The value of magnetic resonance imaging-based tumor shape features for assessing microsatellite instability status in endometrial cancer.

Background: Microsatellite instability (MSI) status can be used for the classification and risk stratification of endometrial cancer (EC). This study aimed to investigate whether magnetic resonance imaging (MRI)-based tumor shape features can help assess MSI status in EC before surgery.
Methods: The medical records of 88 EC patients with MSI status were retrospectively reviewed. Quantitative and subjective shape features based on MRI were used to assess MSI status. Variables were compared using the Student's t-test, χ2 test, or Wilcoxon rank-sum test where appropriate. Univariate and multivariate analyses were performed by the logistic regression model. The area under the curve (AUC) was used to estimate the discrimination performance of variables.
Results: There were 23 patients with MSI, and 65 patients with microsatellite stability (MSS) in this study. Eccentricity and shape type showed significant differences between MSI and MSS (P=0.039 and P=0.033, respectively). The AUC values of eccentricity, shape type, and the combination of 2 features for assessing MSI were 0.662 [95% confidence interval (CI): 0.554-0.770], 0.627 (95% CI: 0.512-0.743), and 0.727 (95% CI: 0.613-0.842), respectively. Considering the International Federation of Gynecology and Obstetrics (FIGO) staging, eccentricity maintained a significant difference in stages I-II (P=0.039), while there was no statistical difference in stages III-IV (P=0.601). Conclusions: It is possible that MRI-based tumor shape features, including eccentricity and shape type, could be promising markers for assessing MSI status. The features may aid in the preliminary screening of EC patients with MSI. 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.

Introduction

Endometrial cancer (EC) ranks the sixth most common cancer type in women worldwide (1). Traditionally, the prognosis of EC has depended on the type of pathology that was first proposed by Bokhman in 1983 (2). Type I cancers (65%) are associated with endometrial hyperplasia, obesity, hyperlipidemia, estrogen excess, moderate or high differentiation, high sensitivity to progestogens, and a favorable outcome. In contrast, type II cancers (35%) often lack the above signs and are associated with poor differentiation and less favorable outcomes (2). However, the classification system cannot completely elucidate the gene heterogeneity and molecular diversity, which could assist treatment options and prognosis evaluations in EC patients (3-5). Microsatellite instability (MSI) status, a hypermutable phenotype commonly resulting from defective mismatch repair (MMR) proteins, affects about 30% of patients with EC (6). Recently, MSI status has been considered a promising molecular subtype for risk stratification and therapeutic interventions in patients with EC (3). The US Food and Drug Administration has approved pembrolizumab for the treatment of all advanced MSI-high or deficient MMR solid tumors. Moreover, MSI status testing is also recommended by the National Comprehensive Cancer Network guidelines in detecting Lynch syndrome for EC patients (7). Identifying EC patients with Lynch syndrome can facilitate early detection of other Lynch syndrome-related cancers and find at-risk relatives (8).

MSI status can be tested by immunohistochemistry, polymerase chain reaction, and next-generation sequencing techniques. These methods are based on tumor specimens from invasive biopsy or surgical excision. Additionally, even though MSI screening is recommended for EC patients, it is rarely used in routine clinical practice due to its high cost and dependence on technology, such as sufficient tissue samples and the lengthy process of ordering the test (9,10). Therefore, it is vital to find a noninvasive, interpretable, and easy-to-use method to assess the MSI status in patients with EC.

In recent years, shape and texture features extracted from medical images have been used to assess MSI status in colorectal cancer and EC (11-16). Moreover, quantitative shape features have also been used to describe tumor aggressiveness and predict the gene expression in other tumors (17,18). Compactness can help differentiate benign from malignant pleural lesions (17) and has a specific correlation with human papillomavirus status in head and neck cancers (18). Moreover, subjective shape features, including the shape type and tumor margin, have been used for diagnosing prostate cancer and breast cancer in clinical practice with reference to the Prostate Imaging Reporting and Data System, version 2 (PI-RADS v.2) and the Breast Imaging Reporting and Data System version 5 (BI-RADS v.5), respectively. In EC, large tumor volumes and diameters based on magnetic resonance imaging (MRI) have been associated with poor prognosis (19,20).

