Impact of PI-RADS Score Combined with SII on Pathological Upgrading in Patients with Localized Prostate Cancer After Radical Prostatectomy

doi:10.21203/rs.3.rs-4704293/v1

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Impact of PI-RADS Score Combined with SII on Pathological Upgrading in Patients with Localized Prostate Cancer After Radical Prostatectomy

https://doi.org/10.21203/rs.3.rs-4704293/v1

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Purpose

To investigate the combined effect of PI-RADS v2.1 scores and the systemic immune-inflammation index (SII) on predicting pathological upgrading in patients with localized prostate cancer after radical prostatectomy.

Methods

The study collected various clinical indicators, imaging examination results, serum inflammation marker levels, and pathological examination results of 76 patients with localized prostate cancer who underwent prostate biopsy followed by radical prostatectomy. Patients were divided into two subgroups based on changes in their International Society of Urological Pathology (ISUP) grades before and after surgery: the pathological upgrading group and the non-upgrading group. Univariate and multivariate logistic regression analyses were used to assess independent risk factors for pathological upgrading. The effectiveness of single indicators, imaging inflammation indicators, traditional clinical pathology indicators, and multi-indicator combined prediction models in predicting pathological upgrading was evaluated using receiver operating characteristic (ROC) curves.

Results

Univariate and multivariate logistic regression analyses revealed that PI-RADS scores, SII, percentage of free PSA (%PSA), and the proportion of tumor tissue in biopsy samples were independent predictors of ISUP grade upgrading after radical prostatectomy. The area under the curve (AUC) values for single indicators PI-RADS, SII, %PSA, and biopsy tumor tissue proportion were 0.607, 0.711, 0.618, and 0.778, respectively. The AUC for the model combining imaging inflammation indicators (PI-RADS and SII) was 0.914.

Conclusions

The PI-RADS v2.1 score and SII are valuable in predicting pathological upgrading after radical prostatectomy in prostate cancer patients. Combining these indicators with traditional clinical pathology indicators significantly improves predictive performance.

Biological sciences/Cancer/Cancer models

Biological sciences/Cancer/Urological cancer

Prostate cancer

PI-RADS score

SII

Pathological escalation

Prediction model

Prostate cancer (PCa) is the second most common malignancy among men worldwide and ranks fifth in cancer-related mortality ^[1]. The gold standard for diagnosing PCa is prostate biopsy, but its Gleason score (GS) often has sampling errors, leading to discrepancies with postoperative GS, which can affect treatment planning and patient prognosis ^[2]. If a biopsy underestimates the malignancy of the tumor, patients may be misdirected to active surveillance instead of radical surgery, potentially missing the optimal treatment window ^[3]. The Prostate Imaging Reporting and Data System (PI-RADS) grades and scores prostate images by combining T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCE) from prostate MRI, providing a semi-quantitative diagnostic standard. PI-RADS scoring has played a significant role in distinguishing prostate cancer and clinically significant prostate cancer ^[4], but its significance in predicting ISUP upgrading after radical prostatectomy remains to be evaluated.

Recent studies have shown that neutrophils, platelets, and lymphocytes in the tumor microenvironment play crucial roles in tumor initiation and progression. Blood inflammation markers such as the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have been proven to be closely related to treatment outcomes, prognosis, pathological features, and biochemical recurrence in PCa patients ^{[5, 6]}. The systemic immune-inflammation index (SII), a novel inflammation marker based on the ratio of neutrophils, platelets, and lymphocytes, has recently shown significant importance in predicting the occurrence and development of various tumors ^[7]. However, research on its association with pathological upgrading in prostate cancer is still limited. Therefore, this study aims to evaluate the impact of combining PI-RADS scores with inflammatory serum markers on pathological upgrading in prostate cancer post-surgery and to explore the predictive performance of adding imaging inflammation indicators to traditional clinical pathology models.

