Quantitative Shape Irregularity and Density Heterogeneity of Preoperative Hematoma Can Predict Rebleeding following Minimally Invasive Catheter Evacuation for Intracerebral Hemorrhage
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* Kaijiang Kang
* Zeqiang Ji
* Yang Du
* Guangshuo Li
* Jing Yan
* Zeyu Ding
* Yiming Shi
* Yanfang Liu
* Jianwei Wu
* Xingquan Zhao

## Abstract

**BACKGROUND AND PURPOSE:** Postoperative rebleeding is a critical factor associated with poor outcomes in patients with intracerebral hemorrhage (ICH) who undergo minimally invasive catheter evacuation (MICE) followed by thrombolysis. This study aimed to explore the association between quantitative shape irregularity and density heterogeneity of preoperative hematoma and rebleeding after MICE.

**MATERIALS AND METHODS:** We analyzed patients with ICH who underwent MICE between February 2021 and January 2024. The surface regularity index (SRI) and density coefficient of variation (DCV) of the hematomas were obtained based on preoperative CT by using 3D Slicer software. Postoperative rebleeding was defined as a hematoma increase of >6 mL or >33% compared with the previous CT. The predictive value of shape irregularity (reflected by SRI) and density heterogeneity (reflected by DCV) for postoperative rebleeding were comprehensively analyzed.

**RESULTS:** In total, 240 patients were included, of whom 45 (18.8%) experienced postoperative rebleeding. Patients with postoperative rebleeding exhibited lower SRI (37.2 versus 51.4, *P* = .001) and higher DCV (13.8% versus 11.7%, *P* < .001) after adjusting for preoperative hematoma volume, surface area, standard deviation of hematoma density, intraventricular hemorrhage (IVH), hematoma expansion (HE), time period from onset to surgery, and catheter misplacement. The combination of SRI, DCV, IVH, and HE demonstrated optimal discrimination in predicting postoperative rebleeding, with an area under the curve (AUC) and 95% CI of 0.880 (0.824–0.935).

**CONCLUSIONS:** Hematoma shape irregularity and density heterogeneity are risk factors for rebleeding after MICE for ICH. SRI and DCV can be used to identify individuals at high risk of postoperative rebleeding.

## ABBREVIATIONS:

AUC
:   area under the curve
DCV
:   density coefficient of variation
HE
:   hematoma expansion
ICH
:   intracerebral hemorrhage
IQR
:   interquartile range
IVH
:   intraventricular hemorrhage
MICE
:   minimally invasive catheter evacuation
OR
:   odds ratio
ROC
:   receiver operating characteristic
SRI
:   surface regularity index

SUMMARY

#### PREVIOUS LITERATURE:

MICE for hematoma evacuation has been widely used as an accessible and practical approach for ICH. However, postoperative rebleeding has been indicated to be one of the key factors affecting the benefit of surgery. Prior research has indicated that several NCCT signs reflecting the shape irregularity and density heterogeneity of the hematoma were associated with postoperative rebleeding. However, these NCCT signs were qualitative, and the interpretation was significantly subjective, resulting in a suboptimal predictive ability for postoperative rebleeding. This study was investigated to explore the association of hematoma characteristics quantitatively with postoperative rebleeding.

#### KEY FINDINGS:

This study indicated that hematoma shape irregularity and density heterogeneity were risk factors for rebleeding after MICE for ICH and demonstrated that SRI and DCV combined with IVH and HE could predict postoperative rebleeding with optimal discrimination.

#### KNOWLEDGE ADVANCEMENT:

Based on the findings of this study, SRI and DCV can be used to identify individuals at high risk of postoperative rebleeding. For patients with elevated SRI and DCV, repeated NCCT assessment for delayed surgery decisions can be considered.

