Tuesday January 21, 2020

Whole-brain radiation technique to treat brain cancer causes memory loss: Study

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Washington: The widely used whole-brain radiation technique to treat brain cancer is not an effective strategy and results in more memory loss than treating patients with radiotherapy alone, study says.

First used in 1954, whole-brain radiation has long been a standard strategy for brain metastases (cancer cells that have spread to the brain from primary tumours in other organs in the body).

“The potential benefits of whole brain radiation therapy are far outweighed by the detriments of the therapy itself,” Paul Brown, professor of radiation oncology at the University of Texas’ MD Anderson Cancer Centre was quoted as saying in a Wall Street Journal report.

For the study, patients were assigned to either radiosurgery followed by whole-brain radiation or radiosurgery alone.

The research involved 213 patients, who had one to three small tumours or metastases in the brain.

Patients treated with both approaches performed significantly worse three months later on tests involving cognitive abilities.

Median overall survival was 7.5 months for those receiving both treatments and 10.7 months for those on radiosurgery alone.

Both whole-brain radiation and recurrent metastases are “bad for the brain.”

Lung cancer is the most common malignancy to spread to the brain, followed by breast cancer and melanoma.

The study was presented at the annual meeting of the American Society of Clinical Oncology on May 31. (IANS)

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AI Can Better Help Doctors to Identify Cancer Cells in Human Body

The process of manually identifying all the cells in a pathology slide is extremely labor intensive and error-prone

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The AI algorithm helps pathologists obtain the most accurate Cancer cell analysis - in a much faster way. Pixabay

Researchers at University of Texas Southwestern have developed a software tool that uses Artificial Intelligence (AI) to recognize Cancer cells from digital pathology images – giving clinicians a powerful way of predicting patient outcomes.

The spatial distribution of different types of cells can reveal a cancer’s growth pattern, its relationship with the surrounding microenvironment, and the body’s immune response.

But the process of manually identifying all the cells in a pathology slide is extremely labor intensive and error-prone.

“To make a diagnosis, pathologists usually only examine several ‘representative’ regions in detail, rather than the whole slide. However, some important details could be missed by this approach,” said Dr. Guanghua “Andy” Xiao, corresponding author of a study published in EbioMedicine.

A major technical challenge in systematically studying the tumor microenvironment is how to automatically classify different types of cells and quantify their spatial distributions.

The AI algorithm that Dr Xiao and his team developed, called “ConvPath”, overcomes these obstacles by using AI to classify cell types from lung cancer pathology images.

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Researchers at University of Texas Southwestern have developed a software tool that uses Artificial Intelligence (AI) to recognize Cancer Cells from digital pathology images – giving clinicians a powerful way of predicting patient outcomes. Pixabay

The ConvPath algorithm can “look” at cells and identify their types based on their appearance in the pathology images using an AI algorithm that learns from human pathologists.

The algorithm helps pathologists obtain the most accurate cancer cell analysis – in a much faster way.

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“It is time-consuming and difficult for pathologists to locate very small tumour regions in tissue images, so this could greatly reduce the time that pathologists need to spend on each image,” said Dr Xiao. (IANS)