LungCT-Diagnosis | Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma

DOI: 10.7937/K9/TCIA.2015.A6V7JIWX | Page Accessibility: Public | Collection

Summary

All the images are diagnostic contrast enhanced CT scans. The images were retrospectively acquired, to ensure sufficient patient follow-up. Slice thickness is variable : between 3 and 6 mm. All images were done at diagnosis and prior to surgery. The objective of the study was to extract prognostic image features that will describe lung adenocarcinomas and will associate with overall survival.   Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity and intratumor density variation using routinely obtained diagnostic CT scans. The features systematically scored tumors and identified imaging phenotypes which exhibited survival differences. The features were extracted from routinely obtained CT images and were reproducible and stable despite the inherent clinical image acquisition variability. Our results suggest that quantitative imaging features can be used as an additional diagnostic tool in management of lung adenocarcinomas. More information is available in the related publication (see Citation tab below).

Acknowledgements

We would like to acknowledge the individual and institution that have provided data for this collection:

• Moffitt Cancer Center (Tampa Florida) - Special thanks to Olya Stringfield, PhD  from the Department of Cancer Imaging and Metabolism.

Data Access

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Additional Resources for this Dataset

The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.

• Imaging Data Commons (IDC) (Imaging Data)

Third Party Analyses of this Dataset

TCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection:

• Long and Short Survival in Adenocarcinoma Lung CTs (LUAD-CT-Survival)

Detailed Description

TCIA DICOM Subject ID, SOP Instance UID, Instance Number, and Image Position (Patient) X-Y-Z  are noted in Representative-Tumor-Slices.xlsx
The accompanying data  are survival data (status: dead or alive, survival time in months) and pathological stage (TNM).

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Analysis Results Using This Data