Intracerebral hemorrhage (ICH) is a devastating form of stroke that accounts for 10% to 30% of all strokes worldwide, with a 30-day mortality rate of 30% to 40%; most patients survive with disabilities, leading to a significant burden.1,2 Hematoma evacuation has long been thought to reduce hematoma volume, decrease intracranial pressure, and alleviate secondary brain injury by facilitating early removal of the hematoma.3 However, the International Surgical Trial in Intracerebral Hemorrhage (STICH) and Surgical Trial in Lobar Intracerebral Haemorrhage (STICH-II) demonstrated no benefit in functional outcomes associated with hematoma removal from early craniotomy when compared with initial conservative treatment.4,5 The Early MiNimally-invasive Removal of IntraCerebral Hemorrhage (ENRICH) trial demonstrated that minimally invasive hematoma evacuation based on the BrainPath-Myriad system (NICO) achieved better long-term functional outcomes for patients with lobar hemorrhage, but still not for those with deep hemorrhage.6

Minimally invasive catheter evacuation (MICE) for hematoma evacuation has been widely used as an accessible and practical approach because of its advantages of shorter operation times and less disruption of unaffected brain tissue compared with conventional craniotomy, which has attracted considerable attention.7 However, the Minimally Invasive Surgery with Plus Rt-PA for ICH Evacuation Phase III (MISTIE III) trial failed to identify any definite therapeutic improvement in the functional outcomes of MICE compared with conventional treatment, one of the reasons that was indicated was the higher rate of postoperative rebleeding in the MICE group.8

Prior research has indicated that several NCCT signs reflecting the shape irregularity and density heterogeneity of the hematoma, such as an irregular shape,9 black hole sign,10 and blend sign,11 are all associated with postoperative rebleeding. However, these NCCT signs were qualitative, and the interpretation was significantly subjective, resulting in a suboptimal predictive ability for postoperative rebleeding.9,10 The 3D Slicer software ([www.slicer.org](http://www.slicer.org)) has previously been utilized in ICH research for hematoma modeling and puncture path selection for MICE and can also provide objective and quantitative measurements of the shape irregularity and density variability of the hematoma.12,13 Herein, we investigated the association between shape regularity and density variability of preoperative hematomas by using 3D Slicer software with postoperative rebleeding in patients with ICH who underwent MICE.

## MATERIALS AND METHODS

### Study Population, Clinical Characteristics, Preoperative Imaging Program, NCCT Sign Assessment, MICE Procedure, and Periprocedural Management

The details of the study population, clinical characteristics, preoperative imaging program, NCCT sign assessment, MICE procedure, and periprocedural management are presented in Supplemental Data.

### Definition and Measurement of Hematoma Shape and Density

The hematoma was segmented and reconstructed from the DICOM data of the preoperative CT by using the 3D Slicer software (Version 4.10.1, [www.slicer.org](http://www.slicer.org)) (Fig 1). Hematomas were semiautomatically identified pixel-by-pixel in each slice, with thresholds ranging from 40 to 100 HU.14 Finally, the model module was utilized to reconstruct the 3D data by adding all pixels from each slice, and the values of hematoma volume and surface area were directly obtained from the 3D Slicer without smoothing processing. The surface regularity index (SRI) was calculated from hematoma volume and surface area, with values between 0 (fractal hematoma with very irregular surfaces) and 1 (spherical hematoma), by using the following formula15: ![Formula][1]</img> 

![FIG 1.](http://www.ajnr.org/https://ajnr-sso.highwirestaging.com/content/ajnr/early/2025/06/12/ajnr.A8680/F1.medium.gif)

[FIG 1.](http://www.ajnr.org/content/early/2025/06/12/ajnr.A8680/F1)

FIG 1. 
Hematoma segmentation, modeling, and postoperative outcomes in 2 exemplar cases. *A1–A5*, Images taken from a patient with ICH who underwent MICE with a high SRI and low DCV of preoperative hematoma, achieving satisfactory hematoma drainage without postoperative rebleeding. *B1–B5*, Images taken from an patient with ICH who underwent MICE with low SRI and high DCV of preoperative hematoma and experienced rebleeding 3 days following the MICE. *A1–A3* and *B1–B3*, Hematoma segmentation and modeling performed by using the 3D Slicer software based on preoperative CT. The results of the modeled hematoma measurements and calculations (hematoma volume, surface area, SRI, mean density, and DCV) are presented in *A3* and *B3*.

