(2024/04/02)

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AI’s ability to detect tumor cells could be key to more accurate bone cancer
prognoses
Researchers have 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.
Fukuoka, Japan - Researchers at Kyushu University have developed and validated a machinelearning model that can accurately evaluate the density of surviving tumor cells after treatment inpathological images of osteosarcoma—the most prevalent malignant bone tumor. The model canassess 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 proportionof dead tissue within a tumor. Unfortunately, these methods are limited by variability between
pathologists’ assessments and may not accurately predict treatment response.
Recognizing the need for faster and more accurate prognoses, co-first authors Dr. Kengo Kawaguchi
and Dr. Kazuki Miyama, from the Department of Orthopedic Surgery, Graduate School of MedicalSciences, Kyushu University, Japan, and Dr. Makoto Endo, a lecturer of Orthopedic Surgery atKyushu University Hospital, along with collaborators, turned to artificial intelligence (AI) for a morenuanced evaluation. The multidisciplinary team headed by Dr. Endo included Dr. Kazuki Miyama,Professor Ryoma Bise, Yoshinao Oda, and Professor Yasuharu Nakashima. Explaining the rationale
behind their research, which was published on 22 January, 2024, in njp Precision Oncology, Dr.
Endo says, “In the traditional method, the necrosis rate is calculated as a necrotic area rather thanindividual cell counts, which is not sufficiently reproducible between assessors and does notadequately reflect the effects of anticancer drugs. We therefore considered using AI to improve theestimation.”
In phase 1 of the study, the team trained a type of AI, called a deep-learning model, to detectsurviving tumor cells and validated its detection performance using patient data. The AI model
showed proficiency in detecting viable tumor cells in pathological images, aligning with expert
pathologists' capabilities. In phase 2, the researchers analyzed two key measures: disease-specificsurvival, 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-estimatedviable tumor 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 tumor cell density
was above or below 400/mm2. The survival analysis revealed that the high-density group showeda worse prognosis, while the low-density group showed a better prognosis for disease-specificsurvival 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 tumor cell density was a more reliable predictor of prognosis thannecrosis rate.
Overall, these findings suggest that the AI-based measurement of viable tumor cells reflects theinherent malignancy (ability of the cancer to spread) and individual tumor cell response ofosteosarcomas. Incorporating AI in the analysis of pathological images improves detection accuracy,
reduces inter-assessor variability, and enables timely assessment. Moreover, the estimation of viable
tumor cells, which reflects their ability to keep multiplying following chemotherapy, is a more reliablepredictor of treatment response than cell death. Large-scale validation of the AI model developedin this study can aid its wider application in real-life clinical settings.
“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, andetiology. Despite the passage of decades, particularly in treatment strategies, substantial progress
remains elusive. By putting AI to the problem, this might finally change,” concludes a hopeful Dr.
Endo.
###For more information about this research, see " Viable tumor cell density after neoadjuvant
chemotherapy assessed using deep learning model reflects the prognosis of osteosarcoma" Kengo
Kawaguchi, Kazuki Miyama, Makoto Endo, Ryoma Bise, Kenichi Kohashi, Takeshi Hirose, Akira
Nabeshima, Toshifumi Fujiwara, Yoshihiro Matsumoto, Yoshinao Oda & Yasuharu Nakashima, npjPrecision Oncology, https://doi.org/10.1038/s41698-024-00515-yFig. 1. Researchers from Kyushu University, Japan develop a novel deep-learning model which canaccurately estimate viable tumor cell density from pathological images of osteosarcoma patients, andpredict their prognosis following chemotherapy.

[Contact]
Makoto Endo, Lecturer
Orthopedic Surgery, Kyushu University Hospital
Tel: +81-92-642-5488
E-mail: endo.m.a40@m.kyushu-u.ac.jp