Personalized optimization of systematic prostate biopsy core number based on mpMRI radiomics features: a large-sample retrospective analysis
Prostate cancer (PCa) is one of the most prevalent malignancies worldwide, with high morbidity rates . Currently, PCa is definitively diagnosed by systematic prostate biopsy (SBx) with 13 cores. This method, however, can also increase the risks of urinary retention, infection and bleeding due to the excessive number of biopsy cores . Methods that can reduce the number of SBx cores and minimize biopsy-related complications while ensuring accurate detection of PCa are therefore needed. Multiparametric magnetic resonance imaging (mpMRI) is an indispensable diagnostic tool for PCa, due to its non-invasive imaging capabilities . Studies have assessed methods by which mpMRI can enhance optimization of SBx. For example, a study comparing the dimensions of a suspected PCa lesion on mpMRI and its actual size found that the actual diameters of the lesions were three times greater than the diameters measured on mpMRI images . SBx biopsy sites closer to lesions may be associated with higher rates of PCa detection . Moreover, the rate of tumor detection was higher when biopsy sites were located within 10 mm of lesions visualized on mpMRI than when biopsy sites were located outside this radius . Efforts are therefore needed to reduce the numbers of SBx cores outside lesions detected by mpMRI, while maintaining or increasing the rate of PCa detection. To date, however, an optimal protocol for reducing the number of SBx cores has not been definitively established. Advances in radiomics and its widespread clinical application have made it possible to better explore the connection between imaging data and tumor biology through the evaluation of multimodal imaging features and model building . Previous studies have primarily focused on enhancing the protocol for SBx by utilizing characteristics observed on MRI, such as diameter, volume and Prostate Imaging - Reporting and Data System (PI-RADS) scores . To date, however, the radiomics features of mpMRI that can also depict the characteristics of cancer lesions, such as energy, entropy and contrast, have not been utilized to optimize SBx. The high-throughput radiomics features of SBx obtained from multiple modalities on mpMRI, such as T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) and diffusion-weighted imaging (DWI) sequences, may help determine the feasibility of reducing the number of cores required for the diagnosis of PCa. The feasibility of personalized reduction in the number of SBx cores was analyzed using a radiomics approach to evaluate high-throughput features. A Prostate Cancer Risk Score (PCS) model was developed that integrated clinical traits with radiomics features on mpMRI. This model can reduce the number of biopsy cores while maintaining a high rate of PCa detection, and may serve as a method in clinical practice to reduce the risk of biopsy-related complications and improve the patient’s biopsy experience. 

