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European Radiology

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21.06.2018 | Hepatobiliary-Pancreas

CT prediction of resectability and prognosis in patients with pancreatic ductal adenocarcinoma after neoadjuvant treatment using image findings and texture analysis

verfasst von: Bo Ram Kim, Jung Hoon Kim, Su Joa Ahn, Ijin Joo, Seo-Youn Choi, Sang Joon Park, Joon Koo Han

Erschienen in: European Radiology | Ausgabe 1/2019

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Abstract

Objectives

To assess utility of CT findings and texture analysis for predicting the resectability and prognosis in patients after neoadjuvant therapy for pancreatic ductal adenocarcinoma (PDAC).

Materials and methods

Among 308 patients, 45 with PDAC underwent neoadjuvant therapy (concurrent-chemoradiation-therapy, CCRT, n = 27 and chemotherapy, ChoT, n = 18) before surgery were included. All underwent baseline and preoperative CT. Two reviewers assessed CT findings and resectability. We analyzed relationship between CT resectability and residual tumor. CT texture values obtained by subtracting preoperative from baseline CT were analyzed using multivariate Cox/logistic regression analysis to identify significant parameters predicting resectability and prognosis.

Results

There were 30 patients without residual tumor (CCRT, n = 20; ChoT, n = 10) and 15 with residual tumor (CCRT, n = 7; ChoT, n = 8). Considering borderline as resectable was more accurate for R0 resectability than considering borderline as unresectable (68.9% vs 55.6% and 51.1%, p < 0.001). Particularly, neoadjuvant CCRT provided better accuracy than that in (p < 0.001). In CT texture analysis, higher subtracted entropy (cut-off: 0.03, HR 0.159, p = 0.005) and lower subtracted GLCM entropy (cut-off: –0.35, HR 10.235, p = 0.036) are important parameters for prediction of longer overall survival.

Conclusion

CT findings with texture analysis can be useful for predicting a patient’s outcome, including resectability and prognosis, after neoadjuvant therapy for PDAC.

Key Points

• Considering borderline resectable tumor as resectable have better accuracy for resectability.

• Considering borderline as resectable, CCRT-patients have better resectability accuracy than chemotherapy-patients.

• Higher subtracted entropy and lower subtracted GLCM entropy are predictors of favorable outcome.

Society AC (2017) Cancer facts & figures 2017. American Cancer Society, Atlanta

Sohn TA, Yeo CJ, Cameron JL et al (2000) Resected adenocarcinoma of the pancreas-616 patients: results, outcomes, and prognostic indicators. J Gastrointest Surg 4:567–579CrossRef

Winter JM, Cameron JL, Campbell KA et al (2006) 1423 pancreaticoduodenectomies for pancreatic cancer: A single-institution experience. J Gastrointest Surg 10:1199–1210 discussion 1210–1191CrossRef

Bilimoria KY, Talamonti MS, Sener SF et al (2008) Effect of hospital volume on margin status after pancreaticoduodenectomy for cancer. J Am Coll Surg 207:510–519CrossRef

Neoptolemos JP, Stocken DD, Dunn JA et al (2001) Influence of resection margins on survival for patients with pancreatic cancer treated by adjuvant chemoradiation and/or chemotherapy in the ESPAC-1 randomized controlled trial. Ann Surg 234:758–768CrossRef

Gillen S, Schuster T, Meyer Zum Buschenfelde C, Friess H, Kleeff J (2010) Preoperative/neoadjuvant therapy in pancreatic cancer: a systematic review and meta-analysis of response and resection percentages. PLoS Med 7:e1000267CrossRef

McClaine RJ, Lowy AM, Sussman JJ, Schmulewitz N, Grisell DL, Ahmad SA (2010) Neoadjuvant therapy may lead to successful surgical resection and improved survival in patients with borderline resectable pancreatic cancer. HPB (Oxford) 12:73–79CrossRef

Addeo P, Rosso E, Fuchshuber P et al (2015) Resection of Borderline Resectable and Locally Advanced Pancreatic Adenocarcinomas after Neoadjuvant Chemotherapy. Oncology 89:37–46CrossRef

Soriano A, Castells A, Ayuso C et al (2004) Preoperative staging and tumor resectability assessment of pancreatic cancer: prospective study comparing endoscopic ultrasonography, helical computed tomography, magnetic resonance imaging, and angiography. Am J Gastroenterol 99:492–501CrossRef

10.

