Clinicoradiologic features and evolutionary characteristics of pulmonary focal mucinous adenocarcinomas across different density patterns: a retrospective multi-center study

Introduction

Mucinous adenocarcinoma (MA) is a rare histological subtype of lung adenocarcinoma, accounting for approximately 3–10% of all cases (1,2). Histologically, MA is characterized by columnar or goblet cells with basally located nuclei and abundant mucin inside the cells (3-5). Mucin extravasation can cause tumors to exhibit an irregular shape and ill-defined boundary on computed tomography (CT) images, which are similar to the radiological manifestations of inflammatory lesions (5-10). Subsequently, MA is frequently misdiagnosed, which results in patients missing the optimal treatment opportunity and leads to a poor prognosis (11,12). Therefore, better understanding the clinical and CT characteristics of MA is necessary for early diagnosis.

Previous studies primarily classified MA into two types: the nodular/mass type and the consolidative/pneumonic type based on their main CT manifestations (4,13,14). Compared to the nodular/mass type, the consolidative/pneumonic type exhibits relatively specific CT features, such as the halo sign, angiogram sign, vacuole sign, dead branch sign, and unevenly low enhancement, making it relatively less likely to be misdiagnosed (12,14,15). However, this type is usually advanced and frequently exhibits spread through air spaces, making it unable to be completely resected and prone to recurrence, and leading to a poor overall survival (10-12,16-19). Therefore, accurate diagnosis of the nodular/mass type, which has a better prognosis, is of great clinical value (17,18). However, the diagnosis of the nodular/mass type, particularly the nodular type, is more challenging due to its lack of characteristic features (9). This study focused on focal MAs (maximum diameter ≤3 cm), which correspond to the nodular-type MA in previous classifications.

The focal MA is further subdivided into subsolid and solid lesions (6). It was revealed that elevated standardized uptake values, ill-defined boundary, air bronchogram, lobulation, and spiculation are their potential predictive indicators (8,20,21). However, no comparative study based on large samples focused on the clinical and radiological characteristics has addressed whether the subsolid and solid ones represent distinct types (17). Additionally, those potential predictive indicators of focal MA are non-specific; thus longitudinal follow-up is often required and the changes of lesions could provide additional information for confident diagnosis (22). However, the individual changes in CT features of subsolid and solid types during progression are not revealed. These issues have limited the comprehensive understanding and accurate diagnosis of early-staged MA.

This study systematically compares the clinical and CT findings of focal subsolid mucinous adenocarcinomas (SS-MAs) and solid mucinous adenocarcinomas (S-MAs), and tracks their longitudinal changes, aiming to enhance the understanding of early-stage MAs and improve their diagnosis. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-0453/rc).

Methods

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of The First Affiliated Hospital of Chongqing Medical University (No. 2025-118-01, date of approval: 27.01.2025) and the Ethics Committee of The Second Affiliated Hospital of Army Medical University (No. 2022-196-01, date of approval: 13.05.2022). The requirement for written informed consent was waived due to the retrospective nature of the study. All the personal identification data were anonymized and de-identified before analysis.

Patients selection

Patients who underwent pulmonary lesion resection in the Department of Thoracic Surgery of The First Affiliated Hospital of Chongqing Medical University and The Second Affiliated Hospital of the Army Medical University between November 2012 and November 2025 were retrospectively identified by searching electronic health record. This search yielded an initial cohort of 34,126 patients. First, 21,030 patients with non-invasive or benign lesions were excluded, comprising 6,118 with precursor lesions, 4,700 with fibrous tissue hyperplasia, 4,336 with inflammatory lesions, 2,633 with infectious lesions, and 3,243 with other benign lesions (including lymphoid tissue, granuloma, hamartoma, and organizing pneumonia, etc.). Subsequently, we excluded 5,632 patients with squamous cell carcinoma, 1,121 with small cell carcinoma, 911 with metastatic carcinoma, 991 with other malignant tumors (including adenosquamous carcinoma, large cell carcinoma, and sarcomatoid carcinoma), 3,698 with minimally invasive adenocarcinoma, 98 with non-mucinous adenocarcinoma (NMA) (minimal mucinous component <10%), and 233 with mixed mucinous/NMA (both non-mucinous and mucinous component ≥10%) (23). These exclusions left 412 patients with pure invasive MA, which defined as adenocarcinoma with a mucinous component of ≥90% (8,10,17,23). All pathology specimens were reviewed and confirmed by an experienced thoracic pathologist according to the 2021 World Health Organization (WHO) classification of lung tumors (3). For brevity, MA refers to invasive MA throughout this study. Then, the preoperative chest CT scans of these patients were reviewed in the picture archiving and communication system and 201 patients were excluded for the following reasons: MAs with a maximum diameter greater than 3 cm (n=165). The measurement was based on the initial CT scan for patients undergoing multiple scans, or on the only available preoperative scan for others; lack of thin-section CT images (slice thickness ≤1.25 mm) (n=22); incomplete clinical data (n=11); or significant image artifacts (n=3). Consequently, a final cohort of 211 patients, each with a single MA, was included in the study. Of these, 51 MAs (24.2%) had follow-up data for at least three months. The patient selection process is detailed in Figure 1.