After segmenting and modeling the hematoma, we applied the “statistics” modules to automatically calculate the mean, median, and SD of the CT values of each pixel. The density coefficient of variation (DCV) was calculated to represent the hematoma density heterogeneity. ![Formula][2]</img> 

### Neuroradiological Image Interpretation

All neuroradiological images were interpreted independently by 2 experienced neuroradiologists (blinded to the outcome), and an interpretation training program was performed for both to ensure consistency of the interpreting standard. After the training program, a consistency test was carried out, and the formal interpretation started when the intraclass correlation coefficient reached above 0.8. The time needed to process each NCCT imaging differed from 5 to 10 minutes. The differences between assessments were resolved by a third senior neuroradiologist (categoric data), or the average of their data (quantitative data) was calculated to reduce the impact of subjective factors.

### Statistical Analysis

Continuous variables were expressed as medians (interquartile range [IQR]) or means ± SD, which were evaluated by using the Shapiro-Wilk test and compared by using the Mann-Whitney or *t* test, respectively. The chi-square test was applied to compare categoric variables, which are expressed as numbers (proportions). Variables with *P* < .05 from the comparison of baseline characteristics and variables significantly influencing the outcome in previous studies were included in the multivariate logistic regression. The ORs and 95% CI for postoperative rebleeding were calculated subsequently. The discriminative abilities of the selected variables were evaluated by using receiver operating characteristic (ROC) curves with the De Long test for comparison of areas under the curve (AUC). Differences with *P* < .05 were considered statistically significant for 2-tailed tests. Statistical analyses were performed using a commercial statistical software package (SPSS for Windows, Version 25.0, IBM-SPSS), and the Delong test was conducted using SAS software (Version 9.4; SAS Institute).

## RESULTS

### Patient Baseline Characteristics

From February 2021 to January 2024, a total of 240 patients (56.0 ± 13.9 years old) were recruited, including 185 (77.1%) men and 55 (22.9%) women. A flowchart of the patient selection process is shown in Fig 2. The median baseline GCS and NIHSS scores were 11 (8–13) and 18 (13–27), respectively. The median periods from ICH onset to baseline CT and MICE surgery were 7.0 (4.0, 13.0) and 48.1 (36.2, 64.9) hours, respectively. The median baseline hematoma volume and ultraearly hematoma growth (uHG) were 36.7 mL (26.9, 53.7) and 6.0 mL/h (2.9, 10.4), respectively. Deep hematomas (including the basal ganglia and thalamus) accounted for 81.2% of all hemorrhages, whereas lobar hematomas accounted for 18.8%. All the baseline characteristics are presented in the Supplemental Data.

![FIG 2.](http://www.ajnr.org/https://ajnr-sso.highwirestaging.com/content/ajnr/early/2025/06/12/ajnr.A8680/F2.medium.gif)

[FIG 2.](http://www.ajnr.org/content/early/2025/06/12/ajnr.A8680/F2)

FIG 2. 
Flowchart of patient selection.

### Periprocedural Characteristics

The median preoperative hematoma volume in this study cohort was 41.8 mL (37.2, 64.9), with 77 patients found to be complicated with intraventricular hemorrhage (IVH). The median surface area of preoperative hematoma was 99.0 cm2 (74.5, 137.5). The average density of the preoperative hematoma was 60.4 ± 3.5 HU. The mean SRI and DCV of the preoperative hematoma were 48.7 ± 15.5 and 12.1 ± 2.0%, respectively. The median depth of the catheter placement was 6.5 cm (6.0, 6.5), and 20.0% (48/240) of catheters were misplaced for the first time. The median volume of the first aspirated hematoma was 19.0 mL, and the drainage was 3.1 days (3.0, 4.0). Of the 240 investigated patients, 45 (18.8%) experienced postoperative rebleeding. The median period from MICE surgery to rebleeding was 3.0 days (3.0, 3.9), ranging from 1.6 to 7 days.

