Objectives To explore intravoxel incoherent motion (IVIM) for evaluation of the perineural invasion (PNI) status and survival in patients with rectal cancer. Materials and methods The true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and microvascular volume fraction (f) were recorded together with histogram metrics. Differences in IVIM histogram metrics between the PNI-positive group and the PNI-negative group were analyzed. Univariable and multivariable logistic regression analysis were used for model construction. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance of the models. Histopathology was used as the PNI endpoint. Kaplan–Meier curve analysis was employed to estimate the disease-free survival (DFS) and overall survival (OS) of patients. Results A total of 175 patients were retrospectively enrolled in this study. Multivariable logistic regression analysis showed that higher D_median (odds ratio (OR) = 2.036, p = 0.003) and D_min (OR = 1.479, p = 0.002) and lower f_SD (OR = 0.697, p < 0.001) and f_kurtosis (OR = 0.485, p < 0.001) were independently associated with PNI-positive. The combined model showed the best performance in predicting the PNI status with AUCs, sensitivity, specificity, and accuracy of 0.885, 81.67%, 82.61%, and 82.29%, respectively. Kaplan–Meier curves analysis revealed that the patients with higher scores (> −1.12) of the combined model showed relatively lower 2-year DFS (81.6% vs 93.2%, p = 0.014) compared to the patients with lower scores (≤ −1.12). Conclusion IVIM histogram metrics could predict the PNI status and serve as a preoperative risk stratification tool. Critical relevance statement The combination of IVIM histogram metrics and clinical characteristics could discriminate the PNI status and serve as a surrogate for PNI. Key Points <UnorderedList Mark="Bullet"> <ItemContent> The PNI is an important prognostic factor in rectal cancer. </ItemContent> <ItemContent> The IVIM histogram metrics were associated with the PNI status. </ItemContent> <ItemContent> The combination of IVIM and clinical factors could serve as a surrogate for PNI. </ItemContent> </UnorderedList> Graphical Abstract

Pretreatment intravoxel incoherent motion histogram metrics and clinical characteristics for prediction of perineural invasion status and survival in patients with rectal cancer

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2025年9月19日

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Ao Yang,
Hao Xu,
Xin Zhang,
Ping Zheng,
Li Bo Lin,
Jie Ke Liu,
Peng Zhou,
Xiao Li Chen

摘要

Objectives

To explore intravoxel incoherent motion (IVIM) for evaluation of the perineural invasion (PNI) status and survival in patients with rectal cancer.

Materials and methods

The true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and microvascular volume fraction (f) were recorded together with histogram metrics. Differences in IVIM histogram metrics between the PNI-positive group and the PNI-negative group were analyzed. Univariable and multivariable logistic regression analysis were used for model construction. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance of the models. Histopathology was used as the PNI endpoint. Kaplan–Meier curve analysis was employed to estimate the disease-free survival (DFS) and overall survival (OS) of patients.

Results

A total of 175 patients were retrospectively enrolled in this study. Multivariable logistic regression analysis showed that higher D_median (odds ratio (OR) = 2.036, p = 0.003) and D_min (OR = 1.479, p = 0.002) and lower f_SD (OR = 0.697, p < 0.001) and f_kurtosis (OR = 0.485, p < 0.001) were independently associated with PNI-positive. The combined model showed the best performance in predicting the PNI status with AUCs, sensitivity, specificity, and accuracy of 0.885, 81.67%, 82.61%, and 82.29%, respectively. Kaplan–Meier curves analysis revealed that the patients with higher scores (> −1.12) of the combined model showed relatively lower 2-year DFS (81.6% vs 93.2%, p = 0.014) compared to the patients with lower scores (≤ −1.12).

Conclusion

IVIM histogram metrics could predict the PNI status and serve as a preoperative risk stratification tool.

Critical relevance statement

The combination of IVIM histogram metrics and clinical characteristics could discriminate the PNI status and serve as a surrogate for PNI.

Key Points

The PNI is an important prognostic factor in rectal cancer.

The IVIM histogram metrics were associated with the PNI status.

The combination of IVIM and clinical factors could serve as a surrogate for PNI.

Graphical Abstract