Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results.
Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results.
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DOI：
10.3390/cancers12123663
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发表时间：
2020-12-07
期刊：
影响因子：
5.2
通讯作者：
Romero E
中科院分区：
文献类型：
--
作者：
Alvarez-Jimenez C;Sandino AA;Prasanna P;Gupta A;Viswanath SE;Romero E
这项工作提出了通过探索计算机断层扫描（CT）图像和相应的数字化病理图像之间的跨尺度关联来区分两种主要非小细胞肺癌（NSCLC）亚型的初步结果。该分析包括三个阶段：（i）多分辨率细胞密度量化，以识别判别性病理模式，（ii）使用Haralick描述符对CT图像进行放射组学表征，以及（iii）多模式特征之间的定量相关性分析，以识别它们之间的潜在关联。该分析使用公开可用的数据库、两个数字化病理学和两个放射学队列进行。使用匹配的标本，在细胞密度统计和CT强度值之间确定了初步但显著的跨尺度关联，用于显著提高放射组学特征在区分NSCLC亚型中的总体区分性能。(1)背景资料：尽管放射学和组织病理学之间的互补性，无论是从诊断和预后的角度来看，这些模式的定量分析通常是在断开筒仓。这项工作提出了通过探索计算机断层扫描（CT）图像和相应的数字化病理图像之间的跨尺度关联来区分两种主要非小细胞肺癌（NSCLC）亚型的初步结果。(2)研究方法：该分析包括三个阶段，（i）多分辨率细胞密度定量以识别用于区分腺癌（ADC）和鳞状细胞癌（SCC）的判别病理学模式，（ii）通过使用Haralick描述符对CT图像进行放射组学表征以定量由灰度同现表示的肿瘤纹理异质性以区分两种病理亚型，以及（iii）多模态特征之间的定量相关性分析，以识别它们之间的潜在关联。该分析使用两个公开的数字化病理学数据库（117例来自TCGA，54例来自CPTAC）和一个公共的CT图像放射学集合（101例来自NSCLC-R）进行。(3)结果如下：来自组织病理学分析的排名最高的细胞密度病理学特征是相关性、对比度、同质性、熵和方差差;其在训练集（CPTAC）上产生了0.72 ± 0.02的交叉验证AUC，在测试集（TCGA）上产生了0.77的保留验证AUC。NSCLC-R内排名最高的共现放射组学特征是对比度、相关性和熵之和，其产生0.72 ± 0.01的交叉验证AUC。使用TCGA队列中可用的匹配标本，在细胞密度统计和CT强度值之间确定了初步但显著的跨尺度关联，用于显著提高放射组学特征在区分NSCLC亚型中的总体区分性能（AUC = 0.78 ± 0.01）。(4)结论：初步结果表明，数字病理学和CT成像之间可能存在跨尺度关联，可用于识别相关的放射组学和组织病理学特征，以准确区分肺腺癌和鳞状细胞癌。
This work presents initial results for differentiating two major non-small cell lung cancer (NSCLC) subtypes by exploring cross-scale associations between Computed Tomography (CT) images and corresponding digitized pathology images. The analysis comprised three phases, (i) a multi-resolution cell density quantification to identify discriminant pathomic patterns, (ii) radiomic characterization of CT images by using Haralick descriptors, and (iii) quantitative correlation analysis between the multi-modal features to identify potential associations between them. This analysis was carried out using publicly available databases, two digitized pathology and two radiology cohorts. Preliminary but significant cross-scale associations were identified between cell density statistics and CT intensity values using matched specimens, which were used to significantly improve the overall discriminatory performance of radiomic features in differentiating NSCLC subtypes. (1) Background: Despite the complementarity between radiology and histopathology, both from a diagnostic and a prognostic perspective, quantitative analyses of these modalities are usually performed in disconnected silos. This work presents initial results for differentiating two major non-small cell lung cancer (NSCLC) subtypes by exploring cross-scale associations between Computed Tomography (CT) images and corresponding digitized pathology images. (2) Methods: The analysis comprised three phases, (i) a multi-resolution cell density quantification to identify discriminant pathomic patterns for differentiating adenocarcinoma (ADC) and squamous cell carcinoma (SCC), (ii) radiomic characterization of CT images by using Haralick descriptors to quantify tumor textural heterogeneity as represented by gray-level co-occurrences to discriminate the two pathological subtypes, and (iii) quantitative correlation analysis between the multi-modal features to identify potential associations between them. This analysis was carried out using two publicly available digitized pathology databases (117 cases from TCGA and 54 cases from CPTAC) and a public radiological collection of CT images (101 cases from NSCLC-R). (3) Results: The top-ranked cell density pathomic features from the histopathology analysis were correlation, contrast, homogeneity, sum of entropy and difference of variance; which yielded a cross-validated AUC of 0.72 ± 0.02 on the training set (CPTAC) and hold-out validation AUC of 0.77 on the testing set (TCGA). Top-ranked co-occurrence radiomic features within NSCLC-R were contrast, correlation and sum of entropy which yielded a cross-validated AUC of 0.72 ± 0.01. Preliminary but significant cross-scale associations were identified between cell density statistics and CT intensity values using matched specimens available in the TCGA cohort, which were used to significantly improve the overall discriminatory performance of radiomic features in differentiating NSCLC subtypes (AUC = 0.78 ± 0.01). (4) Conclusions: Initial results suggest that cross-scale associations may exist between digital pathology and CT imaging which can be used to identify relevant radiomic and histopathology features to accurately distinguish lung adenocarcinomas from squamous cell carcinomas.
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DOI：
10.1158/1078-0432.ccr-18-2013
发表时间：
2019-03-01
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
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10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
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