Related Experiment Video
Updated: Jun 16, 2025
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Using amide proton transfer-weighted magnetic resonance imaging (MRI) in peritumor tissue to predict parametrial
Lianze Du1, Qinghai Yuan1, Mengdi Zhang1
1Department of Radiology, The Second Hospital of Jilin University, Changchun, China.
Background:
Parametrial infiltration (PMI) is an important indicator for staging and treatment of cervical cancer (CC). The potential of amide proton transfer-weighted (APTw) parameters of peritumor tissue in predicting PMI is still uncertain. This study aims to explore whether the APTw parameters of peritumor tissue can improve diagnostic value of diffusion-weighted imaging (DWI) in magnetic resonance imaging (MRI).
Methods:
Eighty-one patients with pathologic analysis-confirmed CC were enrolled in this retrospective study. All patients underwent APTw MRI and DWI. The APTw values of tumor (APTw-t), APTw values in peritumor tissues (APTw-p) and apparent diffusion coefficient (ADC) values were independently reviewed by two radiologists to map the regions of interest and measure the corresponding values. Receiver operating characteristic curves were generated to evaluate the diagnostic performance of these quantitative parameters.
Results:
The study patients were divided into the PMI group (n=22) and non-PMI group (n=59). The APTw-t and APTw-p values (%) of PMI group were higher than those of the non-PMI group [3.71 (interquartile range, IQR, 3.60-3.98) and 2.75 (IQR, 2.68-2.77) vs. 3.33 (IQR, 3.24-3.60) and 1.98 (IQR, 1.82-2.36); P<0.001]. The ADC values of PMI group were lower than those of non-PMI group [0.88 (IQR, 0.83-0.94) ×10-3 vs. 0.95 (IQR, 0.88-1.04)×10-3 mm2/sec; P<0.001]. The area under the curve (AUC) of APTw-t, APTw-p and ADC value for PMI diagnosis were 0.810, 0.831 and 0.806 respectively. In addition, the AUC value (0.918) of APTw-p + ADC was optimal, with a sensitivity and specificity of 91.20% and 87.20% respectively.
Conclusions:
APTw in peritumor tissues, combined with ADC value can be used to efficiently distinguish PMI of CC.
Insights
Amide proton transfer-weighted (APTw) parameters in peritumor tissue, combined with apparent diffusion coefficient (ADC) values, can effectively predict parametrial infiltration (PMI) in cervical cancer (CC). This MRI-based approach improves diagnostic accuracy for staging and treatment planning.
Area of Science:
- Radiology
- Oncology
- Medical Imaging
Background:
- Parametrial infiltration (PMI) is crucial for cervical cancer (CC) staging and treatment.
- The predictive value of peritumor amide proton transfer-weighted (APTw) parameters for PMI remains uncertain.
- This study investigates APTw's potential to enhance diffusion-weighted imaging (DWI) in diagnosing CC PMI.
Purpose of the Study:
- To evaluate the diagnostic performance of APTw parameters in peritumor tissues for predicting PMI in cervical cancer.
- To determine if combining APTw parameters with apparent diffusion coefficient (ADC) values improves the diagnostic accuracy of MRI for PMI detection.
Main Methods:
- Retrospective analysis of 81 patients with pathologically confirmed CC who underwent APTw MRI and DWI.
- Quantitative measurement of APTw values in tumor (APTw-t) and peritumor tissues (APTw-p), and ADC values.
- Receiver operating characteristic (ROC) curve analysis to assess diagnostic performance of individual and combined parameters.
Main Results:
- Patients with PMI showed significantly higher APTw-t and APTw-p values and lower ADC values compared to non-PMI patients (P<0.001).
- Individual parameters showed good diagnostic performance (AUCs: APTw-t 0.810, APTw-p 0.831, ADC 0.806).
- The combination of APTw-p and ADC yielded the optimal AUC of 0.918, with 91.20% sensitivity and 87.20% specificity for PMI detection.
Conclusions:
- APTw parameters in peritumor tissues are valuable indicators for predicting PMI in CC.
- Combining peritumor APTw values with ADC values significantly enhances the diagnostic capability of MRI for distinguishing CC with PMI.
