计算机断层扫描对小细胞肺癌肝转移的自动检测与表征。

IF 1.7 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Sophia Ty, Fahmida Haque, Parth Desai, Nobuyuki Takahashi, Usamah Chaudhary, Peter L Choyke, Anish Thomas, Barış Türkbey, Stephanie A Harmon
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引用次数: 0

摘要

目的:小细胞肺癌(SCLC)是一种具有多种表型的侵袭性疾病,反映了肿瘤相关基因的异质性表达。最近的研究表明,神经内分泌(NE)转录因子可用于区分具有不同治疗反应的SCLC肿瘤。肝脏是SCLC中转移性疾病的常见部位,可导致预后不良。在这里,我们提出了一种计算方法来检测和表征转移性SCLC (mSCLC)肝脏病变及其相关的ne相关表型,作为改善患者管理的一种方法。方法:本研究利用来自两个数据源的肝脏病变患者的计算机断层扫描进行肝脏疾病的分割和分类:(1)来自各种癌症类型患者的公共数据集(分割,n = 131)和(2)SCLC患者的机构队列(分割和分类,n = 86)。我们开发了深度学习分割算法,并比较了它们在自动检测肝脏病变方面的性能,评估了有无纳入SCLC队列的结果。在SCLC队列中进行分割后,从检测到的病变中提取放射学特征,并利用最小绝对收缩和选择算子回归从训练队列中选择特征(80/20分割)。随后,我们训练了基于放射组学的机器学习分类器,根据NE肿瘤特征对患者进行分层,NE肿瘤特征定义为来自大量RNA测序或循环游离DNA染色质免疫沉淀测序的预选基因集的表达水平。结果:我们的肝脏病变检测工具对两个数据集的基于病变的灵敏度为66%-83%。在mSCLC患者中,基于放射组学的NE表型分类器区分患者为NE样肝转移表型阳性或阴性,受试者工作特征曲线下面积为0.73,F1评分为0.88。结论:我们展示了利用基于人工智能(AI)的平台作为临床决策支持系统的潜力,它可以帮助临床医生根据其相关的分子肿瘤特征确定SCLC患者的治疗方案。临床意义:靶向治疗需要准确的疾病分子特征,影像学和人工智能可以帮助确定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated detection and characterization of small cell lung cancer liver metastasis on computed tomography.

Purpose: Small cell lung cancer (SCLC) is an aggressive disease with diverse phenotypes that reflect the heterogeneous expression of tumor-related genes. Recent studies have shown that neuroendocrine (NE) transcription factors may be used to classify SCLC tumors with distinct therapeutic responses. The liver is a common site of metastatic disease in SCLC and can drive a poor prognosis. Here, we present a computational approach to detect and characterize metastatic SCLC (mSCLC) liver lesions and their associated NE-related phenotype as a method to improve patient management.

Methods: This study utilized computed tomography scans of patients with hepatic lesions from two data sources for segmentation and classification of liver disease: (1) a public dataset from patients of various cancer types (segmentation; n = 131) and (2) an institutional cohort of patients with SCLC (segmentation and classification; n = 86). We developed deep learning segmentation algorithms and compared their performance for automatically detecting liver lesions, evaluating the results with and without the inclusion of the SCLC cohort. Following segmentation in the SCLC cohort, radiomic features were extracted from the detected lesions, and least absolute shrinkage and selection operator regression was utilized to select features from a training cohort (80/20 split). Subsequently, we trained radiomics-based machine learning classifiers to stratify patients based on their NE tumor profile, defined as expression levels of a preselected gene set derived from bulk RNA sequencing or circulating free DNA chromatin immunoprecipitation sequencing.

Results: Our liver lesion detection tool achieved lesion-based sensitivities of 66%-83% for the two datasets. In patients with mSCLC, the radiomics-based NE phenotype classifier distinguished patients as positive or negative for harboring NE-like liver metastasis phenotype with an area under the receiver operating characteristic curve of 0.73 and an F1 score of 0.88 in the testing cohort.

Conclusion: We demonstrate the potential of utilizing artificial intelligence (AI)-based platforms as clinical decision support systems, which could help clinicians determine treatment options for patients with SCLC based on their associated molecular tumor profile.

Clinical significance: Targeted therapy requires accurate molecular characterization of disease, which imaging and AI may aid in determining.

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来源期刊
Diagnostic and interventional radiology
Diagnostic and interventional radiology Medicine-Radiology, Nuclear Medicine and Imaging
自引率
4.80%
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0
期刊介绍: Diagnostic and Interventional Radiology (Diagn Interv Radiol) is the open access, online-only official publication of Turkish Society of Radiology. It is published bimonthly and the journal’s publication language is English. The journal is a medium for original articles, reviews, pictorial essays, technical notes related to all fields of diagnostic and interventional radiology.
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