Participants This retrospective study included 480 patients who underwent transrectal prostate biopsy with a 13-core protocol at Fujian Union Hospital between January 2014 and June 2022. The validation set consisted of 101 patients who underwent a transrectal prostate biopsy with a 13-core protocol at Changhai Hospital from June 2021 to June 2022.The process of patient inclusion and study procedures are shown in Fig. . The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of Fujian Union Hospital of Fujian Medical University (protocol code: 2023KY095). Clinical and pathological data Clinical data were collected, including patient age; the results of total prostate-specific antigen (TPSA); imaging data (including PI-RADS v2.1 score derived from prostate mpMRI); and the pathological results of each prostate biopsy core (Tissues from different biopsy sites were placed separately in specimen bottles, and each bottle was labeled with the corresponding anatomic position. The tissues in the bottles were then fixed with 10% formalin.). A Gleason score ≥ 3 + 4 was defined as clinically significant prostate cancer (csPCa). The detailed Gleason score model can be found in the 2014 International Society of Urology and Pathology (ISUP) Gleason Grading Consensus for Prostate Cancer . Prostate biopsy method All prostate biopsy procedures were performed by two urologists, each with over 5 years of professional experience. After completing preoperative examinations, if patients used aspirin, warfarin, and other anticoagulants, they were requested to stop using the drugs for 1 week. On the day of the procedure, patients began to take quinolone antibiotics (levofloxacin). Patients without surgical contraindications underwent preoperative bowel cleansing and were placed in the left lateral decubitus position. The perianal area was disinfected with 0.5% iodine solution. A transrectal ultrasound probe was inserted through the anus. Patients at Fujian Union Hospital and Changhai Hospital underwent 13-core systematic biopsy at the positions shown in Fig. . The biopsy process was performed under local anesthesia. The prostate biopsy tissue specimens were evaluated and interpreted by a pathologist with more than 10 years of experience. MRI equipment and scan parameters MRI was performed using a 3-Tesla MRI system (MAGNETOM Skyra, Siemens Healthcare, Erlangen, Germany) and a standard 18-channel phased-array body coil. Digital Imaging and Communications in Medicine (DICOM) data obtained on mpMRI included T2WI, ADC and DWI. The scanning parameters are detailed in Table S1. Extraction of radiomics features and screening Image acquisition and pre-processing Because the mpMRI data were obtained at two different centers in DICOM format, the data were preprocessed using various image post-processing techniques to minimize the effects of different MRI systems on imaging parameters. Imaging data were preprocessed to reduce noise and processing artifacts, as well as to avoid excessive upsampling of images. To standardize the voxel spacing and ensure isotropy, the imaging data from both centers were converted to a uniform voxel size of 2 mm × 2 mm × 2 mm. Inhomogeneity of the magnetic field in MRI can lead to formation of a bias field through signal intensity deviation in certain areas of the original image. Variations in the bias field across an image can therefore affect image feature extraction and analysis. To mitigate the influence of the bias field, the “N4ITK” algorithm was utilized for T2WI, ADC, and DWI to rectify the inhomogeneity of image intensity caused by magnetic field inhomogeneity during MRI. Outline of the volume of interest (VOI) The preprocessed mpMRI images of the patients were imported into 3D Slicer software (version 4.11.20210226). Two experienced clinicians jointly assessed the quality of the preprocessed MRI images, identified suspicious lesions and determined their boundaries. Any discrepancies were resolved through discussion until consensus was reached. After the location and boundaries of each lesion were identified, Regions of Interest (ROI) were delineated on the T2WI, ADC, and DWI sequences using 3D Slicer software. ROI reconstructions were fused to form VOIs, which were exported and applied to subsequent extraction of radiomics features (Fig. A). Feature extraction The radiomics features of each VOI were extracted using the PyRadiomics package (version3.0.1 https://pypi.org/project/pyradiomics/ ) in Python (version3.7.11). Comprehensive sets of morphological characteristics (42 features), first-order statistical properties (1080 features), and texture attributes (4380 features) were obtained (Fig. B). The latter included an array of specific feature sets, such as measurements of grey-level co-occurrence matrices (GLCM) (1320 features), grey-level dependence matrices (GLDM) (840 features), grey-level run length matrices (GLRLM) (960 features), grey-level size zone matrices (GLSZM) (960 features), and neighborhood gray-tone difference matrices (NGTDM) (300 features). A total of 5502 features were extracted from the T2WI, ADC, and DWI sequences in prostate mpMRI. All of these radiomics features were in agreement with the definitions outlined by the Image Biomarker Standardization Initiative. Screening of radiomics features The data on radiomics features were standardized using Z-scores, and the intraclass correlation coefficients (ICC) were calculated to assess the within-group and between group consistency of feature data delineated by two clinicians. Stable features with ICCs > 0.75 were selected for univariate analysis. Normally distributed features were evaluated by t-tests, whereas non-normally distributed features were analyzed by Mann-Whitney U-tests. The pathological results of prostate biopsy were utilized as the reference standard for preliminary screening of radiomics features, with statistical significance defined as p < 0.05. After eliminating features lacking statistical significance, redundant features were identified by Pearson correlation analysis; if the correlation between any two feature was > 0.2, only one was retained. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was employed for further reduction of dimensionality and feature selection. The regularization parameter (λ) utilized in feature selection was optimized by 10-fold cross-validation to meet the minimum standard. The imaging feature selection and screening process is illustrated in Fig. C. The radiomics (rad) score consisted of 15 radiomics features (Supplementary Table 2) with non-zero coefficients, as determined by the formula in Fig. D and Supplementary Formula 1. Simulated process to reduce the number of cores To determine the optimal numbers and locations of biopsy cores, the present study simulated personalized systematic prostate biopsy core reduction procedures on patients with suspicious PCa. Two urologists, each with over 5 years of experience, simulated the process of personalized SBx core reduction. Studies have reported that positivity rates on prostate biopsy were not only associated with the number of biopsy cores evaluated, but also with the proximity of the biopsy sites to the lesions on mpMRI . The simulated process of reducing the number of cores in this study was based on the aforementioned background. The first urologist described above determined the location of visible lesions on the T2WI, ADC and DWI sequences of each patient’s mpMRI, and measured the distance between the center of the lesion and each biopsy core. The simulated process of reducing the number of cores started with the core farthest from the lesion and continued sequentially until reaching the core closest to the lesion. If there were two or more suspicious lesions, this urologist would initially determine the most suspicious lesion location independently based on clinical experience and then proceed to reduce the number of cores. The second urologist documented the negative and positive outcomes of simulated biopsies based on the numbers of biopsy cores. For example, Fig. illustrates the simulated procedure of reducing the number of cores for a patient who underwent a 13-core SBx at Fujian Union Hospital, with the lesion situated in the right peripheral zone of the prostate. Cores reduction model based on PCS The Prostate Cancer Risk Score (PCS) used in this study was a composite parameter that incorporated clinical features and rad-score, with the PCS for each patient calculated based on the regression coefficients of each indicator. Subsequently, the study subjects were divided into five subgroups based on their percentile ranks in PCS distribution: 0–20%, 20–40%, 40–60%, 60–80%, and 80–100%. The optimal number of SBx cores predicted for each PCS quintile was calculated. The sensitivity, negative predictive value, accuracy, area under the curve (AUC), 95% confidence interval (CI), and corresponding p -value were determined for each biopsy core, from core 1 to core 13. These findings were compared with the results of 13-core systematic prostate biopsy to predict the optimal number of SBx cores for each PCS subgroup. If both the upper and lower limits of the 95% CI exceeded 0.9, it was regarded as a criterion for predicting the optimal number of SBx cores for patients in each PCS subgroup. This criterion enabled prediction of the number of SBx cores that could be reduced for patients in each PCS subgroup. External validation The PCS model constructed from data of Fujian Union Hospital were externally validated in 101 patients ( n = 101) diagnosed with PCa at Changhai Hospital. PCS was calculated for these 101 patients based on PCS formula. Patients diagnosed at Changhai Hospital also were divided into five subgroups based on the same criteria. The simulated pathological results of reducing the core numbers in the external validation set were compared with the actual SBx pathological results. Then we calculated the optimized core numbers, accuracy of optimized SBx for predicting PCa and the leakage rate of PCa or csPCa in validation set. Statistical methods Normally distributed quantitative variables were reported as the mean ± standard deviation (SD) and compared by independent sample t-tests. Non-normally distributed quantitative variables were reported as the median (upper/lower quartiles) and compared by Mann-Whitney U-tests. Categorical variables were reported as frequency (percentage) and compared by chi-squared tests. Logistic regression was used to calculate the PCS model. Statistical analyses were performed and graphs were drawn using Python (version 3.7.11, Python Software Foundation, Wilmington, Delaware, USA. https://www.python.org/ ), R (version 4.1.2, R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ .), and SPSS Statistics (IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp) software, with p < 0.05 defined as statistically significant. 

