Advancing Prognostics in Oncology: Developing a Machine Learning Model for Predicting 2-Year and 5-Year Survival Rates in Patients with Undifferentiated Pleomorphic Sarcoma.

BACKGROUND

Undifferentiated pleomorphic sarcoma (UPS) is a prevalent soft tissue sarcoma subtype associated with poor prognosis. Current prognostic tools lack the ability to incorporate personalized data for predicting survival. Machine learning (ML) offers a potential solution to enhance survival prediction accuracy. This study aimed to develop and validate a new ML algorithm to predict 2- and 5-year overall survival (OS) in patients with UPS.

PATIENTS AND METHODS

We included 3494 patients with a histologic diagnosis of UPS from the Surveillance, Epidemiology, and End Results (SEER) database for model training and internal validation. An institutional database of 288 patients was used for external validation. The development of the ML model involved converting tabular patient data into high-dimensional embeddings using a pre-trained language model. A custom neural network, optimized for high-dimensional data, was then developed to classify survival outcomes. Area under the curve (AUC), precision, and F1-scores were used to assess model performance.

RESULTS

Tumor size, age, metastases, lymph node involvement, and sex were factors associated with OS. On internal validation, our model showed higher performance than standard ML models for both 2-year and 5-year OS (AUC of 0.81 and 0.82, respectively). On external validation, the model showed excellent discriminative performance for the 2-year (AUC = 0.79) and 5-year OS (AUC = 0.81). In addition, we showed that our developed model performed superiorly compared with other models.

CONCLUSIONS

We successfully developed and validated an ML algorithm that accurately predicts 2-year and 5-year OS in patients with UPS. To confirm generalizability, further external validation of this algorithm is encouraged.