Figure 1 Flow chart of patient selection. CT, computed tomography; MA, mucinous adenocarcinoma.

CT protocol

Patients were scanned using one of the following CT scanners: Discovery CT750 HD (GE Healthcare, Milwaukee, WI, USA), SOMATOM Perspective (Siemens Healthineers, Erlangen, Germany), SOMATOM Definition Flash (Siemens Healthineers, Erlangen, Germany), or SOMATOM Force (Siemens Healthineers, Erlangen, Germany). To minimize breathing artifacts, all CT scans were performed at the end of inspiration during a single breath-hold. The scan range was from the thoracic inlet to the costophrenic angle. The analysis was based on the unenhanced CT images, which were acquired with the following parameters: tube voltage, 110–120 kVp; tube current, 50–140 mAs (reference mAs, using automatic current modulation technology); scanning slice thickness, 5 mm; rotation time, 0.5 s; pitch, 1–1.1; collimation, 0.6–0.625 mm; reconstruction slice thickness and interval, 0.625–1 mm; and matrix, 512×512. All images were reconstructed iteratively using a standard algorithm for GE scanners or a medium-sharp algorithm for Siemens scanners.

Clinical data and image analysis

The patients’ clinical characteristics collected included age, sex, smoking history, alcohol consumption, hypertension, family history of lung cancer, personal history of malignancy, diabetes mellitus, and serum levels of lung cancer-associated tumor markers. Primary assessment was performed on axial images in lung window settings (window level: −600 HU; window width: 1,500 HU), supplemented with multiplanar reconstruction and maximum intensity projection images as needed. Two thoracic radiologists (X.F.J. and Z.G.C.), blinded to pathological results, independently evaluated all CT images on a picture archiving and communication system workstation. Disagreements were resolved by consensus or by adjudication from a third senior thoracic radiologist.

The following CT features were assessed: (I) diameter (mean of the longest and its perpendicular diameter on axial images); (II) volume; (III) location (upper, middle, or lower lobe); (IV) subpleural distribution (yes or no); (V) density; (VI) CT pattern [solid (S) or subsolid (SS)]; (VII) shape (patchy or nodular); (VIII) boundary (well-defined or ill-defined); (IX) lobulation; (X) spiculation; (XI) pleural indentation; (XII) vacuole; and (XIII) air bronchogram. A subpleural distribution was defined as a distance of ≤1 cm from the distal lesion margin to the nearest pleura. Density was quantified by placing a circular region of interest encompassing the largest possible area of the lesion while excluding vessels and airways. Measurements were repeated on two adjacent slices (superior and inferior) by both radiologists. All measurements adhered to Fleischner Society guidelines (24). A lesion was defined as solid (S) when it completely obscured the underlying lung parenchyma, and as subsolid (SS) when it contained at least partial ground-glass opacity (GGO) (24). Shape classification (patchy or nodular) was based on the three-planar diameter ratio. The longest and perpendicular diameters were measured on axial, coronal, and sagittal planes, yielding six values. The three-planar ratio was calculated as the ratio of the maximum to minimum diameter (25). Previous studies showed that a three-planar ratio >1.78 was closely associated with flat, benign lesions (26). On CT images, focal MAs may present as nodular or patchy lesions. Compared with nodular lesions, patchy lesions are typically flatter. To distinguish these two morphological patterns, this established cutoff was adopted in this study to classify focal subsolid or solid lesions as patchy (ratio >1.78) versus nodular (ratio ≤1.78).

The changes in the following aspects were evaluated during follow-up: (I) diameter; (II) volume; (III) density; (IV) volume doubling time (VDT); (V) diameter of solid component; (VI) interface between solid and GGO components (well-defined or ill-defined); (VII) morphology of solid component (strip-like, band-like, nodular, or irregular); (VIII) distribution of solid component (centric or eccentric); and (IX) development of new GGO around the lesion. An increase of ≥2 mm in the diameter of the lesion or its solid component was defined as growth. A volume increase was defined as a change ≥25%. An increase in density was defined as an increase in mean CT attenuation of the whole lesion ≥100 HU. VDT was defined as the time required for a lesion to double its volume. VDT was calculated using the modified Schwartz formula as follows: VDT = (ln2×∆t)/ln(V2/V1), where ln is the natural logarithm, V2 and V1 are the final and initial volume, and ∆t is the interval between the final and initial CT scans in days (27,28). This formula assumes exponential tumor growth with a constant growth rate and accurate volume measurements. However, this assumption may not hold for all tumors. The median interval between consecutive CT examinations was 5.2 months (range, 3–12.1 months).

Statistical analyses

Statistical analyses were performed using SPSS software (Version 27; IBM, Armonk, NY, USA), GraphPad Prism software (Version 9.5.0; GraphPad Software, San Diego, CA, USA), and R software (Version 4.2.1; R Foundation for Statistical Computing, Vienna, Austria). The following R packages were used: cmprsk (v2.2.12), ggplot2 (v3.5.1), gridExtra (v2.3), and dplyr (v1.1.4). Continuous variables were presented as mean ± standard deviation or median (interquartile range). Categorical variables were expressed as numbers and percentages. Intragroup comparisons of continuous variables were performed using the paired-samples t-test or Wilcoxon signed-rank test, and the McNemar test for categorical variables. Between-group comparisons were conducted using the unpaired t-test or Mann-Whitney U test, depending on the distribution. Categorical variables were compared using Pearson chi-square test or Fisher’s exact test, with Benjamini-Hochberg procedure applied to adjust for multiple comparisons. Diagnostic performance analyses were performed to assess the predictive value of clinical characteristics and CT features of SS-MAs and S-MAs. These included sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Interobserver agreement was assessed using the intraclass correlation coefficient (ICC) for continuous variables and Cohen’s kappa for categorical ones. ICC values were interpreted as poor (<0.500), moderate (0.500–0.740), good (0.750–0.890), or excellent (≥0.900). Kappa coefficients were categorized as poor (<0.000), slight (0.000–0.200), fair (0.210–0.400), moderate (0.410–0.600), substantial (0.610–0.800), or almost perfect (0.810–1.000) (29). The cumulative incidence function curves for lesion volume doubling were compared between groups using Gray’s test. Surgical resection was treated as censoring; no competing events occurred. A P value <0.05 was considered statistically significant.

Results

Comparison of patients’ clinical characteristics and initial CT features of SS-MAs and S-MAs

Among the 211 MAs, 117 (55.5%) were subsolid and 94 (44.5%) were solid. The clinical characteristics of the patients and initial CT features of the lesions are summarized in Table 1. Both subsolid and solid MAs showed a predominant distribution in the lower lobes (62.4% vs. 62.9%, respectively) and subpleural zones (75.2% vs. 76.3%, respectively), with no significant differences between the two groups (all P>0.05).

Clinically, compared to the solid MA group, the subsolid MA group had a significantly higher proportion of female patients (74.4% vs. 52.1%) and non-smokers (84.6% vs. 71.3%), but a lower proportion of alcohol drinkers (6.0% vs. 18.1%) and individuals with elevated CEA levels (0% vs. 7.4%) (all P<0.05).

