Our study suggests that histogram assessment of the ADC map, a non-invasive tool, could predict aggressiveness in papillary thyroid carcinoma. A total of six features were selected for the final model. ADC_firstorder_Maximum was the most promising predictive parameter with an AUC of 0.83. The final model had a satisfactory potential in predicting PTC aggressiveness, with an accuracy of 0.82 and an AUC of 0.88.
Radiomics has been widely applied in predicting clinical prognosis, pathological grading and response to treatment recently since it enables the quantitative assessment of intratumor parameters, transforming them into high-throughput parameters, mostly comprising histogram and texture features[21, 22]. Histogram analysis through conversion of MRI-based parameters in primary tumors could successfully detect aggressiveness in multiple lesions[23, 24]. This study aimed to examine whole-lesion histogram analysis based on ADC maps for its ability to predict the aggressiveness of PTC. As a result, a predictive model was built, with an improved performance in predicting tumor aggressiveness (AUC of 0.88). The above finding indicates histogram analysis of ADC maps may provide more biological data and constitute a better surrogate imaging-derived tool for detecting PTC aggressiveness. Additionally, histogram assessment may better meet the clinical needs, for its easy implementation and data interpretation without requirement of sound mathematical knowledge.
Routine DWI is not reliable in providing good thyroid image quality because of susceptibility and motion artifacts, potentially rendering lesion determination difficult. Here, we utilized the reduced FOV diffusion strategy in lieu of routine DWI to image the thyroid, which is considered to provide high-resolution and high-quality DWI for small structures[25–27]. An 8-channel special neck surface coil was used to allow higher image quality while reducing susceptibility artifacts and distortions around the thyroid. In addition, ADC obtained according to manually selected ROIs is very subjective and variable. In this study, whole-lesion histogram assessment was utilized to examine the whole tumor, eliminating sample bias and enhancing the evaluation of intra-tumor heterogeneity[17, 28–30]. We found 6 ADC histogram parameters showed reduced values in aggressive PTC compared with non-aggressive cases, and ADC_firstorder_Maximum had the best discriminative performance. The discrepant ADC histogram features may reflect histopathological differences between aggressive and non-aggressive PTCs. For example, severer desmoplastic response and higher cell density in aggressive PTCs reduce diffusion, lowering ADC, while follicle and extracellular fluid abundance as well as reduced cell density in non-aggressive cases yield higher ADC values. These findings indicate the greater the heterogeneity of tumor cellularity, the more aggressive the PTC, reflected by ETE, nodular metastasis and aggressive histopathology.
This study had three major limitations. First, the sample size was small (88 cases), which could result from selection bias due to exclusion criteria including small tumor size and poor image quality. Advances in MRI might help detect smaller PTC lesions and achieve high image quality. Secondly, another selection bias may exist because some PTC cases who underwent ultrasound examination without MR scanning were not enrolled in this study. Thirdly, for predicting PTC aggressiveness, ADC values were not compared to other imaging features, including diffusion kurtosis imaging (DKI), which have also been utilized to assess thyroid nodules and related histological features. Nevertheless, these results were encouraging, and whole-lesion histogram analysis deserves popularization and wide application because it is convenient to carry out as a non-invasive imaging marker for predicting aggressiveness and therapeutic outcome in PTC.