Diagnostic efficacy of dual-energy computed tomography-based fractal analysis for assessing extramural venous invasion/tumor deposits and peripheral nerve invasion in rectal cancer

Introduction

In recent years, with the popularization of rectal cancer (RC) screening modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), and the progress of diagnostic technology, the prevalence of RC has gradually increased, becoming one the most prevalent malignancies globally (1,2). Although the prognosis of RC is informed by tumor-node-metastasis (TNM) staging (3,4), several pathological features independent of the TNM system—including status of extramural venous invasion (EMVI), tumor deposits (TDs), and peripheral nerve invasion (PNI)—have garnered increased attention in guiding treatment strategies for patients with RC.

EMVI, defined histologically as the involvement of veins beyond the muscularis propria, may promote tumor dissemination along vascular structures and is recognized as an independent prognostic factor associated with an increased risk of local recurrence, distant metastasis, and reduced overall survival (5,6). TDs, defined as discrete tumor nodules lacking residual lymph node tissue, are also associated with poor prognosis in colorectal cancer, and this adverse prognostic effect persists independently of lymph node metastasis (7,8). PNI is the infiltration of tumor cells into and along nerve sheaths and is significantly associated with higher postoperative recurrence rates and poorer prognosis in patients with RC (9,10). Preoperative evaluation of EMVI, TD, and PNI allows for the more accurate stratification of patients with RC. This comprehensive approach enhances risk assessment, supports the development of individualized treatment strategies, and ultimately contributes to improved clinical outcomes.

MRI and CT are both recommended as preoperative imaging modalities in current clinical guidelines and are commonly used in combination with TNM staging. Although MRI is considered the gold standard for evaluating high-risk pathological features in patients with RC, its clinical utility is limited by contraindications and its inability to comprehensively assess distant metastases. Contrast-enhanced CT is a more readily applicable imaging modality in current clinical practice, as recommended by existing guidelines. Nevertheless, its inadequate soft tissue resolution complicates the assessment of risk factors, such as PNI, EMVI, and TD in the tumor periphery. Compared with conventional CT, dual-energy CT (DECT) enhances diagnostic accuracy and offers quantitative data to evaluate the biological behavior of malignancies (11). DECT has demonstrated significant applicability in the assessment of colorectal cancer, particularly in refining T staging and forecasting EMVI and lymph node metastases (12-14).

Fractal analysis offers a reliable method for quantifying tumor heterogeneity via fractal dimension (FD) (15,16). FD is a non-integer metric that characterizes the inherent morphology of the tissue, with its magnitude indicating the intricacy of tumor heterogeneity (17). Elevated FD typically indicates increased tumor aggressiveness, and hence, fractal analysis is extensively employed in tumor risk assessment. Fractal analysis has been shown to predict the effectiveness of neoadjuvant radiation in locally advanced RC (18).

We hypothesized that fractal analysis of DECT can elucidate the biological attributes of high-risk factors associated with RC pathology. This study aimed to assess the diagnostic significance of quantitative parameters derived from fractal analysis with DECT in the preoperative prediction of EMVI/TD and PNI status. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-862/rc).

Methods

Patient population

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments and was approved by the Ethics Committees of the Second Affiliated Hospital of Chongqing Medical University (No. 2020-366). Written informed consent was obtained from all participants. This study comprised a retrospective development phase and a prospective validation phase. Patients with histologically confirmed RC who underwent preoperative DECT between April 2021 and November 2024 were consecutively enrolled. Participants were divided into a retrospective development cohort (n=85) enrolled between April 2021 and October 2023 and a prospective independent validation cohort (n=45) enrolled between October 2023 and November 2024. The same exclusion criteria were applied to both cohorts: (I) pathologically confirmed benign lesions or non-adenocarcinomas (n=28); (II) administration of anticancer treatment prior to CT examination (n=56); (III) inadequate visualization of tumor lesions (n=12); (IV) presence of distant metastases (n=8); and (V) absence of surgical or histological examination within 2 weeks after CT examination (n=11) (Figure 1).

