AI-assisted biparametric MRI surveillance of prostate cancer: feasibility study

OBJECTIVES

To evaluate the feasibility of automatic longitudinal analysis of consecutive biparametric MRI (bpMRI) scans to detect clinically significant (cs) prostate cancer (PCa).

METHODS

This retrospective study included a multi-center dataset of 1513 patients who underwent bpMRI (T2 + DWI) between 2014 and 2020, of whom 73 patients underwent at least two consecutive bpMRI scans and repeat biopsies. A deep learning PCa detection model was developed to produce a heatmap of all PIRADS ≥ 2 lesions across prior and current studies. The heatmaps for each patient's prior and current examination were used to extract differential volumetric and likelihood features reflecting explainable changes between examinations. A machine learning classifier was trained to predict from these features csPCa (ISUP > 1) at the current examination according to biopsy. A classifier trained on the current study only was developed for comparison. An extended classifier was developed to incorporate clinical parameters (PSA, PSA density, and age). The cross-validated diagnostic accuracies were compared using ROC analysis. The diagnostic performance of the best model was compared to the radiologist scores.

RESULTS

The model including prior and current study (AUC 0.81, CI: 0.69, 0.91) resulted in a higher (p = 0.04) diagnostic accuracy than the current only model (AUC 0.73, CI: 0.61, 0.84). Adding clinical variables further improved diagnostic performance (AUC 0.86, CI: 0.77, 0.93). The diagnostic performance of the surveillance AI model was significantly better (p = 0.02) than of radiologists (AUC 0.69, CI: 0.54, 0.81).

CONCLUSIONS

Our proposed AI-assisted surveillance of prostate MRI can pick up explainable, diagnostically relevant changes with promising diagnostic accuracy.

KEY POINTS

• Sequential prostate MRI scans can be automatically evaluated using a hybrid deep learning and machine learning approach. • The diagnostic accuracy of our csPCa detection AI model improved by including clinical parameters.

Daily rat tibial growth in vivo following hypothalamic sex reversal with neonatal and pubertal treatments with gonadal steroids

A striking sex-related difference in postpubertal growth and growth hormone (GH) secretory pattern in the rat has been described. Although this sexual dimorphism seems to be determined by the neonatal effects of gonadal steroids on the hypothalamus, peripubertal exposure to steroids also plays an important role. In order to study the real influence of the hypothalamic sex and/or peripubertal gonadal steroids, the growth pattern of female and male rats in response to neonatal and peripubertal sexual steroid treatments was studied using microknemometry, a technique that allows non-invasive daily measurements of rat tibial growth rate. Neonatal steroid environment in males was modified by castration on day 1, whereas in females it was changed by a single neonatal testosterone administration on day 5 followed by castration at 13 days of age. From the onset of puberty to adulthood, both female and male animals received testosterone or estrogens, respectively. Neonatal treatment alone, i.e. androgenization of female and castration of male rats, were only able to induce a partial reversal of the original sex-dependent growth pattern. Additional peripubertal treatments achieved a complete change in the sex-linked growth pattern. Consistent with the effects observed on growth, the pituitary GH concentration was significantly increased in females, and diminished in males, when they were treated both at the neonatal and peripubertal stages. However, only this latter group, whose growth was more seriously compromised, showed decreased plasma insulin-like growth factor-I (IGF-I) levels. In conclusion, a complete feminization of male tibial growth pattern or masculinization of female pattern can only be achieved by maintaining the new steroid environment from puberty to adulthood.