Suppr超能文献

T1期浸润性肺腺癌:薄层CT实性评分和组织学骨膜蛋白表达可预测肿瘤复发。

T1 invasive lung adenocarcinoma: Thin-section CT solid score and histological periostin expression predict tumor recurrence.

作者信息

Iwamoto Ryoji, Tanoue Shuichi, Nagata Shuji, Tabata Kazuhiro, Fukuoka Junya, Koganemaru Masamichi, Sumi Akiko, Chikasue Tomonori, Abe Toshi, Murakami Daigo, Takamori Shinzo, Ishii Hidenobu, Ohshima Koichi, Ohta Shoichiro, Izuhara Kenji, Fujimoto Kiminori

机构信息

Department of Radiology, Kurume University School of Medicine, Kurume, Fukuoka 830-0011, Japan.

Department of Pathology, Nagasaki Graduate School of Biomedical Sciences, Nagasaki 852-8523, Japan.

出版信息

Mol Clin Oncol. 2021 Nov;15(5):228. doi: 10.3892/mco.2021.2391. Epub 2021 Sep 10.

Abstract

Adenocarcinoma is the most common histological type of non-small cell lung cancer (NSCLC), and various biomarkers for predicting its prognosis after surgical resection have been suggested, particularly in early-stage lung adenocarcinoma. Periostin (also referred to as POSTN, PN or osteoblast-specific factor) is an extracellular matrix protein, the expression of which is associated with tumor invasiveness in patients with NSCLC. In the present study, the novel approach, in which the thin-section CT findings prior to surgical resection and periostin expression of resected specimens were analyzed in combination, was undertaken to assess whether the findings could be a biomarker for predicting the outcomes following resection of T1 invasive lung adenocarcinoma. A total of 73 patients who underwent surgical resection between January 2000 and December 2009 were enrolled. A total of seven parameters were assessed in the thin-section CT scans: i) Contour; ii) part-solid ground-glass nodule or solid nodule; iii) percentage of solid component (the CT solid score); iv) presence of air-bronchogram and/or bubble-like lucencies; v) number of involved vessels; vi) shape linear strands between the nodule and the visceral pleura; and vii) number of linear strands between the nodule and the visceral pleura. Two chest radiologists independently assessed the parameters. Periostin expression was evaluated on the basis of the strength and extent of staining. Univariate and multivariate analyses were subsequently performed using the Cox proportional hazards model. There was a substantial to almost perfect agreement between the two observers with regard to classification of the seven thin-section CT parameters (κ=0.64-0.85). In the univariate analysis, a CT solid score >80%, pathological lymphatic invasion, tumor and lymph node status and high periostin expression were significantly associated with recurrence (all P<0.05). Multivariate analysis demonstrated that a CT solid score >80% and high periostin expression were risk factors for recurrence (P=0.002 and P=0.011, respectively). The cumulative recurrence rates among the three groups (both negative, CT solid score >80% or high periostin expression, or both positive) were significantly different (log-rank test, P<0.001). Although the solid component is already known to be a major predictor of outcome in lung adenocarcinomas according to previous studies, the combined analysis of CT solid score and periostin expression might predict the likelihood of tumor recurrence more precisely.

摘要

腺癌是非小细胞肺癌(NSCLC)最常见的组织学类型,目前已提出多种用于预测手术切除后其预后的生物标志物,尤其是在早期肺腺癌中。骨膜蛋白(也称为POSTN、PN或成骨细胞特异性因子)是一种细胞外基质蛋白,其表达与NSCLC患者的肿瘤侵袭性相关。在本研究中,采用了一种新方法,即将手术切除前的薄层CT表现与切除标本的骨膜蛋白表达进行联合分析,以评估这些表现是否可作为预测T1期浸润性肺腺癌切除术后结局的生物标志物。共有73例在2000年1月至2009年12月期间接受手术切除的患者纳入研究。在薄层CT扫描中评估了总共七个参数:i)轮廓;ii)部分实性磨玻璃结节或实性结节;iii)实性成分百分比(CT实性评分);iv)空气支气管征和/或气泡样透亮区的存在情况;v)受累血管数量;vi)结节与脏层胸膜之间的形状线性条索;vii)结节与脏层胸膜之间的线性条索数量。两名胸部放射科医生独立评估这些参数。根据染色强度和范围评估骨膜蛋白表达。随后使用Cox比例风险模型进行单因素和多因素分析。两位观察者在七个薄层CT参数的分类方面存在实质性至几乎完美的一致性(κ=0.64 - 0.85)。在单因素分析中,CT实性评分>80%、病理淋巴浸润、肿瘤和淋巴结状态以及高骨膜蛋白表达与复发显著相关(所有P<0.05)。多因素分析表明,CT实性评分>80%和高骨膜蛋白表达是复发的危险因素(分别为P = 0.002和P = 0.011)。三组(两者均为阴性、CT实性评分>80%或高骨膜蛋白表达阳性、或两者均为阳性)的累积复发率有显著差异(对数秩检验,P<0.001)。尽管根据先前研究,实性成分已被认为是肺腺癌预后的主要预测指标,但CT实性评分与骨膜蛋白表达的联合分析可能更准确地预测肿瘤复发的可能性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0357/8506662/15c67db23bb3/mco-15-05-02391-g00.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验