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基于磁共振成像的希佩尔-林道相关透明细胞肾细胞癌的非侵入性肿瘤分级评估。

Non-Invasive Tumor Grade Evaluation in Von Hippel-Lindau-Associated Clear Cell Renal Cell Carcinoma: A Magnetic Resonance Imaging-Based Study.

机构信息

Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA.

Urology Oncology Branch, National Cancer Institutes, National Institutes of Health, Bethesda, Maryland, USA.

出版信息

J Magn Reson Imaging. 2024 Sep;60(3):1076-1081. doi: 10.1002/jmri.29222. Epub 2024 Feb 1.

Abstract

BACKGROUND

Pathology grading is an essential step for the treatment and evaluation of the prognosis in patients with clear cell renal cell carcinoma (ccRCC).

PURPOSE

To investigate the utility of texture analysis in evaluating Fuhrman grades of renal tumors in patients with Von Hippel-Lindau (VHL)-associated ccRCC, aiming to improve non-invasive diagnosis and personalized treatment.

STUDY TYPE

Retrospective analysis of a prospectively maintained cohort.

POPULATION

One hundred and thirty-six patients, 84 (61%) males and 52 (39%) females with pathology-proven ccRCC with a mean age of 52.8 ± 12.7 from 2010 to 2023.

FIELD STRENGTH AND SEQUENCES

1.5 and 3 T MRIs. Segmentations were performed on the T1-weighted 3-minute delayed sequence and then registered on pre-contrast, T1-weighted arterial and venous sequences.

ASSESSMENT

A total of 404 lesions, 345 low-grade tumors, and 59 high-grade tumors were segmented using ITK-SNAP on a T1-weighted 3-minute delayed sequence of MRI. Radiomics features were extracted from pre-contrast, T1-weighted arterial, venous, and delayed post-contrast sequences. Preprocessing techniques were employed to address class imbalances. Features were then rescaled to normalize the numeric values. We developed a stacked model combining random forest and XGBoost to assess tumor grades using radiomics signatures.

STATISTICAL TESTS

The model's performance was evaluated using positive predictive value (PPV), sensitivity, F1 score, area under the curve of receiver operating characteristic curve, and Matthews correlation coefficient. Using Monte Carlo technique, the average performance of 100 benchmarks of 85% train and 15% test was reported.

RESULTS

The best model displayed an accuracy of 0.79. For low-grade tumor detection, a sensitivity of 0.79, a PPV of 0.95, and an F1 score of 0.86 were obtained. For high-grade tumor detection, a sensitivity of 0.78, PPV of 0.39, and F1 score of 0.52 were reported.

DATA CONCLUSION

Radiomics analysis shows promise in classifying pathology grades non-invasively for patients with VHL-associated ccRCC, potentially leading to better diagnosis and personalized treatment.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 2.

摘要

背景

病理学分级是透明细胞肾细胞癌(ccRCC)患者治疗和评估预后的重要步骤。

目的

探讨纹理分析在评估 Von Hippel-Lindau(VHL)相关 ccRCC 患者肾肿瘤 Fuhrman 分级中的应用,旨在提高非侵入性诊断和个体化治疗水平。

研究类型

前瞻性队列的回顾性分析。

人群

136 名经病理证实的 ccRCC 患者,其中 84 名(61%)为男性,52 名(39%)为女性,平均年龄为 52.8±12.7 岁,病例采集时间为 2010 年至 2023 年。

磁场强度和序列

1.5 和 3T MRI。在 T1 加权 3 分钟延迟序列上进行分割,然后在对比前、T1 加权动脉和静脉序列上进行注册。

评估

对 MRI 的 T1 加权 3 分钟延迟序列上的 404 个病灶,345 个低级别肿瘤和 59 个高级别肿瘤进行了 ITK-SNAP 分割。从对比前、T1 加权动脉、静脉和延迟后对比期序列中提取放射组学特征。采用预处理技术解决类别不平衡问题。对特征进行重新缩放以归一化数值。我们开发了一个结合随机森林和 XGBoost 的堆叠模型,使用放射组学特征评估肿瘤分级。

统计检验

采用阳性预测值(PPV)、敏感度、F1 评分、受试者工作特征曲线下面积和 Matthews 相关系数评估模型性能。采用蒙特卡罗技术报告了 100 个 85%训练和 15%测试的平均性能基准。

结果

最佳模型的准确率为 0.79。对于低级别肿瘤检测,获得了 0.79 的敏感度、0.95 的阳性预测值和 0.86 的 F1 评分。对于高级别肿瘤检测,报告了 0.78 的敏感度、0.39 的阳性预测值和 0.52 的 F1 评分。

数据结论

放射组学分析有望为 VHL 相关 ccRCC 患者的病理分级提供非侵入性诊断,从而可能实现更好的诊断和个体化治疗。

证据水平

1 技术功效:2 级。

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