In Search of Biologic Correlates for Liver Texture on Portal-Phase CT

Ganeshan, Balaji; Miles, Kenneth A.; Young, Rupert; Chatwin, Chris

doi:10.1016/j.acra.2007.05.023

Cited by 64 publications

(44 citation statements)

References 28 publications

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“…Apart from morphologically visible metastases, the presence of tumour may also affect the spatial heterogeneity of the remaining -apparently non-diseased -part of the liver. 21,22 Previous studies in CRC patients without metastases have demonstrated that texture parameters (entropy and uniformity) of the normal liver parenchyma were significantly correlated with the hepatic hemodynamic status and glucose metabolism 23,24 and may also be used as a predictor of survival. 25 Furthermore, results from a study by Ganeshan et al suggest that -in patients with known CRC liver metastases -texture parameters may be more sensitive to tumour-related changes in the apparently disease-free areas of the liver parenchyma than measurements of hepatic attenuation or perfusion.…”

Section: Introductionmentioning

confidence: 99%

Whole‐liver CT texture analysis in colorectal cancer: Does the presence of liver metastases affect the texture of the remaining liver?

et al. 2014

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Background: Liver metastases limit survival in colorectal cancer. Earlier detection of (occult) metastatic disease may benefit treatment and survival. Objective: The objective of this article is to evaluate the potential of whole-liver CT texture analysis of apparently diseasefree liver parenchyma for discriminating between colorectal cancer (CRC) patients with and without hepatic metastases. Methods: The primary staging CT examinations of 29 CRC patients were retrospectively analysed. Patients were divided into three groups: patients without liver metastases (n ¼ 15), with synchronous liver metastases (n ¼ 10) and metachronous liver metastases within 18 months following primary staging (n ¼ 4). Whole-liver texture analysis was performed by delineation of the apparently non-diseased liver parenchyma (excluding metastases or other focal liver lesions) on portal phase images. Mean grey-level intensity (M), entropy (E) and uniformity (U) were derived with no filtration and different filter widths (0.5 ¼ fine, 1.5 ¼ medium, 2.5 ¼ coarse). Results: Mean E 1.5 and E 2.5 for the whole liver in patients with synchronous metastases were significantly higher compared with the non-metastatic patients (p ¼ 0.02 and p ¼ 0.01). Mean U 1.5 and U 2.5 were significantly lower in the synchronous metastases group compared with the non-metastatic group (p ¼ 0.04 and p ¼ 0.02). Texture parameters for the metachronous metastases group were not significantly different from the non-metastatic group or synchronous metastases group (p > 0.05), although -similar to the synchronous metastases group -there was a subtle trend towards increased E 1.5 , E 2.5 and decreased U 1.5 , U 2.5 values. Areas under the ROC curve for the diagnosis of synchronous metastatic disease based on the texture parameters E 1.5,2.5 and U 1.5,2.5 ranged between 0.73 and 0.78. Conclusion: Texture analysis of the apparently non-diseased liver holds promise to differentiate between CRC patients with and without metastatic liver disease. Further research is required to determine whether these findings may be used to benefit the prediction of metachronous liver disease.

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Section: Introductionmentioning

confidence: 99%

Whole‐liver CT texture analysis in colorectal cancer: Does the presence of liver metastases affect the texture of the remaining liver?

et al. 2014

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“…Although the imaging modalities combined in the decision tree can each provide a range of functional or physiologic measurements, the imaging parameters chosen reflected the relationship that KRAS mutational status exhibits with hypoxia and angiogenesis for which prior studies have shown associations with tumor 18 F-FDG uptake, CT texture expressed as MPP, and DCE CT measurements of BF in CRC and other tumors (12,16,18,29,30). Recursive decision trees have advantages over other methods for assessing interactions between biomarkers such as linear and proportional hazard models (19). Recursive methods can identify interactions that are predominantly found within a particular range of values and not detected by a simple product or ratio of 2 variables.…”

