News | Artificial Intelligence | December 27, 2019

Artificial Intelligence Identifies Previously Unknown Features Associated with Cancer Recurrence

The AI also identified features relevant to cancer prognosis that were not previously noted by pathologists, leading to a higher accuracy of prostate cancer recurrence compared to pathologist-based diagnosis

The AIP's RAIDEN AI supercomputer

The AIP's RAIDEN AI supercomputer

December 27, 2019 — Artificial intelligence (AI) technology developed by the RIKEN Center for Advanced Intelligence Project (AIP) in Japan has successfully found features in pathology images from human cancer patients, without annotation, that could be understood by human doctors. Further, the AI identified features relevant to cancer prognosis that were not previously noted by pathologists, leading to a higher accuracy of prostate cancer recurrence compared to pathologist-based diagnosis. Combining the predictions made by the AI with predictions by human pathologists led to an even greater accuracy.

According to Yoichiro Yamamoto, M.D., Ph.D., the first author of the study published in Nature Communications, "This technology could contribute to personalized medicine by making highly accurate prediction of cancer recurrence possible by acquiring new knowledge from images. It could also contribute to understanding how AI can be used safely in medicine by helping to resolve the issue of AI being seen as a 'black box.'"

The research group led by Yamamoto and Go Kimura, in collaboration with a number of university hospitals in Japan, adopted an approach called "unsupervised learning." As long as humans teach the AI, it is not possible to acquire knowledge beyond what is currently known. Rather than being "taught" medical knowledge, the AI was asked to learn using unsupervised deep neural networks, known as autoencoders, without being given any medical knowledge. The researchers developed a method for translating the features found by the AI — only numbers initially — into high-resolution images that can be understood by humans.

To perform this feat the group acquired 13,188 whole-mount pathology slide images of the prostate from Nippon Medical School Hospital (NMSH), The amount of data was enormous, equivalent to approximately 86 billion image patches (sub-images divided for deep neural networks), and the computation was performed on AIP's powerful RAIDEN supercomputer.

The AI learned using pathology images without diagnostic annotation from 11 million image patches. Features found by AI included cancer diagnostic criteria that have been used worldwide, on the Gleason score, but also features involving the stroma — connective tissues supporting an organ — in non-cancer areas that experts were not aware of. In order to evaluate these AI-found features, the research group verified the performance of recurrence prediction using the remaining cases from NMSH (internal validation). The group found that the features discovered by the AI were more accurate (AUC=0.820) than predictions made based on the human-established cancer criteria developed by pathologists, the Gleason score (AUC=0.744). Furthermore, combining both AI-found features and the human-established criteria predicted the recurrence more accurately than using either method alone (AUC=0.842). The group confirmed the results using another dataset including 2,276 whole-mount pathology images (10 billion image patches) from St. Marianna University Hospital and Aichi Medical University Hospital (external validation).

"I was very happy," said Yamamoto, "to discover that the AI was able to identify cancer on its own from unannotated pathology images. I was extremely surprised to see that AI found features that can be used to predict recurrence that pathologists had not identified."

He continued, "We have shown that AI can automatically acquire human-understandable knowledge from diagnostic annotation-free histopathology images. This 'newborn' knowledge could be useful for patients by allowing highly-accurate predictions of cancer recurrence. What is very nice is that we found that combining the AI's predictions with those of a pathologist increased the accuracy even further, showing that AI can be used hand-in-hand with doctors to improve medical care. In addition, the AI can be used as a tool to discover characteristics of diseases that have not been noted so far, and since it does not require human knowledge, it could be used in other fields outside medicine."

For more information: www.riken.jp/en/research/labs/aip/

Related Content

Videos | Ultrasound Imaging | August 13, 2020
This is a tutorial video on how to perform an ...
Rafael Rivero, M.D., Global Head of Medical Affairs at MSI, said: "The importance of MyoStrain cannot be understated because of the test's immense clinical value and ability to quantify intramyocardial dysfunction across 48 segments of the heart. In a six-heartbeat MRI scan, MyoStrain arms physicians with novel clinical information about a patient's heart health."
News | Cardiac Imaging | August 11, 2020
August 11, 2020 — Myocardial Solutions, Inc. and United Imaging, Inc.
Building on its commitment to providing customers with artificial intelligence (AI) solutions that can be utilized across modalities in routine care, Canon Medical Systems USA, Inc. is partnering with Zebra Medical Vision to offer its AI1 automated imaging analysis solutions to help clinicians in the U.S. provide faster, accurate diagnoses for optimized patient care
News | Artificial Intelligence | August 11, 2020
August 11, 2020 — Building on its commitment to providing customers with...
A special issue of the Journal of Medical Imaging and Radiation Sciences features stories about the interpersonal skills beyond the technical aspects to care for and guide patients through medical imaging and radiation therapy procedures

Image courtesy of Canon

News | Patient Engagement | August 11, 2020
August 11, 2020 — "I went into the MRI
Results of Journal of the American College of Surgeons study should reassure breast cancer patients who experienced surgical postponements due to COVID-19 pandemic

Association between Time to Operation and Pathological Stage in DCIS & ER+ Breast Cancer. Courtesy of American College of Surgeons

News | Women's Health | August 07, 2020
August 7, 2020 — A new br...

An oncologist practices social distancing while talking to a cancer patient. Image courtesy of University of Michigan Rogel Cancer Center

News | Coronavirus (COVID-19) | August 07, 2020
August 7, 2020 — When COVID-19 struck, health ca
As part of an international collaboration, researchers from Aarhus University and University of Leicester have succeeded in developing a dynamic 3-D CT scanning method that shows what happens inside the body during simulated heart massage

A look inside cardiopulmonary resuscitation: A 4-D computed tomography model of simulated closed chest compression. A proof of concept. Courtesy of Kasper Hansen/Jonathan Bjerg Moeller/Aarhus University

News | Cardiac Imaging | August 07, 2020
August 7, 2020 — Rapid first aid during...
Global radiotherapy market revenue is set to expand from $7,222.4 million in 2019 to $17,194.4 million by 2030, at an 8.4% CAGR between 2020 and 2030, the key factor driving the market growth is the increasing number of cancer cases, according to the report published by P&S Intelligence.

Image courtesy of Elekta

News | Radiation Therapy | August 06, 2020
August 6, 2020 — Global ...
Collaboration will include data sharing, R&D and an upgrade of RadNet’s fleet of mammography systems to Hologic’s state-of-the-art imaging technology
News | Breast Imaging | August 06, 2020
August 6, 2020 — RadNet, Inc., a national leader in providing hig