This retrospective study included 480 patients who underwent transrectal prostate biopsy with a 13-core protocol at Fujian Union Hospital between January 2014 and June 2022. The validation set consisted of 101 patients who underwent a transrectal prostate biopsy with a 13-core protocol at Changhai Hospital from June 2021 to June 2022.The process of patient inclusion and study procedures are shown in Fig. . The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of Fujian Union Hospital of Fujian Medical University (protocol code: 2023KY095). 

Clinical data were collected, including patient age; the results of total prostate-specific antigen (TPSA); imaging data (including PI-RADS v2.1 score derived from prostate mpMRI); and the pathological results of each prostate biopsy core (Tissues from different biopsy sites were placed separately in specimen bottles, and each bottle was labeled with the corresponding anatomic position. The tissues in the bottles were then fixed with 10% formalin.). A Gleason score ≥ 3 + 4 was defined as clinically significant prostate cancer (csPCa). The detailed Gleason score model can be found in the 2014 International Society of Urology and Pathology (ISUP) Gleason Grading Consensus for Prostate Cancer . 

All prostate biopsy procedures were performed by two urologists, each with over 5 years of professional experience. After completing preoperative examinations, if patients used aspirin, warfarin, and other anticoagulants, they were requested to stop using the drugs for 1 week. On the day of the procedure, patients began to take quinolone antibiotics (levofloxacin). Patients without surgical contraindications underwent preoperative bowel cleansing and were placed in the left lateral decubitus position. The perianal area was disinfected with 0.5% iodine solution. A transrectal ultrasound probe was inserted through the anus. Patients at Fujian Union Hospital and Changhai Hospital underwent 13-core systematic biopsy at the positions shown in Fig. . The biopsy process was performed under local anesthesia. The prostate biopsy tissue specimens were evaluated and interpreted by a pathologist with more than 10 years of experience. 

MRI was performed using a 3-Tesla MRI system (MAGNETOM Skyra, Siemens Healthcare, Erlangen, Germany) and a standard 18-channel phased-array body coil. Digital Imaging and Communications in Medicine (DICOM) data obtained on mpMRI included T2WI, ADC and DWI. The scanning parameters are detailed in Table S1. 

