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A quantitative imaging biomarker for predicting disease-free-survival-associated histologic subgroups in lung adenocarcinoma.

预测肺腺癌无病生存相关组织学亚组的定量成像生物标志物。

  • 影响因子:4.08
  • DOI:10.1007/s00330-020-06663-6
  • 作者列表:"Lu L","Wang D","Wang L","E L","Guo P","Li Z","Xiang J","Yang H","Li H","Yin S","Schwartz LH","Xie C","Zhao B
  • 发表时间:2020-02-21
Abstract

OBJECTIVES:Classification of histologic subgroups has significant prognostic value for lung adenocarcinoma patients who undergo surgical resection. However, clinical histopathology assessment is generally performed on only a small portion of the overall tumor from biopsy or surgery. Our objective is to identify a noninvasive quantitative imaging biomarker (QIB) for the classification of histologic subgroups in lung adenocarcinoma patients. METHODS:We retrospectively collected and reviewed 1313 CT scans of patients with resected lung adenocarcinomas from two geographically distant institutions who were seen between January 2014 and October 2017. Three study cohorts, the training, internal validation, and external validation cohorts, were created, within which lung adenocarcinomas were divided into two disease-free-survival (DFS)-associated histologic subgroups, the mid/poor and good DFS groups. A comprehensive machine learning- and deep learning-based analytical system was adopted to identify reproducible QIBs and help to understand QIBs' significance. RESULTS:Intensity-Skewness, a QIB quantifying tumor density distribution, was identified as the optimal biomarker for predicting histologic subgroups. Intensity-Skewness achieved high AUCs (95% CI) of 0.849(0.813,0.881), 0.820(0.781,0.856) and 0.863(0.827,0.895) on the training, internal validation, and external validation cohorts, respectively. A criterion of Intensity-Skewness ≤ 1.5, which indicated high tumor density, showed high specificity of 96% (sensitivity 46%) and 99% (sensitivity 53%) on predicting the mid/poor DFS group in the training and external validation cohorts, respectively. CONCLUSIONS:A QIB derived from routinely acquired CT was able to predict lung adenocarcinoma histologic subgroups, providing a noninvasive method that could potentially benefit personalized treatment decision-making for lung cancer patients. KEY POINTS:• A noninvasive imaging biomarker, Intensity-Skewness, which described the distortion of pixel-intensity distribution within lesions on CT images, was identified as a biomarker to predict disease-free-survival-associated histologic subgroups in lung adenocarcinoma. • An Intensity-Skewness of ≤ 1.5 has high specificity in predicting the mid/poor disease-free survival histologic patient group in both the training cohort and the external validation cohort. • The Intensity-Skewness is a feature that can be automatically computed with high reproducibility and robustness.

摘要

目的: 组织学亚组分类对接受手术切除的肺腺癌患者具有显著的预后价值。然而,临床组织病理学评估通常仅对活检或手术的总体肿瘤的一小部分进行。我们的目的是确定一种非侵入性定量成像生物标志物 (QIB),用于肺腺癌患者组织学亚组的分类。 方法: 我们回顾性收集并回顾了 2014年1月至 2017年10月期间从两个地理位置较远的机构中切除的肺腺癌患者的 1313 次 ct扫描。创建了三个研究队列,培训、内部验证和外部验证队列,其中肺腺癌被分为两个无病生存 (DFS) 相关组织学亚组, 中/差和良好的 DFS 组。采用基于机器学习和深度学习的综合分析系统,识别可重复的 QIBs,帮助理解 QIBs 的意义。 结果: Intensity-偏度,一种量化肿瘤密度分布的 QIB,被确定为预测组织学亚组的最佳生物标志物。强度偏度在训练上获得了 95% (0.849,0.813) 、 0.881 (0.820,0.781) 和 0.856 (0.863,0.827) 的高 auc (0.895 CI),内部验证, 和外部验证队列分别。表示肿瘤密度高的强度-偏度 ≤ 1.5 的标准显示了 96% (敏感性 46%) 和 99% (敏感性 53%) 的高特异度分别在训练和外部验证队列中预测中/差 DFS 组。 结论: 来源于常规获得性 CT 的 QIB 能够预测肺腺癌组织学亚组,提供了一种无创方法,可能有益于肺癌患者的个性化治疗决策。 要点: • 一种非侵入性成像生物标志物,Intensity-偏度,它描述了 CT 图像上病灶内像素强度分布的失真, 被确定为预测肺腺癌中无病生存相关组织学亚组的生物标志物。•≤ 1.5 的强度偏度在预测训练队列和外部验证队列中的中/差无病生存组织学患者组中具有较高的特异性。•强度偏度是一个可以自动计算的特征,具有很高的重现性和鲁棒性。

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影响因子:1.84
发表时间:2020-01-01
来源期刊:Oncology letters
DOI:10.3892/ol.2019.11149
作者列表:["Das SK","Huang YY","Li B","Yu XX","Xiao RH","Yang HF"]

METHODS::The aim of the present study was to compare the safety and efficacy of cryoablation (CA) and microwave ablation (MWA) as treatments for non-small cell lung cancer (NSCLC). Patients with stage IIIB or IV NSCLC treated with CA (n=45) or MWA (n=56) were enrolled in the present study. The primary endpoint was progression-free survival (PFS); the secondary endpoints included overall survival (OS) time and adverse events (AEs). The median PFS times between the two groups were not significantly different (P=0.36): CA, 10 months [95% confidence interval (CI), 7.5-12.4] vs. MWA, 11 months (95% CI, 9.5-12.4). The OS times between the two groups were also not significantly different (P=0.07): CA, 27.5 months (95% CI, 22.8-31.2 months) vs. MWA, 18 months (95% CI, 12.5-23.5). For larger tumors (>3 cm), patients treated with MWA had significantly longer median PFS (P=0.04; MWA, 10.5 months vs. CA, 7.0 months) and OS times (P=0.04; MWA, 24.5 months vs. CA, 14.5 months) compared patients treated with CA. However, for smaller tumors (≤3 cm), median PFS (P=0.79; MWA, 11.0 months vs. CA, 13.0 months) and OS times (P=0.39; MWA, 30.0 months vs. CA, 26.5 months) between the two groups did not differ significantly. The incidence rates of AEs were similar in the two groups (P>0.05). The number of applicators, tumor size and length of the lung traversed by applicators were associated with a higher risk of pneumothorax and intra-pulmonary hemorrhage in the two groups. Treatment with CA resulted in significantly less intraprocedural pain compared with treatment with MWA (P=0.001). Overall, the present study demonstrated that CA and MWA were comparably safe and effective procedures for the treatment of small tumors. However, treatment with MWA was superior compared with CA for the treatment of large tumors.

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影响因子:8.44
发表时间:2020-02-01
DOI:10.1016/j.annonc.2019.10.022
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