Abstract
(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.
Highlights
According to the World Health Organization (WHO), lung cancer was the most common cancer diagnosed worldwide in 2018 [1]
This study presents initial results for a novel pathomic-radiomic association approach, i.e., identifying pathomic features from digitized histopathology that potentially reflect the tissue composition basis of radiomic descriptors from computed tomography (CT), towards improving the understanding and ability to discriminate ADC from squamous cell carcinoma (SCC)
The opposite trend is observed in SCC (Figure 4c,d), i.e., co-ocurrences are more distributed with a smoother changes in cell density values between adjacent tiles
Summary
According to the World Health Organization (WHO), lung cancer was the most common cancer diagnosed worldwide in 2018 [1]. Between 80% to 85% of lung cancers are non-small cell lung cancer (NSCLC), and the most prevalent subtypes are adenocarcinoma (ADC) and squamous cell carcinoma (SCC) [2,3]. ADC is the most commonly diagnosed type of lung cancer and frequently occurs along the outer periphery of the lung [4,5]. SCC comprises 25–30% of lung cancer cases and usually occurs in the central portion of the lung as well as being considered more aggressive than ADC [4]. Available treatment options include conventional chemotherapy and targeted therapies, and typically differ for ADC and SCC [6,7]. The key modalities in the lung cancer clinical protocol remain standard-of-care computed tomography (CT) imaging (acquired at diagnosis) as well as tissue biopsy specimens (acquired for disease confirmation); both typically requiring subjective expert evaluation in the clinical workflow [9]
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