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

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.

Interobserver Variability and Accuracy of Preoperative CT and MRI in Pancreatic Ductal Adenocarcinoma Size Estimation: A Retrospective Cohort Study

“…A recent study revealed that fractal analysis that allows differentiating between viable tumour and dense fibrosis at the periphery of PDAC improves PDAC size estimates. 19 Our study has some limitations. First, the retrospective design has assumably introduced some inclusion biases.…”

“…A recent study revealed that fractal analysis that allows differentiating between viable tumour and dense fibrosis at the periphery of PDAC improves PDAC size estimates. 19…”

MR Elastography in Cancer

“…Although high-resolution imaging modalities can depict heterogeneity with smaller voxel size than MRE, their intrinsic sensitivity to biophysical tissue properties and their alterations in cancer is often unclear. For example, CT frequently underestimates tumor size due to insufficient sensitivity to small vessels in the rim 118 . By contrast, MRE stiffness of PDAC provides a contrast that reveals tumor boundaries to the naked eye without the need for contrast agent administration 77 .…”

“…For example, CT frequently underestimates tumor size due to insufficient sensitivity to small vessels in the rim. 118 By contrast, MRE stiffness of PDAC provides a contrast that reveals tumor boundaries to the naked eye without the need for contrast agent administration. 77 However, the large heterogeneity of mechanical structures depicted by MRE in brain tumors poses challenges to the quantification of values and tabulation of thresholds for tumor characterization.…”

Resolution of 2 Dimensional Reconstruction of Functions with Nonsmooth Edges from Discrete Radon Transform Data

“…In [25] it was demonstrated that "fractal analysis is a suitable method for quantifying perfusion heterogeneity." In [26] it was established for one type of pancreatic cancer that "fractal analysis visualizes perfusion chaos in the tumor rim and improves size measurement on CT in comparison to" other methods. Thus, more accurate CT reconstruction of rough shapes may lead to better detection and a more accurate assessment of tumors.…”