Contribution of diuresis renogram to the differential diagnosis of upper urinary tract diseases (obstructive uropathy)

Between 1986-1990 in 4.348 admitions to our department, 1201 were diagnosed to have upper urinary tract disease. 214 patients were subjected to renogram, 66 of them were subjected to diuresis renogram, 27 were males and 39 females. The diuresis renogram was alone to patients with: 1) UPJ obstruction, 2) Upper urinary tract stones, 3) Stenosis or tumor of the upper urinary tract, 4) post-traumatic urinoma, 5) Contralateral hydronephrosis, caused by probable Urinary tract tuberculosis, 6) Paraureteral venous plexus dilatation, 7) Chronical Renal failure caused by obstructive uropathy, 8) Tuberculosis of the urinary tract, 9) Multicystic kidney, chronical pyelonephritis, renal failure, 10) injurie to a multicystic kidney, 12) atrophic hypoplastic kidneys. Because all the above mentioned diseases, in spite of their diversity, have many very similar symptoms, a differential diagnosis is required. For that reason we have used the diuresis renogram as an additional differential diagnosis method, as well as a post operative or after conservative therapy follow up method of our patients. The diuresis renogram was the criteria for the differential diagnosis and for the choice of therapeutic treatment to the obstructive uropathy, that is to say diversified the dilatation with obstruction from the dilatation without obstruction (anatomical or functional stenosis). At the same time the diuresis renogram was an exceptional post operative follow up method for those that had been subjected to pyeloplasty. The diuresis renogram useful as an additional method was considered aiding the differential diagnosis, therapy and follow up of patients suffering from upper urinary tract stones and TBC of urinary tract. It proved to be of a minor importance diagnostic method for all other disease of the upper urinary tract that we have dealt with. In conclusion we believe that we have proved, according to the Hellenic and International bibliographic references, that the diuresis renogram is a non-invasive, precise and harmless method, exceptionaly unique for the differential diagnosis of the obstructive uropathy and useful for other upper urinary tract diseases.Από τους 4.348 ασθενείς της πενταετίας 1986-1990 που νοσηλεύθηκαν στο Ουρολογικό Τμήμα του Ν.Γ.Ν.Θ. «Ο Άγιος Δημήτριος» η διάγνωση πάθησης στο ανώτερο ουροποιητικό τέθηκε σε 1201 και ραδιοϊσοτοπικό νεφρόγραμμα τελέσθηκε σε 214 ενώ το διουρητικό νεφρόγραμμα αξιολογήθηκε σε 66 από αυτούς. Οι 27 ήταν άνδρες και 39 γυναίκες. Το διουρητικό νεφρόγραμμα τελέσθηκε σε ασθενείς με: 1) παθήσεις πυελοουρητηρικής συμβολής, 2) λιθίαση ανωτέρου ουροποιητικού, 3) στενώματα ή Ca άνω τριτημορίου ουρητήρος, 4) ουρίνωμα (μετατραυματικό), 5) ετερόπλευρη ουρητηροϋδρονέφρωση λόγω πιθανής TBC ουροποιητικού, 6) κιρσοειδή διάταση παραουρητηρικών φλεβών, 7) χρονία νεφρική ανεπάρκεια αποφρακτικής αιτιολογίας, 8) TBC ουροποιητικού, 9) πολυκυστικούς νεφρούς, χρόνια πυελονεφρίτιδα και χρόνια νεφρική ανεπάρκεια εν γένει, 10) κάκωση νεφρών, 11) κυστεοουρητηρική παλινδρόμηση και μεγαουρητήρες και 12) ρικνούς νεφρούς. Επειδή όλες αυτές οι παθήσεις, παρά την πολυμορφία τους, έχουν τόσο κοινή εκδήλωση που είναι απαραίτητη η διαφορική διάγνωση, χρησιμοποιήσαμε το διουρητικό νεφρόγραμμα σαν πρόσθετο διαφοροδιαγνωστικό μέσο καθώς και στη μετά χειρουργική ή συντηρητική θεραπεία παρακολούθηση των ασθενών μας. Το ραδιενεργό νεφρόγραμμα αποτέλεσε το κριτήριο διαφορικής διάγνωσης της επιλογής θεραπευτικής αντιμετώπισης στην αποφρακτική ουροπάθεια, δηλαδή διαχώρησε τη διάταση με απόφραξη από την διάταση χωρίς απόφραξη (οργανική ή λειτουργική στένωση). Ταυτόχρονα αποτέλεσε την κύρια μέθοδο μετεγχειρητικής παρακολούθησης των ασθενών που είχαν υποστεί πυελοπλαστική. Θεωρήθηκε χρήσιμο συμπλήρωμα στην διαφορική διάγνωση, θεραπεία και παρακολούθηση των ασθενών με λιθίαση του ανωτέρου ουροποιητικού και με TBC ουροποιητικού. Σε όλες τις άλλες παθήσεις του ανωτέρου ουροποιητικού που χρησιμοποιήθηκε, αποτέλεσε διαγνωστικό βοήθημα ήσσονος σημασίας. Συμπερασματικά, το διουρητικό νεφρόγραμμα αποτελεί μία μέθοδο ανώδυνη, ακριβή και ακίνδυνη, μοναδική στη διαφορική διάγνωση της αποφρακτικής ουροπάθειας και χρήσιμη στις υπόλοιπες παθήσεις του ανωτέρου ουροποιητικού

