A team of researchers at Kyushu University has developed a new machine-learning model that can precisely make prognosis predictions for patients with osteosarcoma, based on the density of viable tumor cells post-treatment.
Osteosarcoma is a type of bone cancer that predominantly affects adolescents. Reliable models that can predict prognosis and treatment response can help improve patient outcomes.
“Conventional methods relying on the measurement of necrosis rate are limited by inter-assessor variability and may not accurately predict prognosis. Now, researchers have developed an AI model that can accurately measure the density of viable tumor cells after treatment which correlates well with prognosis and individual tumor cell response, the researchers said.According to the researchers, the model can assess how individual tumor cells respond to treatment and can predict overall patient prognosis more reliably than conventional methods.
“Surgery and chemotherapy have significantly improved the outcomes of patients with localized osteosarcoma. However, patients with advanced metastatic disease (the stage where cancerous cells have spread to distant tissues) have a low survival rate. After a standard treatment of surgery and chemotherapy, assessing the prognosis of patients is essential for determining their subsequent individual treatment plans. However, predicting patient outcomes has many challenges. Currently, prognosis relies on necrosis rate assessment, which involves pathologists evaluating the proportion of dead tissue within a tumor,” the University said in a statement.
Unfortunately, these methods are limited by variability between pathologists’ assessments and may not accurately predict treatment response, the scientists said.
Recognizing the need for faster and more accurate prognoses, Dr. Kengo Kawaguchi and Dr. Kazuki Miyama, from the Department of Orthopedic Surgery, Graduate School of Medical Sciences, Kyushu University, Japan, and Dr. Makoto Endo, a lecturer of Orthopedic Surgery at Kyushu University Hospital, along with collaborators, turned to artificial intelligence (AI) for a more nuanced evaluation.
The multidisciplinary team headed by Dr. Endo included Kyushu University’s Professor Ryoma Bise, Professor Yoshinao Oda, and Professor Yasuharu Nakashima. The findings of the study were published in the NJP Precision Oncology journal.
Dr. Endo says, “In the traditional method, the necrosis rate is calculated as a necrotic area rather than individual cell counts, which is not sufficiently reproducible between assessors and does not adequately reflect the effects of anticancer drugs. We therefore considered using AI to improve the estimation.”
