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Predicting perineural invasion in prostate cancer using chest CT body composition and multiparametric MRI of prostate lesions and periprostatic fat
WANG Siqi  ZHANG Qinhe  WANG Xiulin  CHEN Lihua  WANG Hongkai  SUN Yinan  LIU Ailian 

Cite this article as: WANG S Q, ZHANG Q H, WANG X L, et al. Predicting perineural invasion in prostate cancer using chest CT body composition and multiparametric MRI of prostate lesions and periprostatic fat[J]. Chin J Magn Reson Imaging, 2026, 17(9): 25-33, 48. DOI:10.12015/issn.1674-8034.2026.09.004.


[Abstract] Objective To evaluate the value of preoperative chest CT-derived body composition indices combined with quantitative multiparametric magnetic resonance imaging (mpMRI) features of prostate cancer (PCa) lesions and periprostatic adipose tissue (PPAT), together with clinical laboratory indicators, for predicting perineural invasion (PNI) in PCa.Materials and Methods Seventy five patients with pathologically confirmed PCa were retrospectively enrolled and divided into a PNI-positive group (n = 35) and a PNI-negative group (n = 40). Muscle and adipose tissue areas and their ratios at the T12 vertebral level were automatically quantified from non-contrast chest CT images using Total Segmentator. Quantitative parameters derived from intravoxel incoherent motion (IVIM) and mDIXON-QUANT sequences were extracted from intraprostatic lesions and PPAT on mpMRI. Regions of interest were independently delineated by two radiologists, and mean values were used after interobserver agreement was confirmed by intra-class correlation coefficient. Independent predictors of PNI were identified using univariate analysis followed by multivariate logistic regression. Single-parameter and combined prediction models were constructed and evaluated using receiver operating characteristic (ROC) analysis and area under the curve (AUC). Model performance was compared using the DeLong test, and a nomogram was developed for visualization of the combined model. Model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test, and internal validation was performed using bootstrap resampling (1000 repetitions).Results The PNI-positive group exhibited significantly higher muscle area [(86.54 ± 16.42) cm2 vs. (76.52 ± 14.84) cm2], PPAT-fat fraction (FF) [(69.37% ± 9.33%) vs. (62.07% ± 7.24%)], and IVIM-perfusion fraction (f) values [0.47 (0.33, 0.63) vs. 0.31 (0.25, 0.60)] and significantly lower red blood cell (RBC) levels [(3.90 ± 0.35) /μL vs. (4.26 ± 0.40) /μL] compared with the PNI-negative group (all P < 0.05). Multivariate analysis identified muscle area [AUC = 0.668, 95% confidence interval (CI): 0.545 to 0.790], PPAT-FF (AUC = 0.724, 95% CI: 0.610 to 0.839), and RBC (AUC = 0.673, 95% CI: 0.550 to 0.796) as independent predictors of PNI. The combined model (muscle area + PPAT-FF + RBC) achieved an AUC of 0.848 (95% CI: 0.763 to 0.933), with a sensitivity of 71.4% and a specificity of 80.0%. Its predictive performance was significantly superior to that of each single indicator (DeLong test: combined model vs. muscle area, P = 0.012; combined model vs. PPAT-FF, P = 0.023; combined model vs. RBC, P = 0.008).Conclusions Integration of chest CT-based body composition metrics with mpMRI quantitative features of prostate cancer lesions and periprostatic adipose tissue, together with clinical laboratory indicators, significantly enhances the preoperative prediction of perineural invasion in prostate cancer.
[Keywords] prostate cancer；perineural invasion；quantitative computed tomography；body composition analysis；multiparametric magnetic resonance imaging；prediction model

WANG Siqi1, 2, 3   ZHANG Qinhe1, 2, 3   WANG Xiulin4   CHEN Lihua1, 2, 3   WANG Hongkai3, 5   SUN Yinan5   LIU Ailian1, 2, 3*  

1 Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

2 Technology Innovation Center of Hyperpolarized MRI, Liaoning Province, Dalian 116011, China

3 Dalian Engineering Research Center for Artificial Intelligence in Medical Imaging, Dalian 116011, China

4 Stem Cell Clinical Research Institute of the First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

5 Faculty of Medicine, Dalian University of Technology, Dalian 116011, China

Corresponding author: LIU A L, E-mail: cjr.liuailian@vip.163.com

Conflicts of interest   None.

Received  2026-01-04
Accepted  2026-03-15
DOI: 10.12015/issn.1674-8034.2026.09.004
Cite this article as: WANG S Q, ZHANG Q H, WANG X L, et al. Predicting perineural invasion in prostate cancer using chest CT body composition and multiparametric MRI of prostate lesions and periprostatic fat[J]. Chin J Magn Reson Imaging, 2026, 17(9): 25-33, 48. DOI:10.12015/issn.1674-8034.2026.09.004.

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