We've updated our Privacy Policy to make it clearer how we use your personal data. We use cookies to provide you with a better experience. You can read our Cookie Policy here.


Smart Software Can Diagnose Prostate Cancer as Well as a Pathologist

Listen with
Register for free to listen to this article
Thank you. Listen to this article using the player above.

Want to listen to this article for FREE?

Complete the form below to unlock access to ALL audio articles.

Read time: 1 minute

Chinese scientists and clinicians have developed a learning artificial intelligence system and have reported that it can diagnose and identify cancerous prostate samples as accurately as any pathologist.

Hopefully, this will help streamline cancer diagnosis, and help to eliminate variation in the process. It may also help overcome any local shortage of trained pathologists.

Long term, it may lead to automated or partially-automated prostate cancer diagnosis. Prostate cancer is the most common male cancer, with around 1.1 million diagnoses ever year, worldwide. Diagnosis confirmation normally requires a biopsy sample, which is then examined by a pathologist.

Now an artificial intelligence learning system, presented at the European Association of Urology congress in Copenhagen, has shown similar levels of accuracy to a human pathologist. The software can accurately classify the level of malignancy of the cancer, which eliminates the variability which can creep into diagnosis by humans.

“This is not going to replace a human pathologist” said research leader Hongqian Guo (Nanjing, China), “We still need an experienced pathologist to take responsibility for the final diagnosis. What it will do is help pathologists make better, faster diagnosis, as well as eliminating the day-to-day variation in judgement which can creep into human evaluations.”

Prof. Guo’s group collected 918 prostate whole mount pathology section samples from 283 patients, and processed these through the analysis system, which was able to gradually learn and improve diagnosis.

These pathology images were subdivided into 40,000 smaller samples; 30,000 of these samples were used to ‘train’ the software, and 10,000 were used to test accuracy.

An accurate diagnosis was achieved in 99.38% of cases (using a human pathologist as a ‘gold standard’), which is effectively as accurate as the human pathologist. 

Different Gleason Grades could also be identified in the pathology sections using AI. Ten whole mount prostate pathology sections have been tested so far, with similar Gleason Grade results apparent in the AI and human pathologist’s diagnosis. The group has not started testing the system with human patients.

Prof. Guo described the significance of this work:

“...Until now, automated systems have had limited clinical value, but we believe this is the first automated work to offer an accurate reporting and diagnosis of prostate cancer. In the short-term, this can offer a faster throughput, plus a greater consistency in cancer diagnosis from pathologist to pathologist, hospital to hospital, country to country. Artificial intelligence is advancing at an amazing rate – you only need to look at facial recognition on smartphones, or driverless cars. It is important that cancer detection and diagnosis takes advantage of these changes.”

While the researchers did acknowledge some limitations, the work has gathered a lot of interest. Professor Rodolfo Montironi (Professor of Pathology, Polytechnic University of the Marche, Ancona, Italy) commented:

“This is interesting work which shows how artificial intelligence will increasingly step into clinical practice. This may be very useful in some areas where there is a lack of trained pathologists. Like all automation, this will lead to a lesser reliance on human expertise, but we need to ensure that the final decisions on treatment stay with a trained pathologist. The really important thing though, is that we ensure the highest standard of patient care. The future will be interesting.”