### Factors Associated with Postoperative Rebleeding

Patients with postoperative rebleeding had a larger preoperative hematoma volume (60.0 versus 40.5 mL, *P* < .001) and hematoma surface area (141.2 versus 91.6 cm2, *P* < .001), with a higher prevalence of IVH (48.9% versus 28.2%, *P* = .007) and hematoma expansion (HE) (48.9% versus 23.6%, *P* = .001) (Fig 1). The postoperative rebleeding was associated with a lower SRI (37.2 versus 51.4, *P* < .001) and higher DCV (13.8% versus 11.7%, *P* < .001) (Fig 1). The cutoff points of SRI and DCV for predicting postoperative rebleeding were 43.48 and 12.97%, respectively. There were no significant differences in baseline hematoma volume, period from onset to surgery, catheter depth, aspiration volume, or drainage time between patients with and without postoperative rebleeding. Of the 45 patients with postoperative rebleeding, 27 (61.4%) experienced neurologic deterioration, defined as a ≥2 reduction in GCS or a ≥4 increase in NIHSS score for nonsedatives/sleeping medications compared with the pre-rebleeding status.16 We further observed a higher prevalence of poor drainage (91.1% versus 36.4%, *P* < .001) and longer hospital stay (23 versus 15 days, *P* < .001) in patients with postoperative rebleeding than in those without rebleeding (Table).

View this table:
[Table1](http://www.ajnr.org/content/early/2025/06/12/ajnr.A8680/T1)

Results of the multivariate logistic regression of postoperative rebleeding predictiona

Multivariate logistic regression analysis indicated that preoperative hematoma SRI (odds ratio [OR] [95% CI]: 0.938 [0.905–0.973], *P* = .001), DCV (OR [95% CI]: 1.907 [1.473–2.470], *P* < .001), IVH (OR [95% CI]: 2.630 [1.109–6.235], *P* = .028), and HE (OR [95% CI]: 2.635 [1.127–6.162], *P* = .025) were independent predictors of postoperative rebleeding, after adjusting for associated variables in univariate analysis (including preoperative hematoma volume, surface area, standard deviation of hematoma density) and factors that influenced the outcome in a previous study (time from onset to surgery17 and presence of misplaced catheter18) (Table).

### Predictive Analysis

In univariate prediction analysis, SRI (AUC [95% CI]: 0.781 [0.708–0.854]) and DCV (AUC [95% CI]: 0.789 [0.710–0.867]) exhibited acceptable predictive abilities for postoperative rebleeding. The combination of SRI and DCV also provided significant improvements in rebleeding prediction (AUC [95% CI]: 0.854 [0.791–0.917]) compared with SRI (*P* = .012) and DCV (*P* = .013). In addition, the combination of SRI, DCV, IVH, and HE demonstrated optimal discrimination in the prediction of postoperative rebleeding (AUC [95% CI]: 0.880 [0.824–0.935]), showing significant improvements compared with SRI (*P* = .002) and DCV (*P* = .002) (Fig 3).

![FIG 3.](http://www.ajnr.org/https://ajnr-sso.highwirestaging.com/content/ajnr/early/2025/06/12/ajnr.A8680/F3.medium.gif)

[FIG 3.](http://www.ajnr.org/content/early/2025/06/12/ajnr.A8680/F3)

FIG 3. 
ROC curve analysis of SRI, DCV, and the 2 models for predicting postoperative rebleeding. SRI (AUC [95% CI]: 0.781 [0.708–0.854]) and DCV (AUC [95% CI]: 0.789 [0.710–0.867]) exhibited acceptable predictive abilities for postoperative rebleeding. The combination of SRI and DCV provided significant improvements in rebleeding prediction (AUC [95% CI]: 0.854 [0.791–0.917]) compared with SRI (*P* = .012) and DCV (*P* = .013) alone. In addition, the combination of the 4 selected predictors (SRI, DCV, IVH, and HE) demonstrated optimal discrimination in the prediction of postoperative rebleeding (AUC [95% CI]: 0.880 [0.824–0.935]), showing a significant improvement compared with SRI (*P* = .002) and DCV (*P* = .002).

Additionally, we evaluated the predictive ability of previously reported radiologic signs of postoperative rebleeding in this cohort. NCCT signs (including blend sign, heterogeneous density, hypodensities, black hole sign, island sign, satellite sign, and irregular shape) were observed in all patients. CTA was available for 224 patients (93.3%), with spot signs identified in 32 (14.3%). The median period from onset to CTA was 11 hours (5.0, 20.0). The rebleeding groups had a higher prevalence of heterogeneous density (42.2% versus 27.2%, *P* = .047). However, there was no difference in other NCCT and CTA spot signs between patients with and without postoperative rebleeding. The AUCs of all reported radiologic signs ranged from 0.502 to 0.588 (Supplemental Data).

