Vergleich der diagnostischen Genauigkeit von konventioneller Röntgenmammographie mit der MR-Mammographie im Langzeitverlauf

Die Magnet-Resonanz-Mammographie (MRM) gilt als das sensitivste Verfahren zur Detektion maligner Neoplasien der Mamma, gleichzeitig jedoch ist die Spezifität der Methode umstritten. Die vorliegende Arbeit vergleicht als retrospektive Kohortenstudie die diagnostische Genauigkeit der MRM mit der konventionellen Röntgenmammographie (XRM) in einem klinischen Patientenkollektiv. Erfasst wurden die Befunde aller 342 Patienten, die im Zeitraum vom 01.01.2005 bis zum 20.03.2006 eine MRM an der FSU Jena erhielten und für diese eine Überweisung aus der Klinik für Frauenheilkunde und Geburtshilfe der FSU Jena hatten. Um den direkten Vergleich der Methode mit der XRM anzustellen, wurden die Befunde der zeitlich vor der MRM durchgeführten XRM erfasst (74,6% der Patienten verfügten über eine solche). In 91,6% der Fälle konnte eine Follow-Up-Untersuchung ermittelt werden. Als hinreichend wurden für das Follow-Up eine XRM, MRM, Ultraschalluntersuchung, histopathologische Absicherung oder eine klinische Untersuchung angesehen. In einem zweiten Arbeitsschritt wurden alle histopathologisch gesicherten Herdbefunde (n=216) von zwei Untersuchern reevaluiert und nach den aktuellen BI-RADS Kriterien bewertet. Für die MRM (XRM) resultierten die folgenden statistischen Kennzahlen: Sensitivität 96,9% (64,8%), Spezifität 94,0% (93,0%), positiver prädiktiver Wert 79,1% (70,8%), negativer prädiktiver Wert (NPW) 99,3% (90,9%) sowie diagnostische Genauigkeit 94,5% (87,1%). Statistisch signifikant war die MRM der XRM hinsichtlich Sensitivität, diagnostischen Genauigkeit und ihres NPW überlegen. Im Unterschied zur MRM hing die Genauigkeit der Röntgenmammographie stark von der Dichte des Brustdrüsenparenchyms ab. Die Reevaluation der 216 Herdbefunde zeigte, dass ein überproportional großer Anteil falsch-positiver MRM Befunde der Gruppe der non-mass Läsionen entstammt. Dies legt eine Überarbeitung des Kataloges an Deskriptoren für diese Tumorentität nahe

Living systematic review and meta-analysis of the prostate MRI diagnostic test with Prostate Imaging Reporting and Data System (PI-RADS) assessment for the detection of prostate cancer:study protocol

INTRODUCTION: The Prostate Imaging Reporting and Data System (PI-RADS) standardises reporting of prostate MRI for the detection of clinically significant prostate cancer. We provide the protocol of a planned living systematic review and meta-analysis for (1) diagnostic accuracy (sensitivity and specificity), (2) cancer detection rates of assessment categories and (3) inter-reader agreement. METHODS AND ANALYSIS: Retrospective and prospective studies reporting on at least one of the outcomes of interest are included. Each step that requires literature evaluation and data extraction is performed by two independent reviewers. Since PI-RADS is intended as a living document itself, a 12-month update cycle of the systematic review and meta-analysis is planned. This protocol is in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses—Protocols statement. The search strategies including databases, study eligibility criteria, index and reference test definitions, outcome definitions and data analysis processes are detailed. A full list of extracted data items is provided. Summary estimates of sensitivity and specificity (for PI-RADS ≥3 and PI-RADS ≥4 considered positive) are derived with bivariate binomial models. Summary estimates of cancer detection rates are calculated with random intercept logistic regression models for single proportions. Summary estimates of inter-reader agreement are derived with random effects models. ETHICS AND DISSEMINATION: No original patient data are collected, ethical review board approval, therefore, is not necessary. Results are published in peer-reviewed, open-access scientific journals. We make the collected data accessible as supplemental material to guarantee transparency of results. PROSPERO REGISTRATION NUMBER: CRD42022343931

The Lodwick classification for grading growth rate of lytic bone tumors: a decision tree approach

The estimation of growth rate of lytic bone tumors based on conventional radiography has been extensively studied. While benign tumors exhibit slow growth, malignant tumors are more likely to show fast growth. The most frequently used algorithm for grading of growth rate on conventional radiography was published by Gwilym Lodwick. Based on the evaluation of the four descriptors (1) type of bone destruction (including the subdescriptor 'margin' for geographic lesions), (2) penetration of cortex, (3) presence of a sclerotic rim, and (4) expanded shell, an overall growth grade (IA, IB, IC, II, III) can be assigned, with higher grade representing faster tumor growth. In this article, we provide an easy-to-use decision tree of Lodwick's original grading algorithm, suitable for teaching of students and residents. Subtleties of the grading algorithm and potential pitfalls in clinical practice are explained and illustrated. Exemplary conventional radiographs provided for each descriptor in the decision tree may be used as a guide and atlas for assisting in evaluation of individual features in daily clinical practice

