Classification of early stage non-small cell lung cancers on computed tomographic images into histological types using radiomic features: interobserver delineation variability analysis

Abstract

Radiomics, which involves the extraction of large numbers of quantitative features from medical images, has attracted attention in cancer research. In radiomics analysis, tumor segmentation is a crucial step. In this study, we evaluated the potential application of radiomics for predicting the histology of early stage non-small cell lung cancer (NSCLC) by analyzing interobserver variability in tumor delineation. Forty patient datasets were included in this study, 21 involving adenocarcinomas and 19 involving squamous cell carcinomas. All patients underwent stereotactic body radiotherapy treatment. In total, 476 features were extracted from each dataset, representing treatment planning, computed tomography images, and gross tumor volume (GTV). The definition of GTV can significantly affect the histology prediction. Therefore, in the present study, the effect of interobserver tumor delineation variability on radiomic features was evaluated by preparing 4 volumes of interest (VOIs) for each patient, as follows: the original GTV (which was delineated at treatment planning); two GTVs delineated retrospectively by radiation oncologists; and a semi-automatic GTV contoured by a medical physicist. Radiomic features extracted from each VOI were then analyzed using a naïve Bayesian model. Area-under-the-curve (AUC) analysis showed that interobserver variability in delineation is a significant factor in radiomics performance. Nevertheless, with 8 selected features, AUC values averaged over the VOIs were high (0.725 ± 0.070). The present study indicated that radiomics has potential for predicting early stage NSCLC histology despite variability in delineation. The high prediction accuracy implies that noninvasive histology evaluation by radiomics is a promising clinical application.

Multi‐subtype classification model for non‐small cell lung cancer based on radiomics: SLS model

“…Obtaining more radiomic features will help us get more information from CT images from a more comprehensive perspective. In the classification studies of NSCLC, the number of radiomic features was all below 500. In this study, we increased the number of radiomic features to 1029 by increasing the number of filters to 12.…”

“…used the LASSO logistic regression model to select five key features to construct the radiomic signature for histological subtype classification, and the AUC of the radiomic signature to distinguish between lung ADC and SCC in validation cohorts was 0.893. Haga et al . found that radiomics had potential for predicting early stage NSCLC histology despite variability in delineation by analyzing interobserver variability in tumor delineation.…”

Prediction of the degree of pathological differentiation in tongue squamous cell carcinoma based on radiomics analysis of magnetic resonance images

“…In patients followed closely for N0 disease, 20-30% will subsequently develop cervical lymph node metastases. A 40% incidence of micrometastatic disease was still found when elective neck dissection was performed in patients with T1 and T2 TSCC [32]; most of the patients were poorly differentiated. Therefore, these present ndings have the potential to impact the clinical management of early TSCC.…”

“…However, accurate judgment of the degree of pathological differentiation before surgery may improve this situation. The performance of radiomics analysis varies depending on the MRI scanner, imaging parameters and tumour delineation method used [31,32]. MRI scans acquired by one type of scanner and imaging parameters from the entire dataset were used to reduce the in uence of these variations on performance [33].…”

Anticancer activity of green synthesised gold nanoparticles from Marsdenia tenacissima inhibits A549 cell proliferation through the apoptotic pathway

“…Lung cancer is distinguished into small cell and non-small cell lung cancer based on histologic studies. Generally, an advanced type of non-small cell lung cancer has been thought of as poor prognosis; therefore, new ultimate methods are urgently needed for improving the chances of patient survival [2]. The development of therapies to fight against severely multiplying tumors is very difficult.…”