General Data

This study retrospectively analyzed the clinicopathological data of 76 patients with localized prostate cancer who underwent prostate biopsy and subsequent radical prostatectomy at XX University from September 2019 to May 2024. Inclusion criteria were: (1) histopathological results from targeted prostate biopsy and radical prostatectomy; (2) postoperative pathological diagnosis of prostate cancer; (3) preoperative multiparametric MRI (mpMRI) scan; (4) no signs of metastasis on preoperative enhanced CT and bone scans. Exclusion criteria were: (1) preoperative prostate surgery, radiotherapy, or endocrine therapy; (2) MRI scans not meeting PI-RADS V2.1 standards; (3) poor MRI image quality with artifacts; (4) incomplete clinical data. Patients were divided into non-upgrading and upgrading groups based on pre- and post-surgery ISUP grades. Upgrading was defined as: (1) postoperative ISUP grade higher than biopsy ISUP grade; (2) biopsy pathology showing benign prostate tissue but postoperative diagnosis confirming prostate cancer. The study was approved by the Ethics Review Committee of the Second Affiliated Hospital of Zhengzhou University and informed consent was obtained from all participants and their legal guardians. Studies involving human research participants are fully compliant with the provisions of the Declaration of Helsinki.

MRI Examination and PI-RADS v2.1 Scoring

MRI scans were performed using a Philips Achieva 781 3.0T MRI scanner. Basic scan sequences included T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCE). Two experienced radiologists, blinded to the pathological results, independently scored the prostate MRI according to the latest PI-RADS v2.1 assessment criteria. Discrepancies were resolved through discussion. Based on the PI-RADS scores, patients were categorized into low-score (score ≤ 3) and high-score (score > 3) groups.

Study Indicators

All patients in this study underwent laparoscopic or robot-assisted radical prostatectomy. Collected clinicopathological information included patient age, height, weight, body mass index (BMI), prostate volume (PV), free prostate-specific antigen (fPSA) level, total prostate-specific antigen (tPSA) level, percentage of free PSA (%PSA), and prostate-specific antigen density (PSAD). Imaging characteristics included PI-RADS scores. Serum inflammation markers included neutrophil count, lymphocyte count, platelet count, monocyte count, eosinophil count, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), eosinophil-to-lymphocyte ratio (ELR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), red blood cell distribution width (RDW), and platelet distribution width (PDW). Pathological indicators included the percentage of positive biopsy cores, proportion of tumor tissue in biopsy length, biopsy Gleason score and ISUP grade, and postoperative Gleason score and ISUP grade. BMI was calculated as weight (kg) / height (m)², PV as 0.523 × (length × height × width), %PSA as fPSA / tPSA, SII as neutrophil count × platelet count / lymphocyte count, and SIRI as neutrophil count × monocyte count / lymphocyte count.

Data Analysis

SPSS 25.0 was used for data analysis. For continuous variables, if normally distributed, data were presented as mean ± standard deviation (x¯±s) and compared using independent sample t-tests. If not normally distributed, data were presented as median (interquartile range) [M (P25, P75)] and compared using rank-sum tests. Categorical variables were presented as percentages (%) and compared using χ² tests. Univariate logistic regression analysis was used to assess differences in baseline characteristics between the pathological upgrading and non-upgrading groups. Variables with statistical significance were further included in multivariate logistic regression models to determine independent predictors of postoperative pathological grade upgrading. The predictive performance of the model for diagnosing pathological upgrading was evaluated using receiver operating characteristic (ROC) curves. A significance level of P < 0.05 was set for statistical differences.

General Clinical Data

A total of 76 patients were included in this study. Based on pre- and post-surgery ISUP grades, patients were divided into the non-upgrading group (46 cases) and the upgrading group (30 cases), with an ISUP upgrading rate of 39.5%. There were no statistically significant differences between the two groups in terms of age, height, weight, BMI, and prostate volume (all P > 0.05, Table 1). A Sankey diagram showing changes in biopsy pathology grades and postoperative pathology grades is presented in Fig. 1.

