Wallner Christoph, Schmidt Sonja V, Reinkemeier Felix, Drysch Marius, Sogorski Alexander, von Glinski Maxi, Harenberg Patrick, Becerikli Mustafa, Lehnhardt Marcus, Stricker Ingo, Dadras Mehran, Puscz Flemming
From the Department for Plastic and Hand Surgery, BG University Hospital Bergmannsheil Bochum, Ruhr University Bochum, Bochum, Germany.
Institute of Pathology, Ruhr-University Bochum, Bochum, Germany.
Plast Reconstr Surg Glob Open. 2025 Apr 2;13(4):e6653. doi: 10.1097/GOX.0000000000006653. eCollection 2025 Apr.
Non-otherwise specified (NOS) sarcomas, a diverse and diagnostically challenging group of mesenchymal malignancies, pose significant clinical dilemmas due to their variable clinical trajectories and therapeutic responses. This study utilizes advanced machine learning techniques, namely classification and regression trees and Shapley additive explanation (SHAP) values, to identify predictors of survival, metastatic progression, and recurrence within a well-defined patient cohort, aiming to improve risk stratification and individualized care strategies.
Through the application of classification and regression trees and SHAP values to a cohort of 122 patients with NOS sarcoma, we identified critical factors impacting disease outcomes.
The study findings revealed that age and tumor diameter significantly influenced the development of metastasis, whereas body mass index and tumor grading were key predictors for relapse. Additionally, tumor size, location, and age were identified as influential factors for overall survival in patients with NOS sarcoma. These results have direct clinical relevance and can aid in risk stratification and surgical planning in this challenging patient population.
Considering the comparatively small cohort with which the machine learning algorithm was trained, this study underscores the importance of considering age, tumor size, location, body mass index, and tumor grading in the management of NOS sarcomas, shedding light on factors that may impact clinical outcomes and guide personalized treatment strategies.
未另作特殊说明（NOS）的肉瘤是一组多样且诊断具有挑战性的间充质恶性肿瘤，因其临床病程和治疗反应各异，给临床带来了重大难题。本研究运用先进的机器学习技术，即分类回归树和夏普利加性解释（SHAP）值，在一个明确界定的患者队列中识别生存、转移进展和复发的预测因素，旨在改善风险分层和个体化护理策略。
通过将分类回归树和SHAP值应用于122例NOS肉瘤患者的队列，我们确定了影响疾病预后的关键因素。
研究结果显示，年龄和肿瘤直径对转移的发生有显著影响，而体重指数和肿瘤分级是复发的关键预测因素。此外，肿瘤大小、位置和年龄被确定为NOS肉瘤患者总生存的影响因素。这些结果具有直接的临床相关性，有助于对这一具有挑战性的患者群体进行风险分层和手术规划。
考虑到用于训练机器学习算法的队列相对较小，本研究强调了在NOS肉瘤管理中考虑年龄、肿瘤大小、位置、体重指数和肿瘤分级的重要性，揭示了可能影响临床结果的因素并指导个性化治疗策略。
