Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China.
Shenzhen University General Hospital Clinical Research Centre for Neurological Diseases, Shenzhen, China.
BMC Cancer. 2020 Apr 15;20(1):322. doi: 10.1186/s12885-020-06825-1.
Response evaluation of neoadjuvant chemotherapy (NACT) in patients with osteosarcoma is significant for the termination of ineffective treatment, the development of postoperative chemotherapy regimens, and the prediction of prognosis. However, histological response and tumour necrosis rate can currently be evaluated only in resected specimens after NACT. A preoperatively accurate, noninvasive, and reproducible method of response assessment to NACT is required. In this study, the value of multi-parametric magnetic resonance imaging (MRI) combined with machine learning for assessment of tumour necrosis after NACT for osteosarcoma was investigated.
Twelve patients with primary osteosarcoma of limbs underwent NACT and received MRI examination before surgery. Postoperative tumour specimens were made corresponding to the transverse image of MRI. One hundred and two tissue samples were obtained and pathologically divided into tumour survival areas (non-cartilaginous and cartilaginous tumour viable areas) and tumour-nonviable areas (non-cartilaginous tumour necrosis areas, post-necrotic tumour collagen areas, and tumour necrotic cystic/haemorrhagic and secondary aneurismal bone cyst areas). The MRI parameters, including standardised apparent diffusion coefficient (ADC) values, signal intensity values of T2-weighted imaging (T2WI) and subtract-enhanced T1-weighted imaging (ST1WI) were used to train machine learning models based on the random forest algorithm. Three classification tasks of distinguishing tumour survival, non-cartilaginous tumour survival, and cartilaginous tumour survival from tumour nonviable were evaluated by five-fold cross-validation.
For distinguishing non-cartilaginous tumour survival from tumour nonviable, the classifier constructed with ADC achieved an AUC of 0.93, while the classifier with multi-parametric MRI improved to 0.97 (P = 0.0933). For distinguishing tumour survival from tumour nonviable, the classifier with ADC achieved an AUC of 0.83, while the classifier with multi-parametric MRI improved to 0.90 (P < 0.05). For distinguishing cartilaginous tumour survival from tumour nonviable, the classifier with ADC achieved an AUC of 0.61, while the classifier with multi-parametric MRI parameters improved to 0.81(P < 0.05).
The combination of multi-parametric MRI and machine learning significantly improved the discriminating ability of viable cartilaginous tumour components. Our study suggests that this method may provide an objective and accurate basis for NACT response evaluation in osteosarcoma.
新辅助化疗（NACT）治疗骨肉瘤的疗效评估对于终止无效治疗、制定术后化疗方案和预测预后具有重要意义。然而，目前仅能在 NACT 后的切除标本中评估组织学反应和肿瘤坏死率。因此，需要一种术前准确、无创且可重复的 NACT 反应评估方法。本研究旨在探讨多参数磁共振成像（MRI）联合机器学习在骨肉瘤 NACT 后肿瘤坏死评估中的价值。
12 例四肢原发性骨肉瘤患者接受 NACT 治疗，并在术前进行 MRI 检查。术后肿瘤标本与 MRI 横断位图像相对应。共获得 102 个组织样本，并进行病理分为肿瘤存活区（非软骨性和软骨性肿瘤存活区）和肿瘤非存活区（非软骨性肿瘤坏死区、坏死后肿瘤胶原区和肿瘤坏死囊性/出血性和继发性动脉瘤样骨囊肿区）。基于随机森林算法，使用标准化表观扩散系数（ADC）值、T2 加权成像（T2WI）和减影增强 T1 加权成像（ST1WI）信号强度值等 MRI 参数训练机器学习模型。通过五重交叉验证评估三种分类任务，即区分肿瘤存活、非软骨性肿瘤存活和软骨性肿瘤存活与肿瘤非存活。
在区分非软骨性肿瘤存活与肿瘤非存活方面，基于 ADC 构建的分类器 AUC 为 0.93，而基于多参数 MRI 构建的分类器 AUC 提高至 0.97（P=0.0933）。在区分肿瘤存活与肿瘤非存活方面，基于 ADC 构建的分类器 AUC 为 0.83，而基于多参数 MRI 构建的分类器 AUC 提高至 0.90（P<0.05）。在区分软骨性肿瘤存活与肿瘤非存活方面，基于 ADC 构建的分类器 AUC 为 0.61，而基于多参数 MRI 参数构建的分类器 AUC 提高至 0.81（P<0.05）。
多参数 MRI 与机器学习的结合显著提高了有活力的软骨性肿瘤成分的鉴别能力。本研究表明，该方法可能为骨肉瘤 NACT 反应评估提供客观、准确的依据。
