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OBJECTIVES: This study investigated patients' acceptance of artificial intelligence (AI) for diagnosing prostate cancer (PCa) on MRI scans and the factors influencing their trust in AI diagnoses. MATERIALS AND METHODS: A prospective, multicenter study was conducted between January and November 2023. Patients undergoing prostate MRI were surveyed about their opinions on hypothetical AI assessment of their MRI scans. The questionnaire included nine items: four on hypothetical scenarios of combinations between AI and the radiologist, two on trust in the diagnosis, and three on accountability for misdiagnosis. Relationships between the items and independent variables were assessed using multivariate analysis. RESULTS: A total of 212 PCa suspicious patients undergoing prostate MRI were included. The majority preferred AI involvement in their PCa diagnosis alongside a radiologist, with 91% agreeing with AI as the primary reader and 79% as the secondary reader. If AI has a high certainty diagnosis, 15% of the respondents would accept it as the sole decision-maker. Autonomous AI outperforming radiologists would be accepted by 52%. Higher educated persons tended to accept AI when it would outperform radiologists (p < 0.05). The respondents indicated that the hospital (76%), radiologist (70%), and program developer (55%) should be held accountable for misdiagnosis. CONCLUSIONS: Patients favor AI involvement alongside radiologists in PCa diagnosis. Trust in AI diagnosis depends on the patient's education level and the AI performance, with autonomous AI acceptance by a small majority on the condition that AI outperforms a radiologist. Respondents held the hospital, radiologist, and program developers accountable for misdiagnosis in descending order of accountability. CLINICAL RELEVANCE STATEMENT: Patients show a high level of acceptance for AI-assisted prostate cancer diagnosis on MRI, either alongside radiologists or fully autonomous, particularly if it demonstrates superior performance to radiologists alone. KEY POINTS: Prostate cancer suspicious patients may accept autonomous AI based on performance. Patients prefer AI involvement alongside a radiologist in diagnosing prostate cancer. Patients indicate accountability for AI should be shared among multiple stakeholders.

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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.

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In the past, environmental Phosphorus (P) parameters like soil P indices have been used to catogorize the potential risk of P losses from agricultural land. In order to assess the actual risk of P pollution of groundwater and surface waters, dynamic process oriented soil and water quality models have been frequently used. Recently, an approximating model for phosphorus, called SIMPLE, has been developed. This model approximates the output from a complex dynamic water quality model. The approximating model is called a metamodel. This simple P-model proves to be a powerful tool for quick assessment of the risk of P pollution from agricultural land to surface waters.

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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.

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