AIMC Topic: Neoplasms

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Usefulness of copper filters in digital chest radiography based on the relationship between effective detective quantum efficiency and deep learning-based segmentation accuracy of the tumor area.

Radiological physics and technology
This study aimed to determine the optimal radiographic conditions for detecting lesions on digital chest radiographs using an indirect conversion flat-panel detector with a copper (Cu) filter. First, we calculated the effective detective quantum effi...

Recent advances in artificial intelligence applications for supportive and palliative care in cancer patients.

Current opinion in supportive and palliative care
PURPOSE OF REVIEW: Artificial intelligence (AI) is a transformative technology that has the potential to improve and augment the clinical workflow in supportive and palliative care (SPC). The objective of this study was to provide an overview of the ...

Transfer Learning Based Lightweight Ensemble Model for Imbalanced Breast Cancer Classification.

IEEE/ACM transactions on computational biology and bioinformatics
Automated classification of breast cancer can often save lives, as manual detection is usually time-consuming & expensive. Since the last decade, deep learning techniques have been most widely used for the automatic classification of breast cancer us...

Synthetic biology, genetic circuits and machine learning: a new age of cancer therapy.

Molecular oncology
Synthetic biology has made it possible to rewire natural cellular responses to treat disease, notably demonstrated by chimeric antigen receptor (CAR) T cells as cancer immunotherapy. Building on the success of T-cell activation using synthetic recept...

An End-to-End Natural Language Processing System for Automatically Extracting Radiation Therapy Events From Clinical Texts.

International journal of radiation oncology, biology, physics
PURPOSE: Real-world evidence for radiation therapy (RT) is limited because it is often documented only in the clinical narrative. We developed a natural language processing system for automated extraction of detailed RT events from text to support cl...

A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application.

BMC bioinformatics
BACKGROUND: Using visual, biological, and electronic health records data as the sole input source, pretrained convolutional neural networks and conventional machine learning methods have been heavily employed for the identification of various maligna...

A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer.

PLoS computational biology
One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strat...

Predicting mutational function using machine learning.

Mutation research. Reviews in mutation research
Genetic variations are one of the major causes of phenotypic variations between human individuals. Although beneficial as being the substrate of evolution, germline mutations may cause diseases, including Mendelian diseases and complex diseases such ...

Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers.

Current oncology (Toronto, Ont.)
Cancer significantly contributes to global mortality, with 9.3 million annual deaths. To alleviate this burden, the utilization of artificial intelligence (AI) applications has been proposed in various domains of oncology. However, the potential appl...