Image acquisition and pre-processing Because the mpMRI data were obtained at two different centers in DICOM format, the data were preprocessed using various image post-processing techniques to minimize the effects of different MRI systems on imaging parameters. Imaging data were preprocessed to reduce noise and processing artifacts, as well as to avoid excessive upsampling of images. To standardize the voxel spacing and ensure isotropy, the imaging data from both centers were converted to a uniform voxel size of 2 mm × 2 mm × 2 mm. Inhomogeneity of the magnetic field in MRI can lead to formation of a bias field through signal intensity deviation in certain areas of the original image. Variations in the bias field across an image can therefore affect image feature extraction and analysis. To mitigate the influence of the bias field, the “N4ITK” algorithm was utilized for T2WI, ADC, and DWI to rectify the inhomogeneity of image intensity caused by magnetic field inhomogeneity during MRI. Outline of the volume of interest (VOI) The preprocessed mpMRI images of the patients were imported into 3D Slicer software (version 4.11.20210226). Two experienced clinicians jointly assessed the quality of the preprocessed MRI images, identified suspicious lesions and determined their boundaries. Any discrepancies were resolved through discussion until consensus was reached. After the location and boundaries of each lesion were identified, Regions of Interest (ROI) were delineated on the T2WI, ADC, and DWI sequences using 3D Slicer software. ROI reconstructions were fused to form VOIs, which were exported and applied to subsequent extraction of radiomics features (Fig. A). Feature extraction The radiomics features of each VOI were extracted using the PyRadiomics package (version3.0.1 https://pypi.org/project/pyradiomics/ ) in Python (version3.7.11). Comprehensive sets of morphological characteristics (42 features), first-order statistical properties (1080 features), and texture attributes (4380 features) were obtained (Fig. B). The latter included an array of specific feature sets, such as measurements of grey-level co-occurrence matrices (GLCM) (1320 features), grey-level dependence matrices (GLDM) (840 features), grey-level run length matrices (GLRLM) (960 features), grey-level size zone matrices (GLSZM) (960 features), and neighborhood gray-tone difference matrices (NGTDM) (300 features). A total of 5502 features were extracted from the T2WI, ADC, and DWI sequences in prostate mpMRI. All of these radiomics features were in agreement with the definitions outlined by the Image Biomarker Standardization Initiative. Screening of radiomics features The data on radiomics features were standardized using Z-scores, and the intraclass correlation coefficients (ICC) were calculated to assess the within-group and between group consistency of feature data delineated by two clinicians. Stable features with ICCs > 0.75 were selected for univariate analysis. Normally distributed features were evaluated by t-tests, whereas non-normally distributed features were analyzed by Mann-Whitney U-tests. The pathological results of prostate biopsy were utilized as the reference standard for preliminary screening of radiomics features, with statistical significance defined as p < 0.05. After eliminating features lacking statistical significance, redundant features were identified by Pearson correlation analysis; if the correlation between any two feature was > 0.2, only one was retained. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was employed for further reduction of dimensionality and feature selection. The regularization parameter (λ) utilized in feature selection was optimized by 10-fold cross-validation to meet the minimum standard. The imaging feature selection and screening process is illustrated in Fig. C. The radiomics (rad) score consisted of 15 radiomics features (Supplementary Table 2) with non-zero coefficients, as determined by the formula in Fig. D and Supplementary Formula 1. 

Because the mpMRI data were obtained at two different centers in DICOM format, the data were preprocessed using various image post-processing techniques to minimize the effects of different MRI systems on imaging parameters. Imaging data were preprocessed to reduce noise and processing artifacts, as well as to avoid excessive upsampling of images. To standardize the voxel spacing and ensure isotropy, the imaging data from both centers were converted to a uniform voxel size of 2 mm × 2 mm × 2 mm. Inhomogeneity of the magnetic field in MRI can lead to formation of a bias field through signal intensity deviation in certain areas of the original image. Variations in the bias field across an image can therefore affect image feature extraction and analysis. To mitigate the influence of the bias field, the “N4ITK” algorithm was utilized for T2WI, ADC, and DWI to rectify the inhomogeneity of image intensity caused by magnetic field inhomogeneity during MRI. 

The preprocessed mpMRI images of the patients were imported into 3D Slicer software (version 4.11.20210226). Two experienced clinicians jointly assessed the quality of the preprocessed MRI images, identified suspicious lesions and determined their boundaries. Any discrepancies were resolved through discussion until consensus was reached. After the location and boundaries of each lesion were identified, Regions of Interest (ROI) were delineated on the T2WI, ADC, and DWI sequences using 3D Slicer software. ROI reconstructions were fused to form VOIs, which were exported and applied to subsequent extraction of radiomics features (Fig. A). 