Table 1

Comparison of General Clinical Data Between Non-Upgrading and Upgrading Groups
Variables	Non-upgraded group (n = 46)	Upgraded group (n = 30)	P
Age (years)	68(65, 72)	66(62.75, 70)	0.087
Height (m)	1.7(1.68, 1.73)	1.7(1.68, 1.71)	0.153
Weight (kg)	71.0 ± 8.9	71.7 ± 6.8	0.727
BMI (kg/m²)	24.3 ± 2.8	25.1 ± 2.2	0.215
Prostate volume (ml)	40.9(28.6, 61.2)	47.9(30.9, 57.6)	0.702
fPSA (ng/ml)	2.07(0.95, 3.67)	2.42 (1.31, 4.19)	0.451
tPSA (ng/ml)	14.42(6.99, 28.80)	21(9.65, 34.78)	0.154
%PSA	0.14(0.11, 0.17)	0.13(0.09, 0.15)	0.083
PSAD (ng/ml²)	0.27(0.13, 0.79)	0.45(0.29, 0.83)	0.118
Neutrophil count (10^9/L)	3.45 (2.59, 4.18)	3.58(3.22, 4.38)	0.147
Platelet count (10^9/L)	179.8 ± 53.2	201.0 ± 55.0	0.097
Lymphocyte count (10^9/L)	1.8 ± 0.4	1.7 ± 0.4	0.348
Monocyte count (10^9/L)	0.36(0.3, 0.46)	0.29(0.26, 0.39)	0.071
Eosinophil count (10^9/L)	0.14(0.08, 0.19)	0.1(0.06, 0.29)	0.928
NLR	1.90(1.39, 2.37)	2.07 (1.74, 2.69)	0.053
PLR	95.24(79.30, 116.42)	118.18 (93.45, 139.08)	0.021
LMR	4.94(3.63, 6.80)	6.11 (3.59, 7.03)	0.355
ELR	0.06 (0.05, 0.11)	0.07(0.03, 0.17)	0.588
SII	338.8(217.2, 407.8)	394.8(331, 513.6)	0.002
SIRI	0.78(0.44, 1.01)	0.64 (0.46, 0.91)	0.89
RDW (%)	13(12.9, 13.2)	13(12.7, 13)	0.099
PDW (fL)	15.9(14.3, 16.3)	16(13.6, 16.5)	0.84
Percentage of positive biopsy cores (%)	43.8(21, 92.1)	35. 9(12.6, 51.8)	0.045
Percentage of biopsy tumor tissue (%)	19.3(9.1, 58.4)	6.9(1.3, 20.1)	< 0.001
Biopsy pathology grade			0.006
<3	13(28.3)	18(60)
≥ 3	33(71.7)	12(40)
Surgical pathology grade			0.948
<3	15(32.6)	10(33)
≥ 3	31(67.4)	20(67)
PI-RADS score			0.015
≤ 3	18(39.1)	4(13.3)
>3	28(60.9)	26(86.7)

Logistic Regression

Univariate logistic regression analysis revealed that the upgrading group had higher NLR (P = 0.022), SII (P = 0.008), and PI-RADS scores (P = 0.045), but lower %PSA (P = 0.034), percentage of positive biopsy cores (P = 0.041), proportion of tumor tissue in biopsy length (P = 0.002), and biopsy pathology grades (P = 0.007) compared to the non-upgrading group. These statistically significant variables were included in the multivariate logistic regression analysis, which identified PI-RADS scores (P = 0.005), SII (P = 0.028), %PSA (P = 0.008), and the proportion of tumor tissue in biopsy length (P = 0.004) as independent factors influencing postoperative pathological upgrading (Table 2). Higher PI-RADS v2.1 scores (OR = 17.11, 95% CI 2.39-122.59) and higher SII (OR = 1.009, 95% CI 1.001–1.016) were associated with a greater probability of postoperative pathological upgrading.