## DISCUSSION

To the best of our knowledge, this is the first study to quantitatively and objectively explore the association between shape irregularity and density heterogeneity of hematomas and post-MICE rebleeding. This study demonstrated that SRI and DCV combined with IVH and HE could predict postoperative rebleeding with optimal discrimination.

Minimally invasive hematoma removal has previously been proposed as a strategy to alleviate the mass effect and secondary injuries resulting from the hematoma, reducing the mortality and disability rates of patients with ICH.3,19 While several meta-analyses have indicated that MICE is associated with better functional outcomes and fewer complications than conservative treatment or craniotomy,20,21 the MISTIE III trial failed to demonstrate that MICE could improve long-term functional outcomes in patients with ICH compared with conservative treatment.8 Subsequent research has revealed that poor outcomes after MICE are associated with postoperative rebleeding, along with a high neurologic deterioration rate and large residual hematoma volume resulting from rebleeding.22,23 In this cohort, the postoperative rebleeding rate was 18.8%, which is comparable with that observed in previous studies (about 20%).7,8,21 In addition, patients with postoperative rebleeding had a higher rate of poor drainage than those without rebleeding (91.1% versus 36.4%). As such, it is imperative to identify patients at high risk of postoperative rebleeding when selecting patients with ICH for MICE.

Several studies have previously suggested that NCCT signs, such as an irregular hematoma shape, blend sign, and satellite sign, can predict postoperative rebleeding.9⇓-11 These NCCT signs were initially investigated for HE prediction and can be divided into 2 types according to the essential characteristics of the hematoma24: shape irregularity (such as irregular shape,25 satellite sign,26 and island sign27) and density heterogeneity (such as blend sign,28 hypodensities,29 and black hole sign30). However, NCCT signs reflecting shape irregularity and density heterogeneity are qualitative and require subjective interpretation. The predictive ability of NCCT signs for postoperative rebleeding has been reported to be suboptimal (AUC of 0.688 for black hole sign,10 0.772 for blend sign, and 0.629 for irregular shape31). We further evaluated the predictive ability of NCCT and spot signs for rebleeding. However, neither of these signs exhibited acceptable predictive abilities for postoperative rebleeding compared with DCV and SRI (Supplemental Data).

In this study, hematomas were evaluated objectively and quantitatively by using 3D Slicer software based on the pixels of the hematoma, without making any assumptions.14,32 The SRI was used to describe the irregularity of the hematoma, which was adjusted for the influence of the hematoma volume on the hematoma surface area. The DCV of each pixel was then automatically calculated to represent the hematoma density heterogeneity. This study is the first to use the SRI and DCV to predict post-MICE rebleeding. Additionally, we adopted CT scans with a thickness of 1 mm for segmentation, which is believed to be more accurate and realistic than the regular approach by using 5 mm CT for hematoma modeling in most studies.

Although the exact underlying pathophysiological mechanism remains unclear, the Fisher domino model33 is a widely acknowledged theory that explains the association between shape irregularity and HE or rebleeding. According to the Fisher model, HE or rebleeding is caused by the shearing stress of blood vessels around the site of the primary hemorrhage, which contributes to secondary hemorrhage. Irregularly shaped hematomas, according to different definitions, have further been shown to predict HE with acceptable performance.24,25,34 Nonetheless, the performance of irregular hematomas in predicting postoperative rebleeding has been unsatisfactory. In one previous study, irregular hematoma shape, defined as a categoric scale by Barras et al25 exhibited suboptimal discriminative ability (AUC = 0.629) for postoperative rebleeding.31 In the present study, the shape irregularity of the hematoma was reflected by the SRI, which was calculated mathematically with quantitative and objective measurements of hematoma volume and surface area and exhibited acceptable predictive abilities for postoperative rebleeding with an AUC of 0.781.