Figure 1 Flowchart of patient selection. CT, computed tomography; EMVI, extramural venous invasion; PNI, peripheral nerve invasion; RC, rectal cancer; TD, tumor deposit.

Histological examination

A board-certified gastrointestinal pathologist (>5 years of experience) diagnosed EMVI [tumor within blood vessels beyond muscularis propria confirmed by immunohistochemical (IHC) tests for CD31/CD34], TD (discrete tumor foci in the perirectal fat without regional lymph node or identifiable vascular or neural structures), and PNI (tumor encircling ≥33% of nerve circumference verified by IHC test for S100) according to the International Collaboration on Cancer Reporting (ICCR) guidelines (19). All specimens underwent 10% formalin fixation (24–48 h), 4-µm sectioning, and hematoxylin and eosin staining, with equivocal cases resolved through IHC and senior pathologist review.

DECT protocol and reconstruction

All CT exams were conducted with a DECT SOMATOM Force system (Siemens Healthineers, Erlangen, Germany). The scanning parameters included tube voltages of 100 and 140 kV, a tube current of 350 mA, a pitch of 1, and a gantry rotation time of 0.5 seconds. The contrast agent (370 mg/mL; Ultravist, Schering, Berlin, Germany) was administered into an elbow vein with a dual-head power injector at a prescribed dosage of 1.5 mL/kg and a flow rate of 3 mL/s. The scan commenced automatically in the arterial phase after a 15-second wait, which was triggered by the abdominal aortic threshold reaching 120 Hounsfield units (HU).

The DECT data underwent postprocessing on a commercial 3D multimodality workstation (syngo.via v. VA30A, Siemens Healthineers) with a customized soft tissue convolution kernel (Qr40, Siemens Healthineers) and an iterative reconstruction technique at a strength level of 3 [advanced modeled iterative reconstruction (ADMIRE); Siemens Healthineers]. Standard linear mixed pictures were thereafter created automatically with a mixing ratio of 0.6 (M0.6), with the integration of 60% of the data from a low tube voltage of 100 keV and 40% from a high tube voltage of 140 keV (20). Arterial-phase images with a 1-mm slice thickness were imported into the workstation and displayed in dual-energy mode to automatically generate conventional mixed-energy images, virtual monoenergetic maps, iodine concentration maps, and effective atomic number (Zeff) maps. This study substituted conventional mixed-energy CT pictures with regular linear blend images, as indicated by previous research (21).

Fractal analysis

Fractal analysis was performed with ImageJ software v. 1.54d (National Institutes of Health, Bethesda, MD, USA; http://rsb.info.nih.gov/ij) along with the FracLac plugin version 2.5 (http://rsb.info.nih.gov/ij/plugins/fraclac/FLHelp/Introduction.htm). FracLac computed FD by employing the conventional box-counting technique on images obtained from 12 distinct grid positions. Fractal analyses were conducted by two radiologists (C.Z. and H.W., with 5 and 3 years of abdominal CT experience, respectively) who were blinded to each other’s data and any additional clinical information. C.Z. conducted the fractal analysis of the total datasets, whereas H.W. analyzed the development cohort. The tumor was identified on 40-keV monoenergetic pictures, with the maximal tumor level assessed in the sagittal orientation. Region of interest (ROI) delineation was conducted in the axial position, with the focus on areas of notable lesion enhancement and with prominent blood vessels and the intestinal lumen being excluded (Figure 2). For the standard linear blend image (M0.6), the window width and window level were configured to 400 and 40 HU, respectively. These were subsequently imported to ImageJ software and converted to the 8-bit type image. The window width and window level for the iodine concentration images and the Zeff image were configured to 400 and 40 HU, respectively. The color contrast for the iodine maps (window width, 200; window level, 0) and the Zeff image (window width, 8; window level, 7) was adjusted, and the images were then imported to ImageJ to change the image type to 8-bit color. The minimal box size was established at 2 pixels and progressively augmented throughout the sample duration until it attained 45% of the maximum total area specified (22). After window-width/level optimization, the ROI delineated on the 40-keV monoenergetic image was propagated to the conventional mixed-energy images, iodine concentration images, and Zeff images for the extraction of FD parameters (FD-Con, FD-IC, and FD-Zeff, respectively).

