AIMC Topic: Neoplasms

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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...

Joint triplet loss with semi-hard constraint for data augmentation and disease prediction using gene expression data.

Scientific reports
The accurate prediction of patients with complex diseases, such as Alzheimer's disease (AD), as well as disease stages, including early- and late-stage cancer, is challenging owing to substantial variability among patients and limited availability of...

["New Modalities in Cancer Imaging and Therapy" XVth edition of the workshop organized by the network "Tumor Targeting, Imaging, Radiotherapies" of the Cancéropôle Grand-Ouest, 5-8 October 2022, France].

Bulletin du cancer
The fifteenth edition of the international workshop organized by the "Tumour Targeting and Radiotherapies network" of the Cancéropôle Grand-Ouest focused on the latest advances in internal and external radiotherapy from different disciplinary angles:...

Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types.

PeerJ
Machine learning (ML) includes a broad class of computer programs that improve with experience and shows unique strengths in performing tasks such as clustering, classification and regression. Over the past decade, microbial communities have been imp...

Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space.

Computer methods and programs in biomedicine
Positron emission tomography/computed tomography (PET/CT) is increasingly used in oncology, neurology, cardiology, and emerging medical fields. The success stems from the cohesive information that hybrid PET/CT imaging offers, surpassing the capabili...