Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer

Reliable entity subtyping is paramount for therapy stratification in lung cancer. Morphological evaluation remains the basis for entity subtyping and directs the application of additional methods such as immunohistochemistry (IHC). The decision of whether to perform IHC for subtyping is subjective, and access to IHC is not available worldwide. Thus, the application of additional methods to support morphological entity subtyping is desirable. Therefore, the ability of convolutional neuronal networks (CNNs) to classify the most common lung cancer subtypes, pulmonary adenocarcinoma (ADC), pulmonary squamous cell carcinoma (SqCC), and small-cell lung cancer (SCLC), was evaluated. A cohort of 80 ADC, 80 SqCC, 80 SCLC, and 30 skeletal muscle specimens was assembled; slides were scanned; tumor areas were annotated; image patches were extracted; and cases were randomly assigned to a training, validation or test set. Multiple CNN architectures (VGG16, InceptionV3, and InceptionResNetV2) were trained and optimized to classify the four entities. A quality control (QC) metric was established. An optimized InceptionV3 CNN architecture yielded the highest classification accuracy and was used for the classification of the test set. Image patch and patient-based CNN classification results were 95% and 100% in the test set after the application of strict QC. Misclassified cases mainly included ADC and SqCC. The QC metric identified cases that needed further IHC for definite entity subtyping. The study highlights the potential and limitations of CNN image classification models for tumor differentiation.

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Classification of Non-Small Cell Lung Cancer Based on Copy Number Alterations

PLoS ONE ◽

10.1371/journal.pone.0088300 ◽

2014 ◽

Vol 9 (2) ◽

pp. e88300 ◽

Cited By ~ 21

Author(s):

Bi-Qing Li ◽

Jin You ◽

Tao Huang ◽

Yu-Dong Cai

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Copy Number ◽

Small Cell ◽

Copy Number Alterations ◽

Small Cell Lung

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Prognostic factors for survival in advanced non-small-cell lung cancer: univariate and multivariate analyses including recursive partitioning and amalgamation algorithms in 1,052 patients. The European Lung Cancer Working Party.

Journal of Clinical Oncology ◽

10.1200/jco.1995.13.5.1221 ◽

1995 ◽

Vol 13 (5) ◽

pp. 1221-1230 ◽

Cited By ~ 268

Author(s):

M Paesmans ◽

J P Sculier ◽

P Libert ◽

G Bureau ◽

G Dabouis ◽

...

Keyword(s):

Lung Cancer ◽

Prognostic Factors ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Cox Regression ◽

Recursive Partitioning ◽

Working Party ◽

Small Cell ◽

Small Cell Lung

PURPOSE This study attempted to determine the prognostic value for survival of various pretreatment characteristics in patients with nonresectable non-small-cell lung cancer in the context of more than 10 years of experience of a European Cooperative Group. PATIENTS AND METHODS We included in the analysis all eligible patients (N = 1,052) with advanced non-small-cell lung cancer registered onto one of seven trials conducted by the European Lung Cancer Working Party (ELCWP) during one decade. The patients were treated by chemotherapy regimens based on platinum derivatives. We prospectively collected 23 variables and analyzed them by univariate and multivariate methods. RESULTS The global estimated median survival time was 29 weeks, with a 95% confidence interval of 27 to 30 weeks. After univariate analysis, we applied two multivariate statistical techniques. In a Cox regression model, the selected explanatory variables were disease extent, Karnofsky performance status, WBC and neutrophil counts, metastatic involvement of skin, serum calcium level, age, and sex. These results were confirmed by application of recursive partitioning and amalgamation algorithms (RECPAM), which led to classification of the patients into four homogeneous subgroups. CONCLUSION We confirmed by our analysis the role of well-known independent prognostic factors for survival, but also identified the effect of the neutrophil count, rarely studied, with the use of two methods: a classical Cox regression model and a RECPAM analysis. The classification of patients into the four subgroups we obtained needs to be validated in other series.

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Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results

Cancers ◽

10.3390/cancers12123663 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3663

Author(s):

Charlems Alvarez-Jimenez ◽

Alvaro A. Sandino ◽

Prateek Prasanna ◽

Amit Gupta ◽

Satish E. Viswanath ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Density ◽

Squamous Cell ◽

Cell Lung Cancer ◽

Ct Images ◽

Small Cell ◽

Small Cell Lung ◽

Cancer Subtypes ◽

Initial Results

(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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Identification of Gene Biomarkers for Distinguishing Small-Cell Lung Cancer from Non-Small-Cell Lung Cancer Using a Network-Based Approach

BioMed Research International ◽

10.1155/2015/685303 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 7

Author(s):

Fei Long ◽

Jia-Hang Su ◽

Bin Liang ◽

Li-Li Su ◽

Shu-Juan Jiang

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Expression Patterns ◽

Small Cell ◽

Normal Lung ◽

Specific Gene ◽

Small Cell Lung ◽

Cancer Subtypes ◽

Gene Modules

Lung cancer consists of two main subtypes: small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) that are classified according to their physiological phenotypes. In this study, we have developed a network-based approach to identify molecular biomarkers that can distinguish SCLC from NSCLC. By identifying positive and negative coexpression gene pairs in normal lung tissues, SCLC, or NSCLC samples and using functional association information from the STRING network, we first construct a lung cancer-specific gene association network. From the network, we obtain gene modules in which genes are highly functionally associated with each other and are either positively or negatively coexpressed in the three conditions. Then, we identify gene modules that not only are differentially expressed between cancer and normal samples, but also show distinctive expression patterns between SCLC and NSCLC. Finally, we select genes inside those modules with discriminating coexpression patterns between the two lung cancer subtypes and predict them as candidate biomarkers that are of diagnostic use.

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