This study aimed to explore whether MRI-based tumor shape features could help assess MSI status in patients with EC. Furthermore, most patients with EC are diagnosed at early stages with good prognoses, while those at advanced stages have poor outcomes (21). We intended to conduct a subgroup analysis according to the International Federation of Gynecology and Obstetrics (FIGO) staging system. We present the following article in accordance with the TRIPOD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-22-77/rc).

Methods

Patients

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the research Ethics Committee of Guangdong Provincial People’s Hospital, and individual consent for this retrospective analysis was waived. From August 2016 to June 2021, 100 patients with histologically confirmed EC and preoperative pelvic 3.0 T MRI examinations were eligible for this study. The inclusion criteria were as follows: (I) those without neoadjuvant therapy for EC before surgery; and (II) those with MSI status evaluation. The exclusion criteria were as follows: (I) those without contrast-enhanced T1-weighted imaging (CE-T1WI; n=3); (II) those with severe MRI artifacts (n=2); and (III) those with a tumor diameter of <1 cm or that was hard to identify (n=7). In all, the remaining 88 patients constituted the study population.

Patient baseline clinicopathologic characteristics, including age, menopausal status, histological type, tumor grade, FIGO stage, and tumor markers, were collected from the electronic medical record system. Tumor markers included carbohydrate antigen 125 (CA125), human epididymis protein 4 (HE4), and carbohydrate antigen 19-9 (CA19-9), with thresholds of ≤35 U/mL, ≤70 pmol/L, and ≤27 U/mL, respectively, based on the normal ranges used at our institution.

MSI status assessment

The MSI status was assessed by immunohistochemical staining using formalin-fixed, paraffin-embedded EC tissue samples. Four primary antibodies (MLH1, MSH2, MSH6, and PMS2) were included in the process. The negative nuclear staining of tumor cells was defined as protein deficient, and any positive nuclear staining was defined as protein proficient. Cases with at least 1 deficient protein were defined as MSI; cases with 4 proficient proteins were classified as microsatellite stability (MSS) (22).

MRI protocols

Preoperative pelvic CE-MRI images were acquired with the GE 3.0 T system (Signa Excite; GE Healthcare, Chicago, IL, USA) for 35 patients and the Philips 3.0 T system (Ingenia; Philips Healthcare, Amsterdam, Netherlands) for the remaining 53 patients. The mean interval between MRI and surgical staging was 7.9 (range, 1–59) days. All patients were required to keep an empty stomach and undergo proper bowel preparation to avoid the artifacts of intestinal gas before MRI examination. The MRI protocols included axial-oblique T2-weighted imaging (T2WI), axial-oblique diffusion-weighted imaging (DWI; including b values of 0/600/800/1,000 s/mm2), and CE-T1WI. CE-T1WI was obtained after administration of 0.1 mmol/kg of the gadolinium-based contrast agent, Magnevist (Schering, Berlin, Germany). Apparent diffusion coefficient (ADC) maps were generated by DWI images. The mean ADC values of tumors were recorded for further analysis in our study. The parameters of all the protocols are provided in .

Table 1

MRI scanning parameters for the patients

Scanner	Patients	Sequence	TR/TE (ms)	Matrix	Slice thickness (mm)	Slice gap (mm)	Flip angle	B value (s/mm2)
GE Medical Systems	35	T2WI	2,800/119	512×512	5	6	90°	N/A
		CE-T1WI	4/2	512×512	4	2	12°	N/A
		DWI	5,200/97	256×256	5	6	90°	0/600/800
Philips Medical Systems	53	T2WI	1,500/70	512×512	5	6	90°	N/A
		CE-T1WI	3.9/1.37	432×432	4	2	10°	N/A
		DWI	6,322/83	224×224	5	7	90°	0/600/800/1,000

MRI, magnetic resonance imaging; T2WI, T2-weighted imaging; CE-T1WI, contrast-enhanced T1-weighted imaging; DWI, diffusion-weighted imaging; TR, repetition time; TE, echo time; N/A, not applicable.

