Viable tumor cell density after neoadjuvant chemotherapy assessed using deep learning model reflects the prognosis of osteosarcoma.
NPJ Precis Oncol
; 8(1): 16, 2024 Jan 22.
Article
en En
| MEDLINE
| ID: mdl-38253709
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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Base de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
NPJ Precis Oncol
Año:
2024
Tipo del documento:
Article