Somers I, Bipat S (2017) Contrast-enhanced CT in determining resectability in patients with pancreatic carcinoma: a meta-analysis of the positive predictive values of CT. Eur Radiol 27:3408–3435CrossRef

11.

Morgan DE, Waggoner CN, Canon CL et al (2010) Resectability of pancreatic adenocarcinoma in patients with locally advanced disease downstaged by preoperative therapy: a challenge for MDCT. AJR Am J Roentgenol 194:615–622CrossRef

12.

Cassinotto C, Cortade J, Belleannee G et al (2013) An evaluation of the accuracy of CT when determining resectability of pancreatic head adenocarcinoma after neoadjuvant treatment. Eur J Radiol 82:589–593CrossRef

13.

Kim YE, Park MS, Hong HS et al (2009) Effects of neoadjuvant combined chemotherapy and radiation therapy on the CT evaluation of resectability and staging in patients with pancreatic head cancer. Radiology 250:758–765CrossRef

14.

Cassinotto C, Mouries A, Lafourcade JP et al (2014) Locally advanced pancreatic adenocarcinoma: reassessment of response with CT after neoadjuvant chemotherapy and radiation therapy. Radiology 273:108–116CrossRef

15.

Ahn SJ, Kim JH, Park SJ, Han JK (2016) Prediction of the therapeutic response after FOLFOX and FOLFIRI treatment for patients with liver metastasis from colorectal cancer using computerized CT texture analysis. Eur J Radiol 85:1867–1874CrossRef

16.

Yip C, Landau D, Kozarski R et al (2014) Primary esophageal cancer: heterogeneity as potential prognostic biomarker in patients treated with definitive chemotherapy and radiation therapy. Radiology 270:141–148CrossRef

17.

Cassinotto C, Chong J, Zogopoulos G et al (2017) Resectable pancreatic adenocarcinoma: Role of CT quantitative imaging biomarkers for predicting pathology and patient outcomes. Eur J Radiol 90:152–158CrossRef

18.

Chee CG, Kim YH, Lee KH et al (2017) CT texture analysis in patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy: A potential imaging biomarker for treatment response and prognosis. PLoS One 12:e0182883CrossRef

19.

Goh V, Ganeshan B, Nathan P, Juttla JK, Vinayan A, Miles KA (2011) Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. Radiology 261:165–171CrossRef

20.

Callery MP, Chang KJ, Fishman EK, Talamonti MS, William Traverso L, Linehan DC (2009) Pretreatment assessment of resectable and borderline resectable pancreatic cancer: expert consensus statement. Ann Surg Oncol 16:1727–1733CrossRef

21.

Loyer EM, David CL, Dubrow RA, Evans DB, Charnsangavej C (1996) Vascular involvement in pancreatic adenocarcinoma: reassessment by thin-section CT. Abdom Imaging 21:202–206CrossRef

22.

Tempero MA, Arnoletti JP, Behrman SW et al (2012) Pancreatic Adenocarcinoma, version 2.2012: featured updates to the NCCN Guidelines. J Natl Compr Cancer Netw 10:703–713CrossRef

23.

Sobin L, Gospodarowicz MK, Wittekind C (2010) International Union against Cancer TNM classification of malignant tumors, 7th ed. 2009 edn. Wiley-Blackwell, Chichester

24.

Eilaghi A, Baig S, Zhang Y et al (2017) CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma - a quantitative analysis. BMC Med Imaging 17:38CrossRef

25.

