A computed tomography-based score indicative of lung cancer aggression (SILA) predicts lung adenocarcinomas with low malignant potential or vascular invasion.

IF 2.2 4区 医学 Q3 ONCOLOGY Cancer Biomarkers Pub Date : 2024-05-22 DOI:10.3233/CBM-230456
Dylan Steiner, Ju Ae Park, Sarah Singh, Austin Potter, Jonathan Scalera, Jennifer Beane, Kei Suzuki, Marc E Lenburg, Eric J Burks
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Abstract

Background: Histologic grading of lung adenocarcinoma (LUAD) is predictive of outcome but is only possible after surgical resection. A radiomic biomarker predictive of grade has the potential to improve preoperative management of early-stage LUAD.

Objective: Validate a prognostic radiomic score indicative of lung cancer aggression (SILA) in surgically resected stage I LUAD (n= 161) histologically graded as indolent low malignant potential (LMP), intermediate, or aggressive vascular invasive (VI) subtypes.

Methods: The SILA scores were generated from preoperative CT-scans using the previously validated Computer-Aided Nodule Assessment and Risk Yield (CANARY) software.

Results: Cox proportional regression showed significant association between the SILA and 7-year recurrence-free survival (RFS) in a univariate (p< 0.05) and multivariate (p< 0.05) model incorporating age, gender, smoking status, pack years, and extent of resection. The SILA was positively correlated with invasive size (spearman r= 0.54, p= 8.0 × 10 - 14) and negatively correlated with percentage of lepidic histology (spearman r=-0.46, p= 7.1 × 10 - 10). The SILA predicted indolent LMP with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.74 and aggressive VI with an AUC of 0.71, the latter remaining significant when invasive size was included as a covariate in a logistic regression model (p< 0.01).

Conclusions: The SILA scoring of preoperative CT scans was prognostic and predictive of resected pathologic grade.

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基于计算机断层扫描的肺癌侵袭性评分(SILA)可预测恶性程度低或有血管侵犯的肺腺癌。
背景:肺腺癌(LUAD)的组织学分级可预测预后,但只有在手术切除后才能进行分级。预测分级的放射生物标志物有望改善早期肺腺癌的术前管理:目的:在手术切除的 I 期 LUAD(161 例)患者中验证肺癌侵袭性放射学评分(SILA),该评分在组织学上被分级为轻度低恶性潜能亚型(LMP)、中度亚型或侵袭性血管浸润亚型(VI):SILA评分由术前CT扫描结果生成,使用的是之前经过验证的计算机辅助结节评估和风险收益(CANARY)软件:结果:Cox比例回归显示,在单变量(p< 0.05)和多变量(p< 0.05)模型中,SILA与7年无复发生存率(RFS)之间存在显著关联,多变量模型包括年龄、性别、吸烟状况、包年和切除范围。SILA与浸润性大小呈正相关(spearman r=0.54,p= 8.0 × 10 - 14),与鳞状组织学百分比呈负相关(spearman r=-0.46,p= 7.1 × 10 - 10)。SILA预测惰性LMP的接收者操作特征曲线(ROC)下面积(AUC)为0.74,预测侵袭性VI的接收者操作特征曲线(ROC)下面积(AUC)为0.71,当在逻辑回归模型中将侵袭性大小作为协变量时,后者仍然显著(p< 0.01):结论:术前CT扫描的SILA评分可预测预后并预示切除的病理分级。
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 Biomarkers
Cancer Biomarkers ONCOLOGY-
CiteScore
5.20
自引率
3.20%
发文量
195
审稿时长
3 months
期刊介绍: Concentrating on molecular biomarkers in cancer research, Cancer Biomarkers publishes original research findings (and reviews solicited by the editor) on the subject of the identification of markers associated with the disease processes whether or not they are an integral part of the pathological lesion. The disease markers may include, but are not limited to, genomic, epigenomic, proteomics, cellular and morphologic, and genetic factors predisposing to the disease or indicating the occurrence of the disease. Manuscripts on these factors or biomarkers, either in altered forms, abnormal concentrations or with abnormal tissue distribution leading to disease causation will be accepted.
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