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Predicting perineural invasion using histogram analysis of zoomed EPI diffusion-weighted imaging in rectal cancer

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Abstract

Purpose

To investigate the utility of histogram analysis of zoomed EPI diffusion-weighted imaging (DWI) for predicting the perineural invasion (PNI) status of rectal cancer (RC).

Methods

This prospective study evaluated 94 patients diagnosed with histopathologically confirmed RC between July 2020 and July 2021. Patients underwent preoperative rectal magnetic resonance imaging (MRI) examinations, including the zoomed EPI DWI sequence. Ten whole-tumor histogram parameters of each patient were derived from zoomed EPI DWI. Reproducibility was evaluated according to the intra-class correlation coefficient (ICC). The association of the clinico-radiological and histogram features with PNI status was assessed using univariable analysis for trend and multivariable logistic regression analysis with β value calculation. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance.

Results

Forty-two patients exhibited positive PNI. The inter- and intraobserver agreements were excellent for the histogram parameters (all ICCs > 0.80). The maximum (p = 0.001), energy (p = 0.021), entropy (p = 0.021), kurtosis (p < 0.001), and skewness (p < 0.001) were significantly higher in the positive PNI group than in the negative PNI group. Multivariable analysis showed that higher MRI T stage [β = 2.154, 95% confidence interval (CI) 0.932–3.688; p = 0.002] and skewness (β = 0.779, 95% CI 0.255–1.382; p = 0.006) were associated with positive PNI. The model combining skewness and MRI T stage had an area under the ROC curve of 0.811 (95% CI 0.724–0.899) for predicting PNI status.

Conclusion

Histogram parameters in zoomed EPI DWI can help predict the PNI status in RC.

Graphical abstract

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Abbreviations

AUC:

Area under the ROC curve

CI:

Confidence interval

DTA:

Distance from the inferior edge of the tumor to the anal verge

DWI:

Diffusion-weighted imaging

EMVI:

Extramural venous invasion

EPI:

Echo-planar imaging

ICC:

Intraclass correlation coefficient

MRF:

Mesorectal fascia

MRI:

Magnetic resonance imaging

MTL:

Maximum tumor length

MTT:

Maximum tumor thickness

PNI:

Perineural invasion

RC:

Rectal cancer

ROC:

Receiver operating characteristic

ROI:

Regions of interest

SD:

Short-axis diameter

ss-EPI:

Single-shot echo-planar imaging

T2WI:

T2-weighted image

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Funding

This research is supported by the National Natural Science Foundation of China (Grant Number 81971589); CAMS Innovation Fund for Medical Sciences (CIFMS, Grant Number 2021-I2M-C&T-A-017); 2020 SKY Imaging Research Fund (Grant Number Z-2014-07-2003-01); and Youth Research Fund of Peking Union Medical College, the Fundamental Research Funds for the Central Universities (Grant Number 3332021098).

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Authors and Affiliations

  1. Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China

    Lijuan Wan, Wenjing Peng, Peihua Wu, Qing Zhao, Feng Ye, Xinming Zhao & Hongmei Zhang

  2. Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China

    Shuangmei Zou

  3. MR Scientific Marketing, Siemens Healthineers Ltd., Beijing, 100021, China

    Qinglei Shi

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  1. Lijuan Wan
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  8. Xinming Zhao
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Contributions

Study concepts/study design, data acquisition, or data analysis/interpretation, all authors; quality control of date and algorithms, FY, XMZ, and HMZ; statistical analysis, LJW and HMZ; drafting the article or revising it critically for important intellectual content, all authors; final approval of the version to be submitted, all authors.

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Correspondence to Hongmei Zhang.

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All authors (Lijuan Wan, Wenjing Peng, Shuangmei Zou, Qinglei Shi, Peihua Wu, Qing Zhao, Feng Ye, Xinming Zhao, Hongmei Zhang) have no conflicts of interest to be disclosed related to this article.

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Wan, L., Peng, W., Zou, S. et al. Predicting perineural invasion using histogram analysis of zoomed EPI diffusion-weighted imaging in rectal cancer. Abdom Radiol 47, 3353–3363 (2022). https://doi.org/10.1007/s00261-022-03579-1

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