Tumor annotation and quantitative shape features extraction

We retrieved the equilibrium phases of axial-oblique CE-T1WI sequences (2–3 min after injection of contrast agents), which showed the maximum contrast between EC tumor and normal myometrium for tumor annotations (23). Axial-oblique T2WI sequences were also retrieved for tumor annotations to check the consistency of quantitative shape features with CE-T1WI. On T2WI, the tumor region was defined as the area of intermediate signal intensity that was different from normal hypointense myometrium and hyperintense endometrium. Tumor annotations were performed with ITK-SNAP software (version 3.8.0; http://www.itksnap.org) (24). Regions of interest (ROIs) of tumors were manually annotated slice by slice by radiologist A (with 4 years of experience in reading pelvic MRI) for all cases on CE-T1WI and T2WI, respectively, to analyze the correlation of quantitative shape features. The ROIs included areas of hemorrhage, necrosis, and cystic tissue. Volumes of interest (VOIs) were then constructed by integrating ROIs of all slices for each tumor. We randomly selected 30 cases for tumor annotation by radiologist B (with 7 years of experience in reading pelvic MRI) on CE-T1WI. The two radiologists were blinded to MSI status during segmentation.

Four quantitative shape features, including the longest diameter, volume, eccentricity, and solidity, were extracted and analyzed with MATLAB R2019a (MathWorks Inc., Natick, MA, USA). The quantitative shape feature definitions and calculation methods are shown in . displays the 4 quantitative shape features based on CE-T1WI.

Table 2

Shape feature definition or calculation

Shape feature	Definition or calculation
Longest diameter	Longest diameter of the ROI, in cm
Volume	Number of voxels in the ROI, in mm3
Solidity	Ratio of the number of voxels in the ROI to the number of voxels in the 3D convex hull of the ROI (smallest polyhedron containing the ROI)
Eccentricity	Metric given by 1−abc^2 where c, a, and b are the longest semi-principal axes, the second and third longest semi-principal axes of the ellipsoid, respectively

ROI, region of interest; 3D, three-dimensional.

Figure 1

Two cases of EC with quantitative shape features. (A,C) A tumor with MSI. (B,D) A tumor with MSS. 3D, three-dimensional; EC, endometrial cancer; MSI, microsatellite instability; MSS, microsatellite stability.

Subjective shape features evaluation

Four subjective shape features, including margin (distinct or indistinct), shape type (regular or irregular), extension beyond corpus (yes or no), and myometrial invasion (≤50% or >50%), were evaluated by radiologist A using the in-house picture archiving and communication system. The margin of the tumor was evaluated on T2WI, which was classified as “distinct” if the tumor margin was clear to identify and “indistinct” if the tumor margin was fuzzy. The shape type of tumor was evaluated on CE-T1WI, which was classified as “regular” if the tumor shape type was oval or round and “irregular” if the tumor shape type was irregular. The extension beyond the corpus was also evaluated on CE-T1WI, which was classified as “yes” if the tumor grew out of the uterine corpus into the cervix or outside the uterus and “no” if the tumor was confined to the uterine corpus. The depth of myometrial invasion was evaluated on CE-T1WI, T2WI, and the DWI, which was classified as “≤50%” if the tumor invaded less than or equal to 50% of the myometrial thickness and “>50%” if the tumor invaded more than 50% of the myometrium thickness. A total of 30 patients were randomly selected by stratified sampling for subjective features assessment in the same way by radiologist B.

