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Improving bone cancer detection with AI



A team of researchers have developed a new machine-learning model that can precisely make prognosis predictions for patients with osteosarcoma, based on the density of viable tumour cells post-treatment.

Researchers at Kyushu University have developed the model which accurately evaluates the density of surviving tumour cells after treatment in pathological images of osteosarcoma.

It can also assess how individual tumour cells respond to treatment and can predict overall patient prognosis more reliably than conventional methods.

Typically, patients with advanced metastatic disease 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 such as reliance on necrosis rate assessment, which involves pathologists evaluating the proportion of dead tissue within a tumour. Unfortunately, these methods are limited by variability between pathologists’ assessments and may not accurately predict treatment response.

So-author, Dr. Makoto Endo, a lecturer of Orthopaedic Surgery at Kyushu University Hospital stated: “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.”

For the study, the team trained a type of AI, called a deep-learning model, to detect surviving tumour cells and validated its detection performance using patient data. The AI model showed proficiency in detecting viable tumour cells in pathological images, aligning with expert pathologists’ capabilities. 

The researchers then analysed two key measures: disease-specific survival, which tracks the duration after diagnosis or treatment without death directly caused by the disease, and metastasis-free survival, which monitors the time post-treatment without cancer cells spreading to distant body parts. 

They also explored the correlation between AI-estimated viable tumour cell density and prognosis. Notably, the AI model demonstrated comparable detection performance and precision to that of the pathologist, with good reproducibility.

Next, the researchers sorted the patients into groups based on whether the viable tumour cell density was above or below 400/mm2. The survival analysis revealed that the high-density group showed a worse prognosis, while the low-density group showed a better prognosis for disease-specific survival and metastasis-free survival. Necrosis rate, on the other hand, was not associated with disease-specific survival or metastasis-free survival. 

Furthermore, analysis of individual cases revealed that AI-estimated viable tumour cell density was a more reliable predictor of prognosis than necrosis rate.

The findings suggest that the AI-based measurement of viable tumour cells reflects the inherent malignancy and individual tumour cell response of osteosarcomas. 

Dr Endo concluded: “This new approach has the potential to enhance the accuracy of prognoses for osteosarcoma patients treated with chemotherapy. In the future, we intend to actively apply AI to rare diseases such as osteosarcoma, which have seen limited advancements in epidemiology, pathogenesis, and aetiology. Despite the passage of decades, particularly in treatment strategies, substantial progress remains elusive. By putting AI to the problem, this might finally change.”

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