Figure 2 Maximum axial image of an arterial phase-enhanced computed tomography in a 55-year-old female patient with rectal cancer. (A) A 40-keV virtual monoenergetic image. Tumor areas are outlined in red. (B) Iodine map (system default window width and window level). (C) Effective atomic number map (system default window width and window level). (D) Mixed energy image (M0.6). (E) Iodine map (color contrast: window width, 200; window level, 0). (F) Effective atomic number map (color contrast: window width, 8; window level, 7).

Statistical analysis

The Chi-squared test or Fisher exact test was employed for categorical variables. Normally distributed continuous data are presented as the mean ± standard deviation and were analyzed with the t-test; nonnormally distributed continuous variables are presented as medians and quartiles [median (interquartile range)] and were analyzed with the Mann-Whitney test. Bland-Altman plots and the intraclass correlation coefficient (ICC) were employed to assess the concordance between two radiologists evaluating FD. Receiver operating characteristic (ROC) curves were constructed, and the area under the curve (AUC) and Youden J index were computed. The DeLong test was employed to compare the AUCs of various approaches. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors for PNI and EMVI/TD status. Statistical analysis was conducted via MedCalc v. 19.2 (MedCalc Software, Ostend, Belgium) and GraphPad Prism v. 9.4.1 (Dotmatics, Boston, MA, USA), with P<0.05 indicating statistical significance.

Results

Clinicopathological characteristics

The study included 130 patients, with 85 and 45 in the development and validation cohorts, respectively. Furthermore, in the development cohort, the PNI subgroup had 37 positive and 48 negative individuals, whereas the EMVI/TD subgroup included 19 positive and 66 negative patients. In the validation cohort, the PNI subgroup had 22 positive and 23 negative patients, whereas the EMVI/TD subgroup included 16 positive and 29 negative patients. In the EMVI/TD subgroup, the percentage of patients with carcinoembryonic antigen levels beyond 5 ng/mL was significantly greater in the positive group than in negative group in both the development and validation cohorts (P=0.038 and P=0.007, respectively). No significant difference was observed between the PNI-positive and PNI-negative groups (Table 1).

Interobserver concordance of FD parameters

Excellent interobserver agreement was confirmed for all FD parameters in the development cohort, which was supported by both the ICC and Bland-Altman analyses. The ICCs for FD-Con, FD-IC, and FD-Zeff were 0.973 [95% confidence interval (CI): 0.959–0.982], 0.971 (95% CI: 0.955–0.981), and 0.965 (95% CI: 0.947–0.971), respectively. Concordantly, the Bland-Altman plots revealed minimal bias with mean differences of 0.004, −0.002, and −0.003 and narrow limits of agreement for FD-Con, FD-IC, and FD-Zeff, respectively (Figure 3).

Figure 3 Bland-Altman plots of interobserver agreement based on the fractal analysis of different image types. The mean difference between observers for (A) FD-Con, (B) FD-IC, and (C) FD-Zeff was 0.004, −0.002, and −0.003, respectively, with 95% limits of agreement of −0.054 to 0.047, −0.066 to 0.063, and −0.064 to 0.058. FD, fractal dimension; FD-Con, FD from conventional mixed-energy images; FD-IC, FD from iodine concentration images; FD-Zeff, FD from effective atomic number images; SD, standard deviation.

Analysis of fractal parameters

Table 2 summarizes the distribution of FD values in the two subgroups. In the PNI subgroup, the positive group, as compared to the negative group, had a significantly higher mean FD-IC [development cohort: 0.986±0.118 vs. 0.865 (0.796, 1.015), P=0.001; validation cohort: 0.985±0.105 vs. 0.914±0.093, P=0.019] and FD-Zeff (development cohort: 0.947±0.125 vs. 0.868±0.103, P=0.002; validation cohort: 1.002±0.110 vs. 0.890±0.130, P=0.003), while the FD-Con was not significantly different. In the EMVI/TD subgroup, the negative group, as compared to the positive group, had a significantly higher mean FD-IC (development cohort: 1.013±0.097 vs. 0.908±0.136, P=0.002; validation cohort: 1.014±0.087 vs. 0.913±0.096, P=0.001) and FD-Zeff (development cohort: 1.006±0.080 vs. 0.873±0.112, P<0.001; validation cohort: 1.048±0.097 vs. 0.887±0.113, P=0.001), while the FD-Con was not significantly different.

