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European Radiology
Published in: European Radiology 8/2022

Open Access 24-02-2022 | Magnetic Resonance Imaging | Gastrointestinal

Fractal analysis improves tumour size measurement on computed tomography in pancreatic ductal adenocarcinoma: comparison with gross pathology and multi-parametric MRI

Authors: Florian Michallek, Mohamed Amine Haouari, Ophélie Dana, Antoine Perrot, Stéphane Silvera, Axel Dallongeville, Marc Dewey, Marc Zins

Published in: European Radiology | Issue 8/2022

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Abstract

Objectives

Tumour size measurement is pivotal for staging and stratifying patients with pancreatic ductal adenocarcinoma (PDA). However, computed tomography (CT) frequently underestimates tumour size due to insufficient depiction of the tumour rim. CT-derived fractal dimension (FD) maps might help to visualise perfusion chaos, thus allowing more realistic size measurement.

Methods

In 46 patients with histology-proven PDA, we compared tumour size measurements in routine multiphasic CT scans, CT-derived FD maps, multi-parametric magnetic resonance imaging (mpMRI), and, where available, gross pathology of resected specimens. Gross pathology was available as reference for diameter measurement in a discovery cohort of 10 patients. The remaining 36 patients constituted a separate validation cohort with mpMRI as reference for diameter and volume.

Results

Median RECIST diameter of all included tumours was 40 mm (range: 18–82 mm). In the discovery cohort, we found significant (p = 0.03) underestimation of tumour diameter on CT compared with gross pathology (Δdiameter3D = −5.7 mm), while realistic diameter measurements were obtained from FD maps (Δdiameter3D = 0.6 mm) and mpMRI (Δdiameter3D = −0.9 mm), with excellent correlation between the two (R2 = 0.88). In the validation cohort, CT also systematically underestimated tumour size in comparison to mpMRI (Δdiameter3D = −10.6 mm, Δvolume = −10.2 mL), especially in larger tumours. In contrast, FD map measurements agreed excellently with mpMRI (Δdiameter3D = +1.5 mm, Δvolume = −0.6 mL). Quantitative perfusion chaos was significantly (p = 0.001) higher in the tumour rim (FDrim = 4.43) compared to the core (FDcore = 4.37) and remote pancreas (FDpancreas = 4.28).

Conclusions

In PDA, fractal analysis visualises perfusion chaos in the tumour rim and improves size measurement on CT in comparison to gross pathology and mpMRI, thus compensating for size underestimation from routine CT.

