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Neoplasms

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Quantitative image analysis pipeline for detecting circulating hybrid cells in immunofluorescence images with human-level accuracy.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Circulating hybrid cells (CHCs) are a newly discovered, tumor-derived cell population found in the peripheral blood of cancer patients and are thought to contribute to tumor metastasis. However, identifying CHCs by immunofluorescence (IF) imaging of ...

Robot-assisted thoracic surgery for benign tumors at the cervicothoracic junction: a propensity-matched study.

Scientific reports
This study aimed to assess the feasibility and safety of robot-assisted thoracic surgery (RATS) for resecting benign tumors of the cervicothoracic junction. Between 2017 and 2021, a total of 54 patients with benign cervicothoracic junction tumors wer...

Artificial intelligence in immunotherapy PET/SPECT imaging.

European radiology
OBJECTIVE: Immunotherapy has dramatically altered the therapeutic landscape for oncology, but more research is needed to identify patients who are likely to achieve durable clinical benefit and those who may develop unacceptable side effects. We inve...

Machine Learning to Allocate Palliative Care Consultations During Cancer Treatment.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE: For patients with advanced cancer, early consultations with palliative care (PC) specialists reduce costs, improve quality of life, and prolong survival. However, capacity limitations prevent all patients from receiving PC shortly after diag...

PECAN Predicts Patterns of Cancer Cell Cytostatic Activity of Natural Products Using Deep Learning.

Journal of natural products
Many machine learning techniques are used as drug discovery tools with the intent to speed characterization by determining relationships between compound structure and biological function. However, particularly in anticancer drug discovery, these mod...

Multicancer screening test based on the detection of circulating non haematological proliferating atypical cells.

Molecular cancer
BACKGROUND: the problem in early diagnosis of sporadic cancer is understanding the individual's risk to develop disease. In response to this need, global scientific research is focusing on developing predictive models based on non-invasive screening ...

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides.

Nature communications
Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothe...

AIEgen-deep: Deep learning of single AIEgen-imaging pattern for cancer cell discrimination and preclinical diagnosis.

Biosensors & bioelectronics
This study introduces AIEgen-Deep, an innovative classification program combining AIEgen fluorescent dyes, deep learning algorithms, and the Segment Anything Model (SAM) for accurate cancer cell identification. Our approach significantly reduces manu...

A systematic analysis of deep learning in genomics and histopathology for precision oncology.

BMC medical genomics
BACKGROUND: Digitized histopathological tissue slides and genomics profiling data are available for many patients with solid tumors. In the last 5 years, Deep Learning (DL) has been broadly used to extract clinically actionable information and biolog...

Cancer detection and classification using a simplified binary state vector machine.

Medical & biological engineering & computing
Cancer is an invasive and malignant growth of cells and is known to be one of the most fatal diseases. Its early detection is essential for decreasing the mortality rate and increasing the probability of survival. This study presents an efficient mac...