Diagnostic efficacy of FD parameters

Table 3 and Figure 4 show the diagnostic performance of significant FD characteristics. In the PNI subgroup, the AUC with 95% CI, sensitivity, and specificity of FD-IC were 0.706 (95% CI: 0.595–0.816), 0.892, and 0.521 for the development cohort, respectively, while for the validation cohort, they were 0.732 (95% CI: 0.579–0.885), 0.727, and 0.783, respectively; meanwhile, these parameters for FD-Zeff were 0.710 (95% CI: 0.594–0.852), 0.649, and 0.750 in the development cohort, respectively, while they were 0.752 (95% CI: 0.630–0.912), 0.682, and 0.826 in the validation cohort, respectively. In the EMVI/TD subgroup, the AUC for FD-IC was 0.739 (95% CI: 0.629–0.849) in the development cohort, and the sensitivity and specificity were 0.895 and 0.561, respectively; while in the validation cohort, the AUC was 0.795 (95% CI: 0.662–0.929), with a sensitivity and specificity of 0.938 and 0.586, respectively; meanwhile, these parameters for FD-Zeff were 0.845 (95% CI: 0.758–0.931), 0.895, and 0.727 for the development cohort, respectively, while they were 0.855 (95% CI: 0.740–0.969), 0.750, and 0.862 for the validation cohort, respectively. Table 4 presents the variations in AUC observed for the FD parameters. In the PNI subgroup, the AUC of FD-Zeff was significantly higher than that of FD-Con in the validation cohort (P=0.044). In the EMVI/TD subgroup, both FD-IC and FD-Zeff exhibited significantly elevated AUCs compared to FD-Con (development cohort: P=0.008 and P<0.001; validation cohort: P=0.030 and P=0.002).

Figure 4 Receiver operating characteristic curves of fractal parameters in diagnosing PNI and EMVI/TD with P<0.05 in the subgroup analyses. EMVI, extramural venous invasion; FD-IC, FD from iodine concentration images; FD-Zeff, FD from effective atomic number images; PNI, peripheral nerve invasion; TD, tumor deposit.

Univariate and multivariate analyses

Table 5 presents the results of the univariate and multivariate logistic regression analyses for the development cohort. FD-IC was identified as an independent risk factor for predicting PNI [odds ratio (OR) 77.873; 95% CI: 1.362–5.447×10³; P=0.039], while FD-Zeff was as an independent risk factor for EMVI/TD status (OR 1.109×10⁴, 95% CI: 25.550–1.907×10⁷; P=0.006).

Discussion

The preoperative diagnosis of PNI and EMVI/TD is particularly valuable for personalized treatment planning and improving the prognosis of patients with RC (23,24). This study primarily assessed the diagnostic efficacy of FD parameters derived from conventional and functional DECT imaging of RC. FD-IC and FD-Zeff were elevated in positive patients compared to negative patients in both the PNI and EMVI/TD groups. Multivariate logistic regression analysis indicated that FD-IC was an independent predictor of PNI, while FD-Zeff served as an independent predictor of EMVI/TD. Additionally, we employed these factors to develop integrated models for PNI and EMVI/TD. In the validation cohort, the AUC, sensitivity, and specificity of the PNI combination model were 0.771, 0.682, and 0.826, respectively, whereas the corresponding parameters for the EMVI/TD combination model were 0.879, 0.862, and 0.750, respectively. The diagnostic accuracy of PNI and EMVI/TD was significantly enhanced with the combination of multiparameter risk variables.

