Computed tomography-based nutritional associated nomogram on machine learning predicts survival outcomes in patients with resectable soft-tissue sarcoma

Abstract

BACKGROUND

Soft-tissue sarcomas (STS) are heterogeneous mesenchymal malignancies for which surgery remains the mainstay of curative treatment, yet recurrence and mortality rates remain substantial. Nutritional status and body composition measured via routine blood tests and computed tomography-derived metrics such as skeletal muscle and adipose tissue areas have emerged as important determinants of outcomes in cancer. Advanced machine-learning methods can integrate high-dimensional nutritional and radiologic variables to improve individualized survival prediction.

AIM

To identify prognostic value of nutrition-associated factors for patients with STS treated with excision and to construct a predictive model for nutritional assessment by traditional survival analysis and a random forest machine learning method.

METHODS

We retrospectively included 638 patients who were diagnosed with STS and underwent surgical excision from January 2009 to June 2018. Nutrition-associated indicators from peripheral blood tests and routine computed tomography were collected. The primary outcome was overall survival (OS). The secondary outcomes were progression-free survival and length of postoperative hospital stay. The random survival forest (RSF) analysis selected important variables for re progression-free survival and OS, and the random forest analysis selected important variables for length of hospitalization. Nomograms were constructed by the prognostic features to predict survival probabilities.

RESULTS

The RSF analysis identified stage, hospital duration, subtype, body mass index, and tumour size as important variables for OS. The RSF-based nomogram on nutritional indexes for various clinical outcomes showed consistent calibration capacities on calibration plots and great discriminative abilities on the C-index and Brier score.

CONCLUSION

Our study implicated the prognostic value of multiple nutritional assessment indexes for prediction of clinical outcomes in STS, and patients’ nutrition status need long-term surveillance and management.

Keywords: Soft-tissue sarcoma; Random survival forest; Systemic immune-inflammatory index; Controlling nutritional score; Malnutrition universal screening tool; Radiomic score; Nomogram; Prognosis

Core Tip: This study explored the prognostic value of various nutritional assessment indicators for patients with soft tissue sarcomas receiving surgical resection. We constructed the prediction model for short- and long-term prognoses via multi-sided variables with potential prognostication in patients with resectable sarcomas. We applied machine learning methods to increase the prediction abilities of our model for identification of individual survival risk. The sample size was nearly 700 larger than most studies about nutritional prognostic factors especially for sarcomas.