Skip to main content
Erschienen in: European Radiology 4/2018

21.11.2017 | Gastrointestinal

CT texture features of liver parenchyma for predicting development of metastatic disease and overall survival in patients with colorectal cancer

verfasst von: Scott J. Lee, Ryan Zea, David H. Kim, Meghan G. Lubner, Dustin A Deming, Perry J. Pickhardt

Erschienen in: European Radiology | Ausgabe 4/2018

Einloggen, um Zugang zu erhalten

Abstract

Objectives

To determine if identifiable hepatic textural features are present at abdominal CT in patients with colorectal cancer (CRC) prior to the development of CT-detectable hepatic metastases.

Methods

Four filtration–histogram texture features (standard deviation, skewness, entropy and kurtosis) were extracted from the liver parenchyma on portal venous phase CT images at staging and post-treatment surveillance. Surveillance scans corresponded to the last scan prior to the development of CT-detectable CRC liver metastases in 29 patients (median time interval, 6 months), and these were compared with interval-matched surveillance scans in 60 CRC patients who did not develop liver metastases. Predictive models of liver metastasis-free survival and overall survival were built using regularised Cox proportional hazards regression.

Results

Texture features did not significantly differ between cases and controls. For Cox models using all features as predictors, all coefficients were shrunk to zero, suggesting no association between any CT texture features and outcomes. Prognostic indices derived from entropy features at surveillance CT incorrectly classified patients into risk groups for future liver metastases (p < 0.001).

Conclusions

On surveillance CT scans immediately prior to the development of CRC liver metastases, we found no evidence suggesting that changes in identifiable hepatic texture features were predictive of their development.

