Magnetic resonance imaging -based radiomics of the pituitary gland is highly predictive of precocious puberty in girls: a pilot study

Abstract

Background: The aim of the study was to explore a radiomic model that could assist physicians in the diagnosis of central precocious puberty (CPP). A predictive model based on radiomic features (RFs), extracted form magnetic resonance imaging (MRI) of the pituitary gland, was thus developed to distinguish between CPP and control subjects.

Methods: 45 girls with confirmed diagnosis of CPP (CA:8.4 ± 0.9 yr) according to the current criteria and 47 age-matched pre-pubertal control subjects (CA:8.7 ± 1.2 yr) were retrospectively enrolled. Two readers (R1, R2) blindly segmented the pituitary gland on MRI studies for RFs and performed a manual estimation of the pituitary volume. Radiomics was compared against pituitary volume in terms of predictive performances (metrics: ROC-AUC, accuracy, sensitivity and specificity) and reliability (metric: intraclass correlation coefficient, ICC). Pearson correlation between RFs and auxological, biochemical, and ultrasound data was also computed.

Results: Two different radiomic parameters, Shape Surface Volume Ratio and Glrlm Gray Level Non-Uniformity, predicted CPP with a high diagnostic accuracy (ROC-AUC 0.81 ± 0.08) through the application of our ML algorithm. Anthropometric variables were not confounding factors of these RFs suggesting that premature thelarche and/or pubarche would not be potentially misclassified. The selected RFs correlated with baseline and peak LH (p < 0.05) after GnRH stimulation. The diagnostic sensitivity was improved compared to pituitary volume only (0.76 versus 0.68, p<0.001) and demonstrated higher inter-reader reliability (ICC>0.57 versus ICC=0.46).

Discussion: Radiomics is a promising tool to diagnose CPP as it reflects also functional aspects. Further studies are warranted to validate these preliminary data.

Keywords: central precocious puberty; machine learning; magnetic resonance imaging; pituitary gland; precocious puberty, puberty; radiomics.

Figure 1

Cumulative scores of the top 15 radiomic features, calculated as the sum of scores obtained over 100 rounds. (A) Feature scores derived from R1 segmentation and (B) feature scores obtained from R2 segmentation.

Figure 2

Model performances of radiomic and reference models. Error bars represent 95% confidence intervals. Single thick line: training set; Double thin lines: validation set; Orange: radiomics; Blue: Pituitary Volume using the ellipsoid method.

Figure 3

Scatter plots of training cases as determined by Surface Volume Ratio (Shape) and Gray Level Non-Uniformity (Glrlm) for both readers. The line that better discriminated the two classes is drawn. N (negative class): control cases; Y (positive class): CPP cases. Black cases denote correct predictions, while red cases are wrong predictions.

Figure 4

Scatter plot of validation cases as determined by Surface Volume Ratio (Shape) and Gray Level Non-Uniformity (Glrlm). Red color (negative class): control cases. Green color (positive class): CPP cases. Circle: correctly classified patients. Triangle: patients wrongly classified.

Figure 5

Graphical representation of four representative cases that help to interpret the classification mechanism of the developed predictive models.