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

Yip, Connie; Landau, D. P.; Kozarski, Robert; Ganeshan, Balaji; Thomas, Robert; Michaelidou, A.

doi:10.1148/radiology.13122869

Cited by 21 publications

(22 citation statements)

References 20 publications

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“…Ganeshan et al found that lower histogram uniformity (with Gaussian filtration) from unenhanced CT images before start of treatment was an independent predictor for poorer overall-survival [36].Yip et al analyzed contrast-enhanced images of 36 patients before and after treatment and found a significant decrease in histogram entropy and increase in uniformity (with Gaussian filtration) between the two time points. Higher post treatment entropy was associated with poorer overall-survival [37]. For gastric cancer (Table 4), three studies have assessed the potential of radiomic approaches for classification.…”

Section: Ct Radiomicsmentioning

confidence: 99%

Radiomics in esophageal and gastric cancer

et al. 2018

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Esophageal, esophago-gastric, and gastric cancers are major causes of cancer morbidity and cancer death. For patients with potentially resectable disease, multimodality treatment is recommended as it provides the best chance of survival. However, quality of life may be adversely affected by therapy, and with a wide variation in outcome despite multi-modality therapy, there is a clear need to improve patient stratification. Radiomic approaches provide an opportunity to improve tumor phenotyping. In this review we assess the evidence to date and discuss how these approaches could improve outcome in esophageal, esophago-gastric, and gastric cancer.

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Section: Ct Radiomicsmentioning

confidence: 99%

Radiomics in esophageal and gastric cancer

et al. 2018

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“…Regions of interest (ROIs) enclosing the largest cross-sectional area of tumour area were manually delineated on the axial images under the supervision of a gastrointestinal radiologist (AA) with 7 years' experience. The ROIs underwent TA under the supervision of an imaging scientist (BG) with 9 years' experience in TA using proprietary commercially available TEXRAD RESEARCH software (version 3.3, TexRAD Ltd, www.texrad.com, part of Feedback Plc, Cambridge, UK) [26]. MRTA comprised an image filtration-histogram approach where the filtration step employed a Laplacian of Gaussian band-pass spatial scale filter (SSF) to highlight features ranging from SSF = 2 mm (fine) to SSF = 6 mm (coarse) in radius with SSF = 3-5 mm in radius corresponding to medium texture scales ( Fig.…”

Section: Mr Textural Analysis (Mrta)mentioning

confidence: 99%

Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long‐term survival in locally advanced rectal cancer treated by chemoradiotherapy

et al. 2017

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Aim The study aimed to investigate whether textural features of rectal cancer on MRI can predict long-term survival in patients treated with long-course chemoradiotherapy.Method Textural analysis (TA) using a filtrationÀhis-togram technique of T2-weighted pre-and 6-week post-chemoradiotherapy MRI was undertaken using TexRAD, a proprietary software algorithm. Regions of interest enclosing the largest cross-sectional area of the tumour were manually delineated on the axial images and the filtration step extracted features at different anatomical scales (fine, medium and coarse) followed by quantification of statistical features [mean intensity, standard deviation, entropy, skewness, kurtosis and mean of positive pixels (MPP)] using histogram analysis. Cox multiple regression analysis determined which univariate features including textural, radiological and histological independently predicted overall survival (OS), disease-free survival (DFS) and recurrence-free survival (RFS).Results MPP [fine texture, hazard ratio (HR) 6.9, 95% CI: 2.43-19.55, P < 0.001], mean (medium texture, HR 5.6, 95% CI: 1.4-21.7, P = 0.007) and extramural venous invasion (EMVI) on MRI (HR 2.96, 95% CI: 1.04-8.37, P = 0.041) independently predicted OS while mean (medium texture, HR 4.53, 95% CI: 1.58-12.94, P = 0.003), MPP (fine texture, HR 3.36, 95% CI: 1.36-8.31, P = 0.008) and threatened circumferential resection margin (CRM) on MRI (HR 3.1, 95% CI: 1.01-9.46, P = 0.046) predicted DFS. For OS, EMVI on MRI (HR 4.23, 95% CI: 1.41-12.69, P = 0.01) and for DFS kurtosis (medium texture, HR 3.97, 95% CI: 1.44-10.94, P = 0.007) and CRM involvement on MRI (HR 3.36, 95% CI: 1.21-9.32, P = 0.02) were the independent post-treatment factors. Only TA independently predicted RFS on pre-or post-treatment analyses.Conclusion MR based TA of rectal cancers can predict outcome before undergoing surgery and could potentially select patients for individualized therapy.

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“…The authors performed texture analysis on CT images using in-house software, which has now became commercially available by the name of TexRAD (Tex-RAD Ltd, Cambridge, UK), and a number of studies using this software have been published. [32][33][34][35][36][37][38][39] The group suggested the use of five LoG filters ranging from fine to coarse textures (filter widths: fine 5 4 image pixels and coarse 5 12 image pixels) and quantifying tumour heterogeneity by measuring the mean, SD, skewness, mean value of positive pixels (MPP), uniformity and entropy from the CT slice with the largest cross section of the tumour. The group reported uniformity, which is inversely proportional to entropy, to show the most significant results in predicting patient survival.…”

Section: Texture Analysis In Ctmentioning

confidence: 99%

The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning

Alobaidli

McQuaid²,

South³

et al. 2014

BJR

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Cite this article as: Alobaidli S, McQuaid S, South C, Prakash V, Evans P, Nisbet A. The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning. Br J Radiol 2014;87:20140369. REVIEW ARTICLEThe role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning ABSTRACTPredicting a tumour's response to radiotherapy prior to the start of treatment could enhance clinical care management by enabling the personalization of treatment plans based on predicted outcome. In recent years, there has been accumulating evidence relating tumour texture to patient survival and response to treatment. Tumour texture could be measured from medical images that provide a non-invasive method of capturing intratumoural heterogeneity and hence could potentially enable a prior assessment of a patient's predicted response to treatment. In this article, work presented in the literature regarding texture analysis in radiotherapy in relation to survival and outcome is discussed. Challenges facing integrating texture analysis in radiotherapy planning are highlighted and recommendations for future directions in research are suggested.

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

Cited by 21 publications

References 20 publications

Radiomics in esophageal and gastric cancer

Radiomics in esophageal and gastric cancer

Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long‐term survival in locally advanced rectal cancer treated by chemoradiotherapy

The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning

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