.

The British journal of radiology
OBJECTIVE: Robotic-guided interventions are emerging techniques that are gradually becoming a common tool for performing biopsies and tumor ablations in liver. This systematic review aims to evaluate their advancements, challenges, and outcomes.

Translation of tissue-based artificial intelligence into clinical practice: from discovery to adoption.

Oncogene
Digital pathology (DP), or the digitization of pathology images, has transformed oncology research and cancer diagnostics. The application of artificial intelligence (AI) and other forms of machine learning (ML) to these images allows for better inte...