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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. [ABSTRACT FROM AUTHOR]

De Wet Affectieschade in de praktijk; duidelijke afbakening of onsmakelijke discussies?: In dit artikel bespreken de auteurs de sinds de invoering van de Wet Affectieschade – en binnen het temporele toepassingsbereik van deze wet – verschenen rechtspraak, voor zover daarin de vraag of aanspraak kon worden gemaakt op vergoeding van affectieschade onderwerp van (partij)debat is geweest. Zij bezien in hoeverre in de rechtspraktijk tegemoet wordt gekomen aan de doelstellingen van de wetgever, en bespreken welke vragen (kunnen) rijzen bij de uitleg van deze wettelijke regeling. [ABSTRACT FROM AUTHOR]

De actualiteit en toekomst van de toepassing van whiplashjurisprudentie buiten whiplashzaken: Steeds vaker wordt in niet-whiplashzaken een beroep gedaan op de zogenaamde whiplashjurisprudentie. Met een beroep op de redeneringen uit deze jurisprudentie wordt door benadeelden getracht om het bestaan van veelal substraatloze klachten en het (juridisch) causaal verband tussen deze klachten en beperkingen en het incident aan te tonen. Is toepassing van de whiplashjurisprudentie in niet-whiplashzaken gerechtvaardigd, of is daarmee het spreekwoordelijke hek van de dam? Door middel van een analyse van de whiplashjurisprudentie en recente jurisprudentie in niet-whiplashzaken komen de auteurs tot de conclusie dat een juiste toepassing van de zogenoemde causaliteitsregels uit de whiplashjurisprudentie in niet-whiplashzaken tot rechtvaardige uitkomsten leidt, mits men daarbij de hoofdregel en de beginselen van het bewijsrecht niet uit het oog verliest. [ABSTRACT FROM AUTHOR]

North and south of the equator a band with water-scarce countries is found (Middle East, North Africa, Asia, the Americas). Water-scarce areas also frequently have to deal with soil salinity and brackish water conditions. Due to these problems food self-sufficiency is at stake in many of these countries. A logical choice for Governments therefore would be to focus on food crops that have a low water footprint and that are salt tolerant. Potato is the champion staple crop with a high nutritional value and a low water footprint. Potato is therefore an excellent crop to be grown under water scarce conditions. Salt tolerant potatoes are not yet commercially available. Egypt is a typical example of a water scarce country with ample brackish water available and with a considerable food production deficit. Egypt has a strong potato growing and processing industry. Potato growers are running into the problems of increasing soil and water salinity. The paper reports on the results of a pilot project that combines the “more crop per drop” expertise of Wageningen UR with expertise on highly advanced soil moisture monitoring systems (DACOM) and innovative potato breeding (MEIJER), reinforced by the knowledge of local market and crop cultivation practices brought in by the Egyptian partners, the „Plant Systems‟ company and the Domiatec Group. The pilot project had as main objective to select for salt tolerant potato cultivars in order to make brackish waters available for food production. Results include that (i) several potato varieties were found to perform well, for irrigating with water of a salinity up to 7.5 dS/m. Adequate water and crop management are a pre-requisite for this; (ii) there are substantial differences between potato varieties. This offers opportunities for improvement by plant breeders; (iii) soil moisture monitoring has been extended with monitoring of soil salinity and sensors proved to be important tools to extend knowledge of sols conditions. Placement of sensors in combination with non-uniform water application is not easy. [ABSTRACT FROM AUTHOR]

Summary. 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. [ABSTRACT FROM AUTHOR]

The increased input of fertilizers and animal wastes after 1950 has boosted agricultural crop production to a high level in many industrialized countries, but it has also contributed to increased nitrogen and phosphorus emissions from agriculture to groundwaters and surface waters. This paper summarizes the pathways and controls of nitrogen and phosphorus losses to surface waters, and it presents estimates andpredictions of the losses from agricultural soils in The Netherlandsinto surface waters, before and after the implementation of policiesand measures to reduce nutrient losses from agriculture. Implementation of the nutrient accounting system MINAS, aiming at a step-wise lowering of nitrogen and phosphorus surpluses at farm level, will decrease the total nitrogen and phosphorus surpluses between the years 1985 and 2008 by 58 and 82%, respectively. These large decreases are theresult of a strong decrease in the input via fertilizers and animal wastes, combined with only a minor decrease in the output via harvested products. Nitrogen emissions from agricultural land to surface waters will decrease by 38% between 1985 and 2008. Phosphorus emissions from agricultural land to surface waters are expected not to decreaseon the short term. [ABSTRACT FROM AUTHOR]

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.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.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.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.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.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.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.Materials and methods: 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.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.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.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.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.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.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.Results: 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.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.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.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.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.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.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.Conclusions: 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.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.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.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.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.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.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.Clinical relevance statement: 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.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.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.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.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.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.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.Key Points: 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.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.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.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.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.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.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. [ABSTRACT FROM AUTHOR]