Artificial intelligence can diagnose prostate cancer as well as a pathologist

Chinese researchers have developed an artificial intelligence system which can diagnose cancerous prostate samples as accurately as any pathologist, holding out the possibility of streamlining and eliminating variation in the process of cancer diagnosis.

The system may also help overcome shortages of trained pathologists and in the longer term lead to automated or partially-automated prostate diagnosis.

Confirmation of a prostate cancer diagnosis normally requires a biopsy sample to be examined by a pathologist. Now the Chinese AI system has shown similar levels of accuracy to pathologists and can also accurately classify the level of malignancy of the cancer, eliminating the variability which can creep into human diagnoses.

Details of the AI system were presented at the European Association of Urology Congress taking place in Copenhagen 16 – 20 March.

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

Guo’s group took 918 prostate samples from 283 patients and ran these through the AI system, with the software gradually learning and improving diagnosis. The pathology images were subdivided into 40,000 smaller samples of which 30,000 were used to train the software while the remaining 10,000 were used to test accuracy.

The results showed an accurate diagnosis in 99.38 per cent of cases, using a human pathologist as a gold standard. Guo said that means the AI system is as accurate as a pathologist.

The system was programmed to learn and gradually improve how it interpreted the samples. “Our results show that the diagnoses the AI system reported were at a level comparable to that of a pathologist. Furthermore, the system could accurately classify the malignancy level of prostate cancer,” said Guo.

Until now, automated systems have had limited clinical value. “We believe this is the first automated system 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,” Guo said. “It is important that cancer detection and diagnosis takes advantage of these changes.”

“This is interesting work which shows how artificial intelligence will increasingly step into clinical practice,” said Rodolfo Montironi, professor of pathology, Polytechnic University of the Marche, Ancona, Italy. “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.”