Key Points

No correlation between liver texture features and metastasis-free survival was observed.
Liver texture features incorrectly classified patients into risk groups for liver metastases.
Standardised texture analysis workflows need to be developed to improve research reproducibility.
Literatur
1.
Zurück zum Zitat Steele CB, Rim SH, Joseph DA et al (2013) Colorectal cancer incidence and screening - United States, 2008 and 2010. MMWR Suppl 62:53–60PubMed Steele CB, Rim SH, Joseph DA et al (2013) Colorectal cancer incidence and screening - United States, 2008 and 2010. MMWR Suppl 62:53–60PubMed
2.
Zurück zum Zitat Report MW (2011) Vital signs: colorectal cancer screening, incidence, and mortality–United States, 2002-2010. MMWR Morb Mortal Wkly Rep 60:884–889 Report MW (2011) Vital signs: colorectal cancer screening, incidence, and mortality–United States, 2002-2010. MMWR Morb Mortal Wkly Rep 60:884–889
3.
Zurück zum Zitat Manfredi S, Bouvier AM, Lepage C et al (2006) Incidence and patterns of recurrence after resection for cure of colonic cancer in a well defined population. J Br Surg 93:1115–1122CrossRef Manfredi S, Bouvier AM, Lepage C et al (2006) Incidence and patterns of recurrence after resection for cure of colonic cancer in a well defined population. J Br Surg 93:1115–1122CrossRef
4.
Zurück zum Zitat Desch CE, Benson AB, Somerfield MR et al (2005) Colorectal cancer surveillance: 2005 update of an American Society of Clinical Oncology practice guideline. J Clin Oncol 23:8512–8519CrossRefPubMed Desch CE, Benson AB, Somerfield MR et al (2005) Colorectal cancer surveillance: 2005 update of an American Society of Clinical Oncology practice guideline. J Clin Oncol 23:8512–8519CrossRefPubMed
5.
Zurück zum Zitat Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRefPubMed Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRefPubMed
6.
Zurück zum Zitat Ganeshan B, Miles KA, Young RCD, Chatwin CR (2007) In search of biologic correlates for liver texture on portal-phase CT. Acad Radiol 14:1058–1068CrossRefPubMed Ganeshan B, Miles KA, Young RCD, Chatwin CR (2007) In search of biologic correlates for liver texture on portal-phase CT. Acad Radiol 14:1058–1068CrossRefPubMed
7.
Zurück zum Zitat Miles KA, Hayball MP, Dixon AK (1993) Functional images of hepatic perfusion obtained with dynamic CT. Radiology 188:405–411CrossRefPubMed Miles KA, Hayball MP, Dixon AK (1993) Functional images of hepatic perfusion obtained with dynamic CT. Radiology 188:405–411CrossRefPubMed
8.
Zurück zum Zitat Ganeshan B, Miles KA, Young RCD, Chatwin CR (2007) Hepatic enhancement in colorectal cancer. Texture analysis correlates with hepatic hemodynamics and patient survival. Acad Radiol 14:1520–1530CrossRefPubMed Ganeshan B, Miles KA, Young RCD, Chatwin CR (2007) Hepatic enhancement in colorectal cancer. Texture analysis correlates with hepatic hemodynamics and patient survival. Acad Radiol 14:1520–1530CrossRefPubMed
9.
Zurück zum Zitat Haralick RM, Dinstein I, Shanmugam K (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3:610–621CrossRef Haralick RM, Dinstein I, Shanmugam K (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3:610–621CrossRef
10.
Zurück zum Zitat Kruskal JB, Thomas P, Kane RA, Goldberg SN (2004) Hepatic perfusion changes in mice livers with developing colorectal cancer metastases. Radiology 231:482–490CrossRefPubMed Kruskal JB, Thomas P, Kane RA, Goldberg SN (2004) Hepatic perfusion changes in mice livers with developing colorectal cancer metastases. Radiology 231:482–490CrossRefPubMed
11.
Zurück zum Zitat Cuenod C, Leconte I, Siauve N et al (2001) Early changes in liver perfusion caused by occult metastases in rats: detection with quantitative CT. Radiology 218:556–561CrossRefPubMed Cuenod C, Leconte I, Siauve N et al (2001) Early changes in liver perfusion caused by occult metastases in rats: detection with quantitative CT. Radiology 218:556–561CrossRefPubMed
12.
Zurück zum Zitat Miles KA, Ganeshan B, Griffiths MR et al (2009) Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology 250:444–452CrossRefPubMed Miles KA, Ganeshan B, Griffiths MR et al (2009) Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology 250:444–452CrossRefPubMed
14.
Zurück zum Zitat Simon RM, Subramanian J, Li M-C, Menezes S (2011) Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data. Brief Bioinform 12:203–214CrossRefPubMedPubMedCentral Simon RM, Subramanian J, Li M-C, Menezes S (2011) Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data. Brief Bioinform 12:203–214CrossRefPubMedPubMedCentral
15.
16.
Zurück zum Zitat Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning: data mining, inference, and prediction. Springer, New YorkCrossRef Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning: data mining, inference, and prediction. Springer, New YorkCrossRef
17.
Zurück zum Zitat Witten DM, Tibshirani R (2010) Survival analysis with high-dimensional covariates. Stat Methods Med Res 19:29–51CrossRefPubMed Witten DM, Tibshirani R (2010) Survival analysis with high-dimensional covariates. Stat Methods Med Res 19:29–51CrossRefPubMed
18.
Zurück zum Zitat Core Team R (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna Core Team R (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
19.
Zurück zum Zitat Rao S-X, Lambregts DM, Schnerr RS et al (2014) Whole-liver CT texture analysis in colorectal cancer: does the presence of liver metastases affect the texture of the remaining liver? United Eur Gastroenterol J 2:530–538CrossRef Rao S-X, Lambregts DM, Schnerr RS et al (2014) Whole-liver CT texture analysis in colorectal cancer: does the presence of liver metastases affect the texture of the remaining liver? United Eur Gastroenterol J 2:530–538CrossRef
21.
Zurück zum Zitat Hatt M, Tixier F, Pierce L et al (2017) Characterization of PET/CT images using texture analysis: the past, the present… any future? Eur J Nucl Med Mol Imaging 44:151–165CrossRefPubMed Hatt M, Tixier F, Pierce L et al (2017) Characterization of PET/CT images using texture analysis: the past, the present… any future? Eur J Nucl Med Mol Imaging 44:151–165CrossRefPubMed
22.
Zurück zum Zitat Chalkidou A, O’Doherty MJ, Marsden PK (2015) False discovery rates in PET and CT studies with texture features: a systematic review. PLoS One 10:e0124165CrossRefPubMedPubMedCentral Chalkidou A, O’Doherty MJ, Marsden PK (2015) False discovery rates in PET and CT studies with texture features: a systematic review. PLoS One 10:e0124165CrossRefPubMedPubMedCentral
23.
Zurück zum Zitat Court LE, Fave X, Mackin D et al (2016) Computational resources for radiomics. Transl Cancer Res 5:340–348CrossRef Court LE, Fave X, Mackin D et al (2016) Computational resources for radiomics. Transl Cancer Res 5:340–348CrossRef
25.
Zurück zum Zitat Mackin D, Fave X, Zhang L et al (2015) Measuring computed tomography scanner variability of radiomics features. Invest Radiol 50:1–9CrossRef Mackin D, Fave X, Zhang L et al (2015) Measuring computed tomography scanner variability of radiomics features. Invest Radiol 50:1–9CrossRef
26.
Zurück zum Zitat Zhang L, Fried DV, Fave XJ et al (2015) ibex: an open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys 42:1341–1353CrossRefPubMedPubMedCentral Zhang L, Fried DV, Fave XJ et al (2015) ibex: an open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys 42:1341–1353CrossRefPubMedPubMedCentral
Metadaten
Titel
CT texture features of liver parenchyma for predicting development of metastatic disease and overall survival in patients with colorectal cancer
verfasst von
Scott J. Lee
Ryan Zea
David H. Kim
Meghan G. Lubner
Dustin A Deming
Perry J. Pickhardt
Publikationsdatum
21.11.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
European Radiology / Ausgabe 4/2018
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-017-5111-6