Statistical analysis

The differences in clinicopathological characteristics and shape features were assessed by using the Student’s t-test, χ2 test, or Wilcoxon rank-sum test, as appropriate. For measuring interrater reliability, the intraclass correlation coefficient (ICC) was used for continuous quantitative variables, while kappa statistics were used for categorical variables. Pearson correlation coefficient (Pearson r) was used to assess the correlation of continuous quantitative variables. Univariate and multivariate analyses were performed by logistic regression model for MSI status. The feature with a P value <0.05 in the univariate analysis was included in the multivariate analysis. The receiver operating characteristic (ROC) curve was plotted, and the area under the curve (AUC) was calculated to evaluate the predictive performance of shape features for MSI status. A P value <0.05 was considered statistically significant. All statistical analyses were performed with R language (v.4.0.3; The R Project for Statistical Computing, Vienna, Austria).

Results

A total of 88 patients with EC were finally included in our study. The MSI group included 23 patients (26.1%), and the MSS group included 65 patients (73.9%). The mean age at diagnosis was 54.95 years, with a standard deviation (SD) of 9.08 years. Histologically, 84 were endometrioid, 2 were serous, and 2 were mixed carcinomas (endometrioid mixed clear cell). Among the 84 endometrioid tumors, there were 28, 45, and 11 for grade 1, grade 2, and grade 3 tumors, respectively. For the FIGO stage, 65 of the tumors were stage I, 2 were stage II, 18 were stage III, and 3 were stage IV. The clinicopathological characteristics are summarized in . No significant differences were found between MSI and MSS in all clinicopathological characteristics (all P values >0.05; ).

Table 3

Characteristics of patients in the whole cohort

Characteristics	MSS (n=65)	MSI (n=23)	P value
Age (years), mean ± SD	54.88±10.08	55.17±5.47	0.861
Menopausal status, n (%)			1.00
Yes	38 (58.5)	13 (56.5)
No	27 (41.5)	10 (43.5)
Histology, n (%)			1.00
Endometrioid	62 (95.4)	22 (95.7)
Serous	2 (3.1)	0 (0.0)
Mixed	1 (1.5)	1 (4.3)
Tumor grade, n (%)			0.738
Grade I	22 (33.9)	6 (26.1)
Grade II	31 (47.7)	14 (60.9)
Grade III	9 (13.8)	2 (8.7)
Not available	3 (4.6)	1 (4.3)
FIGO stage, n (%)			0.522
I	48 (73.8)	17 (73.9)
II	2 (3.1)	0 (0.0)
III	12 (18.5)	6 (26.1)
IV	3 (4.6)	0 (0.0)
LVSI, n (%)			0.188
Absent	53 (81.5)	15 (65.2)
Present	12 (18.5)	8 (34.8)
CA125, n (%)			1.00
Normal	35 (53.8)	13 (56.5)
Abnormal	30 (46.2)	10 (43.5)
HE4, n (%)			0.835
Normal	17 (26.2)	6 (26.1)
Abnormal	47 (72.3)	17 (73.9)
Unknown	1 (1.5)	0 (0.0)
CA19-9, n (%)			0.698
Normal	38 (58.5)	12 (52.2)
Abnormal	26 (40.0)	11 (47.8)
Unknown	1 (1.5)	0 (0.0)

SD, standard deviation; FIGO, International Federation of Gynecology and Obstetrics; LVSI, lymphovascular space invasion; CA125, carbohydrate antigen 125; HE4, human epididymis protein 4; CA19-9, carbohydrate antigen 19-9; MSI, microsatellite instability; MSS, microsatellite stability.

