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首页> 外文期刊>Japanese journal of radiology >A multi-scanner study of MRI radiomics in uterine cervical cancer: prediction of in-field tumor control after definitive radiotherapy based on a machine learning method including peritumoral regions
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A multi-scanner study of MRI radiomics in uterine cervical cancer: prediction of in-field tumor control after definitive radiotherapy based on a machine learning method including peritumoral regions

机译：子宫宫颈癌MRI辐射瘤的多扫描仪研究：基于机器学习方法的明确放疗后现场肿瘤对照预测

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Purpose This study aimed to identify the most appropriate volume of interest (VOI) setting in prognostic prediction using pretreatment magnetic resonance imaging (MRI) radiomic analysis for cervical cancer (CC) treated with definitive radiotherapy. Materials and methods The study participants were 87 patients who had undergone pretreatment MRI and definitive radiotherapy for CC. VOItumor was created with tumor alone and VOI+4 mm-VOI+20 mm mechanically expanded by 4-20 mm around each VOItumor in axial T2-weighted images (T2WI) and an apparent diffusion coefficient (ADC) map. A model was constructed to predict recurrence within the irradiation field within 2 years after treatment using imaging features from the VOI of each sequence. Sorting ability was evaluated by area under the receiver operator characteristic curve (AUC-ROC) analysis. Results VOI expansion improved AUC-ROCs compared with the predictive models of VOItumor (0.59 and 0.67 in T2WI and ADC, respectively). The AUC-ROCs of the models with imaging features from expanded VOI+4 mm in T2WI and VOI+4 mm and VOI+8 mm in ADC were 0.82, 0.82, and 0.86, respectively. Conclusion Recurrence could be predicted with high accuracy using expanded VOI for CC treated with definitive radiotherapy, suggesting that including the pathological characteristics of invasive margins in radiomics may improve predictive ability.
机译：目的本研究旨在鉴定利用确定放疗治疗的预处理磁共振成像（MRI）辐射学分析（CC）对预后预测中最合适的兴趣（VOI）设置。材料和方法研究参与者是87名患者，该患者经历了预处理MRI和CC的明确放疗。在轴向T2加权图像（T2WI）的每个VOITumor周围，在轴向T2加权图像（T2WI）中，使用肿瘤和围绕每个VOITumor机械地扩展的肿瘤和voi + 4mm-Voi + 20mm的voitumor。构建模型以预测使用来自每个序列的VoI的成像特征的成像特征后2年内的辐射场内的复发。按接收器操作员特征曲线（AUC-ROC）分析的区域评估分选能力。结果VOI扩展改进的AUC-ROC与Voitumor的预测模型（0.59和T2WI和ADC中的0.59和0.67）相比。具有在T2WI和VOI + 4mm的扩展VOI + 4 mm的成像特征的模型的AUC-ROC分别为0.82,0.82和0.86分别为0.82,0.82和0.86。结论可通过使用明确放射治疗的扩展VOI对CC进行高精度来预测再现，表明包括侵入式侵入物中的侵袭性余量的病理特征可以提高预测能力。

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