Application of CT texture analysis in predicting histopathological characteristics of gastric cancers

Liu, Shunli; Liu, Song; Ji, Chen; Zheng, Haixue; Pan, Xia; Zhang, Yujuan; Guan, Wei; Chen, Ling; Guan, Yue; Liu, Weifeng; He, Jian; Ge, Yun; Zhou, Zhengyang

doi:10.1007/s00330-017-4881-1

Cited by 116 publications

(104 citation statements)

References 29 publications

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“…Similar results have been published by Liu et al [60]; the authors found that texture parameters can predict the degree of differentiation, Lauren classification and vascular invasion. There is also evidence that textural features can predict treatment response [61] when the response rate is based on TRG [49].…”

Section: Ce-mdct and Imaging Biomarkerssupporting

confidence: 89%

“…)YearCountryType of studyNo. of patientsROI selectionCE-MDCT phaseReference standardTexture featuresKey messageBa-Ssalamah et al [59]2013AustriaRetrospective47Single sliceArterial; venousBiopsy2nd order statisticsTexture analysis can differentiate histological subtypesGiganti et al [62]2016ItalyRetrospective56Whole tumourLate arterialRadical resection1st and 2nd order statisticsTexture analysis reflects tumour aggressiveness and is related to prognosisYoon et al [63]2016KoreaRetrospective26Single sliceVenousBiopsy2nd order statisticsTexture features are associated with better survival in HER2-positive patients receiving trastuzumabGiganti et al [61]2017ItalyRetrospective34Whole tumourLate arterialRadical resection1st and 2nd order statisticsTexture analysis provides important information on the response rate to neoadjuvant therapyLiu et al [60]2017ChinaRetrospective107Single sliceArterial; venousRadical resection1st and 2nd order statisticsTexture parameters can predict the degree of differentiation, Lauren classification and vascular invasion ROI region of interest, CE-MDCT contrast-enhanced multidetector computed tomography, HER human epidermal growth factor receptor…”

Section: Ce-mdct and Imaging Biomarkersmentioning

confidence: 99%

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Gastric cancer and imaging biomarkers: Part 1 – a critical review of DW-MRI and CE-MDCT findings

2018

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The current standard of care for gastric cancer imaging includes heterogeneity in image acquisition techniques and qualitative image interpretation. In addition to qualitative assessment, several imaging techniques, including diffusion-weighted magnetic resonance imaging (DW-MRI), contrast-enhanced multidetector computed tomography (CE-MDCT), dynamic-contrast enhanced MRI and 18F-fluorodeoxyglucose positron emission tomography, can allow quantitative analysis. However, so far there is no consensus regarding the application of functional imaging in the management of gastric cancer. The aim of this article is to specifically review two promising biomarkers for gastric cancer with reasonable spatial resolution: the apparent diffusion coefficient (ADC) from DW-MRI and textural features from CE-MDCT. We searched MEDLINE/ PubMed for manuscripts published from inception to 6 February 2018. Initially, we searched for (gastric cancer OR gastric tumour) AND diffusion weighted magnetic resonance imaging. Then, we searched for (gastric cancer OR gastric tumour) AND texture analysis AND computed tomography. We collated the results from the studies related to this query. There is evidence that: (1) the ADC is a promising biomarker for the evaluation of the aggressiveness (T and N stage), treatment response and prognosis of gastric cancer; (2) textural features are related to the degree of differentiation, Lauren classification, treatment response and prognosis of gastric cancer. We conclude that these imaging biomarkers hold promise as effective additional tools in the diagnostic pathway of gastric cancer and may facilitate the multidisciplinary work between the radiologist and clinician, and across different institutions, to provide a greater biological understanding of gastric cancer.Key Points • Quantitative imaging is the extraction of quantifiable features from medical images for the assessment of normal or pathological conditions and represents a promising area for gastric cancer. • Quantitative analysis from CE-MDCT and DW-MRI allows the extrapolation of multiple imaging biomarkers. • ADC from DW-MRI and CE- MDCT-based texture features are non-invasive, quantitative imaging biomarkers that hold promise in the evaluation of the aggressiveness, treatment response and prognosis of gastric cancer.