Key Points

• CT-based measurement of tumour size in pancreatic adenocarcinoma systematically underestimates both tumour diameter (Δdiameter = −10.6 mm) and volume (Δvolume = −10.2 mL), especially in larger tumours.
• Fractal analysis provides maps of the fractal dimension (FD), which enable a more reliable and size-independent measurement using gross pathology or multi-parametric MRI as reference standards.
• FD quantifies perfusion chaos—the underlying pathophysiological principle—and can separate the more chaotic tumour rim from the tumour core and adjacent non-tumourous pancreas tissue.
Literature
1.
Amin MB, American Joint Committee on Cancer., American Cancer Society (2017) AJCC cancer staging manual. Eight edition / editor-in-chief, Mahul B. Amin, MD, FCAP ; editors, Stephen B. Edge, MD, FACS and 16 others ; Donna M. Gress, RHIT, CTR - Technical editor ; Laura R. Meyer, CAPM - Managing editor. edn American Joint Committee on Cancer, Springer, Chicago IL
2.
Allen PJ, Kuk D, Castillo CF et al (2017) Multi-institutional validation study of the American Joint Commission on Cancer (8th edition) changes for T and N staging in patients with pancreatic adenocarcinoma. Ann Surg 265:185–191CrossRef
3.
Saka B, Balci S, Basturk O et al (2016) Pancreatic ductal adenocarcinoma is spread to the peripancreatic soft tissue in the majority of resected cases, rendering the AJCC T-stage protocol (7th edition) inapplicable and insignificant: a size-based staging system (pT1: </=2, pT2: >2-</=4, pT3: >4 cm) is more valid and clinically relevant. Ann Surg Oncol 23:2010–2018CrossRef
4.
Li D, Hu B, Zhou Y, Wan T, Si X (2018) Impact of tumor size on survival of patients with resected pancreatic ductal adenocarcinoma: a systematic review and meta-analysis. BMC Cancer 18:985CrossRef
5.
Forssell H, Proh K, Wester M, Krona H (2012) Tumor size as measured at initial X-ray examination, not length of bile duct stricture, predicts survival in patients with unresectable pancreatic cancer. BMC Cancer 12:429CrossRef
6.
Arvold ND, Niemierko A, Mamon HJ, Fernandez-del Castillo C, Hong TS (2011) Pancreatic cancer tumor size on CT scan versus pathologic specimen: implications for radiation treatment planning. Int J Radiat Oncol Biol Phys 80:1383–1390CrossRef
7.
Kassardjian A, Stanzione N, Wang HL (2019) Comparative accuracy of tumor size assessment and stage analysis by imaging modalities versus gross examination for pancreatic ductal adenocarcinoma. Pancreas 48:223–227CrossRef
8.
Park HS, Lee JM, Choi HK, Hong SH, Han JK, Choi BI (2009) Preoperative evaluation of pancreatic cancer: comparison of gadolinium-enhanced dynamic MRI with MR cholangiopancreatography versus MDCT. J Magn Reson Imaging 30:586–595CrossRef
9.
Legrand L, Duchatelle V, Molinie V, Boulay-Coletta I, Sibileau E, Zins M (2015) Pancreatic adenocarcinoma: MRI conspicuity and pathologic correlations. Abdom Imaging 40:85–94CrossRef
10.
Nishikawa Y, Tsuji Y, Isoda H, Kodama Y, Chiba T (2014) Perfusion in the tissue surrounding pancreatic cancer and the patient’s prognosis. Biomed Res Int 2014:648021CrossRef
11.
Xu J, Liang Z, Hao S et al (2009) Pancreatic adenocarcinoma: dynamic 64-slice helical CT with perfusion imaging. Abdom Imaging 34:759–766CrossRef
12.
Michallek F, Dewey M (2014) Fractal analysis in radiological and nuclear medicine perfusion imaging: a systematic review. Eur Radiol 24:60–69CrossRef
13.
Michallek F, Dewey M (2017) Fractal analysis of the ischemic transition region in chronic ischemic heart disease using magnetic resonance imaging. Eur Radiol 27:1537–1546CrossRef
14.
Michallek F, Dewey M (2017) Patent application: method for characterizing perfusion abnormalities by means of fractal analysis of the interface region. (WO 2017/046082 A1, Application PCT/EP2016/071551).
15.
Annese T, Tamma R, Ruggieri S, Ribatti D (2019) Angiogenesis in pancreatic cancer: pre-clinical and clinical studies. Cancers (Basel) 11
16.
van der Zee JA, van Eijck CH, Hop WC et al (2011) Angiogenesis: a prognostic determinant in pancreatic cancer? Eur J Cancer 47:2576–2584CrossRef
17.
Zhang Z, Ji S, Zhang B et al (2018) Role of angiogenesis in pancreatic cancer biology and therapy. Biomed Pharmacother 108:1135–1140CrossRef