Radiologically, subsolid MAs more frequently presented with an ill-defined boundary (62.4% vs. 20.2%) and a patchy shape (43.6% vs. 24.5%), but less frequently showed lobulation (16.2% vs. 47.9%) or spiculation (8.5% vs. 38.3%) compared to solid MAs (all P<0.05).

In multivariate logistic regression analysis, female, ill-defined boundary, patchy shape, lobulation, and spiculation were identified as independent predictors (all P<0.05). Diagnostic performance analyses revealed that female exhibited the highest sensitivity for predicting SS-MA (74.4%), spiculation demonstrated the highest specificity for predicting S-MA (91.5%), ill-defined boundary showed the highest PPV for SS-MA (79.3%), and lobulation had the highest NPV for S-MA (66.7%) (Table S1).

Comparison of initial and follow-up CT characteristics of SS-MAs and S-MAs

Among the 117 SS-MAs, 31 (26.5%) had follow-up CT data, with a median follow-up duration of 28 months (range, 3–63 months). The initial and follow-up CT features of lesions are summarized in Table 2. A significant increase was observed in lesion size [median: 14.1 (IQR, 12.5–20.7) vs. 11.0 (8.0–16.4) mm], volume [2,453.8 (953.0–4,927.4) vs. 968.6 (464.7–2,290.5) mm³], and density [−283.2 (−387.0 to −99.0) vs. −363.2 (−526.0 to −185.0) HU] on follow-up CT compared with initial imaging (all P<0.05). Additionally, the solid components exhibited significant growth in diameter [12.4 (7.5–19.3) vs. 8.6 (4.8–16.0) mm] and a higher frequency of well-defined boundary on follow-up CT (54.8% vs. 19.4%; both P<0.05) (Figure 2). All 31 SS-MAs (100%) maintained their original CT pattern throughout follow-up (Figures 2,3), and 5 (16.1%) developed new GGO around the existing solid components (Figure 2) and 1 (3.2%) transformed into the consolidative/pneumonic type. Compared with lesions without follow-up CT data, those with follow-up were smaller in size (11.0 vs. 13.4 mm) and less frequently patchy (25.8% vs. 50.0%) (all P<0.05) (Table S2).

Figure 2 Serial CT and histopathology of SS-MA in a 62-year-old female. (A) A 62-year-old female with a SS-MA. An initial CT image shows a subsolid nodule (13.5 mm, −331.0 HU) located in the subpleural region of the right middle lobe (arrow). (B) A follow-up CT scan performed 36 months later shows that the lesion significantly increases in size (20.1 mm) and density (−188.0 HU), while retaining ill-defined boundary. Additionally, the internal solid component increases (12.6 mm) and new GGO develops at its periphery region (arrow). (C) After operation, it is confirmed as mucinous adenocarcinoma with columnar cells exhibiting basal nuclei and abundant intracellular mucus (hematoxylin and eosin stain; original magnification ×100). CT, computed tomography; GGO, ground-glass opacity; HU, Hounsfield unit; SS-MA, subsolid mucinous adenocarcinoma.

Figure 3 Serial CT and histopathology of SS-MA in a 65-year-old female. (A) An initial CT image shows a subsolid nodule with little GGO (11.9 mm, −306.0 HU) located in the left lower lobe (arrow). (B) A follow-up CT scan performed 64 months later shows that the lesion increases in both size (16.3 mm) and density (−194.0 HU), and its boundary becomes well-defined. Additionally, the internal solid component increases (13.9 mm) and becomes irregular (arrow). (C) After operation, it is confirmed as mucinous adenocarcinoma with columnar cells exhibiting basal nuclei and abundant intracellular mucus (hematoxylin and eosin stain; original magnification ×100). CT, computed tomography; GGO, ground-glass opacity; HU, Hounsfield unit; SS-MA, subsolid mucinous adenocarcinoma.

Among the 94 S-MAs, 20 (21.3%) cases had follow-up CT data, with a median follow-up duration of 22 months (range, 3–35 months). The initial and follow-up CT features of lesions are listed in Table 3. A significant increase was observed in lesion size [15.6 (11.5–23.8) vs. 11.8 (9.5–15.7) mm], volume [3,692.0 (2,214.3–8,674.8) vs. 1,268.1 (728.5–3,244.6) mm³], and density [−80.9 (−178.0 to −15.0) vs. −170.8 (−253.0 to −91.0) HU] (each P<0.05) on follow-up CT (Figures 4,5). Additionally, three (15.0%) S-MAs developed new GGO components around the lesions and two (10.0%) transformed into the consolidative/pneumonic type. Compared with lesions without follow-up CT data, those with follow-up were smaller in size (11.8 vs. 14.5 mm) (P<0.05) (Table S3).