The DCV of the hematoma, which reflects the density heterogeneity, was also identified as an independent predictor of postoperative rebleeding for the first time in this study, yielding an AUC of 0.789. From our perspective, there are 2 possible explanations for a hematoma with heterogeneous density: first, the hematoma comprises different stages or components of hemorrhage, reflecting the instability of the hematoma; and second, the hemorrhage itself is more dispersed and mixed with the brain tissue. The first case indicated bleeding at different time points, reflecting the instability of the hematoma with a tendency for hematoma expansion. In the second case, the hematoma was mixed with the brain tissue, possibly indicating that the drainage catheter may have penetrated more brain tissue with cerebral vessels during the surgery, resulting in an increased risk of postoperative rebleeding.

Prior studies on the prediction of postoperative rebleeding by using the black hole sign10 and blend sign11 were based primarily on baseline (within hours of ICH onset) rather than on preoperative CT. However, the shape and density of hematomas may change over time following ICH onset. In the present study, preoperative CT was applied to evaluate the hematoma that was closest to the hematoma during surgery. The median period from ICH onset to preoperative CT was approximately 2 days, indicating that the time window of shape irregularity and density heterogeneity for predicting postoperative rebleeding could be extended from 6 to 48 hours in clinical practice.

Our results further suggest that IVH and HE were independently associated with postoperative rebleeding. The combination of SRI and DCV with IVH and HE demonstrated optimal discrimination in predicting postoperative rebleeding with an AUC of 0.880.

Notably, the latest ENRICH trial demonstrated that minimally invasive hematoma evacuation based on the BrainPath-Myriad system achieved better functional outcomes at 180 days for patients with lobar hemorrhage.6 The results of the Minimally Invasive Endoscopic Surgical Treatment With Apollo/Artemis in Patients With Brain Hemorrhage (INVEST) and Artemis in the Removal of Intracerebral Hemorrhage (MIND) trials (by using the Apollo MIES and Artemis systems [Penumbra], respectively) are also forthcoming. Additionally, several ICH trials on minimally invasive hematoma evacuation are ongoing, including Early Minimally Invasive Image Guided Endoscopic Evacuation of Intracerebral Haemorrhage (EMINENT-ICH), Dutch Intracerebral Hemorrhage Surgery Trial (DIST), and Ultra-Early, Minimally inVAsive intraCerebral Haemorrhage evacUATion Versus Standard trEatment (EVACUATE). However, the minimally invasive visual devices used in these trials are expensive and less accessible, particularly in developing countries. Nonvisual, minimally invasive hematoma excavation will continue to be an essential surgical procedure in many places, and preoperative hematoma evaluation and patient selection are critical for reducing the risk of postoperative rebleeding.

This study has some limitations that should be considered when interpreting the results. First, the sample size was small, and all the patients were enrolled from a single center; therefore, a potential selection bias may be inevitable. Second, SRI and DCV must be acquired via manual image processing, which may limit their clinical application. Automatic analysis software that calculates and processes these parameters will be developed in future studies. Third, the thrombolytic agent used in this study was urokinase, not alteplase or tenecteplase, as used in other clinical trials. However, urokinase is relatively more commonly used in China, especially in areas that are economically less developed.

## CONCLUSIONS

Hematoma shape irregularity and density heterogeneity are risk factors for rebleeding after MICE for ICH. SRI and DCV can be used to identify individuals at high risk of postoperative rebleeding.

## Acknowledgments

We thank all of the patients and health care providers who participated in this study.

## Footnotes

*   Kaijiang Kang, Zeqiang Ji, and Yang Du contributed equally to this article.

*   Prof. Xingquan Zhao, Jianwei Wu and Yanfang Liu are co-corresponding authors.

*   This work was supported by the National Natural Science Foundation of China (Grant number: 82471489 to K.K. and 82371320 to X.Z.), Health China·BuChang ZhiYuan Public Welfare Projects for Heart and Brain Health (Grant number: HIGHER2023074 to K.K.), and Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (Grant number: 2019-I2M-5-029 to X.Z.).

*   [Disclosure forms](https://www.ajnr.org/sites/default/files/additional-assets/Disclosures/July%202025/1018.pdf) provided by the authors are available with the full text and PDF of this article at [www.ajnr.org](http://www.ajnr.org).

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*   Received October 13, 2024.
*   Accepted after revision December 20, 2024.


*   © 2025 by American Journal of Neuroradiology

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