Assessment of invasion of prostate cancer with multiparametric MRI-based radiomics

Jing YANG; Doudou HUANG; Junfan CHEN; Yindeng LUO; Yuexi LIU

doi:10.16016/j.2097-0927.202307044

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Assessment of invasion of prostate cancer with multiparametric MRI-based radiomics

Clinical Medicine | 更新时间：2026-01-09

- Assessment of invasion of prostate cancer with multiparametric MRI-based radiomics
- Journal of Army Medical University Vol. 46, Issue 2, Pages: 170-180(2024)
- 作者机构：
  
  400010 重庆，重庆医科大学附属第二医院放射科
- 作者简介：
- 基金信息：
  
  the Special Project of Technology Innovation and Application Development of Chongqing Science and Technology Bureau(cstc2019jscx-msxmX0253)
- DOI：10.16016/j.2097-0927.202307044
  CLC： R445.2;R73-37;R737.25
- Received：08 July 2023，
  
  Revised：2023-09-04，
  
  Published：30 January 2024
- 稿件说明：
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Jing YANG, Doudou HUANG, Junfan CHEN, et al. Assessment of invasion of prostate cancer with multiparametric MRI-based radiomics[J]. Journal of Army Medical University, 2024, 46(2): 170-180.
DOI：

Jing YANG, Doudou HUANG, Junfan CHEN, et al. Assessment of invasion of prostate cancer with multiparametric MRI-based radiomics[J]. Journal of Army Medical University, 2024, 46(2): 170-180. DOI： 10.16016/j.2097-0927.202307044.

摘要

目的

探讨多参数MRI上不同感兴趣区的影像组学模型和结合影像组学、PI-RADS 2.1评分、临床变量的综合模型在评估前列腺癌侵袭性方面的价值。

方法

收集本院2018年5月至2022年9月2个医疗中心经病理确诊为前列腺癌患者245例：渝中院区176例，其中低侵袭性组[Gleason评分≤7(3+4)

]

77例，高侵袭性组[Gleason评分≥7(4+3)

]

99例；江南院区69例(低侵袭性组33例，高侵袭性组36例)。所有患者行多参数MRI检查后，在多参数MRI图像上分割2种ROI：肿瘤病变区域(tumor region，TR)和前列腺区域(prostate gland，PG)。评估与前列腺癌侵袭性相关的临床变量，并记录每位患者的PI-RADS 2.1评分。使用逻辑回归算法作为机器学习算法，建立多个前列腺癌侵袭性分层模型：影像组学模型(Model

、Model

PG+TR

)、影像组学-临床联合模型、影像组学-PIRADS联合模型、临床-PIRADS联合模型和影像组学-临床-PIRADS综合模型。分别采用受试者工作特性(ROC)曲线、曲线下面积(AUC)和决策曲线分析(DCA)比较各模型的诊断价值和临床收益。结合影像组学评分、独立临床变量和PI-RADS2.1评分构建影像组学列线图。通过校准度、区分度和临床应用评价列线图性能。

结果

在3种影像组学模型中，Model

PG+TR

的AUC值为0.919，高于Model

(AUC=0.874)和Model

(AUC=0.887)。在联合模型中，影像组学-PIRADS-临床综合模型的AUC为0.954，优于影像组学模型(AUC=0.919)、影像组学-临床联合模型(AUC=0.919)、影像组学-PIRADS联合模型(AUC=0.921)和临床-PIRADS联合模型(AUC=0.769)。列线图在评估前列腺癌侵袭性方面显示出良好的风险分层性能(AUC=0.919)和校准效能。决策曲线分析显示，影像组学模型Model

PG+TR

和影像组学-临床-PIRADS综合模型获得了较高的临床净收益。

结论

结合前列腺和肿瘤区域特征的影像组学模型可准确评估前列腺癌的侵袭性；结合影像组学、PI-RADS 2.1评分和临床变量的综合模型能进一步提高对前列腺癌侵袭性的评估性能。

Abstract

Objective

To investigate the value of a radiomic model of multiparametric MRI different regions of interest (ROI) in assessment of the invasion of prostate cancer (PCa)

and explore the evaluation value of an integrated model based on above radiomic model combined with radiomics

PI-RADS 2.1 score

and clinical variables.

Methods

A total of 245 patients with pathologically-confirmed PCa admitted to 2 medical centers in our hospital from May 2018 to September 2022 were collected in this retrospective study. Among them

176 cases were collected in Yuzhong Medical Center

including 77 cases in the low invasive group [Gleason score ≤7 (3+4)

]

and 99 cases in the high invasive group [Gleason score ≥7 (4+3)

]

; 69 cases were collected in Jiangnan Medical Center

including 33 cases in the low invasive group and 36 cases in the high invasive group. All patients underwent multiparametric MRI

and then 2 types of ROI

the tumor region (TR) and the prostate gland (PG)

were segmented on the multiparametric MRI images. Clinical variables related with PCa invasion were assessed

and PI-RADS 2.1 score was recorded for each patient. Logistic regression algorithm was employed as a machine learning method to develop following invasive stratification models for PCa: radiomics models (Model

Model

and Model

PG+TR

)

Radiomics-Clinical model

Radiomics-PIRADS model

PIRADS-Clinical model and Radiomics-PIRADS-Clinical combined model. Receiver operating characteristic (ROC) curve

area under ROC curve (AUC) and decision curve analysis (DCA) were applied to compare the diagnostic efficacy and clinical benefit of each model. A nomogram was constructed by combining Radiomics score (Radscore)

PI-RADS 2.1 score and independent clinical variables

and its performance was evaluated by calibration

differentiation and clinical application.

Results

the above 3 radiomics models

the AUC value was 0.919 for Model

PG+TR

which was higher than that of Model

(0.874) and of Model

(0.887). And the AUC value of the Radiomics-PIRADS-Clinical combined model was 0.954

superior to that of the radiomics model (0.919)

Radiomics-PIRADS model (0.921)

Radiomics-Clinical model (0.919)

and PIRADS-Clinical model (0.769) respectively

The nomogram model showed good performance (AUC=0.919) and calibration efficacy in risk stratification. DSA revealed that both the Model

PG+TR

model and Radiomics-PIRADS-Clinical combined models achieved a higher net clinical benefit.

Conclusion

Our radiomic model of combining features of the prostate and tumor regions can more accurately assess the invasion of PCa

and our integrated model of combining radiomics

PI-RADS 2.1 score and clinical variables can further improve the assessment.

关键词

Keywords

references

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