Impact of Interobserver Variability in Manual Segmentation of Non-Small Cell Lung Cancer (NSCLC) Applying Low-Rank Radiomic Representation on Computed Tomography.
Impact of Interobserver Variability in Manual Segmentation of Non-Small Cell Lung Cancer (NSCLC) Applying Low-Rank Radiomic Representation on Computed Tomography.
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DOI：
10.3390/cancers13235985
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发表时间：
2021-11-28
期刊：
影响因子：
5.2
通讯作者：
Katz SI
中科院分区：
文献类型：
--
作者：
Hershman M;Yousefi B;Serletti L;Galperin-Aizenberg M;Roshkovan L;Luna JM;Thompson JC;Aggarwal C;Carpenter EL;Kontos D;Katz SI
发现癌症的预测和预后放射学特征是目前放射学和肿瘤学界非常感兴趣的。肿瘤表型和预后信息可以通过提取肿瘤分割上的特征来获得，通常是影像学分析师、实习医师和主治医师提供这些标记数据集进行分析。研究了专业培训的水平和类型对非小细胞肺癌人工分割中观察者间变异的潜在影响。虽然阅读器之间的分割存在一些可变性，但随后提取的放射学特征总体上具有良好的相关性。高保真放射学特征提取依赖于从成像中准确提取特征，从而产生可靠的预后和预测性放射学NSCLC生物标志物。本研究的结论是，这一目标可以通过不同水平的培训和临床经验来实现。本研究解决了非小细胞肺癌（NSCLC）人工分割专业培训方面的观察者间差异。用于分割的四名读者是：一名数据科学家（BY），一名医科学生（LS），一名放射学实习生（MH）和一名经过专业培训的放射科医生（SK），共来自两个公开数据库的293名患者。计算429个放射组学的Sørensen-Dice （SD）系数和低秩Pearson相关系数（CC），以评估观察者间的变异性。生成各数据集总生存期（OS）预测的Cox比例风险（CPH）模型和Kaplan-Meier （KM）曲线。两个数据库的SD和CC具有较高的相似性，平均SD: 0.79和CC: 0.92 (BY-SK), SD: 0.81和CC: 0.83 (LS-SK), SD: 0.84和CC: 0.91 （MH-SK）。当将放射组学和临床变量（性别、分期/形态状态和组织学）加在一起时，通过最大CPH模型对两个数据集的OS产生的c统计量分别为0.7 （95% CI）和0.69 （95% CI）。KM曲线在高、低危患者之间也存在显著差异（p值< 0.005）。这支持读者的培训水平和临床经验可能不会显著影响在CT上提取准确的NSCLC放射学特征的能力。这可能允许灵活性所需的培训，以产生强大的预后成像生物标志物，用于潜在的临床转化。
Discovery of predictive and prognostic radiomic features in cancer is currently of great interest to the radiologic and oncologic community. Tumor phenotypic and prognostic information can be obtained by extracting features on tumor segmentations, and it is typically imaging analysts, physician trainees, and attending physicians who provide these labeled datasets for analysis. The potential impact of level and type of specialty training on interobserver variability in manual segmentation of NSCLC was examined. Although there was some variability in segmentation between readers, the subsequently extracted radiomic features were overall well correlated. High fidelity radiomic feature extraction relies on accurate feature extraction from imaging that produce robust prognostic and predictive radiomic NSCLC biomarkers. This study concludes that this goal can be obtained using segmenters of different levels of training and clinical experience. This study tackles interobserver variability with respect to specialty training in manual segmentation of non-small cell lung cancer (NSCLC). Four readers included for segmentation are: a data scientist (BY), a medical student (LS), a radiology trainee (MH), and a specialty-trained radiologist (SK) for a total of 293 patients from two publicly available databases. Sørensen–Dice (SD) coefficients and low rank Pearson correlation coefficients (CC) of 429 radiomics were calculated to assess interobserver variability. Cox proportional hazard (CPH) models and Kaplan-Meier (KM) curves of overall survival (OS) prediction for each dataset were also generated. SD and CC for segmentations demonstrated high similarities, yielding, SD: 0.79 and CC: 0.92 (BY-SK), SD: 0.81 and CC: 0.83 (LS-SK), and SD: 0.84 and CC: 0.91 (MH-SK) in average for both databases, respectively. OS through the maximal CPH model for the two datasets yielded c-statistics of 0.7 (95% CI) and 0.69 (95% CI), while adding radiomic and clinical variables (sex, stage/morphological status, and histology) together. KM curves also showed significant discrimination between high- and low-risk patients (p-value < 0.005). This supports that readers’ level of training and clinical experience may not significantly influence the ability to extract accurate radiomic features for NSCLC on CT. This potentially allows flexibility in the training required to produce robust prognostic imaging biomarkers for potential clinical translation.
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影响因子：
9.8
作者：
Bakr S;Gevaert O;Echegaray S;Ayers K;Zhou M;Shafiq M;Zheng H;Benson JA;Zhang W;Leung ANC;Kadoch M;Hoang CD;Shrager J;Quon A;Rubin DL;Plevritis SK;Napel S
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影响因子：
3.5
作者：
Buch, K.;Li, B.;Sakai, O.
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Sakai, O.
影响因子：
4.4
作者：
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通讯作者：
Prior, Fred
影响因子：
5.9
作者：
Ganeshan, Balaji;Panayiotou, Elleny;Miles, Ken
通讯作者：
Miles, Ken
影响因子：
3.7
作者：
Forgacs A;Pall Jonsson H;Dahlbom M;Daver F;D DiFranco M;Opposits G;K Krizsan A;Garai I;Czernin J;Varga J;Tron L;Balkay L
通讯作者：
Balkay L