基于超声的放射组学机器学习模型诊断非小细胞肺癌患者颈淋巴结转移：一项多中心研究。

Ultrasound-based radiomics machine learning models for diagnosing cervical lymph node metastasis in patients with non-small cell lung cancer: a multicentre study.

机构信息

Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China.

Department of Oncology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.

出版信息

BMC Cancer. 2024 Apr 27;24(1):536. doi: 10.1186/s12885-024-12306-6.

DOI:10.1186/s12885-024-12306-6

PMID:38678211

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11055367/

Abstract

BACKGROUND

Cervical lymph node metastasis (LNM) is an important prognostic factor for patients with non-small cell lung cancer (NSCLC). We aimed to develop and validate machine learning models that use ultrasound radiomic and descriptive semantic features to diagnose cervical LNM in patients with NSCLC.

METHODS

This study included NSCLC patients who underwent neck ultrasound examination followed by cervical lymph node (LN) biopsy between January 2019 and January 2022 from three institutes. Radiomic features were extracted from the ultrasound images at the maximum cross-sectional areas of cervical LNs. Logistic regression (LR) and random forest (RF) models were developed. Model performance was assessed by the area under the curve (AUC) and accuracy, validated internally and externally by fivefold cross-validation and hold-out method, respectively.

RESULTS

In total, 313 patients with a median age of 64 years were included, and 276 (88.18%) had cervical LNM. Three descriptive semantic features, including long diameter, shape, and corticomedullary boundary, were selected by multivariate analysis. Out of the 474 identified radiomic features, 9 were determined to fit the LR model, while 15 fit the RF model. The average AUCs of the semantic and radiomics models were 0.876 (range: 0.781-0.961) and 0.883 (range: 0.798-0.966), respectively. However, the average AUC was higher for the semantic-radiomics combined LR model (0.901; range: 0.862-0.927). When the RF algorithm was applied, the average AUCs of the radiomics and semantic-radiomics combined models were improved to 0.908 (range: 0.837-0.966) and 0.922 (range: 0.872-0.982), respectively. The models tested by the hold-out method had similar results, with the semantic-radiomics combined RF model achieving the highest AUC value of 0.901 (95% CI, 0.886-0.968).

CONCLUSIONS

The ultrasound radiomic models showed potential for accurately diagnosing cervical LNM in patients with NSCLC when integrated with descriptive semantic features. The RF model outperformed the conventional LR model in diagnosing cervical LNM in NSCLC patients.

摘要

背景

颈部淋巴结转移（LNM）是非小细胞肺癌（NSCLC）患者的重要预后因素。我们旨在开发和验证机器学习模型，利用超声放射组学和描述性语义特征来诊断 NSCLC 患者的颈部 LNM。

方法

本研究纳入了 2019 年 1 月至 2022 年 1 月期间，来自三个机构的经颈超声检查后行颈部淋巴结（LN）活检的 NSCLC 患者。从颈部 LN 的最大横截面积提取放射组学特征。建立逻辑回归（LR）和随机森林（RF）模型。通过曲线下面积（AUC）和准确性评估模型性能，分别通过五重交叉验证和留一法进行内部和外部验证。

结果

共纳入 313 例中位年龄为 64 岁的患者，其中 276 例（88.18%）有颈部 LNM。多变量分析选择了 3 个描述性语义特征，包括长径、形状和皮质-髓质边界。在 474 个确定的放射组学特征中，有 9 个符合 LR 模型，15 个符合 RF 模型。语义和放射组学模型的平均 AUC 分别为 0.876（范围：0.781-0.961）和 0.883（范围：0.798-0.966）。然而，语义-放射组学联合 LR 模型的平均 AUC 更高（0.901；范围：0.862-0.927）。当应用 RF 算法时，放射组学和语义-放射组学联合模型的平均 AUC 提高到 0.908（范围：0.837-0.966）和 0.922（范围：0.872-0.982）。留一法测试的模型也得到了相似的结果，语义-放射组学联合 RF 模型的 AUC 值最高，为 0.901（95%CI，0.886-0.968）。