Histogram-based analysis of diffusion-weighted imaging for predicting aggressiveness in papillary thyroid carcinoma

Area of Science:

• Radiology
• Oncology
• Medical Imaging

Background:

• Papillary thyroid carcinoma (PTC) aggressiveness impacts treatment decisions.
• Accurate prediction of PTC aggressiveness is crucial for patient management.
• Non-invasive imaging biomarkers are needed to assess PTC tumor behavior.

Purpose of the Study:

• To evaluate the utility of apparent diffusion coefficient (ADC) maps in predicting PTC aggressiveness.
• To assess the performance of whole-tumor histogram-based analysis of ADC maps for PTC.
• To develop a predictive model for PTC aggressiveness using MRI-derived histogram features.

Main Methods:

• Retrospective analysis of 88 PTC patients who underwent neck MRI.
• Extraction and comparison of whole-lesion histogram features from ADC maps between aggressive and non-aggressive PTC groups.
• Multivariable logistic regression and ROC curve analysis to develop a predictive model.

Main Results:

• Five histogram features were incorporated into the final predictive model.
• ADC_firstorder_TotalEnergy demonstrated significant predictive performance (AUC = 0.77).
• The combined predictive model achieved an optimal performance with an AUC of 0.88 and accuracy of 0.75.

Conclusions:

• Whole-lesion histogram analysis of ADC maps is a valuable tool for evaluating PTC aggressiveness.
• ADC map-based histogram analysis can aid in non-invasively predicting tumor behavior in PTC.
• This imaging approach offers potential for improved risk stratification in PTC patients.