The radiomics features of each VOI were extracted using the PyRadiomics package (version3.0.1 https://pypi.org/project/pyradiomics/ ) in Python (version3.7.11). Comprehensive sets of morphological characteristics (42 features), first-order statistical properties (1080 features), and texture attributes (4380 features) were obtained (Fig. B). The latter included an array of specific feature sets, such as measurements of grey-level co-occurrence matrices (GLCM) (1320 features), grey-level dependence matrices (GLDM) (840 features), grey-level run length matrices (GLRLM) (960 features), grey-level size zone matrices (GLSZM) (960 features), and neighborhood gray-tone difference matrices (NGTDM) (300 features). A total of 5502 features were extracted from the T2WI, ADC, and DWI sequences in prostate mpMRI. All of these radiomics features were in agreement with the definitions outlined by the Image Biomarker Standardization Initiative. 

The data on radiomics features were standardized using Z-scores, and the intraclass correlation coefficients (ICC) were calculated to assess the within-group and between group consistency of feature data delineated by two clinicians. Stable features with ICCs > 0.75 were selected for univariate analysis. Normally distributed features were evaluated by t-tests, whereas non-normally distributed features were analyzed by Mann-Whitney U-tests. The pathological results of prostate biopsy were utilized as the reference standard for preliminary screening of radiomics features, with statistical significance defined as p < 0.05. After eliminating features lacking statistical significance, redundant features were identified by Pearson correlation analysis; if the correlation between any two feature was > 0.2, only one was retained. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was employed for further reduction of dimensionality and feature selection. The regularization parameter (λ) utilized in feature selection was optimized by 10-fold cross-validation to meet the minimum standard. The imaging feature selection and screening process is illustrated in Fig. C. The radiomics (rad) score consisted of 15 radiomics features (Supplementary Table 2) with non-zero coefficients, as determined by the formula in Fig. D and Supplementary Formula 1. 

To determine the optimal numbers and locations of biopsy cores, the present study simulated personalized systematic prostate biopsy core reduction procedures on patients with suspicious PCa. Two urologists, each with over 5 years of experience, simulated the process of personalized SBx core reduction. Studies have reported that positivity rates on prostate biopsy were not only associated with the number of biopsy cores evaluated, but also with the proximity of the biopsy sites to the lesions on mpMRI . The simulated process of reducing the number of cores in this study was based on the aforementioned background. The first urologist described above determined the location of visible lesions on the T2WI, ADC and DWI sequences of each patient’s mpMRI, and measured the distance between the center of the lesion and each biopsy core. The simulated process of reducing the number of cores started with the core farthest from the lesion and continued sequentially until reaching the core closest to the lesion. If there were two or more suspicious lesions, this urologist would initially determine the most suspicious lesion location independently based on clinical experience and then proceed to reduce the number of cores. The second urologist documented the negative and positive outcomes of simulated biopsies based on the numbers of biopsy cores. For example, Fig. illustrates the simulated procedure of reducing the number of cores for a patient who underwent a 13-core SBx at Fujian Union Hospital, with the lesion situated in the right peripheral zone of the prostate. 

The Prostate Cancer Risk Score (PCS) used in this study was a composite parameter that incorporated clinical features and rad-score, with the PCS for each patient calculated based on the regression coefficients of each indicator. Subsequently, the study subjects were divided into five subgroups based on their percentile ranks in PCS distribution: 0–20%, 20–40%, 40–60%, 60–80%, and 80–100%. The optimal number of SBx cores predicted for each PCS quintile was calculated. The sensitivity, negative predictive value, accuracy, area under the curve (AUC), 95% confidence interval (CI), and corresponding p -value were determined for each biopsy core, from core 1 to core 13. These findings were compared with the results of 13-core systematic prostate biopsy to predict the optimal number of SBx cores for each PCS subgroup. If both the upper and lower limits of the 95% CI exceeded 0.9, it was regarded as a criterion for predicting the optimal number of SBx cores for patients in each PCS subgroup. This criterion enabled prediction of the number of SBx cores that could be reduced for patients in each PCS subgroup. 

The PCS model constructed from data of Fujian Union Hospital were externally validated in 101 patients ( n = 101) diagnosed with PCa at Changhai Hospital. PCS was calculated for these 101 patients based on PCS formula. Patients diagnosed at Changhai Hospital also were divided into five subgroups based on the same criteria. The simulated pathological results of reducing the core numbers in the external validation set were compared with the actual SBx pathological results. Then we calculated the optimized core numbers, accuracy of optimized SBx for predicting PCa and the leakage rate of PCa or csPCa in validation set. 