Table 2

Univariate and Multivariate Logistic Regression Results of Various Indicators
Variables	Univariate model		Multivariate model
Variables	OR(95%CI)	P	OR(95%CI)	P
Age	0.959(0.890–1.035)	0.283
BMI	1.126(0.934–1.357)	0.214
Prostate volume	0.993(0.980–1.007)	0.344
tPSA	1.014(0.989–1.041)	0.275
%PSA	0.002(0.001–0.371)	0.034	0.003(0.002–0.004)	0.008
PSAD	1.177(0.558–2.481)	0.669
NLR	2.121(1.115–4.033)	0.022	1.960(0.449–8.552)	0.370
PLR	1.013(1.000-1.026)	0.055
LMR	0.992(0.849–1.158)	0.916
SII	1.006(1.001–1.010)	0.008	1.009(1.001–1.016)	0.028
SIRI	1.582(0.779–3.212)	0.204
PI-RADS score	3.467(1.029–11.684)	0.045	17.111(2.388-122.592)	0.005
Percentage of positive biopsy cores	0.984(0.969–0.999)	0.041	1.027(0.985–1.071)	0.208
Percentage of biopsy tumor tissue	0.955(0.928–0.983)	0.002	0.899(0.837–0.966)	0.004
Biopsy pathology ISUP grade	0.263(0.099–0.694)	0.007	0.660(0.106–4.121)	0.657

Predictive Performance of Various Indicators

As shown in the figure, the ROC curve analysis indicates that when diagnosing with single indicators, the AUC for PI-RADS score, SII, %PSA, and the proportion of tumor tissue in biopsy length are 0.607, 0.711, 0.618, and 0.778, respectively. The AUC for the novel imaging indicator PI-RADS score combined with the inflammation indicator SII is 0.769. The AUC for traditional clinical pathology indicators (%PSA combined with the proportion of tumor tissue in biopsy length) is 0.791. When imaging inflammation indicators (PI-RADS score and SII) are added to this model, the AUC increases to 0.914 (Table 3, Fig. 2).

Table 3

AUC of Various Schemes for Diagnosing Postoperative Pathological Upgrading
Variables	AUC	Cutoff Value	Sensitivity/%	Specificity/%
PI-RADS Score	0.607	0.5	86.7	34.8
SII	0.711	318.44	86.7	45.7
%PSA	0.618	0.099	33.3	91.3
Biopsy Tumor Tissue Proportion	0.778	8.15	60	84.8
New indicators	0.769	0.37	76.7	69.6
Traditional indicators	0.791	0.19	96.7	50
Combined indicators	0.914	0.691	80	89.1

The diagnosis and pathological staging of prostate cancer primarily rely on prostate biopsy. However, biopsy only samples limited tissue, and prostate cancer often exhibits multifocality and heterogeneity, leading to discrepancies between biopsy results and actual postoperative pathology. A study ^[8] reported that the proportion of ISUP grade ≥ 2 at biopsy was 37.3%, but this increased to 71.8% postoperatively. Biopsy pathology grading is crucial for deciding between active surveillance and radical surgery. Studies have shown that patients with pathological upgrading postoperatively have shorter biochemical recurrence times ^[9]. Therefore, accurately assessing the Gleason score preoperatively is critical for formulating individualized treatment plans and predicting prognosis. Our study found that the rate of ISUP upgrading postoperatively in patients with localized prostate cancer undergoing radical surgery was 39.5%, consistent with domestic and international research.

PI-RADS v2.1, based on multiparametric MRI, scores prostate cancer by evaluating lesion performance on T2WI, DWI, and DCE sequences to estimate the probability of clinically significant prostate cancer. Multiple studies ^[10] have demonstrated that PI-RADS v2.1 has high efficacy in diagnosing prostate cancer and predicting the Gleason score. One study ^[11] found that for patients with a biopsy Gleason score of 3 + 3, the pathological upgrading rate after radical prostatectomy was 38.1% when the PI-RADS score was < 3, but increased to 85.7% when the PI-RADS score was > 3. This suggests that patients with a low Gleason score but a high PI-RADS score may need further biopsy to reassess risk. Our multivariate logistic regression analysis also showed that a PI-RADS score > 3 is an independent risk factor for postoperative pathological upgrading (OR = 17.111, 95% CI 2.388-122.592, P = 0.005). This should be included in preoperative risk assessments to guide clinical decision-making.

