Applying Quantitative CT Image Feature Analysis to Predict Response of Ovarian Cancer Patients to Chemotherapy

Danala, Gopichandh; Thai, Theresa C.; Gunderson, Camille C.; Moxley, Katherine M.; Moore, Kathleen N.; Mannel, Robert S.; Lv, Hong; Zheng, Bin; Qiu, Yuchen

doi:10.1016/j.acra.2017.04.014

Cited by 43 publications

(20 citation statements)

References 26 publications

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“…However, in the early stage, using the image marker computed from both prior and post-treatment ultrasound images can yield substantially higher prediction accuracy as compared to using the prior treatment images only (i.e., correlation coefficients of 0.375 vs. 0.679 as shown in Tables 3 and 5). This observation is consistent with our previous study of developing quantitative image markers computed from prior and post-chemotherapy CT images to predict the response of ovarian cancer patients to chemotherapy in the clinical trials 16 .…”

Section: Discussionsupporting

confidence: 92%

“…In cancer research, many previous studies have reported to develop and apply either molecular biomarkers (i.e., 21–23 ) or quantitative image markers (i.e. 15,16,24–26 ) to predict tumor response to chemotherapies and/or other therapeutic methods at an early stage. In this study, we investigated and demonstrated the feasibility of identifying new quantitative image markers computed from ultrasound images.…”

Section: Discussionmentioning

confidence: 99%

“…A total of 284 image features are computed from each segmented tumor region. The similar tumor-related image features have been computed from other imaging modalities (i.e., CT and MRI) in our previous studies to develop quantitative image markers for predicting tumor response to chemotherapies of treating breast and ovarian cancer 15,16 . These features can be categorized into 4 groups as summarizied in Table 1, which include (1) 9 morphology-based image features; (2) 21 tumor density distribution related image features; (3) 44 grayscale run length (GSRL) based texture related image features 17 , which include (a) Short Run Emphasis (SRE), (b) Long Run Emphasis (LRE), (c) Gray-Level Nonuniformity (GLN), (d) Run Length Nonuniformity (RLN), (e) Run Percentage (RP), (f) Low Gray-Level Run Emphasis (LGRE), (g) High Gray-Level Run Emphasis (HGRE), (h) Short Run Low Gray-Level Emphasis (SRLGE), (i) Short Run High Gray-Level Emphasis (SRHGE), (j) Long Run Low Gray-Level Emphasis (LRLGE), and (k) Long Run High Gray-Level Emphasis (LRHGE) computed in four different directions (0°, 45°, 90°, and 135°), respectively; and (4) 210 image features computed from the wavelet transformation maps.

Figure 3Illustration of applying Gaussian filter to the ultrasound image, which shows ( A ) manually marked tumor boundary contour, ( B ) the segmented tumor region and ( C ) tumor image after applying the Gaussian filter.…”

Section: Methodsmentioning

confidence: 99%

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Developing a Quantitative Ultrasound Image Feature Analysis Scheme to Assess Tumor Treatment Efficacy Using a Mouse Model

Mirniaharikandehei

VanOsdol

Heidari

et al. 2019

Sci Rep

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The aim of this study is to investigate the feasibility of identifying and applying quantitative imaging features computed from ultrasound images of athymic nude mice to predict tumor response to treatment at an early stage. A computer-aided detection (CAD) scheme with a graphic user interface was developed to conduct tumor segmentation and image feature analysis. A dataset involving ultrasound images of 23 athymic nude mice bearing C26 mouse adenocarcinomas was assembled. These mice were divided into 7 treatment groups utilizing a combination of thermal and nanoparticle-controlled drug delivery. Longitudinal ultrasound images of mice were taken prior and post-treatment in day 3 and day 6. After tumor segmentation, CAD scheme computed image features and created four feature pools including features computed from (1) prior treatment images only and (2) difference between prior and post-treatment images of day 3 and day 6, respectively. To predict tumor treatment efficacy, data analysis was performed to identify top image features and an optimal feature fusion method, which have a higher correlation to tumor size increase ratio (TSIR) determined at Day 10. Using image features computed from day 3, the highest Pearson Correlation coefficients between the top two features selected from two feature pools versus TSIR were 0.373 and 0.552, respectively. Using an equally weighted fusion method of two features computed from prior and post-treatment images, the correlation coefficient increased to 0.679. Meanwhile, using image features computed from day 6, the highest correlation coefficient was 0.680. Study demonstrated the feasibility of extracting quantitative image features from the ultrasound images taken at an early treatment stage to predict tumor response to therapies.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Developing a Quantitative Ultrasound Image Feature Analysis Scheme to Assess Tumor Treatment Efficacy Using a Mouse Model

