LI-RADS-based hepatocellular carcinoma risk mapping using contrast-enhanced MRI and self-configuring deep learning.

IF 3.5 2区 医学 Q2 ONCOLOGY
Róbert Stollmayer, Selda Güven, Christian Marcel Heidt, Kai Schlamp, Pál Novák Kaposi, Oyunbileg von Stackelberg, Hans-Ulrich Kauczor, Miriam Klauss, Philipp Mayer
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引用次数: 0

Abstract

Background: Hepatocellular carcinoma (HCC) is often diagnosed using gadoxetate disodium-enhanced magnetic resonance imaging (EOB-MRI). Standardized reporting according to the Liver Imaging Reporting and Data System (LI-RADS) can improve Gd-MRI interpretation but is rather complex and time-consuming. These limitations could potentially be alleviated using recent deep learning-based segmentation and classification methods such as nnU-Net. The study aims to create and evaluate an automatic segmentation model for HCC risk assessment, according to LI-RADS v2018 using nnU-Net.

Methods: For this single-center retrospective study, 602 patients at risk for HCC were included, who had dynamic EOB-MRI examinations between 05/2005 and 09/2022, containing ≥ LR-3 lesion(s). Manual lesion segmentations in semantic segmentation masks as LR-3, LR-4, LR-5 or LR-M served as ground truth. A set of U-Net models with 14 input channels was trained using the nnU-Net framework for automatic segmentation. Lesion detection, LI-RADS classification, and instance segmentation metrics were calculated by post-processing the semantic segmentation outputs of the final model ensemble. For the external evaluation, a modified version of the LiverHccSeg dataset was used.

Results: The final training/internal test/external test cohorts included 383/219/16 patients. In the three cohorts, LI-RADS lesions (≥ LR-3 and LR-M) ≥ 10 mm were detected with sensitivities of 0.41-0.85/0.40-0.90/0.83 (LR-5: 0.85/0.90/0.83) and positive predictive values of 0.70-0.94/0.67-0.88/0.90 (LR-5: 0.94/0.88/0.90). F1 scores for LI-RADS classification of detected lesions ranged between 0.48-0.69/0.47-0.74/0.84 (LR-5: 0.69/0.74/0.84). Median per lesion Sørensen-Dice coefficients were between 0.61-0.74/0.52-0.77/0.84 (LR-5: 0.74/0.77/0.84).

Conclusion: Deep learning-based HCC risk assessment according to LI-RADS can be implemented as automatically generated tumor risk maps using out-of-the-box image segmentation tools with high detection performance for LR-5 lesions. Before translation into clinical practice, further improvements in automatic LI-RADS classification, for example through large multi-center studies, would be desirable.

基于li - rad的肝细胞癌风险定位,使用对比增强MRI和自配置深度学习。
背景:肝细胞癌(HCC)通常使用加多赛特二钠增强磁共振成像(EOB-MRI)诊断。肝脏成像报告和数据系统(LI-RADS)的标准化报告可以改善Gd-MRI的解释,但相当复杂和耗时。使用最近的基于深度学习的分割和分类方法(如nnU-Net)可以潜在地减轻这些限制。根据使用nnU-Net的LI-RADS v2018,该研究旨在创建和评估用于HCC风险评估的自动分割模型。方法:在这项单中心回顾性研究中,纳入602例HCC风险患者,这些患者在2005年5月至2022年9月期间进行了动态EOB-MRI检查,包含≥LR-3的病变。语义分割掩码LR-3、LR-4、LR-5、LR-M中的人工损伤分割作为ground truth。利用nnU-Net框架对具有14个输入通道的U-Net模型进行自动分割。通过对最终模型集成的语义分割输出进行后处理,计算病变检测、LI-RADS分类和实例分割度量。对于外部评估,使用了LiverHccSeg数据集的修改版本。结果:最终训练/内部测试/外部测试队列包括383/219/16例患者。在三个队列中,LI-RADS病变(≥LR-3和LR-M)≥10 mm的检测灵敏度为0.41-0.85/0.40-0.90/0.83 (LR-5: 0.85/0.90/0.83),阳性预测值为0.70-0.94/0.67-0.88/0.90 (LR-5: 0.94/0.88/0.90)。检测到病变的LI-RADS分级F1评分范围为0.48-0.69/0.47-0.74/0.84 (LR-5: 0.69/0.74/0.84)。每个病灶的Sørensen-Dice系数中位数在0.61-0.74/0.52-0.77/0.84之间(LR-5: 0.74/0.77/0.84)。结论:基于LI-RADS的基于深度学习的HCC风险评估可以使用现成的图像分割工具自动生成肿瘤风险图,对LR-5病变具有较高的检测性能。在转化为临床实践之前,需要进一步改进LI-RADS自动分类,例如通过大型多中心研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cancer Imaging
Cancer Imaging ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
7.00
自引率
0.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology. The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include: Breast Imaging Chest Complications of treatment Ear, Nose & Throat Gastrointestinal Hepatobiliary & Pancreatic Imaging biomarkers Interventional Lymphoma Measurement of tumour response Molecular functional imaging Musculoskeletal Neuro oncology Nuclear Medicine Paediatric.
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