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Updated: Jun 7, 2026
An In Vivo Murine Sciatic Nerve Model of Perineural Invasion
Published on: April 23, 2018
A multivariate IVIM-DWI-based model for preoperative prediction of perineural invasion status in rectal cancer: a
Yantong Sun1,2, Yi Wang1, Hongyu Zhao3
1Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Abdominal Medical Imaging, Shandong Lung Cancer Institute, Shandong Institute of Neuroimmunology, No.16766 Jingshi Road, Lixia District, Jinan, 250014, Shandong, China.
Objective:
To evaluate intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for preoperative diagnosis of perineural invasion (PNI) in rectal cancer (RC).
Materials And Methods:
A total of 148 patients with pathology-confirmed RC (PNI+, n = 72; PNI-, n = 76) were enrolled. Parameters from mono-exponential (ADC), bi-exponential (D, D*, f), and stretched-exponential (DDC, α) IVIM models were analyzed. Univariate and multivariate logistic regression analyses were used to construct diagnostic models. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. The DeLong test was used to compare the AUC of the models. Internal validation was employed to assess model performance. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI), along with calibration metrics and decision curve analysis, were used to further evaluate model performance. P-value < 0.05 was considered statistically significant.
Results:
ADC, D, f, and DDC differed significantly between groups. Multivariate analysis identified ADC and D as independent PNI predictors. The D value yielded the highest AUC (0.84), while ADC showed the highest sensitivity (81.94%). A combined model (ADC + D) achieved an AUC of 0.85, sensitivity of 86.10%, specificity of 73.70%, and accuracy of 77.00%. The fivefold internal validation mean AUC was 0.84 ± 0.04. No significant AUC differences were found among parameters or models (DeLong test, P > 0.05). Further analyses revealed that the combined model provided significant improvements over the ADC model in individual risk reclassification (continuous NRI = 0.65, 95% CI 0.33-0.95), overall predictive accuracy (IDI = 0.07, 95% CI excluding 0), and calibration (Brier score: 0.16 vs. 0.17; MAE: 0.01 vs. 0.04; MSE: 2.3×10⁻⁴ vs. 1.91×10⁻³). Decision curve analysis demonstrated consistently higher net benefit for the combined model across threshold probabilities of 0-0.50.
Conclusion:
IVIM-DWI demonstrates potential value for the preoperative assessment of PNI status in rectal cancer and may facilitate individualized treatment planning.
Insights
Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) shows promise for diagnosing perineural invasion (PNI) in rectal cancer (RC) before surgery. A combined model using ADC and D parameters achieved high accuracy, aiding in personalized treatment planning.
Area of Science:
- Radiology
- Oncology
- Medical Imaging
Background:
- Perineural invasion (PNI) is a critical prognostic factor in rectal cancer (RC), influencing treatment decisions and patient outcomes.
- Accurate preoperative detection of PNI remains challenging, necessitating advanced imaging techniques.
- Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) offers insights into tissue microstructure and cellularity.
Purpose of the Study:
- To evaluate the efficacy of IVIM-DWI parameters in the preoperative diagnosis of PNI in patients with RC.
- To compare the diagnostic performance of different IVIM-DWI models and their parameters.
- To develop and validate a predictive model for PNI using IVIM-DWI data.
Main Methods:
- A cohort of 148 patients with pathologically confirmed RC (PNI+, n=72; PNI-, n=76) underwent IVIM-DWI.
- Analysis included parameters from mono-exponential (ADC), bi-exponential (D, D*, f), and stretched-exponential (DDC, α) IVIM models.
- Logistic regression, ROC analysis, internal validation, NRI, IDI, and decision curve analysis were employed to assess diagnostic performance and model utility.
Main Results:
- Significant differences in ADC, D, f, and DDC values were observed between PNI-positive and PNI-negative groups.
- Multivariate analysis identified ADC and D as independent predictors of PNI.
- A combined model (ADC + D) demonstrated a high area under the curve (AUC) of 0.85, with sensitivity of 86.10% and accuracy of 77.00%, outperforming individual parameters and showing improved reclassification and calibration.
Conclusions:
- IVIM-DWI shows significant potential for the preoperative assessment of PNI status in rectal cancer.
- The combined ADC and D model offers improved diagnostic performance and aids in risk stratification.
- These findings suggest that IVIM-DWI can facilitate more individualized treatment planning for rectal cancer patients.
