Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

Objective: We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user. Study Design and Setting: Ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group greater or equal 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach. Results: Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history

Rahmenkonzept der Hochschulen des Landes Baden-Württemberg für datenintensive Dienste – bwDATA Phase III (2020-2024)

Das zentrale Ziel von bwDATA in Phase III ist die optimale Unterstützung der Wissenschaft in den Belangen der Datenspeicherung und des nachhaltigen Forschungsdatenmanagements ebenso wie die Versorgung der Landeshochschulen mit auf ihre jeweiligen Belange und Bedürfnisse angepassten Speicherstrukturen und darauf basierenden Diensten. Dem Beispiel des bwHPC-Konzepts folgend werden hierbei enge Abstimmung, Kooperation und Arbeitsteilung zwischen den beteiligten Einrichtungen vertieft. Das vorliegende Rahmenkonzept soll dabei nicht als absoluter Leitfaden für die Periode 2020 bis 2024 dienen, es will vielmehr für die verschiedenen Bereiche der Wissenschaft, für Forschung, Lehre und Administration die Rahmenbedingungen für den koordinierten Aufbau und Betrieb speicherintensiver Dienste definieren. bwDATA basiert dabei auf einer gemeinsamen, strategischen Vorgehensweise aller Universitäten, Hochschulen der angewandten Wissenschaften, Pädagogischen Hochschulen, Kunst- und Musikhochschulen, der Dualen Hochschule Baden-Württembergs, der Landesbibliotheken und des Landesarchivs. Ein wesentliches Ziel von bwDATA Phase III ist der verbesserte Umgang mit großen wissenschaftlichen Datenmengen über den gesamten Data Life Cycle in der BaWü-Datenföderation und damit auch der verstärkte Aufbau des Forschungsdatenmanagements für die beteiligten wissenschaftlichen Einrichtungen bis hin zu Backup und Langzeitarchivierung. Das Rahmenkonzept bwDATA definiert die Möglichkeit, die Wissenschaft in den Teilgebieten Forschung, Lehre und Administration durch Verbessern vorhandener und Aufbau neuer Lösungen flexibel zu unterstützen

Borexino : geo-neutrino measurement at Gran Sasso, Italy

Geo-neutrinos, electron anti-neutrinos produced in beta-decays of naturally occurring radioactive isotopes in the Earth, are a unique direct probe of our planet's interior. After a brief introduction of the geo-neutrinos' properties and of the main aims of their study, we discuss the features of a detector which has recently provided breakthrough achievements in the field, Borexino, a massive, calorimetric liquid scintillator detector installed at the underground Gran Sasso Laboratory. With its unprecedented radiopurity levels achieved in the core of the detection medium, it is the only experiment in operation able to study in real time solar neutrino interactions in the challenging sub-MeV energy region. Its superior technical properties allowed Borexino also to provide a clean detection of terrestrial neutrinos. Therefore, the description of the characteristics of the detected geo-neutrino signal and of the corresponding geological implications are the main core of the discussion contained in this work

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction

BACKGROUND We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user. METHODS Ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach. RESULTS Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history. CONCLUSION Developers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction.

BackgroundWe compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user.MethodsTen North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach.ResultsAmong 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history.ConclusionDevelopers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors

Borexino: Geo-neutrino measurement at Gran Sasso, Italy

Geo-neutrinos, electron anti-neutrinos produced in b-decays of naturally occurring radioactive isotopes in the Earth, are a unique direct probe of our planet\u2019s interior. After a brief introduction of the geo-neutrinos\u2019 properties and of the main aims of their study, we discuss the features of a detector which has recently provided breakthrough achievements in the field, Borexino, a massive, calorimetric liquid scintillator detector installed at the underground Gran Sasso Laboratory. With its unprecedented ra-diopurity levels achieved in the core of the detection medium, it is the only experiment in operation able to study in real time solar neutrino interactions in the challenging sub-MeV energy region. Its superior technical properties allowed Borexino also to provide a clean detection of terrestrial neutrinos. Therefore, the description of the characteristics of the detected geo-neutrino signal and of the corresponding geological implications are the main core of the discussion contained in this work

Solar Neutrinos Spectroscopy with Borexino Phase-II

International audienceSolar neutrinos have played a central role in the discovery of the neutrino oscillation mechanism. They still are proving to be a unique tool to help investigate the fusion reactions that power stars and further probe basic neutrino properties. The Borexino neutrino observatory has been operationally acquiring data at Laboratori Nazionali del Gran Sasso in Italy since 2007. Its main goal is the real-time study of low energy neutrinos (solar or originated elsewhere, such as geo-neutrinos). The latest analysis of experimental data, taken during the so-called Borexino Phase-II (2011-present), will be showcased in this talk—yielding new high-precision, simultaneous wide band flux measurements of the four main solar neutrino components belonging to the “pp” fusion chain (pp, pep, 7 Be, 8 B), as well as upper limits on the remaining two solar neutrino fluxes (CNO and hep)