AI Medical Compendium Topic:
Neoplasms

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Exploring the NRF2-TP53 Signaling Network Through Machine Learning and Pan-Cancer Analysis: Identifying Potential targets for Cancer Prognosis Related to Oxidative Stress.

Advanced biology
Oxidative stress (OXS) is closely related to tumor prognosis and immune response, while TP53 integrated with NRF2 is closely associated with the regulation of cancer-related OXS. Hence, constructing a TP53-NRF2 integrated OXS signature of pan-cancer ...

Decoding the glycoproteome: a new frontier for biomarker discovery in cancer.

Journal of hematology & oncology
Cancer early detection and treatment response prediction continue to pose significant challenges. Cancer liquid biopsies focusing on detecting circulating tumor cells (CTCs) and DNA (ctDNA) have shown enormous potential due to their non-invasive natu...

Robotic Actuation-Mediated Quantitative Mechanogenetics for Noninvasive and On-Demand Cancer Therapy.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Cell mechanotransduction signals are important targets for physical therapy. However, current physiotherapy heavily relies on ultrasound, which is generated by high-power equipment or amplified by auxiliary drugs, potentially causing undesired side e...

Application of deep learning in radiation therapy for cancer.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
In recent years, with the development of artificial intelligence, deep learning has been gradually applied to clinical treatment and research. It has also found its way into the applications in radiotherapy, a crucial method for cancer treatment. Thi...

[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...