目标:目前,目前缺乏良好的临床工具来评估术前化疗对原发性高级别骨肉瘤的影响。我们的目标是研究临床发现的预测价值,并建立一个评分系统来预测化疗反应。
方法:我们进行了一项回顾性多中心队列研究,并回顾了322例原发性高级别骨肉瘤患者。本研究纳入常规接受新辅助化疗并接受原发性肿瘤切除术并评估肿瘤坏死率(TNR)的患者。患者病历于2011年11月1日至2018年3月1日在北京大学人民医院(PKUPH)和北京大学首钢医院(PKUSH)收集。患者的平均年龄为16.2岁(范围3-52岁),其中65.5%为男性。新辅助化疗前后的临床资料包括疼痛程度,实验室检查,X光片,CT,对比增强磁共振(MR),和正电子发射断层扫描-计算机断层扫描(PET-CT)。几种机器学习模型,包括逻辑回归,决策树,支持向量机,和神经网络,用于对化疗反应进行分类。用于预测化疗反应的评分系统的曲线下面积(AUC)是主要结果量度。
结果:对于没有事件的患者,至少随访24个月.中位随访时间为43.3个月,从24个月到84个月不等。纳入患者的5年无进展生存率(PFS)为54.1%。不良反应者的5年PFS率为39.7%,良好反应者为74.9%。特征,如最长直径减小比(最多三个点),清晰的骨骼边界形成(最多两个点),通过磁共振测量肿瘤坏死(最多两点),最大标准吸收值(SUVmax)降低(最多三个点),和显著的碱性磷酸酶下降(高达1分)被确定为良好组织学反应的显著预测因子,并构成了评分系统。评分≥4预示化疗反应良好。基于上述因素的评分系统表现良好,达到0.893的AUC。对于不可测量的病变(根据经修订的实体瘤反应评估标准[RECIST1.1]分类),AUC为0.901。
结论:我们首先设计了一个性能良好的综合评分系统来预测原发性高级别骨肉瘤对新辅助化疗的反应。
OBJECTIVE: Currently, there is a lack of good clinical tools for evaluating the effect of chemotherapy preoperatively on primary high-grade bone sarcomas. Our goal was to investigate the predictive value of the clinical findings and establish a scoring system to predict chemotherapy response.
METHODS: We conducted a retrospective multicenter cohort study and reviewed 322 patients with primary high-grade bone sarcomas. Patients who routinely received neoadjuvant chemotherapy and underwent primary tumor resection with an assessment of tumor necrosis rate (TNR) were enrolled in this study. The medical records of patients were collected from November 1, 2011, to March 1, 2018, at Peking University People\'s Hospital (PKUPH) and Peking University Shougang Hospital (PKUSH). The mean age of the patients was 16.2 years (range 3-52 years), of whom 65.5% were male. The clinical data collected before and after neoadjuvant chemotherapy included the degree of pain, laboratory inspection, X-ray, CT, contrast-enhanced magnetic resonance (MR), and positron emission tomography-computed tomography (PET-CT). Several machine learning models, including logistic regression, decision trees, support vector machines, and neural networks, were used to classify the chemotherapy responses. Area under the curve (AUC) of the scoring system to predict chemotherapy response is the primary outcome measure.
RESULTS: For patients without events, a minimum follow-up of 24 months was achieved. The median follow-up time was 43.3 months, and it ranged from 24 to 84 months. The 5 years progression-free survival (PFS) of the included patients was 54.1%. The 5 years PFS rate was 39.7% for poor responders and 74.9% for good responders. Features such as longest diameter reduction ratio (up to three points), clear bone boundary formation (up to two points), tumor necrosis measured by magnetic resonance (up to two points), maximum standard uptake value (SUVmax ) decrease (up to three points), and significant alkaline phosphatase decrease (up to 1 point) were identified as significant predictors of good histological response and constituted the scoring system. A score ≥4 predicts a good response to chemotherapy. The scoring system based on the above factors performed well, achieving an AUC of 0.893. For nonmeasurable lesions (classified by the revised Response Evaluation Criteria in Solid Tumors [RECIST 1.1]), the AUC was 0.901.
CONCLUSIONS: We first devised a well-performing comprehensive scoring system to predict the response to neoadjuvant chemotherapy in primary high-grade bone sarcomas.