Shape features and MSI status

Quantitative shape features were normalized based on z score normalization, as shown in . Among quantitative shape features, no significant difference was found in the longest diameter, volume, or solidity between the two groups (all P values >0.05; ). Eccentricity had a statistically significant difference between the MSI and MSS groups (P=0.039; ). Tumors with MSI often showed higher eccentricities than those with MSS in EC. Among the 30 randomly selected patients, eccentricity showed a good interrater reliability [ICC =0.981; 95% confidence interval (CI): 0.962–0.991; ]. Moreover, a strong correlation was observed between CE-T1WI–derived and T2WI-derived eccentricity (Pearson r=0.68; P<0.001; ). Using the median value of 0.74 as the threshold, patients were stratified into two groups: eccentricity low (≤0.74) and eccentricity high (>0.74). The percentages of patients with MSI vs. MSS in the two groups were 13.6% vs. 86.4%, and 38.6% vs. 61.4%, respectively (P=0.032; ).

Figure 2

Heatmap of quantitative shape features in all cases. MSI, microsatellite instability; MSS, microsatellite stability.

Figure 3

Quantitative shape features compared between patients with MSS and MSI. (A) Longest diameter, (B) volume, and (C) solidity were not significantly different between the MSI group and the MSS group. (D) Eccentricity was significantly different between the MSI group and the MSS group (P<0.05), and (E) shape features had good interrater consistency (ICC =0.981; 95% CI: 0.962–0.991). (F) The eccentricity calculated from T2WI showed a strong correlation with CE-T1WI (Pearson r=0.68, P<0.001). (G) Eccentricity was stratified into 2 groups according to the median value of 0.74 and was significantly different between the MSI group and the MSS group (P<0.05). *, P<0.05. MSI, microsatellite instability; MSS, microsatellite stability; ICC, intraclass correlation coefficient; T2WI, T2-weighted imaging; CE-T1WI, contrast enhanced T1-weighted imaging; Pearson r, Pearson correlation coefficient.

For subjective shape features, shape type (regular vs. irregular) showed a significant difference between the MSI and MSS groups (P=0.033). In the MSI group, 14 (61%) tumors were classified as regular shape, compared with 23 (35%) tumors in the MSS group. The assessment of shape type showed good interrater reliability (kappa value =0.789). In contrast, no significant difference was found in the margin, extension beyond corpus, or myometrial invasion between MSI and MSS (all P values >0.05).

Univariate and multivariate analyses

In the univariate analysis, eccentricity and shape type showed significant differences between MSI and MSS [high vs. low, odds ratio (OR) =3.99, 95% CI: 1.39–11.43, P=0.010; irregular vs. regular, OR =0.35, 95% CI: 0.13–0.94, P=0.037; ]. No significant differences were found in the ADC value, clinicopathological characteristics, and other shape features. Finally, eccentricity and shape type were included in the multivariate analysis, and both remained as independent indicators of MSI status [high vs. low, adjusted OR (AOR) =4.49, 95% CI: 1.50–13.5, P=0.007; irregular vs. regular, AOR =0.30, 95% CI: 0.11–0.86, P=0.026; ].

Table 4

Univariable and multivariable logistic regression analyses

Risk factors	Univariable analysis			Multivariable analysis
	OR (95% CI)	P value		AOR (95% CI)	P value
Age (years)	1.00 (0.95–1.06)	0.892
HE4
Normal	Ref
Abnormal	1.02 (0.35–3.03)	0.965
CA125
Normal	Ref
Abnormal	0.90 (0.34–2.34)	0.825
ADC	0.65 (0.15–2.83)	0.552
Tumor grade
Grade I	Ref
Grade II	1.66 (0.55–4.98)	0.369
Grade III	0.81 (0.14–4.82)	0.821
FIGO stage
I	Ref
II	0 (0–Inf)	0.995
III	1.41 (0.46–4.35)	0.548
IV	0 (0–Inf)	0.994
Longest diameter	1.00 (0.79–1.28)	0.977
Volume	1.00 (0.98–1.02)	0.962
Solidity	9.62 (0.01–8389)	0.512
Eccentricity
Low	Ref			Ref
High	3.99 (1.39–11.43)	0.010*		4.49 (1.50–13.5)	0.007*
Margin
Distinct	Ref
Indistinct	0.44 (0.15–1.25)	0.124
Shape
Regular	Ref			Ref
Irregular	0.35 (0.13–0.94)	0.037*		0.30 (0.11–0.86)	0.026*
Extension beyond corpus
No	Ref
Yes	1.00 (0.34–2.94)	0.995
Myometrial invasion
≤50%	Ref
>50%	0.95 (0.36–2.45)	0.908