Disordered angiogenesis is a fundamental characteristic of malignant tumors, being directly associated with the proliferation and metabolism of cancer cells. The irregular blood supply distribution within the tumor microenvironment, along with the influence of various factors, alters the invasiveness and self-renewal capacity of cancer cells at the tumor periphery. This leads to disordered vascular growth patterns and induces hypoxia and necrosis within the tumor, which exhibit distinct fractal characteristics (25). Tumor heterogeneity is associated with aggressiveness and affects treatment choices and prognosis (26). Fractal analysis has been shown to be capable of efficiently evaluating tumor heterogeneity (27). Previous research on fractal analysis has predominantly used conventional CT images (28); however, DECT offers enhanced information relative to traditional CT, including perfusion parameters (29), which might directly indicate the angiogenesis and blood supply of tumor tissue (30). This study used a multidimensional approach to examine fractal analysis with iodine and Zeff maps to predict EMVI/TD and PNI status in patients with RC. Our findings indicate that FD-IC and FD-Zeff may effectively evaluate PNI and EMVI/TD status, with both the PNI- and EMVI/TD-positive groups exhibiting higher FD than their negative counterparts. These results aligned with a previous study that used fractal analysis to predict advanced T stage and pathological stage in gastric cancer, with FD serving as an independent predictive factor in the lymph node-positive subgroup of gastric cancer (31). Highly invasive tumor tissues exhibit elevated microvessel density, which augments the proliferative capacity and disarray of cancer cells (21). Meanwhile, elevated tumor heterogeneity is induced by factors such as hypoxia and necrosis, ultimately resulting in a rise in FD (18). The data of our study indicated that a high FD value correlate with an increased likelihood for tumor invasion.

We subsequently compared the diagnostic efficacy of FD-Con with that of FD-IC and FD-Zeff parameters in the study groups. Within the PNI subgroup, both FD-IC and FD-Zeff yielded a higher AUC than did FD-Con; however, significant differences were observed between FD-Zeff and FD-Con only in the validation cohort (P=0.044). In the EMVI/TD subgroup, we observed that FD-IC and FD-Zeff exhibited considerably superior diagnostic accuracy for the evaluation of EMVI/TD status compared to FD-Con in both the development and validation cohorts (all P values <0.01). Given the significant interobserver variability of FD-Con, we hypothesize that FD-IC and FD-Zeff offer more benefits in evaluating these high-risk factors.

In one study, an iodine map–based radiomics model exhibited superior diagnostic efficacy in predicting peritoneal metastasis in patients with gastric cancer compared to the standard CT-based radiomics model, which is in line with our findings (32). In contrast to gross pathology and multiparametric MRI, standard CT may inadequately represent the tumor size, hence diminishing the precision of the real FD measurement of the tumor (33). DECT enhances tumor identification accuracy by reflecting the internal perfusion heterogeneity of tumor tissue, thereby improving the diagnostic utility of fractal analysis in the assessment of PNI and EMVI/TD status in patients with RC as compared to conventional CT (11,34,35).

At present, a dependable noninvasive method for predicting PNI and EMVI/TD status in patients with RC is absent for clinical settings, and fractal analysis with DECT may represent a viable option. From a clinical standpoint, compared with radiomics and deep learning approaches, fractal analysis demonstrates superior interpretability and reduced computational demands in assessing tumor aggressiveness, thereby offering a novel strategy for the pretherapeutic evaluation of RC (36,37).

This study involved several limitations that should be addressed. First, owing to the modest cohort size and the low counts of EMVI-positive and TD-positive patients, EMVI and TD were analyzed in a combined subgroup. Second, patients with distant metastases were excluded to avoid confounding effects on tumor vascular invasion patterns. Third, all data were acquired with a fixed CT scanner and imaging protocol at a single center, and external validation and protocol refinements will be implemented in subsequent studies. Finally, regions of interest were delineated according to the maximum axial area image. However, malignancies can be more precisely recognized in a sequence of consecutive cross-axis images in 3D analysis than they can be in a single cross-axis image in 2D analysis (38).

Conclusions

DECT-based fractal analysis can effectively assess PNI and EMVI/TD status in patients with RC, complementing existing clinical guidelines.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-862/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-862/dss

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-862/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Written informed consent was obtained from all participants. The study protocol was approved by the Ethics Committees of the Second Affiliated Hospital of Chongqing Medical University (No. 2020-366).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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