Chae HD, Park CM, Park SJ, Lee SM, Kim KG, Goo JM (2014) Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas. Radiology 273:285–293CrossRef

26.

Katz MH, Fleming JB, Bhosale P et al (2012) Response of borderline resectable pancreatic cancer to neoadjuvant therapy is not reflected by radiographic indicators. Cancer 118:5749–5756CrossRef

27.

White RR, Paulson EK, Freed KS et al (2001) Staging of pancreatic cancer before and after neoadjuvant chemoradiation. J Gastrointest Surg 5:626–633CrossRef

28.

Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V (2013) Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology 266:177–184CrossRef

29.

Ng F, Kozarski R, Ganeshan B, Goh V (2013) Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? Eur J Radiol 82:342–348CrossRef

30.

Chee CG, Kim YH (2017) CT texture analysis in patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy: A potential imaging biomarker for treatment response and prognosis. 12:e0182883

31.

Zhao Q, Shi CZ, Luo LP (2014) Role of the texture features of images in the diagnosis of solitary pulmonary nodules in different sizes. Chin J Cancer Res 26:451–458PubMedPubMedCentral

32.

Ryu YJ, Choi SH, Park SJ, Yun TJ, Kim JH, Sohn CH (2014) Glioma: application of whole-tumor texture analysis of diffusion-weighted imaging for the evaluation of tumor heterogeneity. PLoS One 9:e108335CrossRef

33.

Yip C, Davnall F, Kozarski R et al (2015) Assessment of changes in tumor heterogeneity following neoadjuvant chemotherapy in primary esophageal cancer. Dis Esophagus 28:172–179CrossRef

34.

Chatterjee D, Katz MH, Rashid A et al (2012) Histologic grading of the extent of residual carcinoma following neoadjuvant chemoradiation in pancreatic ductal adenocarcinoma: a predictor for patient outcome. Cancer 118:3182–3190CrossRef

35.

Hartman DJ, Krasinskas AM (2012) Assessing treatment effect in pancreatic cancer. Arch Pathol Lab Med 136:100–109CrossRef

36.

Zhan Y, Shen D (2006) Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method. IEEE Trans Med Imaging 25:256–272CrossRef

37.

Albregtsen F (2008) Statistical texture measures computed from gray level coocurrence matrices. Image Processing Laboratory, Department of Informatics, University of Oslo Web site; Available via http://www.uio.no/studier/emner/matnat/ifi/INF4300/h08/undervisningsmateriale/glcm.pdf

38.

Partio M, Cramariuc B, Gabbouj M, Visa A (2002) Rock texture retrieval using gray level co-occurrence matrix Proc of 5th Nordic Signal Processing Symposium

39.

Oettle H, Neuhaus P, Hochhaus A et al (2013) Adjuvant chemotherapy with gemcitabine and long-term outcomes among patients with resected pancreatic cancer: the CONKO-001 randomized trial. JAMA 310:1473–1481CrossRef

40.

Neoptolemos JP, Stocken DD, Friess H et al (2004) A randomized trial of chemoradiotherapy and chemotherapy after resection of pancreatic cancer. N Engl J Med 350:1200–1210CrossRef

Titel: CT prediction of resectability and prognosis in patients with pancreatic ductal adenocarcinoma after neoadjuvant treatment using image findings and texture analysis
verfasst von: Bo Ram Kim
Jung Hoon Kim
Su Joa Ahn
Ijin Joo
Seo-Youn Choi
Sang Joon Park
Joon Koo Han
Publikationsdatum: 21.06.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 1/2019
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-018-5574-0

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CT prediction of resectability and prognosis in patients with pancreatic ductal adenocarcinoma after neoadjuvant treatment using image findings and texture analysis

Abstract

Objectives

Materials and methods

Results

Conclusion

Key Points

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Abstract

Objectives

Materials and methods

Results

Conclusion

Key Points

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