Figure 4 Serial CT and histopathology of S-MA in a 75-year-old male. (A) An initial CT image shows a lobulated solid nodule (12.4 mm, −111.0 HU) located in the right upper lobe (arrow). (B) A follow-up CT scan performed 26 months later shows that the lesion increases in size (34.3 mm) and density (−6.0 HU), and develops a vacuole (arrow). (C) After operation, it is confirmed as invasive mucinous adenocarcinoma with abundant intracellular and extracellular mucin deposition (hematoxylin and eosin stain; original magnification ×100). CT, computed tomography; HU, Hounsfield unit; S-MA, solid mucinous adenocarcinoma.

Figure 5 Comparison between initial and follow-up CT features of SS-MAs and S-MAs. * indicates the difference is significant (P<0.05), and “ns” indicates the difference is not significant (P>0.05). CT, computed tomography; HU, Hounsfield unit; S-MA, solid mucinous adenocarcinoma; SS-MA, subsolid mucinous adenocarcinoma.

Comparison of the cumulative incidence of lesion volume doubling in patients with SS-MA or S-MA

The cumulative incidence of volume doubling of SS-MA or S-MA with at least three months of follow-up is presented in Figure 6. Compared to SS-MAs, S-MAs had a significantly higher cumulative incidence of volume doubling and a shorter VDT [336.5 (254.1–401.2) vs. 470.8 (400.8–566.3) days] (each P<0.05).

Figure 6 Cumulative incidence function curves for lesion volume doubling (a 100% increase from baseline lesion volume). Compared to SS-MAs, S-MAs have a significantly higher cumulative incidence of volume doubling and a shorter volume doubling time (each P<0.05). CT, computed tomography; S-MA, solid mucinous adenocarcinoma; SS-MA, subsolid mucinous adenocarcinoma.

Interobserver agreement

Table S4 summarizes the interobserver agreement for the CT features. For continuous features, agreement was almost perfect for size (ICC =0.908), volume (ICC =0.892) and density (ICC =0.878). For categorical features, agreement was also almost perfect (κ=0.827–0.942).

Discussion

In the present study, we evaluated and compared the clinical and CT features as well as the follow-up CT changes of SS-MAs and S-MAs. Although both subtypes shared a similar distribution, various demographic characteristics and CT features differed significantly between them. During follow-up, both subtypes increased in size and density but overall maintained their respective initial CT patterns. However, S-MAs demonstrated a higher cumulative incidence of volume doubling and a shorter VDT than SS-MAs. Additionally, the solid components in SS-MAs significantly increased in size and became more well-defined, while the density of S-MAs was consistently negative in progression. These findings indicate that the SS-MA and S-MA are two distinct radiologic phenotypes, and their respective radiological changes during follow-up may provide unique information for early diagnosis.

In this study, both S-MAs and SS-MAs were mainly located in the lower lobes and subpleural zones, which is consistent with previous findings (8,30). It could be attributed to the effect of gravity on mucin (15). Clinically, females were more common in patients with SS-MAs; this characteristic have also been reported in patients with neoplastic subsolid nodules in previous studies on NMAs (31,32). This demographic similarity suggests that there may be similar pathogenic mechanisms between MAs and NMAs manifested as subsolid lesions. Additionally, compared to SS-MAs, S-MAs more commonly exhibited lobulation and spiculation as well as a higher cumulative incidence of volume doubling and a shorter VDT. This suggests that SS-MAs are relatively indolent, whereas S-MAs may be more invasive. These significant differences between SS-MAs and S-MAs in clinical and radiological characteristics indicate that they may be two distinct radiologic phenotypes.

