ArticleNPJ precision oncology2024
Viable tumor cell density after neoadjuvant chemotherapy assessed using deep learning model reflects the prognosis of osteosarcoma.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- Engineering Osteoimmune Responses with Functionalized Orthopedic Implants for Post-Operative Osteosarcoma Treatment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Article
- A self-cascade nanoCRISPR prompts transcellular penetration to potentiate gene editing and tumor killing.Acta pharmaceutica Sinica. B · 2025Article
- A Need for Multi-Institutional Collaboration for Deep Learning-Driven Assessment of Osteosarcoma Treatment Response.The American journal of pathology · 2025Article
- Mixed reality infrastructure based on deep learning medical image segmentation and 3D visualization for bone tumors using DCU-Net.Journal of bone oncology · 2025Article
- AI based diagnostics product design for osteosarcoma cells microscopy imaging of bone cancer patients using CA-MobileNet V3.Journal of bone oncology · 2024Article
- A novel prognostic signature related to programmed cell death in osteosarcoma.Frontiers in immunology · 2024Article
- Real World Data from Patients With Osteosarcoma Treated in 8 Medical Centers in Greece: Ninety Percent or Greater Tumor Necrosis Is Associated With Disease-free Survival.Cancer diagnosis & prognosisArticle
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
Prognosis after neoadjuvant chemotherapy (NAC) for osteosarcoma is generally predicted using manual necrosis-rate assessments; however, necrosis rates obtained in these assessments are not reproducible and do not adequately reflect individual cell responses. We aimed to investigate whether viable tumor cell density assessed using a deep-learning model (DLM) reflects the prognosis of osteosarcoma. Seventy-one patients were included in this study. Initially, the DLM was trained to detect viable tumor cells, following which it calculated their density. Patients were stratified into high and low-viable tumor cell density groups based on DLM measurements, and survival analysis was performed to evaluate disease-specific survival and metastasis-free survival (DSS and MFS). The high viable tumor cell density group exhibited worse DSS (p = 0.023) and MFS (p = 0.033). DLM-evaluated viable density showed correct stratification of prognosis groups. Therefore, this evaluation method may enable precise stratification of the prognosis in osteosarcoma patients treated with NAC.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.