Primary Esophageal Cancer: Heterogeneity as Potential Prognostic Biomarker in Patients Treated with Definitive Chemotherapy and Radiation Therapy

Yip, Connie; Landau, David; Kozarski, Robert; Ganeshan, Balaji; Thomas, R. E.; Michaelidou, A.; Goh, Vicky

doi:10.1148/radiol.13122869

Cited by 192 publications

(138 citation statements)

References 20 publications

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“…They found that variations in short run emphasis remained predictive of pathologic response for all image registration algorithms used for contour propagation. In a previous study, 88 the same group considered a combination of textural and morphological properties, finding that lower post-treatment intratumoural heterogeneity on CECT images was associated with improved overall survival time in patients who completed definitive CRT for primary oesophageal cancer. Moreover, they observed that the combination of pre-treatment entropy, uniformity and proportional difference in entropy with maximal wall thickness changes performed better than morphological indices alone in predicting overall survival.…”

Section: Application Of Texture Analysis In Radiotherapymentioning

confidence: 99%

Texture analysis of medical images for radiotherapy applications

Scalco¹,

Rizzo²

2017

The British Journal of Radiology

120

108

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The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.

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Section: Application Of Texture Analysis In Radiotherapymentioning

confidence: 99%

Texture analysis of medical images for radiotherapy applications

Scalco¹,

Rizzo²

2017

The British Journal of Radiology

120

108

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show abstract

“…Radiomics has shown promise in improving cancer diagnosis and prognosis in several tumor types including lung [5–10], brain [11,12], liver [13], kidney [14] and esophageal [15] cancers. While the field of radiomics continues to evolve, a potential limitation in image analysis is the heterogeneous imaging acquisition parameters being used, i.e., there exists a wide range of imaging equipment, acquisition techniques and reconstruction parameters in clinical practice and even in clinical trials.…”

Section: Introductionmentioning

confidence: 99%

Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings

et al. 2016

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ObjectivesRadiomics utilizes quantitative image features (QIFs) to characterize tumor phenotype. In practice, radiological images are obtained from different vendors’ equipment using various imaging acquisition settings. Our objective was to assess the inter-setting agreement of QIFs computed from CT images by varying two parameters, slice thickness and reconstruction algorithm.Materials and MethodsCT images from an IRB-approved/HIPAA-compliant study assessing thirty-two lung cancer patients were included for the analysis. Each scan’s raw data were reconstructed into six imaging series using combinations of two reconstruction algorithms (Lung[L] and Standard[S]) and three slice thicknesses (1.25mm, 2.5mm and 5mm), i.e., 1.25L, 1.25S, 2.5L, 2.5S, 5L and 5S. For each imaging-setting, 89 well-defined QIFs were computed for each of the 32 tumors (one tumor per patient). The six settings led to 15 inter-setting comparisons (combinatorial pairs). To reduce QIF redundancy, hierarchical clustering was done. Concordance correlation coefficients (CCCs) were used to assess inter-setting agreement of the non-redundant feature groups. The CCC of each group was assessed by averaging CCCs of QIFs in the group.ResultsTwenty-three non-redundant feature groups were created. Across all feature groups, the best inter-setting agreements (CCCs>0.8) were 1.25S vs 2.5S, 1.25L vs 2.5L, and 2.5S vs 5S; the worst (CCCs<0.51) belonged to 1.25L vs 5S and 2.5L vs 5S. Eight of the feature groups related to size, shape, and coarse texture had an average CCC>0.8 across all imaging settings.ConclusionsVarying degrees of inter-setting disagreements of QIFs exist when features are computed from CT images reconstructed using different algorithms and slice thicknesses. Our findings highlight the importance of harmonizing imaging acquisition for obtaining consistent QIFs to study tumor imaging phonotype.

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“…As a non-invasive imaging method, computed tomography (CT) has been widely available, and easily used for tumor prognostic evaluation89. Tumor heterogeneity, which described by quantitative intratumoral features, can be assessed in a user-defined region of interest (ROI) on CT images.…”

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confidence: 99%

Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis

Song

Liu

Zhong

et al. 2016

Sci Rep

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This was a retrospective study to investigate the predictive and prognostic ability of quantitative computed tomography phenotypic features in patients with non-small cell lung cancer (NSCLC). 661 patients with pathological confirmed as NSCLC were enrolled between 2007 and 2014. 592 phenotypic descriptors was automatically extracted on the pre-therapy CT images. Firstly, support vector machine (SVM) was used to evaluate the predictive value of each feature for pathology and TNM clinical stage. Secondly, Cox proportional hazards model was used to evaluate the prognostic value of these imaging signatures selected by SVM which subjected to a primary cohort of 138 patients, and an external independent validation of 61 patients. The results indicated that predictive accuracy for histopathology, N staging, and overall clinical stage was 75.16%, 79.40% and 80.33%, respectively. Besides, Cox models indicated the signatures selected by SVM: “correlation of co-occurrence after wavelet transform” was significantly associated with overall survival in the two datasets (hazard ratio [HR]: 1.65, 95% confidence interval [CI]: 1.41–2.75, p = 0.010; and HR: 2.74, 95%CI: 1.10–6.85, p = 0.027, respectively). Our study indicates that the phenotypic features might provide some insight in metastatic potential or aggressiveness for NSCLC, which potentially offer clinical value in directing personalized therapeutic regimen selection for NSCLC.

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Primary Esophageal Cancer: Heterogeneity as Potential Prognostic Biomarker in Patients Treated with Definitive Chemotherapy and Radiation Therapy

Cited by 192 publications

References 20 publications

Texture analysis of medical images for radiotherapy applications

Texture analysis of medical images for radiotherapy applications

Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings

Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis

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