Related Experiment Video
Updated: Jan 24, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
Predictive ability of DWI/ADC and DCE-MRI kinetic parameters in differentiating benign from malignant breast lesions
Atiya Allarakha1, Yan Gao1, Hong Jiang1
1Department of Radiology and Medical Imaging, Tongji Hospital affiliated to Tongji University School of Medicine, Shanghai 200065, China.
Objectives:
To analyze DWI/ADC and DCE-MRI kinetic parameters on breast MRI between benign and malignant lesions, to obtain independent predictors of malignancy, and to build a possible future predictive model.
Methods:
121 patients (151 breast lesions) were evaluated by 3.0T breast MRI. Based on their post-operative histopathology, we divided them into two groups, benign and malignant. We processed their DCE-MRI images to obtain size of lesions and several kinetic parameters like Time Intensity Curve (TIC), Time To Peak (TTP), Area Under Curve (AUC), Maximum Enhancement, Wash-In (WI), and Wash-Out (WO). We also processed their DWI/ADC maps to obtain their Signal Intensity (SI) ADC values. These parameters were compared between the two groups.
Results:
The age of the patients was higher in the malignant group as compared to the benign group (mean=55.9 years and 47.3 years, respectively, P<0.001). Malignant lesions were of greater size than benign ones (mean 2.55 cm and 1.82 cm, respectively, P<0.01). On DCE-MRI, parameters such as TIC, AUC, WI, and WO were statistically significantly different (all P values <0.05). On DWI/ADC, malignant lesions had statistically significant lower ADC values than benign lesions (mean of 1.02x0.001 and 1.72x0.001 mm2/s, respectively, P<0.001). We obtained a cutoff ADC value of 1.18x0.001 mm2/s to differentiate between benign and malignant lesions. Our predictive model results showed ADCmean and TIC as independent predictors of malignancy with an excellent combined model of fit (P=0.000).
Conclusion:
Kinetic DCE-MRI and DWI parameters are significantly different between benign and malignant breast lesions. ADCmean and TIC are reliable independent predictors of malignancy in our prediction model, with excellent goodness of fit to predict cancer with a sensitivity of 94.2%.
Related Concept Videos
Predicting Molecular Geometry
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
End Point Prediction: Gran Plot
For potentiometric titration, the Gran plot is created by plotting...
Sensitivity, Specificity, and Predicted Value
Sensitivity is the...
Predicting Reaction Outcomes
Predicting Products: Substitution vs. Elimination
The following factors can influence the mechanisms competing against each other:

