AI Medical Compendium Topic:
Neoplasms

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Graph based multi-scale neighboring topology deep learning for kidney and tumor segmentation.

Physics in medicine and biology
Effective learning and modelling of spatial and semantic relations between image regions in various ranges are critical yet challenging in image segmentation tasks.We propose a novel deep graph reasoning model to learn from multi-order neighborhood t...

Artificial intelligence for prediction of response to cancer immunotherapy.

Seminars in cancer biology
Artificial intelligence (AI) indicates the application of machines to imitate intelligent behaviors for solving complex tasks with minimal human intervention, including machine learning and deep learning. The use of AI in medicine improves health-car...

Multiple instance neural networks based on sparse attention for cancer detection using T-cell receptor sequences.

BMC bioinformatics
Early detection of cancers has been much explored due to its paramount importance in biomedical fields. Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to ...

Microfluidics guided by deep learning for cancer immunotherapy screening.

Proceedings of the National Academy of Sciences of the United States of America
Immunocyte infiltration and cytotoxicity play critical roles in both inflammation and immunotherapy. However, current cancer immunotherapy screening methods overlook the capacity of the T cells to penetrate the tumor stroma, thereby significantly lim...

Fractional transit compartment model for describing drug delayed response to tumors using Mittag-Leffler distribution on age-structured PKPD model.

PloS one
The response of a cell population is often delayed relative to drug injection, and individual cells in a population of cells have a specific age distribution. The application of transit compartment models (TCMs) is a common approach for describing th...

Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy.

Nature communications
In radiotherapy for cancer patients, an indispensable process is to delineate organs-at-risk (OARs) and tumors. However, it is the most time-consuming step as manual delineation is always required from radiation oncologists. Herein, we propose a ligh...

Artificial Intelligence: The Elephant in the Tumor Board Room.

Academic medicine : journal of the Association of American Medical Colleges

Prediction of chemotherapy-related complications in pediatric oncology patients: artificial intelligence and machine learning implementations.

Pediatric research
Although the overall incidence of pediatric oncological diseases tends to increase over the years, it is among the rare diseases of the pediatric population. The diagnosis, treatment, and healthcare management of this group of diseases are important....

Magnetic torque-driven living microrobots for increased tumor infiltration.

Science robotics
Biohybrid bacteria-based microrobots are increasingly recognized as promising externally controllable vehicles for targeted cancer therapy. Magnetic fields in particular have been used as a safe means to transfer energy and direct their motion. Thus ...

Clinical applicability of deep learning-based respiratory signal prediction models for four-dimensional radiation therapy.

PloS one
For accurate respiration gated radiation therapy, compensation for the beam latency of the beam control system is necessary. Therefore, we evaluate deep learning models for predicting patient respiration signals and investigate their clinical feasibi...