Non–Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data—Methods and Preliminary Results

Gevaert, Olivier; Xu, Jiajing; Hoang, Chuong D.; Leung, Ann N.; Xu, Yue; Quon, Andrew; Rubin, Daniel L.; Napel, Sandy; Plevritis, Sylvia K.

doi:10.1148/radiol.12111607

Cited by 352 publications

(286 citation statements)

References 31 publications

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“…8 studies used data from The Cancer Genome Atlas (TCGA) and/or The Cancer Imaging Archive (TCIA); 1,16,23,27,32,34,38,41 2 studies were multi-institutional; 9,19 and the remaining 17 studies used local institutional data. 15,17,18,[20][21][22][24][25][26]28,30,31,33,[35][36][37]39,40 26 out of 27 studies were retrospective in design. The number of patients ranged from 10 to 104 patients, with a median of 38 patients.…”

Section: Published Studies Using a Radiogenomic Approach In Cancer Rementioning

confidence: 99%

“…18,20 Internal air bronchogram was a specific feature extracted for NSCLC. 15 Most MRI studies focused on GBM and breast cancer. For GBM, three studies used Visually Accessible Rembrandt Images (VASARI), a comprehensive feature set consisting of 24 observations familiar to neuroradiologists to describe the morphology of brain tumours on routine contrast-enhanced MRI.…”

Section: Imaging Features and Extractionmentioning

confidence: 99%

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Imaging genomics in cancer research: limitations and promises

Bai

Lee

Yang

et al. 2016

BJR

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Recently, radiogenomics or imaging genomics has emerged as a novel high-throughput method of associating imaging features with genomic data. Radiogenomics has the potential to provide comprehensive intratumour, intertumour and peritumour information non-invasively. This review article summarizes the current state of radiogenomic research in tumour characterization, discusses some of its limitations and promises and projects its future directions. Semiradiogenomic studies that relate specific gene expressions to imaging features will also be briefly reviewed.

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Section: Published Studies Using a Radiogenomic Approach In Cancer Rementioning

confidence: 99%

Section: Imaging Features and Extractionmentioning

confidence: 99%

Imaging genomics in cancer research: limitations and promises

Bai

Lee

Yang

et al. 2016

BJR

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“…These datasets were then deposited in The Cancer Imaging Archive (TCIA: http://cancerimagingarchive.net/) and are available to the public through a shared list for easy download [20]. They consisted of 52 tumors in 41 CT volumes from 5 sub-collections: (A) one CT study of a phantom containing 12 synthetic nodules scanned at Columbia University Medical Center (CUMC) [21], (B) 10 CT studies selected from the publicly available Lung Imaging Database Consortium (LIDC) [22][23][24], (C) 10 CT studies from the Reference Image Database to Evaluate Response (RIDER) [21] to therapy in cancer collections, (D) 10 CT studies from Moffitt Cancer Center (MCC), and (E) 10 CT studies from Stanford University (SU) [25]. Table 1 contains demographic and key scanning parameters for all the sub-collections.…”

Section: Datasetsmentioning

confidence: 99%

“…While all 41 CT volumes have been used in prior studies [21][22][23][25][26][27], they have never been used as a set for the comparison of segmentation algorithms, as we present here, and there therefore is no scientific overlap between the work described here and prior publications.…”

Section: Datasetsmentioning

confidence: 99%

A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study

et al. 2016

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Tumor volume estimation, as well as accurate and reproducible borders segmentation in medical images, are important in the diagnosis, staging, and assessment of response to cancer therapy. The goal of this study was to demonstrate the feasibility of a multi-institutional effort to assess the repeatability and reproducibility of nodule borders and volume estimate bias of computerized segmentation algorithms in CT images of lung cancer, and to provide results from such a study. The dataset used for this evaluation consisted of 52 tumors in 41 CT volumes (40 patient datasets and 1 dataset containing scans of 12 phantom nodules of known volume) from five collections available in The Cancer Imaging Archive. Three academic institutions developing lung nodule segmentation algorithms submitted results for three repeat runs for each of the nodules. We compared the performance of lung nodule segmentation algorithms by assessing several measurements of spatial overlap and volume measurement. Nodule sizes varied from 29 μl to 66 ml and demonstrated a diversity of shapes. Agreement in spatial overlap of segmentations was significantly higher for multiple runs of the same algorithm than between segmentations generated by different algorithms (p < 0.05) and was significantly higher on the phantom dataset compared to the other datasets (p < 0.05). Algorithms differed significantly in the bias of the measured volumes of the phantom nodules (p < 0.05) underscoring the need for assessing performance on clinical data in addition to phantoms. Algorithms that most accurately estimated nodule volumes were not the most repeatable, emphasizing the need to evaluate both their accuracy and precision. There were considerable differences between algorithms, especially in a subset of heterogeneous nodules, underscoring the recommendation that the same software be used at all time points in longitudinal studies.

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“…More recently, combination of the molecular findings with imagebased features of lung cancer on chest CT has emerged as new tool that can potentially impact both the diagnostic and prognostic spaces [8,9]. A few prospective studies have been developed in this regards.…”

Section: Studies That Collect Molecular and Imaging Featuresmentioning

confidence: 99%