通过故障检测自动分割肿瘤总体积,安全实施局部晚期宫颈癌治疗

IF 3.4 Q2 ONCOLOGY
Rahimeh Rouhi , Stéphane Niyoteka , Alexandre Carré , Samir Achkar , Pierre-Antoine Laurent , Mouhamadou Bachir Ba , Cristina Veres , Théophraste Henry , Maria Vakalopoulou , Roger Sun , Sophie Espenel , Linda Mrissa , Adrien Laville , Cyrus Chargari , Eric Deutsch , Charlotte Robert
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引用次数: 0

摘要

背景与目的自动分割方法极大地改变了放射治疗(RT)工作流程,但仍需扩展到靶体积。本文比较了用于局部晚期宫颈癌总肿瘤体积(GTV)分割的深度学习(DL)模型,并利用放射学特征对失败检测进行了新的研究。在交叉验证过程中,将单独训练的模型进行组合,生成最终的分割结果。为了检测失败,使用从分割的 GTV 提取的放射学特征作为输入,训练二元分类器,目的是根据轮廓的 Dice 相似系数 (DSC)<T 和 DSC⩾T 对其进行分类。我们使用了两组不同的以二维序列捕获的 T2 加权(T2W)RT 前 MR 图像:一组是由来自 30 台扫描仪的 115 名 LACC 患者组成的回顾性队列,另一组是由来自 7 台扫描仪的 51 名患者组成的前瞻性队列,用于测试。结果通过二维-SegResNet 进行的分割获得了最佳 DSC、表面 DSC(SDSC3mm)和第 95 次 Hausdorff 距离(95HD):DSC=0.72±0.16,SDSC3mm=0.66±0.17,95HD=14.6±9.0 mm,测试队列无分割缺失(M=0)。我们的研究表明,不同 DL 方法的分割准确性略有不同,在二维 MRI 序列中,二维网络的表现优于三维网络。医生们发现了省时的优势。所提出的故障检测可在敏感病例中为医生提供指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic gross tumor volume segmentation with failure detection for safe implementation in locally advanced cervical cancer

Background and Purpose

Automatic segmentation methods have greatly changed the RadioTherapy (RT) workflow, but still need to be extended to target volumes. In this paper, Deep Learning (DL) models were compared for Gross Tumor Volume (GTV) segmentation in locally advanced cervical cancer, and a novel investigation into failure detection was introduced by utilizing radiomic features.

Methods and materials

We trained eight DL models (UNet, VNet, SegResNet, SegResNetVAE) for 2D and 3D segmentation. Ensembling individually trained models during cross-validation generated the final segmentation. To detect failures, binary classifiers were trained using radiomic features extracted from segmented GTVs as inputs, aiming to classify contours based on whether their Dice Similarity Coefficient (DSC)<T and DSCT. Two distinct cohorts of T2-Weighted (T2W) pre-RT MR images captured in 2D sequences were used: one retrospective cohort consisting of 115 LACC patients from 30 scanners, and the other prospective cohort, comprising 51 patients from 7 scanners, used for testing.

Results

Segmentation by 2D-SegResNet achieved the best DSC, Surface DSC (SDSC3mm), and 95th Hausdorff Distance (95HD): DSC = 0.72 ± 0.16, SDSC3mm=0.66 ± 0.17, and 95HD = 14.6 ± 9.0 mm without missing segmentation (M=0) on the test cohort. Failure detection could generate precision (P=0.88), recall (R=0.75), F1-score (F=0.81), and accuracy (A=0.86) using Logistic Regression (LR) classifier on the test cohort with a threshold T = 0.67 on DSC values.

Conclusions

Our study revealed that segmentation accuracy varies slightly among different DL methods, with 2D networks outperforming 3D networks in 2D MRI sequences. Doctors found the time-saving aspect advantageous. The proposed failure detection could guide doctors in sensitive cases.

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来源期刊
Physics and Imaging in Radiation Oncology
Physics and Imaging in Radiation Oncology Physics and Astronomy-Radiation
CiteScore
5.30
自引率
18.90%
发文量
93
审稿时长
6 weeks
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