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Section: Ce-mdct and Imaging Biomarkerssupporting

confidence: 89%

Section: Ce-mdct and Imaging Biomarkersmentioning

confidence: 99%

Gastric cancer and imaging biomarkers: Part 1 – a critical review of DW-MRI and CE-MDCT findings

2018

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“…Kurtosis and entropy could, respectively, represent the shape and irregularity of the voxel distribution, which could represent tumor heterogeneity from different perspectives . Liu SL and Feng Z reported that kurtosis and entropy based on CTTA could help differentiate benign and malignant tumors. However, we did not find significant difference in kurtosis and entropy between PDAC and atypical pNET.…”

Section: Discussionmentioning

confidence: 99%

“…Texture analysis is an emerging and noninvasive method of assessing organizational characteristics, which can provide objectively quantified parameters for differential diagnosis independent of subjective analysis . Many prior studies have proved that CT texture analysis (CTTA) can be utilized to characterize a variety of tumors such as gastric cancer, renal neoplasms, and colorectal cancer . However, the utility of CT texture analysis to differentiate atypical pNET and PDAC has not been widely reported.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: Using whole‐tumor CT texture analysis as quantitative biomarkers

Liang

et al. 2018

Cancer Medicine

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BackgroundTo explore the application value of computed tomography (CT) texture analysis in differentiating atypical pancreatic neuroendocrine tumors (pNET) from pancreatic ductal adenocarcinomas (PDAC).Materials and methodsThis single‐center retrospective study was approved by local institutional review board, and the requirement for informed consent was waived. We retrospectively analyzed 127 patients with 50 PDACs and 77 pNETs in pathology database between January 2012 and May 2017.These patients successfully finished preoperative contrast‐enhanced CT test. Texture parameters (mean, median, 5th, 10th, 25th, 75th, 90th percentiles, skewness, kurtosis and entropy) were extracted from portal images and compared between PDAC and 77 pNET groups using proper statistical method. The optimal parameters for differentiating PDACs and atypical pNETs were gained through receiver operating characteristic (ROC) curves.ResultsOn the basis of arterial enhancement, 52 pNETs (67%, 52/77) were typical hypervascular and 25 pNETs (32%, 25/77) were atypical hypovascular. Compared with PDACs, atypical pNETs had statistically higher mean, median, 5th, 10th, and 25th percentiles (P = 0.006, 0.024, 0.000, 0.001, 0.021, respectively) and statistically lower skewness (P = 0.017). However, there were no difference for 75th, 90th percentiles, kurtosis and entropy between these two tumors (P = 0.232, 0.415, 0.143, 0.291, respectively). For differentiating PDACs and atypical pNETs, 5th percentile and 5th+skewness were optimal parameters for alone and combined diagnosis, respectively.ConclusionVolumetric CT texture features, especially combined diagnosis of 5th+skewness can be used as a quantitative tool to distinguish atypical pNETs from PDACs.

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“…Studies have found that first and second-order analysis in the contrast-enhanced images may help in differentiation of lymphoma from gastrointestinal stromal cancer [38] or adenocarcinoma [39]. Another study in 107 patients found that arterial phase standard deviation and entropy were correlated with poorer differentiation [40]. For prognostication, Yoon et al investigated 26 HER2 + gastric cancer patients before trastuzumabtreatment.…”

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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Application of CT texture analysis in predicting histopathological characteristics of gastric cancers

Abstract: • CT texture analysis is noninvasive and effective for gastric cancer. • Portal venous CT images correlated significantly with differentiation degree and Lauren classification. • Standard deviation, entropy and minimum attenuation in arterial phase reflect vascular invasion.

Cited by 116 publications

References 29 publications

Gastric cancer and imaging biomarkers: Part 1 – a critical review of DW-MRI and CE-MDCT findings

Gastric cancer and imaging biomarkers: Part 1 – a critical review of DW-MRI and CE-MDCT findings

Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: Using whole‐tumor CT texture analysis as quantitative biomarkers

Radiomics in esophageal and gastric cancer

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