Mirniaharikandehei

VanOsdol

Heidari

et al. 2019

Sci Rep

Self Cite

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“…They showed that MRI-derived radiomic features can discriminate between benign and malignant ovarian masses, with a high accuracy of 87% [12]. Furthermore, CT radiomic features of patients with ovarian cancer correlate with response to therapy [13], progression-free survival [14,15], and overall survival [15], and can identify patients at higher risk for recurrence [16]. Recent work by our group focused on evaluating the possible associations between CT imaging traits and texture metrics with genomics data and patient outcome [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis

et al. 2020

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Objectives To investigate the association between CT imaging traits and texture metrics with proteomic data in patients with high-grade serous ovarian cancer (HGSOC). Methods This retrospective, hypothesis-generating study included 20 patients with HGSOC prior to primary cytoreductive surgery. Two readers independently assessed the contrast-enhanced computed tomography (CT) images and extracted 33 imaging traits, with a third reader adjudicating in the event of a disagreement. In addition, all sites of suspected HGSOC were manually segmented texture features which were computed from each tumor site. Three texture features that represented intra-and inter-site tumor heterogeneity were used for analysis. An integrated analysis of transcriptomic and proteomic data identified proteins with conserved expression between primary tumor sites and metastasis. Correlations between protein abundance and various CT imaging traits and texture features were assessed using the Kendall tau rank correlation coefficient and the Mann-Whitney U test, whereas the area under the receiver operating characteristic curve (AUC) was reported as a metric of the strength and the direction of the association. P values < 0.05 were considered significant. Results Four proteins were associated with CT-based imaging traits, with the strongest correlation observed between the CRIP2 protein and disease in the mesentery (p < 0.001, AUC = 0.05). The abundance of three proteins was associated with texture features that represented intra-and inter-site tumor heterogeneity, with the strongest negative correlation between the CKB protein and cluster dissimilarity (p = 0.047, τ = 0.326). Conclusion This study provides the first insights into the potential associations between standard-of-care CT imaging traits and texture measures of intra-and inter-site heterogeneity, and the abundance of several proteins. Key Points • CT-based texture features of intra-and inter-site tumor heterogeneity correlate with the abundance of several proteins in patients with HGSOC. • CT imaging traits correlate with protein abundance in patients with HGSOC.

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“…Radiomics, in which voxel relationships are evaluated to identify textural patterns, has shown promise in separating patients into low‐ and high‐risk groups for assessment of survival . This separation of patients demonstrates the ability of radiomics features to identify small textural differences on CT images.…”

Section: Introductionmentioning

confidence: 93%

Technical Note: Proof of concept for radiomics‐based quality assurance for computed tomography

Branco

Ger

Mackin

et al. 2019

J Applied Clin Med Phys

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Purpose: Routine quality assurance (QA) testing to identify malfunctions in medical imaging devices is a standard practice and plays an important role in meeting quality standards. However, current daily computed tomography (CT) QA techniques have proven to be inadequate for the detection of subtle artifacts on scans. Therefore, we investigated the ability of a radiomics phantom to detect subtle artifacts not detected in conventional daily QA.Methods: An updated credence cartridge radiomics phantom was used in this study, with a focus on two of the cartridges (rubber and cork) in the phantom. The phantom was scanned using a Siemens Definition Flash CT scanner, which was reported to produce a subtle line pattern artifact. Images were then imported into the IBEX software program, and 49 features were extracted from the two cartridges using four different preprocessing techniques. Each feature was then compared with features for the same scanner several months previously and with features from controlled CT scans obtained using 100 scanners.Results: Of 196 total features for the test scanner, 79 (40%) from the rubber cartridge and 70 (36%) from the cork cartridge were three or more standard deviations away from the mean of the controlled scan population data. Feature values for the artifact-producing scanner were closer to the population mean when features were preprocessed with Butterworth smoothing. The feature most sensitive to the artifact was co-occurrence matrix maximum probability. The deviation from the mean for this feature was more than seven times greater when the scanner was malfunctioning (7.56 versus 1.01).Conclusions: Radiomics features extracted from a texture phantom were able to identify an artifact-producing scanner as an outlier among 100 CT scanners. This preliminary analysis demonstrated the potential of radiomics in CT QA to identify subtle artifacts not detected using the currently employed daily QA techniques.

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Applying Quantitative CT Image Feature Analysis to Predict Response of Ovarian Cancer Patients to Chemotherapy

Cited by 43 publications

References 26 publications

Developing a Quantitative Ultrasound Image Feature Analysis Scheme to Assess Tumor Treatment Efficacy Using a Mouse Model

Developing a Quantitative Ultrasound Image Feature Analysis Scheme to Assess Tumor Treatment Efficacy Using a Mouse Model

Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis

Technical Note: Proof of concept for radiomics‐based quality assurance for computed tomography

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