The systemic immune-inflammation index (SII) is an immunoinflammatory marker based on neutrophil, platelet, and lymphocyte counts. Elevated SII levels indicate a high and uncontrolled tumor-associated inflammatory state. Previous studies have shown that SII is closely associated with the prognosis of various cancers such as renal, bladder, and prostate cancers. Rajwa et al. ^[12] demonstrated that elevated preoperative SII is significantly associated with adverse pathological features and biochemical recurrence in patients with localized prostate cancer undergoing radical prostatectomy (RP). However, its role in pathological upgrading after prostate cancer surgery remains unclear. Our study found that a high preoperative SII level is significantly associated with pathological upgrading post-radical prostatectomy (OR = 1.009, 95% CI 1.001–1.016, P = 0.028).

Prostate-specific antigen (PSA) is widely used for screening, assessing disease severity, and evaluating prognosis in prostate cancer due to its low cost and ease of use. However, PSA lacks specificity for prostate cancer and is influenced by various factors. Therefore, we introduced %PSA, combining free PSA and total PSA levels for a comprehensive evaluation. Studies have shown that %PSA is a protective factor against postoperative Gleason score upgrading, with a critical value of 0.16 ^[13]. We found that when %PSA is below 0.099, the risk of pathological upgrading post-surgery significantly increases. The prostate health index (PHI), which combines tPSA, fPSA, and p2PSA, has shown greater advantages in predicting the occurrence and progression of prostate cancer ^[14]. Due to data limitations in this study, we could not analyze PHI, which may be an important direction for future research.

Biopsy pathology results significantly impact postoperative pathological upgrading. Leeman et al. ^[15] indicated that the number of positive biopsy cores and the proportion of tumor tissue are independent predictors of pathological upgrading in patients with ISUP1 from biopsy. In our study, the proportion of biopsy tumor tissue was defined as the proportion of tumor tissue obtained from the biopsy relative to the total tissue. When this proportion was below 8.15%, the risk of pathological upgrading post-surgery significantly increased. This may be due to the multifocal nature of prostate cancer leading to sampling errors and missing critical tumor tissue. With advancements in medical imaging technology, the accuracy of tumor lesion biopsies will continue to improve, reducing sampling errors.

Our multivariate logistic regression analysis found that imaging indicators PI-RADS v2.1 score, inflammatory marker SII, clinical pathological marker %PSA, and biopsy tumor tissue proportion are risk factors for pathological upgrading post-radical prostatectomy in patients with localized prostate cancer (AUCs were 0.607, 0.711, 0.618, and 0.778, respectively). The AUC for traditional clinical pathological indicators (%PSA combined with biopsy tumor tissue proportion) was 0.791. After incorporating new imaging and inflammatory markers (PI-RADS score, SII), the combined model's predictive ability significantly improved, with an AUC reaching 0.914. This indicates that PI-RADS score and SII have significant predictive value for pathological upgrading in prostate cancer, providing strong references for treatment decisions and showing clinical application prospects.

This study has certain limitations. First, as a retrospective study, data collection has inherent limitations that may lead to selection bias; second, the high proportion of high Gleason score (GS) patients in our hospital may cause potential evaluation bias; finally, this is a single-center study with a limited number of cases, and large-scale, multicenter, prospective studies are needed in the future for further validation. In summary, imaging indicator PI-RADS score and inflammatory marker SII play a role in predicting pathological upgrading post-radical prostatectomy in prostate cancer patients. Diagnostic models combining these new indicators have better predictive performance than traditional clinical pathological indicators.

Author Contribution

Huang Zhiheng is responsible for data collection, data statistical analysis, paper writing and mapping; Wei Haiyang is responsible for data statistical analysis; Zhang Tianhe is responsible for data collation; Xu Changbao was in charge of paper review; Fan Changhui is responsible for research guidance and financial support

Acknowledgement

We thank our mentors and colleagues for their guidance and support during the writing of this paper.

Data Availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Statements and Declarations：The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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You are reading this latest preprint version

Impact of PI-RADS Score Combined with SII on Pathological Upgrading in Patients with Localized Prostate Cancer After Radical Prostatectomy

Status:

Version 1

Abstract

Purpose

Methods

Results

Conclusions

Figures

Introduction

Materials and Methods

General Data

MRI Examination and PI-RADS v2.1 Scoring

Study Indicators

Data Analysis

Results

General Clinical Data

Logistic Regression

Predictive Performance of Various Indicators

Discussion

Declarations

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Status:

Version 1