Normally distributed quantitative variables were reported as the mean ± standard deviation (SD) and compared by independent sample t-tests. Non-normally distributed quantitative variables were reported as the median (upper/lower quartiles) and compared by Mann-Whitney U-tests. Categorical variables were reported as frequency (percentage) and compared by chi-squared tests. Logistic regression was used to calculate the PCS model. Statistical analyses were performed and graphs were drawn using Python (version 3.7.11, Python Software Foundation, Wilmington, Delaware, USA. https://www.python.org/ ), R (version 4.1.2, R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ .), and SPSS Statistics (IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp) software, with p < 0.05 defined as statistically significant. 

Baseline clinical characteristics of patients Of the 480 patients in the training set who underwent a 13-core transrectal systematic prostate biopsy at Fujian Union Hospital, 343 (71.5%) were positive and 137 (28.5%) were negative for PCa. Age ( p = 0.009), TPSA ( p < 0.0001), and PI-RADS score ( p < 0.0001) differed significantly in these two groups (Table ). Positive SBx result of 343 patients was used for constructing PCS model. PCS subgroups The formula for calculating the Prostate Cancer Risk Score (PCS) by the model is as follows: [12pt]{minimal}
				
				 
				 
				 
				
				
				
				
				$$\:PCS=1/\{1+[e^-(\:0.003\;\;X1+0.920\;\;X2+0.044\;\;X3+5.062\;\;X4-7.603)]\}$$ P C S = 1 / { 1 + e - 0.003 * X 1 + 0.920 * X 2 + 0.044 * X 3 + 5.062 * X 4 - 7.603 } X1 = age(year); X2 = PI-RADS score(number); X3 = TPSA(ng/ml); X4 = rad score(number). Patients were divided into five subgroups based on PCS percentiles, specifically 0–20%, 20–40%, 40–60%, 60–80%, and 80–100%. Subsequently, the patients in each group were divided into those with low and high PCSs (Table ). Establishment of an SBx core reduction model The sensitivity, accuracy, negative predictive value, AUC, and corresponding P -values with 95% CI were calculated for each PCS subgroup based on the number of cores, which ranged from 1 to 13. A lower boundary of the two-sided 95% CI of AUC ≥ 0.9 was the criterion to predict the optimal number of cores for SBx. Table illustrates the statistical outcomes of the fourth PCS subgroup (60–80%), with the results of the remaining four subgroups provided in Supplementary Tables 3–6. This criterion enabled determination of the predicted number of SBx cores to be reduced in each PCS subgroup. The optimal numbers of cores for PCS subgroups 1–5 were calculated to be 13, 10, 8, 6, and 6, respectively. The accuracies of the predicted numbers of SBx cores were high, being 100%, 95.8%, 91.7%, 90.6%, and 92.7% for PCS subgroups 1–5, respectively. This analysis was accurate for all 24 patients in PCS subgroup 1, but missed 4 PCa (1 csPCa) patients of the 49 in PCS subgroup 2; 8 PCa (3 csPCa) patients of the 82 in PCS subgroup 3; 9 PCa (3 csPCa) patients of the 92 in PCS subgroup 4; and 7 PCa (2 csPCa) patients of the 94 in PCS subgroup 5. The optimized SBx reduced the core rate by 41.9% (1859/4433). The overall leakage rate of PCa for the SBx core reduction model was 8.2% (28/341), while the leakage rate for csPCa was 3.4% (9/262). External validation of the SBx core reduction model The results of the SBx Core Reduction Model were validated in 101 patients at Changhai Hospital. PCS scores were determined and stratified based on the SBx optimization model described above (Table ). The optimized number of cores of SBx showed very high accuracies, being 100%, 93.5%, 97.2%, 100% and 100% for PCS subgroups 1–5, respectively. The optimized SBx reduced the core rate 28.8% (378/1313). The overall leakage rate of PCa for optimized SBx was only 2.97% (3/101), and csPCa was 3.4% (3/87). 

Of the 480 patients in the training set who underwent a 13-core transrectal systematic prostate biopsy at Fujian Union Hospital, 343 (71.5%) were positive and 137 (28.5%) were negative for PCa. Age ( p = 0.009), TPSA ( p < 0.0001), and PI-RADS score ( p < 0.0001) differed significantly in these two groups (Table ). Positive SBx result of 343 patients was used for constructing PCS model. 

The formula for calculating the Prostate Cancer Risk Score (PCS) by the model is as follows: [12pt]{minimal}
				
				 
				 
				 
				
				
				
				
				$$\:PCS=1/\{1+[e^-(\:0.003\;\;X1+0.920\;\;X2+0.044\;\;X3+5.062\;\;X4-7.603)]\}$$ P C S = 1 / { 1 + e - 0.003 * X 1 + 0.920 * X 2 + 0.044 * X 3 + 5.062 * X 4 - 7.603 } X1 = age(year); X2 = PI-RADS score(number); X3 = TPSA(ng/ml); X4 = rad score(number). Patients were divided into five subgroups based on PCS percentiles, specifically 0–20%, 20–40%, 40–60%, 60–80%, and 80–100%. Subsequently, the patients in each group were divided into those with low and high PCSs (Table ). 

