用定量 US Delta 放射线组学预测头颈部鳞状细胞癌患者的放射反应。

IF 5.6 Q1 ONCOLOGY
Laurentius Oscar Osapoetra, Archya Dasgupta, Daniel DiCenzo, Kashuf Fatima, Karina Quiaoit, Murtuza Saifuddin, Irene Karam, Ian Poon, Zain Husain, William T Tran, Lakshmanan Sannachi, Gregory J Czarnota
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引用次数: 0

摘要

目的 研究放射治疗(RT)第一周后获得的定量 US(QUS)放射组学数据在预测头颈部鳞状细胞癌(HNSCC)患者治疗反应中的作用。材料与方法 这项前瞻性研究纳入了 55 名在 2015 年 1 月至 2019 年 10 月期间接受根治性 RT 治疗的大结节阳性 HNSCC 患者(21 名完全反应患者[中位年龄 65 岁{IQR:47-80 岁},20 名男性,1 名女性;34 名不完全反应患者[中位年龄 59 岁{IQR:39-79 岁},33 名男性,1 名女性])。所有参与者均在 6-7 周内接受了 70 Gy 放射治疗,分 33-35 次进行。在接受 RT 治疗 1 周之前和之后,采集了转移淋巴结的 US 射频数据。QUS 分析产生了五个频谱图,并从中提取了平均值。我们采用灰度共现矩阵技术进行纹理分析,得出了 20 个 QUS 纹理参数和 80 个纹理衍生参数。以 RT 3 个月后的反应作为终点。模型的建立和评估采用了嵌套留一交叉验证。结果 五个 delta (Δ) 参数的差异具有统计学意义(P < .05)。支持向量机分类器在测试集上的灵敏度为 71%(21 个中的 15 个),特异度为 76%(34 个中的 26 个),平衡准确率为 74%,接收器工作特征曲线下面积为 0.77。所有分类器的性能在治疗第一周后都有所提高。结论 利用HNSCC患者RT第一周后获得的数据建立的QUS Δ-放射组学模型可预测治疗结束3个月后的反应,准确性较高。关键词计算机辅助诊断(CAD) 超声波 放射治疗/肿瘤学 头颈部 放射组学 定量 US 放射治疗 头颈部鳞状细胞癌 机器学习 Clinicaltrials.gov registration no.NCT03908684 本文有补充材料。© RSNA, 2024.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantitative US Delta Radiomics to Predict Radiation Response in Individuals with Head and Neck Squamous Cell Carcinoma.

Purpose To investigate the role of quantitative US (QUS) radiomics data obtained after the 1st week of radiation therapy (RT) in predicting treatment response in individuals with head and neck squamous cell carcinoma (HNSCC). Materials and Methods This prospective study included 55 participants (21 with complete response [median age, 65 years {IQR: 47-80 years}, 20 male, one female; and 34 with incomplete response [median age, 59 years {IQR: 39-79 years}, 33 male, one female) with bulky node-positive HNSCC treated with curative-intent RT from January 2015 to October 2019. All participants received 70 Gy of radiation in 33-35 fractions over 6-7 weeks. US radiofrequency data from metastatic lymph nodes were acquired prior to and after 1 week of RT. QUS analysis resulted in five spectral maps from which mean values were extracted. We applied a gray-level co-occurrence matrix technique for textural analysis, leading to 20 QUS texture and 80 texture-derivative parameters. The response 3 months after RT was used as the end point. Model building and evaluation utilized nested leave-one-out cross-validation. Results Five delta (Δ) parameters had statistically significant differences (P < .05). The support vector machines classifier achieved a sensitivity of 71% (15 of 21), a specificity of 76% (26 of 34), a balanced accuracy of 74%, and an area under the receiver operating characteristic curve of 0.77 on the test set. For all the classifiers, the performance improved after the 1st week of treatment. Conclusion A QUS Δ-radiomics model using data obtained after the 1st week of RT from individuals with HNSCC predicted response 3 months after treatment completion with reasonable accuracy. Keywords: Computer-Aided Diagnosis (CAD), Ultrasound, Radiation Therapy/Oncology, Head/Neck, Radiomics, Quantitative US, Radiotherapy, Head and Neck Squamous Cell Carcinoma, Machine Learning Clinicaltrials.gov registration no. NCT03908684 Supplemental material is available for this article. © RSNA, 2024.

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