Weitere Artikel der Ausgabe 4/2018

European Radiology 4/2018 Zur Ausgabe

Screening-Mammografie offenbart erhöhtes Herz-Kreislauf-Risiko

26.04.2024 Mammografie Nachrichten

Routinemäßige Mammografien helfen, Brustkrebs frühzeitig zu erkennen. Anhand der Röntgenuntersuchung lassen sich aber auch kardiovaskuläre Risikopatientinnen identifizieren. Als zuverlässiger Anhaltspunkt gilt die Verkalkung der Brustarterien.

S3-Leitlinie zu Pankreaskrebs aktualisiert

23.04.2024 Pankreaskarzinom Nachrichten

Die Empfehlungen zur Therapie des Pankreaskarzinoms wurden um zwei Off-Label-Anwendungen erweitert. Und auch im Bereich der Früherkennung gibt es Aktualisierungen.

Fünf Dinge, die im Kindernotfall besser zu unterlassen sind

18.04.2024 Pädiatrische Notfallmedizin Nachrichten

Im Choosing-Wisely-Programm, das für die deutsche Initiative „Klug entscheiden“ Pate gestanden hat, sind erstmals Empfehlungen zum Umgang mit Notfällen von Kindern erschienen. Fünf Dinge gilt es demnach zu vermeiden.

„Nur wer sich gut aufgehoben fühlt, kann auch für Patientensicherheit sorgen“

13.04.2024 Klinik aktuell Kongressbericht

Die Teilnehmer eines Forums beim DGIM-Kongress waren sich einig: Fehler in der Medizin sind häufig in ungeeigneten Prozessen und mangelnder Kommunikation begründet. Gespräche mit Patienten und im Team können helfen.

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.