AIMC Topic: Neoplasms

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[Development of an artificial intelligence system to improve cancer clinical trial eligibility screening].

Bulletin du cancer
INTRODUCTION: The recruitment step of all clinical trials is time consuming, harsh and generate extra costs. Artificial intelligence tools could improve recruitment in order to shorten inclusion phase. The objective was to assess the performance of a...

Technical and functional design considerations for a real-world interpretable AI solution for NIR perfusion analysis (including cancer).

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Near infrared (NIR) analysis of tissue perfusion via indocyanine green fluorescence assessment is performed clinically during surgery for a range of indications. Its usefulness can potentially be further enhanced through the application of interpreta...

Deep learning algorithm for the automated detection and classification of nasal cavity mass in nasal endoscopic images.

PloS one
Nasal endoscopy is routinely performed to distinguish the pathological types of masses. There is a lack of studies on deep learning algorithms for discriminating a wide range of endoscopic nasal cavity mass lesions. Therefore, we aimed to develop an ...

MOB-CBAM: A dual-channel attention-based deep learning generalizable model for breast cancer molecular subtypes prediction using mammograms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Deep Learning models have emerged as a significant tool in generating efficient solutions for complex problems including cancer detection, as they can analyze large amounts of data with high efficiency and performance. Recen...

An integrative machine learning model for the identification of tumor T-cell antigens.

Bio Systems
The escalating global incidence of cancer poses significant health challenges, underscoring the need for innovative and more efficacious treatments. Cancer immunotherapy, a promising approach leveraging the body's immune system against cancer, emerge...

Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review.

Expert review of pharmacoeconomics & outcomes research
INTRODUCTION: Patient-reported outcomes (PROs; symptoms, functional status, quality-of-life) expressed in the 'free-text' or 'unstructured' format within clinical notes from electronic health records (EHRs) offer valuable insights beyond biological a...

Pan-cancer image segmentation based on feature pyramids and Mask R-CNN framework.

Medical physics
BACKGROUND: Cancer, a disease with a high mortality rate, poses a great threat to patients' physical and mental health and can lead to huge medical costs and emotional damage. With the continuous development of artificial intelligence technologies, d...

Assessment of bias in scoring of AI-based radiotherapy segmentation and planning studies using modified TRIPOD and PROBAST guidelines as an example.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Studies investigating the application of Artificial Intelligence (AI) in the field of radiotherapy exhibit substantial variations in terms of quality. The goal of this study was to assess the amount of transparency and bias in...

Comparison of perioperative outcomes between robot-assisted adrenalectomy and laparoscopic adrenalectomy: a propensity score matching analysis.

Journal of robotic surgery
This study aimed to evaluate and compare the perioperative outcomes of robot-assisted adrenalectomy (RAA) and laparoscopic adrenalectomy (LA) using propensity score matching. This retrospective study included 395 patients who underwent minimally inva...