The sensitivity, accuracy, negative predictive value, AUC, and corresponding P -values with 95% CI were calculated for each PCS subgroup based on the number of cores, which ranged from 1 to 13. A lower boundary of the two-sided 95% CI of AUC ≥ 0.9 was the criterion to predict the optimal number of cores for SBx. Table illustrates the statistical outcomes of the fourth PCS subgroup (60–80%), with the results of the remaining four subgroups provided in Supplementary Tables 3–6. This criterion enabled determination of the predicted number of SBx cores to be reduced in each PCS subgroup. The optimal numbers of cores for PCS subgroups 1–5 were calculated to be 13, 10, 8, 6, and 6, respectively. The accuracies of the predicted numbers of SBx cores were high, being 100%, 95.8%, 91.7%, 90.6%, and 92.7% for PCS subgroups 1–5, respectively. This analysis was accurate for all 24 patients in PCS subgroup 1, but missed 4 PCa (1 csPCa) patients of the 49 in PCS subgroup 2; 8 PCa (3 csPCa) patients of the 82 in PCS subgroup 3; 9 PCa (3 csPCa) patients of the 92 in PCS subgroup 4; and 7 PCa (2 csPCa) patients of the 94 in PCS subgroup 5. The optimized SBx reduced the core rate by 41.9% (1859/4433). The overall leakage rate of PCa for the SBx core reduction model was 8.2% (28/341), while the leakage rate for csPCa was 3.4% (9/262). 

The results of the SBx Core Reduction Model were validated in 101 patients at Changhai Hospital. PCS scores were determined and stratified based on the SBx optimization model described above (Table ). The optimized number of cores of SBx showed very high accuracies, being 100%, 93.5%, 97.2%, 100% and 100% for PCS subgroups 1–5, respectively. The optimized SBx reduced the core rate 28.8% (378/1313). The overall leakage rate of PCa for optimized SBx was only 2.97% (3/101), and csPCa was 3.4% (3/87). 