Decentralized semantic provision of personal health streams

Personalized healthcare is nowadays driven by the increasing volumes of patient data, observed and produced continuously thanks to medical devices, mobile sensors, patient-reported outcomes, among other data sources. This data is made available as streams, due to their dynamic nature, which represents an important challenge for processing, querying and interpreting the incoming information. In addition, the sensitive nature of healthcare data poses significant restrictions regarding privacy, which has led to the emergence of decentralized personal data management systems. Data semantics play a key role in order to enable both decentralization and integration of personal health data, as they introduce the capability to represent knowledge and information using ontologies and semantic vocabularies. In this paper we describe the SemPryv system, which provides the means to manage personal health data streams enriched with semantic information. SemPryv is designed as a decentralized system, so that users have the possibility of hosting their personal data at different sites, while keeping control of access rights. The semantization of data in SemPryv is implemented through different strategies, ranging from rule-based annotation to machine learning-based suggestions, fed from third-party specialized healthcare metadata providers. The system has been made available as Open Source, and is integrated as part of the Pryv.io platform used and commercialized in the healthcare and personal data management industr

QuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research

Background: Radiomics, the field of image-based computational medical biomarker research, has experienced rapid growth over the past decade due to its potential to revolutionize the development of personalized decision support models. However, despite its research momentum and important advances toward methodological standardization, the translation of radiomics prediction models into clinical practice only progresses slowly. The lack of physicians leading the development of radiomics models and insufficient integration of radiomics tools in the clinical workflow contributes to this slow uptake. Methods: We propose a physician-centered vision of radiomics research and derive minimal functional requirements for radiomics research software to support this vision. Free-to-access radiomics tools and frameworks were reviewed to identify best practices and reveal the shortcomings of existing software solutions to optimally support physician-driven radiomics research in a clinical environment. Results: Support for user-friendly development and evaluation of radiomics prediction models via machine learning was found to be missing in most tools. QuantImage v2 (QI2) was designed and implemented to address these shortcomings. QI2 relies on well-established existing tools and open-source libraries to realize and concretely demonstrate the potential of a one-stop tool for physician-driven radiomics research. It provides web-based access to cohort management, feature extraction, and visualization and supports “no-code” development and evaluation of machine learning models against patient-specific outcome data. Conclusions: QI2 fills a gap in the radiomics software landscape by enabling “no-code” radiomics research, including model validation, in a clinical environment. Further information about QI2, a public instance of the system, and its source code is available at https://medgift.github.io/quantimage-v2-info/. Key points As domain experts, physicians play a key role in the development of radiomics models.Existing software solutions do not support physician-driven research optimally.QuantImage v2 implements a physician-centered vision for radiomics research.QuantImage v2 is a web-based, “no-code” radiomics research platform.</p

QuantImage v2 ::a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research

Background : Radiomics, the field of image-based computational medical biomarker research, has experienced rapid growth over the past decade due to its potential to revolutionize the development of personalized decision support models. However, despite its research momentum and important advances toward methodological standardization, the translation of radiomics prediction models into clinical practice only progresses slowly. The lack of physicians leading the development of radiomics models and insufficient integration of radiomics tools in the clinical workflow contributes to this slow uptake. Methods We propose a physician-centered vision of radiomics research and derive minimal functional requirements for radiomics research software to support this vision. Free-to-access radiomics tools and frameworks were reviewed to identify best practices and reveal the shortcomings of existing software solutions to optimally support physician-driven radiomics research in a clinical environment. Results : Support for user-friendly development and evaluation of radiomics prediction models via machine learning was found to be missing in most tools. QuantImage v2 (QI2) was designed and implemented to address these shortcomings. QI2 relies on well-established existing tools and open-source libraries to realize and concretely demonstrate the potential of a one-stop tool for physician-driven radiomics research. It provides web-based access to cohort management, feature extraction, and visualization and supports “no-code” development and evaluation of machine learning models against patient-specific outcome data. Conclusions : QI2 fills a gap in the radiomics software landscape by enabling “no-code” radiomics research, including model validation, in a clinical environment. Further information about QI2, a public instance of the system, and its source code is available at https://medgift.github.io/quantimage-v2-info/. Key points: - As domain experts, physicians play a key role in the development of radiomics models. - Existing software solutions do not support physician-driven research optimally. - QuantImage v2 implements a physician-centered vision for radiomics research. - QuantImage v2 is a web-based, “no-code” radiomics research platform

Prediction of inactive disease in juvenile idiopathic arthritis : A multicentre observational cohort study

Objectives To predict the occurrence of inactive disease in JIA in the first 2 years of disease. Methods An inception cohort of 152 treatment-naïve JIA patients with disease duration <6 months was analysed. Potential predictors were baseline clinical variables, joint US, gut microbiota composition and a panel of inflammation-related compounds in blood plasma. Various algorithms were employed to predict inactive disease according to Wallace criteria at 6-month intervals in the first 2 years. Performance of the models was evaluated using the split-cohort technique. The cohort was analysed in its entirety, and separate models were developed for oligoarticular patients, polyarticular RF negative patients and ANA positive patients. Results All models analysing the cohort as a whole showed poor performance in test data [area under the curve (AUC): <0.65]. The subgroup models performed better. Inactive disease was predicted by lower baseline juvenile arthritis DAS (JADAS)-71 and lower relative abundance of the operational taxonomic unit Mogibacteriaceae for oligoarticular patients (AUC in test data: 0.69); shorter duration of morning stiffness, higher haemoglobin and lower CXCL-9 levels at baseline for polyarticular RF negative patients (AUC in test data: 0.69); and shorter duration of morning stiffness and higher baseline haemoglobin for ANA positive patients (AUC in test data: 0.72). Conclusion Inactive disease could not be predicted with satisfactory accuracy in the whole cohort, likely due to disease heterogeneity. Interesting predictors were found in more homogeneous subgroups. These need to be validated in future studies