Prediction of biochemical recurrence of prostate cancer based on periprostatic fat MRI radiomics model

ZHANG Yufeng; LIU Dong; WANG Ximing; SONG Yang

doi:10.19732/j.cnki.2096-6210.2023.01.005

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Prediction of biochemical recurrence of prostate cancer based on periprostatic fat MRI radiomics model

更新时间：2025-12-15

- Prediction of biochemical recurrence of prostate cancer based on periprostatic fat MRI radiomics model
- Vol. 32, Issue 1, Pages: 33-40(2023)
- 作者机构：
  
  1. 苏州大学附属第一医院放射科,江苏,苏州,215006
  2. 西门子医疗系统有限公司磁共振科研市场部,上海,200120
- 作者简介：
- 基金信息：
- DOI：10.19732/j.cnki.2096-6210.2023.01.005
  CLC：
- Published Online：28 February 2023，
  
  Published：28 February 2023
- 稿件说明：
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张玉峰,刘冬,王希明,等. 基于前列腺周围脂肪MRI影像组学模型预测前列腺癌生化复发的研究[J]. 肿瘤影像学, 2023, 32(1): 33-40 https://doi.

org/10.19732/j.cnki.2096-6210.2023.01.005
张玉峰,刘冬,王希明,等. 基于前列腺周围脂肪MRI影像组学模型预测前列腺癌生化复发的研究[J]. 肿瘤影像学, 2023, 32(1): 33-40 https://doi. DOI： 10.19732/j.cnki.2096-6210.2023.01.005.

org/10.19732/j.cnki.2096-6210.2023.01.005 DOI：

摘要

目的：

探讨前列腺周围脂肪组织（periprostatic fat，PPF）的磁共振成像（magnetic resonance imaging，MRI）影像组学参数预测前列腺癌根治术（radical prostatectomy，RP）术后生化复发的价值。

方法：

回顾并分析2016年1月2020年12月在苏州大学附属第一医院行RP共114例（无生化复发及生化复发分别为64例和50例）患者的MRI图像、临床信息及随访结果。所有患者术前均行MRI扫描，选取横断位T2加权成像（T2-weighted imaging，T2WI）、弥散加权成像（diffusion-weighted imaging，DWI）、表观弥散系数（apparent diffusion coefficient，ADC）和矢状位T2WI序列图像，使用3D slicer4.13.0软件在T2WI横断位图像进行三维手动分隔PPF，利用FAE软件中的Feature Extraction模块，经过10种图像转换类型和7种特征提取方法提取影像组学特征。T2WI序列提取1 647个特征，DWI和ADC序列提取3 290个特征，T2WI联合DWI及ADC序列提取4 937个特征。通过建立影像组学模型，经机器学习、模型验证，最后将训练集及和测试集的曲线下面积（area under curve，AUC）、准确度、阳性预测值和阴性预测值作为评估影像组学模型的定量表现。

结果：

T2WI序列选取特征排序前13的特征模型，训练集AUC=0.982，测试集AUC=0.912。对于T2WI联合DWI及ADC序列选取特征排序前12的特征模型，训练集AUC=0.916，测试集AUC=0.814。对于DWI和ADC序列选取特征排序前9的特征模型，训练集AUC=0.94，测试集AUC=0.86。

结论：

基于PPF的MRI影像组学模型，尤其是T2WI序列模型对于PCa患者RP术后生化复发有较高的预测能力。

Abstract

Objective:

To investigate the value of magnetic resonance imaging (MRI) radiomics parameters of periprostatic fat (PPF) in predicting biochemical recurrence of prostate c

ancer after radical prostatectomy (RP).

Methods:

MRI images

clinical information and follow-up results of 114 patients (without biochemical recurrence 64 case) and (biochemical recurrence 50 case) who underwent radical prostatectomy (RP) in the First Affiliated Hospital of Soochow University from January 2016 to December 2020 were retrospectively analyzed. All patients underwent MRI scan before surgery. Transverse T2-weighted imaging (T2WI)

diffusion-weighted imaging (DWI)

apparent diffusion coefficient (ADC) and sagittal T2WI sequences were selected

and 3D manual separation of the fat area around the prostate was performed on the transverse T2WI images using 3D Slicer 4.13.0. The feature extraction module of open-source software FAE is used to extract image omics features through 10 image conversion types and 7 feature extraction methods. 1 647 features were extracted from T2WI sequence

3 290 features were extracted from DWI and ADC sequence

and 4 937 features were extracted from T2WI combined with DWI and ADC sequence. The area under curve (AUC)

accuracy

positive predictive value of the training set and the test set are determined by establishing the image omics model

machine learning and model verification. Positive predictive value and negative predictive value were used to evaluate the quantitative performance of the radiomics model.

Results:

The top 13 feature models in T2WI sequence were selected

with AUC of training set was 0.982 and test set was 0.912. The AUC of training set and test set was 0.916 and 0.814 for the top 12 feature models with T2WI combined with DWI and ADC sequences. The AUC of training set and test set was 0.94 and 0.86

respectively

for the feature model with the top 9 selected features in DWI and ADC sequences.

Conclusion:

The MRI PPF radiomics model around prostate has a high ability to predict biochemical recurrence after RP

especially the T2WI sequence model.

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