Predicting distant metastasis in nasopharyngeal carcinoma using gradient boosting tree model based on detailed magnetic resonance imaging reports.

IF 1.4 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING World journal of radiology Pub Date : 2024-06-28 DOI:10.4329/wjr.v16.i6.203
Yu-Liang Zhu, Xin-Lei Deng, Xu-Cheng Zhang, Li Tian, Chun-Yan Cui, Feng Lei, Gui-Qiong Xu, Hao-Jiang Li, Li-Zhi Liu, Hua-Li Ma
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Abstract

Background: Development of distant metastasis (DM) is a major concern during treatment of nasopharyngeal carcinoma (NPC). However, studies have demonstrated improved distant control and survival in patients with advanced NPC with the addition of chemotherapy to concomitant chemoradiotherapy. Therefore, precise prediction of metastasis in patients with NPC is crucial.

Aim: To develop a predictive model for metastasis in NPC using detailed magnetic resonance imaging (MRI) reports.

Methods: This retrospective study included 792 patients with non-distant metastatic NPC. A total of 469 imaging variables were obtained from detailed MRI reports. Data were stratified and randomly split into training (50%) and testing sets. Gradient boosting tree (GBT) models were built and used to select variables for predicting DM. A full model comprising all variables and a reduced model with the top-five variables were built. Model performance was assessed by area under the curve (AUC).

Results: Among the 792 patients, 94 developed DM during follow-up. The number of metastatic cervical nodes (30.9%), tumor invasion in the posterior half of the nasal cavity (9.7%), two sides of the pharyngeal recess (6.2%), tubal torus (3.3%), and single side of the parapharyngeal space (2.7%) were the top-five contributors for predicting DM, based on their relative importance in GBT models. The testing AUC of the full model was 0.75 (95% confidence interval [CI]: 0.69-0.82). The testing AUC of the reduced model was 0.75 (95%CI: 0.68-0.82). For the whole dataset, the full (AUC = 0.76, 95%CI: 0.72-0.82) and reduced models (AUC = 0.76, 95%CI: 0.71-0.81) outperformed the tumor node-staging system (AUC = 0.67, 95%CI: 0.61-0.73).

Conclusion: The GBT model outperformed the tumor node-staging system in predicting metastasis in NPC. The number of metastatic cervical nodes was identified as the principal contributing variable.

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利用基于详细磁共振成像报告的梯度增强树模型预测鼻咽癌的远处转移
背景:远处转移(DM)的发生是鼻咽癌(NPC)治疗过程中的一个主要问题。然而,有研究表明,在化疗的同时加用化疗放疗可改善晚期鼻咽癌患者的远处转移控制和生存率。因此,准确预测鼻咽癌患者的转移至关重要。目的:利用详细的磁共振成像(MRI)报告建立鼻咽癌转移预测模型:这项回顾性研究纳入了792例非远处转移性鼻咽癌患者。从详细的磁共振成像报告中获得了总共 469 个成像变量。数据被分层并随机分成训练集(50%)和测试集。建立梯度提升树(GBT)模型,用于选择预测 DM 的变量。建立了一个包含所有变量的完整模型和一个包含前五个变量的简化模型。通过曲线下面积(AUC)评估模型性能:结果:在792名患者中,有94人在随访期间发生了DM。根据其在GBT模型中的相对重要性,转移性颈结节数量(30.9%)、鼻腔后半部肿瘤侵犯(9.7%)、两侧咽凹(6.2%)、输卵管环(3.3%)和单侧咽旁间隙(2.7%)是预测DM的前五大因素。完整模型的测试AUC为0.75(95%置信区间[CI]:0.69-0.82)。简化模型的测试 AUC 为 0.75(95% 置信区间:0.68-0.82)。就整个数据集而言,完整模型(AUC = 0.76,95%CI:0.72-0.82)和简化模型(AUC = 0.76,95%CI:0.71-0.81)的表现优于肿瘤结节分期系统(AUC = 0.67,95%CI:0.61-0.73):结论:在预测鼻咽癌转移方面,GBT模型优于肿瘤结节分期系统。结论:在预测鼻咽癌转移方面,GBT 模型优于肿瘤结节分期系统。
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来源期刊
World journal of radiology
World journal of radiology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
自引率
8.00%
发文量
35
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