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AI model to improve patient response to cancer therapy

Date:
July 3, 2024
Source:
Australian National University
Summary:
A new artificial intelligence tool that can help to select the most suitable treatment for cancer patients has been developed.
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A new artificial intelligence (AI) tool that can help to select the most suitable treatment for cancer patients has been developed by researchers at The Australian National University (ANU).

DeepPT, developed in collaboration with scientists at the National Cancer Institute in America and pharmaceutical company Pangea Biomed, works by predicting a patient's messenger RNA (mRNA) profile. This mRNA -- essential for protein production -- is also the key molecular information for personalised cancer medicine.

According to lead author Dr Danh-Tai Hoang from ANU, when combined with a second tool called ENLIGHT, DeepPT was found to successfully predict a patient's response to cancer therapies across multiple types of cancer.

"We know that selecting a suitable treatment for cancer patients can be integral to patient outcomes," Dr Hoang said.

"DeepPT was trained on over 5,500 patients across 16 prevalent cancer types, including breast, lung, head and neck, cervical and pancreatic cancers.

"We saw an improvement in patient response rate from 33.3 per cent without using our model to 46.5 per cent with using our model."

DeepPT builds on previous work by the same ANU researchers to develop a tool to help classify brain tumours.

Both AI tools draw on microscopic pictures of patient tissue called histopathology images, also providing another key benefit for patients.

"This cuts down on delays in processing complex molecular data, which can take weeks," Dr Hoang said.

"Any kind of delay obviously poses a real challenge when dealing with patients with high-grade tumours who might require immediate treatment.

"In contrast, histopathology images are routinely available, cost-effective and timely."

The study has been published in Nature Cancer.


Story Source:

Materials provided by Australian National University. Note: Content may be edited for style and length.


Journal Reference:

  1. Danh-Tai Hoang, Gal Dinstag, Eldad D. Shulman, Leandro C. Hermida, Doreen S. Ben-Zvi, Efrat Elis, Katherine Caley, Stephen-John Sammut, Sanju Sinha, Neelam Sinha, Christopher H. Dampier, Chani Stossel, Tejas Patil, Arun Rajan, Wiem Lassoued, Julius Strauss, Shania Bailey, Clint Allen, Jason Redman, Tuvik Beker, Peng Jiang, Talia Golan, Scott Wilkinson, Adam G. Sowalsky, Sharon R. Pine, Carlos Caldas, James L. Gulley, Kenneth Aldape, Ranit Aharonov, Eric A. Stone, Eytan Ruppin. A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics. Nature Cancer, 2024; DOI: 10.1038/s43018-024-00793-2

Cite This Page:

Australian National University. "AI model to improve patient response to cancer therapy." ScienceDaily. ScienceDaily, 3 July 2024. <www.sciencedaily.com/releases/2024/07/240703131814.htm>.
Australian National University. (2024, July 3). AI model to improve patient response to cancer therapy. ScienceDaily. Retrieved July 22, 2024 from www.sciencedaily.com/releases/2024/07/240703131814.htm
Australian National University. "AI model to improve patient response to cancer therapy." ScienceDaily. www.sciencedaily.com/releases/2024/07/240703131814.htm (accessed July 22, 2024).

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