The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study

Abstract

This study aims the ability of first-order histogram-based features, derived from ADC maps, to predict the occurrence of metachronous metastases (MM) in rectal cancer. A total of 52 patients with pathologically confirmed rectal adenocarcinoma were retrospectively enrolled and divided into two groups: patients who developed metachronous metastases (n = 15) and patients without metachronous metastases (n = 37). We extracted 17 first-order (FO) histogram-based features from the pretreatment ADC maps. Student’s t-test and Mann–Whitney U test were used for the association between each FO feature and presence of MM. Statistically significant features were combined into a model, using the binary regression logistic method. The receiver operating curve analysis was used to determine the diagnostic performance of the individual parameters and combined model. There were significant differences in ADC 90th percentile, interquartile range, entropy, uniformity, variance, mean absolute deviation, and robust mean absolute deviation in patients with MM, as compared to those without MM (p values between 0.002–0.01). The best diagnostic was achieved by the 90th percentile and uniformity, yielding an AUC of 0.74 [95% CI: 0.60–0.8]). The combined model reached an AUC of 0.8 [95% CI: 0.66–0.90]. Our observations point out that ADC first-order features may be useful for predicting metachronous metastases in rectal cancer.

Whole-Lesion Apparent Diffusion Coefficient Histogram Analysis for Assessing Normal-Sized Lymph Node Metastasis in Cervical Cancer: Comparison Between Readout-Segmented and Single-Shot Echo-Planar Diffusion-Weighted Imaging

“…In our study, ADC 90 values of both SS-EPI and RS-EPI in group B were lower than those in group A. A possible explanation for this finding could be that more aggressive tumors may contain many invisible cystic and necrotic components 30 . However, Zhang et al 6 reported that ADC min had the highest value in distinguishing normal-sized LNM in cervical cancer.…”

“…A possible explanation for this finding could be that more aggressive tumors may contain many invisible cystic and necrotic components. 30 However, Zhang et al 6 reported that ADC min had the highest value in distinguishing normal-sized LNM in cervical cancer. Although ADC min theoretically best represents the most aggressive component of a tumor, it is more susceptible to noise, artifacts, and adjacent structures.…”

Predicting Response to Exclusive Combined Radio-Chemotherapy in Naso-Oropharyngeal Cancer: The Role of Texture Analysis

“…While the first order overall describes the distribution of voxel intensity within the image region through commonly used and basic metrics, the histogram feature specifically describe the frequency distribution of voxel intensity. Histogram features on ADC maps have been shown to yield significant predictive performance in a multitude of studies-such as in Boca Petresc et al [43], Lenoir et al [27], Rodrigues et al [44], and Fujima et al [45]-and, as such, it is intuitive that they would reveal more about microstructural alterations in the primary lesion in this current study.…”