*, P<0.05. HE4, human epididymis protein 4; CA125, carbohydrate antigen 125; ADC, apparent diffusion coefficient; FIGO, International Federation of Gynecology and Obstetrics; OR, odds ratio; AOR, adjusted odds ratio; CI, confidence interval; Ref, reference; Inf, infinity.

Patients with MSI were grouped based on the eccentricity and shape type: eccentricity high and regular shape, eccentricity high and irregular shape, eccentricity low and regular shape, and eccentricity low and irregular shape; the percentages of MSI status patients were 55%, 27%, 21%, and 8%, respectively (). The ROC curves are depicted in . The AUC values of the eccentricity and the shape type to assess MSI were 0.662 (95% CI: 0.554–0.770) and 0.627 (95% CI: 0.512–0.743), respectively. When combining the eccentricity and the shape type to assess MSI, the AUC value increased to 0.727 (95% CI: 0.613–0.842).

Figure 4

Shape features for assessing MSI status. (A) The distribution of shape and eccentricity in the MSI group. (B) ROC curves of shape features for assessing MSI status. AUC values of shape type, eccentricity, and the combined 2 features to assess MSI were 0.627 (95% CI: 0.512–0.743), 0.662 (95% CI: 0.554–0.770), and 0.727 (95% CI: 0.613–0.842), respectively. (C) Subgroup analysis of eccentricity according to the FIGO stage. Eccentricity remained significant difference in stages I–II (P=0.039, left panel), while no significant difference was found in stages III–IV between the MSI group and the MSS group (P=0.601, right panel). *, P<0.05. MSI, microsatellite instability; FIGO, International Federation of Gynecology And Obstetrics; MSS, microsatellite stability; ROC, receiver operating characteristic; AUC, area under the curve; CI, confidence interval.

Subgroup analysis

Furthermore, we conducted a subgroup analysis of eccentricity according to the FIGO stage. Eccentricity still showed a significant difference in stages I–II (P=0.039; , left panel). There was no statistical difference in stages III–IV between the MSI and MSS groups (P=0.601; , right panel).

Discussion

Our study indicated that MRI-based tumor quantitative and subjective shape features might help identify MSI status in EC patients. On univariate and multivariate analyses, eccentricity and shape type showed significant differences between MSI and MSS, and those with higher eccentricity and regular shape based on CE-T1WI were more prone to tumors with MSI than those with MSS. Multivariate analysis showed that eccentricity and shape type were independent indicators of MSI status. In the FIGO stage subgroup analysis, the difference of eccentricity remained significant in stages I–II but not significant in stages III–IV.

Quantitative features, including shape features extracted from radiological images, have been used to assess MSI status in colorectal cancer and have achieved good performances (11-13). Moreover, quantitative shape features such as compactness can help differentiate benign from malignant pleural lesions (17) and are related to human papillomavirus status in head and neck cancers (18). In EC, the quantitative features extracted from the CE-T1WI sequence have been used for predicting prognosis and detecting lymphovascular space invasion (LVSI) and deep myometrial infiltration in EC (25-27). Some studies have also confirmed the valuable role of quantitative shape features based on MRI in the assessment of EC (19,20). Tumor volume measured on MRI is highly associated with disease progression in patients with EC (19). The potential value of tumor size based on MRI has also been investigated in assessing the tumor aggressiveness of EC (20). Different from previous assessments of the disease progression and tumor aggressiveness, we attempted to investigate the value of quantitative shape features based on CE-T1WI in assessing MSI status. In this study, we found that the eccentricities of tumors with MSI were higher than those with MSS in EC and showed independent predictive value for MSI status. Our study may add insights into MRI-based shape features for MSI assessment to the literature.