18.
O’Malley RB, Soloff EV, Coveler AL et al (2021) Feasibility of wide detector CT perfusion imaging performed during routine staging and restaging of pancreatic ductal adenocarcinoma. Abdom Radiol (NY) 46:1992–2002CrossRef
19.
Delrue L, Blanckaert P, Mertens D, Cesmeli E, Ceelen WP, Duyck P (2011) Assessment of tumor vascularization in pancreatic adenocarcinoma using 128-slice perfusion computed tomography imaging. J Comput Assist Tomogr 35:434–438CrossRef
20.
Di Maggio F, Arumugam P, Delvecchio FR et al (2016) Pancreatic stellate cells regulate blood vessel density in the stroma of pancreatic ductal adenocarcinoma. Pancreatology 16:995–1004CrossRef
21.
Qiu H, Wild AT, Wang H et al (2012) Comparison of conventional and 3-dimensional computed tomography against histopathologic examination in determining pancreatic adenocarcinoma tumor size: implications for radiation therapy planning. Radiother Oncol 104:167–172CrossRef
22.
Kondo H, Kanematsu M, Goshima S et al (2007) MDCT of the pancreas: optimizing scanning delay with a bolus-tracking technique for pancreatic, peripancreatic vascular, and hepatic contrast enhancement. AJR Am J Roentgenol 188:751–756CrossRef
23.
Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Computer Vision, 1998. Sixth Int Conference on, 4-7 Jan 1998. 839-846.
24.
Novianto S, Suzuki Y, Maeda J (2003) Near optimum estimation of local fractal dimension for image segmentation. Pattern Recognition Letters 24:365–374CrossRef
25.
Peleg S, Naor J, Hartley R, Avnir D (1984) Multiple resolution texture analysis and classification. IEEE Trans Pattern Anal Mach Intell 6:518–523CrossRef
26.
Eisenhauer EA, Therasse P, Bogaerts J et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45:228–247CrossRef
27.
Welsh JL, Bodeker K, Fallon E, Bhatia SK, Buatti JM, Cullen JJ (2012) Comparison of response evaluation criteria in solid tumors with volumetric measurements for estimation of tumor burden in pancreatic adenocarcinoma and hepatocellular carcinoma. Am J Surg 204:580–585CrossRef
28.
Yap FY, Bui JT, Knuttinen MG et al (2013) Quantitative morphometric analysis of hepatocellular carcinoma: development of a programmed algorithm and preliminary application. Diagn Interv Radiol 19:97–105PubMed
29.
Harris C, Alcock A, Trefan L et al (2018) Optimising the measurement of bruises in children across conventional and cross polarized images using segmentation analysis techniques in Image J, Photoshop and circle diameter measurements. J Forensic Leg Med 54:114–120CrossRef
30.
Lee CU, Chong S, Choi HW, Choi JC (2018) Quantitative image analysis using chest computed tomography in the evaluation of lymph node involvement in pulmonary sarcoidosis and tuberculosis. PLoS One 13:e0207959CrossRef
31.
Cai X, Gao F, Qi Y et al (2020) Pancreatic adenocarcinoma: quantitative CT features are correlated with fibrous stromal fraction and help predict outcome after resection. Eur Radiol 30:5158–5169CrossRef
32.
Nagayama Y, Tanoue S, Inoue T et al (2020) Dual-layer spectral CT improves image quality of multiphasic pancreas CT in patients with pancreatic ductal adenocarcinoma. Eur Radiol 30:394–403CrossRef
33.
Hall WA, Mikell JL, Mittal P et al (2013) Tumor size on abdominal MRI versus pathologic specimen in resected pancreatic adenocarcinoma: implications for radiation treatment planning. Int J Radiat Oncol Biol Phys 86:102–107CrossRef
34.
Ma C, Yang P, Li J, Bian Y, Wang L, Lu J (2020) Pancreatic adenocarcinoma: variability in measurements of tumor size among computed tomography, magnetic resonance imaging, and pathologic specimens. Abdom Radiol (NY) 45:782–788CrossRef
Metadata
Title
Fractal analysis improves tumour size measurement on computed tomography in pancreatic ductal adenocarcinoma: comparison with gross pathology and multi-parametric MRI
Authors
Florian Michallek
Mohamed Amine Haouari
Ophélie Dana
Antoine Perrot
Stéphane Silvera
Axel Dallongeville
Marc Dewey
Marc Zins
Publication date
24-02-2022
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 8/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-022-08631-8

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