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Proceedings Paper

Differentiating invasive and pre-invasive lung cancer by quantitative analysis of histopathologic images
Author(s): Chuan Zhou; Hongliu Sun; Heang-Ping Chan; Aamer Chughtai; Jun Wei; Lubomir Hadjiiski; Ella Kazerooni
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Paper Abstract

We are developing automated radiopathomics method for diagnosis of lung nodule subtypes. In this study, we investigated the feasibility of using quantitative methods to analyze the tumor nuclei and cytoplasm in pathologic wholeslide images for the classification of pathologic subtypes of invasive nodules and pre-invasive nodules. We developed a multiscale blob detection method with watershed transform (MBD-WT) to segment the tumor cells. Pathomic features were extracted to characterize the size, morphology, sharpness, and gray level variation in each segmented nucleus and the heterogeneity patterns of tumor nuclei and cytoplasm. With permission of the National Lung Screening Trial (NLST) project, a data set containing 90 digital haematoxylin and eosin (HE) whole-slide images from 48 cases was used in this study. The 48 cases contain 77 regions of invasive subtypes and 43 regions of pre-invasive subtypes outlined by a pathologist on the HE images using the pathological tumor region description provided by NLST as reference. A logistic regression model (LRM) was built using leave-one-case-out resampling and receiver operating characteristic (ROC) analysis for classification of invasive and pre-invasive subtypes. With 11 selected features, the LRM achieved a test area under the ROC curve (AUC) value of 0.91±0.03. The results demonstrated that the pathologic invasiveness of lung adenocarcinomas could be categorized with high accuracy using pathomics analysis.

Paper Details

Date Published: 27 February 2018
PDF: 6 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751H (27 February 2018); doi: 10.1117/12.2293601
Show Author Affiliations
Chuan Zhou, Univ. of Michigan (United States)
Hongliu Sun, Univ. of Toledo Medical Ctr. (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Aamer Chughtai, Univ. of Michigan (United States)
Jun Wei, Univ. of Michigan (United States)
Lubomir Hadjiiski, Univ. of Michigan (United States)
Ella Kazerooni, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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