On CT images, the SS-MAs closely mimic the SS-NMAs in morphological features. However, an ill-defined boundary was more common in the SS-MAs in our study (62.4%) than in previously reported SS-NMAs (9.4%) (25). This distinct feature of SS-MA could be attributed to the mucin extravasation (5-10). However, this manifestation is also very common in inflammatory lesions, while most of them will be quickly absorbed during follow-up. In contrast, SS-MAs will show sustainable growth with increases in size and density, while maintaining an ill-defined boundary. Additionally, the solid components significantly increased in size and became more well-defined, which may be related to the increased mucus production and higher mucin viscosity (4,33). Therefore, if a focal subsolid lesion with an ill-defined boundary exhibits the changes of SS-MA during follow-up, MA should be suspected.

Regarding the S-MAs, they exhibited features similar to those of S-NMA, such as lobulation and spiculation, and the increase in size during follow-up, which offered limited differential diagnosis value (20). In contrast, the present study found that the density of S-MAs increased but persistently measured as negative value both on the initial and follow-up images (−178 to −15 HU). This finding may be due to the higher mucin viscosity (4,33), and differs greatly from the soft-tissue density (13 to 43 HU) of S-NMA (34,35). Therefore, if solid lesions with lobulation and spiculation increase in size and density during follow-up, attention should be paid to the density values and their changes over time, which could aid in differentiating MA from NMA.

Goto et al. proposed a stepwise progression of MA: solitary type with GGO, solitary type without GGO, pneumonic type without crazy-paving appearance, and pneumonic type with crazy-paving appearance (36). However, this progression was mainly based on their observation that tumor size and CT attenuation gradually increased across this sequence. In the present study, we observed the transition from SS-MA or S-MA to consolidative/pneumonic type of MA. However, no SS-MA transformed into S-MA and only 15% of S-MAs developed new GGO around the lesions during follow-up. Therefore, there may not be an essential transformation between SS-MA and S-MA, which further confirms that they are two independent radiologic phenotypes. In conclusion, we speculate that both SS-MA and S-MA could progress to consolidative/pneumonic type MA, while there is no stepwise progression from subsolid lesions to solid ones.

Our study has several limitations. First, this was a retrospective study, which may be subject to inherent selection bias. Second, the sample size remained relatively small, as only pathologically confirmed pure MAs were included. Third, only a subset of lesions (24.2%) had follow-up CT data, and the reasons for follow-up (e.g., suspicion of benign etiology or small lesion size) may introduce selection bias, potentially limiting the generalizability of the observed evolutionary changes to all focal MAs. Fourth, follow-up CT intervals were variable, which may introduce variability in VDT estimation because short intervals may increase measurement error, particularly for lesions that grow more slowly. Fifth, the follow-up CT changes observed in this study were limited to conventional morphological features identifiable by visual inspection. Thus, deep learning should be applied to identify additional patterns invisible to the human eye (37,38). Sixth, although SS-MA and S-MA demonstrated significant differences in clinical and radiological findings that may facilitate the recognition of distinct radiologic phenotypes, these findings should be interpreted cautiously due to the substantial overlap of features between these two groups. In conclusion, our findings regarding the follow-up CT changes of these lesions require further validation in future studies.

Conclusions

SS-MA and S-MA showed similar distribution but exhibited distinct clinical and CT features as well as follow-up CT changes. In females, a patchy subsolid lesion with an ill-defined boundary should be highly suspected as SS-MA if its solid component becomes larger and more well-defined during follow-up. In contrast, in males, a solid lesion exhibiting lobulation and/or spiculation may represent S-MA if its CT attenuation, despite increasing over time, remains consistently negative. These findings are helpful for better understanding and early diagnosing focal MAs.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-0453/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-0453/dss

Funding: This work was supported by the Project of Chongqing Natural Science Foundation (No. CSTB2024NSCQ-MSX0655) and the Senior Medical Talents Program of Chongqing for Young and Middle-aged from Chongqing Health Commission (receptor: Z.G.C.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-0453/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. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study protocol was approved by the Ethics Committee of The First Affiliated Hospital of Chongqing Medical University (No. 2025-118-01, date of approval: 27.01.2025) and the Ethics Committee of The Second Affiliated Hospital of Army Medical University (No. 2022-196-01, date of approval: 13.05.2022). The requirement for written informed consent was waived due to the retrospective nature of the study.

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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