In clinical practice, subjective shape features are vital for diagnosing disease and assessing tumor aggression, such as shape type and tumor margin evaluated by referring to PI-RADS v.2 in prostate cancer and BI-RADS v.5 in breast cancer. Pelvic MRI images are widely performed preoperatively for local staging in EC, and the statuses of extension beyond corpus and myometrial invasion influence the FIGO stage of EC (28,29). In this study, we assessed the shape type, margin, extension beyond corpus, and myometrial invasion based on relative MRI sequences. We found that regular shapes based on CE-T1WI were more typical of tumors with MSI than those with MSS in EC.

Tumors with MSI are characterized by sequence alterations in microsatellites and can accumulate thousands of mutations, which results in tumor heterogeneity (30-32). At the molecular level, MSI tumors show substantive intratumor and intertumor heterogeneity in the genomic influence (30). At the cellular and histological level, 3 histomorphological features (more than 2 tumor-infiltrating lymphocytes per high-power field, differentiation of mucinous, and lack of dirty necrosis) are associated with MSI in colorectal cancer (32), suggesting that the morphology of the tumor with MSI is more complex. Likewise, our study tried to identify the complex morphology of the tumor with MSI at the macro level. We found that the tumor with MSI had significantly high eccentricity, reflecting the macroscopical isotropy. However, the significant difference of eccentricity only remained in stages I–II, presumably because of the small sample size. Moreover, we found that the tumor with MSI often showed a regular shape, which seemed quite the opposite of the tumor heterogeneity. This may be related to the structure of the endometrial cavity limiting the growth of the tumor in some conditions. Nonetheless, shape features based on MRI may offer more clarity in assessing MSI status in EC.

To the best of our knowledge, there have only been several studies assessing MSI status in EC based on radiological images, using traditional methods or radiomics methods (14-16). As for traditional methods, EC tumors with MSI had significantly lower signal intensities than those with MSS on CE images in a study with 71 patients (14). We also chose the CE-T1WI sequences to segment tumors. A similar analysis for assessing MSI status within 6 tumors with MSI and 6 tumors with MSS using reduced field of view diffusion sequences indicated that tumors with MSS had significantly higher ADC values than tumors with MSI (15). In contrast, our data set showed no statistical difference in ADC values between the MSI and MSS groups. This may be because the ADC values in our study are calculated from traditional DWI sequences. As for radiomics methods, a clinical-radiomic machine learning model to predict MSI based on computer tomography (CT) in EC and yielded an AUC value of 0.78 in the training set (n=102) and 0.78 in the test set (n=42) (16). The radiomic method has been widely used in other tumors (33-37), but it faces great challenges in the clinic due to limited interpretability. It is also challenging to delineate the boundaries of EC tumors on CE-CT images. Meanwhile, the simple shape features based on MRI images in our study to assess MSI status achieved an AUC value of 0.727.

Our study had some limitations. First, the sample size of our study was small (n=88) because of the limited EC cases with MSI status results. However, the sample size was good relative to previous similar MRI-based studies in EC (14,15). Second, we only focused on analyzing shape features, which might not have covered some other features strongly associated with MSI status. Although the shape features are interpretable, investigating other features may be our future research direction. Third, as this was a retrospective study at a single center, the results need to be verified by multicenter studies in the future.

Conclusions

In conclusion, quantitative and subjective shape features based on MRI may be potential markers for assessing MSI status in EC. These features may aid in the preliminary screening of EC patients with MSI.

The article’s supplementary files as