Fractal analysis improves tumour size measurement on computed tomography in pancreatic ductal adenocarcinoma: comparison with gross pathology and multi-parametric MRI.
Carcinoma, Pancreatic ductal
Four-dimensional computed tomography
Fractals
Multi-parametric magnetic resonance imaging
Perfusion imaging
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Aug 2022
Aug 2022
Historique:
received:
16
07
2021
accepted:
31
01
2022
revised:
06
12
2021
pubmed:
25
2
2022
medline:
16
7
2022
entrez:
24
2
2022
Statut:
ppublish
Résumé
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. 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. 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 (Δdiameter 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. • 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.
Identifiants
pubmed: 35201407
doi: 10.1007/s00330-022-08631-8
pii: 10.1007/s00330-022-08631-8
pmc: PMC9279218
doi:
Types de publication
Comparative Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5053-5063Informations de copyright
© 2022. The Author(s).
Références
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
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 265:185–191
doi: 10.1097/SLA.0000000000001763
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 23:2010–2018
doi: 10.1245/s10434-016-5093-7
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 18:985
doi: 10.1186/s12885-018-4901-9
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 12:429
doi: 10.1186/1471-2407-12-429
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 80:1383–1390
doi: 10.1016/j.ijrobp.2010.04.058
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 48:223–227
doi: 10.1097/MPA.0000000000001233
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 30:586–595
doi: 10.1002/jmri.21889
Legrand L, Duchatelle V, Molinie V, Boulay-Coletta I, Sibileau E, Zins M (2015) Pancreatic adenocarcinoma: MRI conspicuity and pathologic correlations. Abdom Imaging 40:85–94
doi: 10.1007/s00261-014-0196-8
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 2014:648021
doi: 10.1155/2014/648021
Xu J, Liang Z, Hao S et al (2009) Pancreatic adenocarcinoma: dynamic 64-slice helical CT with perfusion imaging. Abdom Imaging 34:759–766
doi: 10.1007/s00261-009-9564-1
Michallek F, Dewey M (2014) Fractal analysis in radiological and nuclear medicine perfusion imaging: a systematic review. Eur Radiol 24:60–69
doi: 10.1007/s00330-013-2977-9
Michallek F, Dewey M (2017) Fractal analysis of the ischemic transition region in chronic ischemic heart disease using magnetic resonance imaging. Eur Radiol 27:1537–1546
doi: 10.1007/s00330-016-4492-2
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).
Annese T, Tamma R, Ruggieri S, Ribatti D (2019) Angiogenesis in pancreatic cancer: pre-clinical and clinical studies. Cancers (Basel) 11
van der Zee JA, van Eijck CH, Hop WC et al (2011) Angiogenesis: a prognostic determinant in pancreatic cancer? Eur J Cancer 47:2576–2584
doi: 10.1016/j.ejca.2011.08.016
Zhang Z, Ji S, Zhang B et al (2018) Role of angiogenesis in pancreatic cancer biology and therapy. Biomed Pharmacother 108:1135–1140
doi: 10.1016/j.biopha.2018.09.136
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) 46:1992–2002
doi: 10.1007/s00261-020-02786-y
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 35:434–438
doi: 10.1097/RCT.0b013e318223f0c5
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 16:995–1004
doi: 10.1016/j.pan.2016.05.393
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 104:167–172
doi: 10.1016/j.radonc.2012.07.004
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 188:751–756
doi: 10.2214/AJR.06.0372
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.
Novianto S, Suzuki Y, Maeda J (2003) Near optimum estimation of local fractal dimension for image segmentation. Pattern Recognition Letters 24:365–374
doi: 10.1016/S0167-8655(02)00261-1
Peleg S, Naor J, Hartley R, Avnir D (1984) Multiple resolution texture analysis and classification. IEEE Trans Pattern Anal Mach Intell 6:518–523
doi: 10.1109/TPAMI.1984.4767557
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 45:228–247
doi: 10.1016/j.ejca.2008.10.026
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 204:580–585
doi: 10.1016/j.amjsurg.2012.07.007
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 19:97–105
pubmed: 23233403
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 54:114–120
doi: 10.1016/j.jflm.2017.12.020
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:e0207959
doi: 10.1371/journal.pone.0207959
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 30:5158–5169
doi: 10.1007/s00330-020-06853-2
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 30:394–403
doi: 10.1007/s00330-019-06337-y
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 86:102–107
doi: 10.1016/j.ijrobp.2012.11.019
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) 45:782–788
doi: 10.1007/s00261-019-02125-w