Section: Discussionmentioning

confidence: 99%

“…This benefit is clearly evident in our tree in which the relationship between CT texture and KRAS mutational status is reversed depending on the level of 18 F-FDG uptake within the tumor. Decision trees are also easy to understand and implement by health care professionals (19).…”

Section: Discussionmentioning

confidence: 99%

“…Hence a combination of these imaging biomarkers could potentially provide an imaging signature for the KRAS mutation. The feasibility of acquiring this combination of imaging parameters has been confirmed (17)(18)(19)(20), but methods for determining the optimal way of combining multiple imaging biomarkers are underdeveloped. One approach increasingly used in genetic research entails recursive decisiontree analysis, which can model the sequential application of predictive biomarkers and evaluate overall performance (21).…”

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confidence: 99%

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Multifunctional Imaging Signature for V-KI-RAS2 Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS) Mutations in Colorectal Cancer

et al. 2014

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This study explores the potential for multifunctional imaging to provide a signature for V-KI-RAS2 Kirsten rat sarcoma viral oncogene homolog (KRAS) gene mutations in colorectal cancer. Methods: This prospective study approved by the institutional review board comprised 33 patients undergoing PET/CT before surgery for proven primary colorectal cancer. Tumor tissue was examined histologically for presence of the KRAS mutations and for expression of hypoxia-inducible factor-1 (HIF-1) and minichromosome maintenance protein 2 (mcm2). The following imaging parameters were derived for each tumor: 18 F-FDG uptake ( 18 F-FDG maximum standardized uptake value [SUV max ]), CT texture (expressed as mean of positive pixels [MPP]), and blood flow measured by dynamic contrast-enhanced CT. A recursive decision tree was developed in which the imaging investigations were applied sequentially to identify tumors with KRAS mutations. Monte Carlo analysis provided mean values and 95% confidence intervals for sensitivity, specificity, and accuracy. Results: The final decision tree comprised 4 decision nodes and 5 terminal nodes, 2 of which identified KRAS mutants. The true-positive rate, false-positive rate, and accuracy (95% confidence intervals) of the decision tree were 82.4% (63.9%-93.9%), 0% (0%-10.4%), and 90.1% (79.2%-96.0%), respectively. KRAS mutants with high 18 F-FDG SUV max and low MPP showed greater frequency of HIF-1 expression (P 5 0.032). KRAS mutants with low 18 F-FDG SUV max , high MPP, and high blood flow expressed mcm2 (P 5 0.036). Conclusion: Multifunctional imaging with PET/CT and recursive decision-tree analysis to combine measurements of tumor 18 F-FDG uptake, CT texture, and perfusion has the potential to identify imaging signatures for colorectal cancers with KRAS mutations exhibiting hypoxic or proliferative phenotypes.

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“…13 Novel utilization of 18 F-FDG-PET/CT in recent years, such as texture analysis on CT imaging, has been shown to reflect tumour heterogeneity and associated prognosis. This has been examined in multiple tumour types, including lung, [14][15][16] colorectal [17][18][19] and oesophageal 20 cancers. There are a number of ways to extract texture elements in images.…”

Section: Various Imaging Modalities and Methods That Can Help To Map mentioning

confidence: 99%

The use of molecular imaging combined with genomic techniques to understand the heterogeneity in cancer metastasis

Chowdhury¹,

Ganeshan²,

Irshad³

et al. 2014

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In Search of Biologic Correlates for Liver Texture on Portal-Phase CT

Cited by 64 publications

References 28 publications

Whole‐liver CT texture analysis in colorectal cancer: Does the presence of liver metastases affect the texture of the remaining liver?

Whole‐liver CT texture analysis in colorectal cancer: Does the presence of liver metastases affect the texture of the remaining liver?

Multifunctional Imaging Signature for V-KI-RAS2 Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS) Mutations in Colorectal Cancer

The use of molecular imaging combined with genomic techniques to understand the heterogeneity in cancer metastasis

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