Systematic prostate biopsy (SBx) is a standard diagnostic method for newly suspected PCa. Most patients benefit diagnostically from evaluation of 10 or more cores widespread throughout the prostate. Obtaining an excess number of biopsy cores not only prolongs the procedural time, but also exposes patients to a greater risk of subsequent complications, such as bleeding and infection . Optimizing the number of SBx cores can reduce the number of unnecessary cores while ensuring the detection of PCa. Improvements in medical imaging diagnostic techniques have resulted in the emergence of mpMRI as a crucial method for the diagnosis and evaluation of PCa. The sensitivity of mpMRI for the diagnosis of PCa has been reported to range from 33 to 77%, thus affecting diagnostic accuracy. The apparent discrepancies between imaging reports and pathological diagnoses may be due to subjective evaluations by radiologists . Radiomics is a novel computer-aided diagnostic technique increasingly used in medical imaging. Radiomics mainly employs high-throughput feature extraction and conversion algorithms to identify intricate characteristics (such as gradients, pixels, and grayscale) embedded in patients’ imaging data that cannot be identified by conventional methods. These data are subsequently transformed, using machine learning and artificial intelligence methods, into information that can be objectively assessed and analyzed, enabling clinicians to construct scientific and operational diagnostic and prognostic models for investigating tumor heterogeneity, classifying diseases, and monitoring the effects of treatment . Clinical radiomics models have been constructed utilizing imaging traits, such as GLRLM, GLCM, and GLSZM, enabling evaluation of the diagnosis and prognosis of various cancers including brain malignancies, breast cancer, gastric cancer, and rectal cancer . For example, a radiomics prognostic model based on enhanced CT scans showed high accuracy in predicting recurrence-free survival of patients with locally advanced gastric cancer (AUC = 0.616 ) . Another radiomics model that included GLRLM showed high predictive value in distinguishing between benign and malignant thyroid nodules, as well as reflecting the heterogeneity of these lesions. This model had a sensitivity of 75%, a specificity of 80%, and an accuracy of 77.8% . A radiomics model, which incorporated DWI-GLCM and reflected the heterogeneity of rectal cancers, was found to accurately predict the risk of early postoperative metastasis in patients with locally advanced rectal cancer who underwent radical surgery, achieving a predictive accuracy of 80% (AUC = 0.833) . Although radiomics models have the potential to enhance the accuracy of tumor diagnosis, radiomics model derived from mpMRI had not been previously reported to optimize core numbers and locations of SBx. Characteristics of MRI-positive lesions, including lesion size, location, texture and number, can potentially influence the detection of PCa and the required number of SBx cores . For example, larger lesions may require fewer biopsy cores, whereas smaller lesions may require more cores to maintain the same PCa detection rate. The present study represents a pioneering application of radiomics from mpMRI images, such as NGTDM, GLCM, and GLSZM features to predict PCa, thereby optimizing systematic prostate biopsy. This study constructs a personalized PCS risk scoring system based on radiomics features and clinical features. Moreover, this study devised an innovative optimization model to reduce the number of SBx cores required for each PCS subgroup. This optimized model effectively reduced the number of SBx cores by 41.9% (1859/4433), while maintaining low rates of missed diagnoses of PCa (8.2%) and csPCa (3.4%). This model provides more precise and personalized systematic prostate biopsies for patients. Innovative approaches for optimizing transrectal ultrasonography (TRUS)-guided prostate biopsies have been evaluated. Although one study found that the distribution of biopsy cores can be reduced to ipsilateral SBx based on the MRI-determined lesion location, this method could not optimize the number of SBx cores for individual PCa patients, as it did not adjust core numbers and locations based on mpMRI radiomics features and clinical factors such as age, TPSA, and PI-RADS . PI-RADS scores based on mpMRI scans can affect the number of prostate biopsy cores, as higher PI-RADS scores patient with fewer SBx cores did not affect the rate of PCa detection . Higher TPSA concentrations were associated with a wider distribution of cancerous lesions in patients with PCa, enabling these lesions to be easily detected through biopsy . Therefore, a limited number of biopsy cores was found to be sufficient for the accurate diagnosis of patients with TPSA levels ≥ 20 ng/ml . In our study, a feasible model of core reduction was developed that considers individual factors, such as age, TPSA and PI-RADS, with the integration of mpMRI radiomics factors into this model resulting in a gradual reduction in the number of cores and a more individualized approach to biopsy. Because PCa exhibits considerable and significant interpatient heterogeneity, these clinical indicators did not include all the features of malignancy. To our knowledge, the model described in the present study is the first to integrate mpMRI-associated radiomics features of the prostate with clinical factors, optimizing the reduction of SBx cores. The transformation of mpMRI imaging data into quantifiable radiomics features has facilitated the highly precise and objective analyses of internal lesion characteristics and the heterogeneity of PCa. This method effectively reduced the number of SBx cores, while reducing the rates of missed diagnoses of PCa and csPCa compared with other models. Reducing the number of biopsy cores would shorten operation time and minimize patient discomfort, as well as alleviating the workload for pathologists . The present study had several limitations: First, the number of patients in the external validation set was relatively small, with PCS subgroups 4 and 5 including only 15 patients. Therefore, results in this set were likely not as objective or stable as the results in the training set. This limitation may be overcome by a prospective study of larger numbers of patients, including larger numbers in external validation sets. Second, the present study only enrolled patients who underwent SBx at two hospitals, thus limiting the generalizability of the model. The use of data from multiple centers and conduct for further verification may enhance the performance of the model. Finally, several factors used to evaluate biopsies and images were subjective. The objectivity of this model may be enhanced by investigating the results of targeted biopsies and robot-assisted learning delineations. The present study described the construction of an innovative and optimized model using the radiomics features of mpMRI and clinical risk parameters to optimize the number of SBx cores required for the diagnosis of PCa. Calculation of PCS risk scores in individual patients can determine the ideal biopsy site and number of cores required, minimizing biopsy-related complications while ensuring accurate detection of PCa. This model may therefore enhance patient experiences during prostate biopsies. 

Supplementary Material 1. Supplemental Table 1. MRI Scan Parameters at Changhai Hospital and Fujian Union Hospital. Supplemental Table 2. Radiomics Features for Rad Score Construction. Supplemental Table 3. Comparative Rates of Prostate Cancer Detection by Simulated Biopsy Sampling Schemes and 13-core Systematic Biopsy in Patients in PCS Subgroup 1. Supplemental Table 4. Comparative Rates of Prostate Cancer Detection by Simulated Biopsy Sampling Schemes and 13-core Systematic Biopsy in Patients in PCS Subgroup 2. Supplemental Table 5. Comparative Rates of Prostate Cancer Detection by Simulated Biopsy Sampling Schemes and 13-core Systematic Biopsy in Patients in PCS Subgroup 3. Supplemental Table 6. Comparative Rates of Prostate Cancer Detection by Simulated Biopsy Sampling Schemes and 13-core Systematic Biopsy in Patients in PCS Subgroup 5. Supplementary Material 2. Supplemental Formula 1. Formula for Calculating Rad Scores. 

