Open AccessThree dimensional texture analysis of noncontrast chest CT in differentiating solitary solid lung squamous cell carcinoma from adenocarcinoma and correlation to immunohistochemical markers
Author(s) -
Han Rui,
Arjal Roshan,
Dong Jin,
Jiang Hong,
Liu Huan,
Zhang Dongyou,
Huang Lu
Publication year - 2020
Publication title -
thoracic cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.823
H-Index - 28
eISSN - 1759-7714
pISSN - 1759-7706
DOI - 10.1111/1759-7714.13592
Subject(s) - medicine , correlation , immunohistochemistry , adenocarcinoma , basal cell , receiver operating characteristic , carcinoma , lung , nuclear medicine , spearman's rank correlation coefficient , pathology , radiology , cancer , geometry , statistics , mathematics
Background The aim of the study was to investigate 3D texture analysis (3D‐TA) in noncontrast enhanced computed tomography (CT) (NCECT) to differentiate squamous cell carcinoma (SCC) from adenocarcinoma (AC), and the correlation with immunohistochemical markers. Methods A total of 70 patients confirmed with SCC ( n = 29) and AC ( n = 41) were enrolled in this retrospective study. 3D‐TA was utilized to calculate TA parameters of all the tumor lesions based on NCECT images, and all the patients were divided into the training and the test groups. The TA parameters were selected by dimensionality reduction, and the model was established to differentiate SCC from AC according to the training group. The ROC curve was used to evaluate the diagnostic efficiency of the model in both the training and the test groups. Spearman correlation were used to assess the correlation between the selected feature parameters and immunohistochemical markers (P63, P40, and TTF‐1). Results Five TA parameters, including volume count, relative deviation, Haralick correlation, gray‐level nonuniformity and run length nonuniformity, were obtained to differentiate SCC from AC by multistep dimensionality reduction. The new model combined with all five TA parameters yielded a high diagnostic performance to differentiate SCC from AC (AUC 0.803) in test group, with a specificity of 89% and a sensitivity of 77%. There was weak correlation between the five texture feature parameters and P63 as well as P40 in all patients ( P < 0.05), respectively. Conclusions The model including five TA parameters on NECT has a good diagnostic performance in differentiating SCC from AC. Key points • Significant findings of the study The model created by five selected textural feature parameters can differentiate solid SCC from AC without contrast media. The selected five texture feature parameters are correlated to the immunohistochemical markers P63 and P40. • What this study adds The textural feature parameters' model can identify SCC from AC without contrast media.