Updating the coal quality parameters in multiple production benches based on combined material measurement: a full case study

An efficient resource model updating framework concept was proposed aiming for the improvement of raw material quality control and process efficiency in any type of mining operation. The concept integrates sensor data measured online on the production line into the resource or grade/quality control model and continuously provides locally more accurate estimates. The concept has been applied in a lignite field with the aim of identifying local impurities in a coal seam and to improve the prediction of coal quality attributes in neighbouring blocks. A significant improvement was demonstrated which led to better coal quality management. So far, the proposed concept and the application in coal mining was limited to a case where online measurements were unambiguously trackable due to a single extraction face being the point of origin for the material. This contribution presents an extension to the case, where characteristics from blended material, originating from two or three simultaneously operating extraction faces, are measured. The challenge tackled in this contribution is the updating of local coal quality estimates in different production benches based on measurements of a blended material stream. For a practical application of the updating concept, which is based on the Ensemble Kalman Filter, a simple method for generating prior ensemble members based on block geometries defined in the short-term model and the variogram, is discussed. This method allows for a fast, semi-automated and rather simple generation of prior models instead of generating a fully simulated deposit model using conditional simulation in geostatistics. It should foster operational implementation in an industrial environment. The main purpose of this article is to investigate the applicability of the developed framework with a simplified prior resource model. In addition to this any model improvements due to the integration of sensor data obtained by observing a blend of coal from multiple extraction faces is investigated.</p

Cancer detection rates of the PI-RADSv2.1 assessment categories: systematic review and meta-analysis on lesion level and patient level

Background!#!The Prostate Imaging Reporting and Data System, version 2.1 (PI-RADSv2.1) standardizes reporting of multiparametric MRI of the prostate. Assigned assessment categories are a risk stratification algorithm, higher categories indicate a higher probability of clinically significant cancer compared to lower categories. PI-RADSv2.1 does not define these probabilities numerically. We conduct a systematic review and meta-analysis to determine the cancer detection rates (CDR) of the PI-RADSv2.1 assessment categories on lesion level and patient level.!##!Methods!#!Two independent reviewers screen a systematic PubMed and Cochrane CENTRAL search for relevant articles (primary outcome: clinically significant cancer, index test: prostate MRI reading according to PI-RADSv2.1, reference standard: histopathology). We perform meta-analyses of proportions with random-effects models for the CDR of the PI-RADSv2.1 assessment categories for clinically significant cancer. We perform subgroup analysis according to lesion localization to test for differences of CDR between peripheral zone lesions and transition zone lesions.!##!Results!#!A total of 17 articles meet the inclusion criteria and data is independently extracted by two reviewers. Lesion level analysis includes 1946 lesions, patient level analysis includes 1268 patients. On lesion level analysis, CDR are 2% (95% confidence interval: 0-8%) for PI-RADS 1, 4% (1-9%) for PI-RADS 2, 20% (13-27%) for PI-RADS 3, 52% (43-61%) for PI-RADS 4, 89% (76-97%) for PI-RADS 5. On patient level analysis, CDR are 6% (0-20%) for PI-RADS 1, 9% (5-13%) for PI-RADS 2, 16% (7-27%) for PI-RADS 3, 59% (39-78%) for PI-RADS 4, 85% (73-94%) for PI-RADS 5. Higher categories are significantly associated with higher CDR (P &amp;lt; 0.001, univariate meta-regression), no systematic difference of CDR between peripheral zone lesions and transition zone lesions is identified in subgroup analysis.!##!Conclusions!#!Our estimates of CDR demonstrate that PI-RADSv2.1 stratifies lesions and patients as intended. Our results might serve as an initial evidence base to discuss management strategies linked to assessment categories

Artificial Intelligence in Magnetic Resonance Imaging-based Prostate Cancer Diagnosis: Where Do We Stand in 2021?

CONTEXT Men suspected of harboring prostate cancer (PCa) increasingly undergo multiparametric magnetic resonance imaging (mpMRI) and mpMRI-guided biopsy. The potential of mpMRI coupled to artificial intelligence (AI) methods to detect and classify PCa before decision-making requires investigation. OBJECTIVE To review the literature for studies addressing the diagnostic performance of combined mpMRI and AI approaches to detect and classify PCa, and to provide selection criteria for relevant articles having clinical significance. EVIDENCE ACQUISITION We performed a nonsystematic search of the English language literature using the PubMed-MEDLINE database up to October 30, 2020. We included all original studies addressing the diagnostic accuracy of mpMRI and AI to detect and classify PCa with histopathological analysis as a reference standard. EVIDENCE SYNTHESIS Eleven studies assessed AI and mpMRI approaches for PCa detection and classification based on a ground truth that referred to the entire prostate either with radical prostatectomy specimens (RPS) or relocalization of positive systematic and/or targeted biopsy. Seven studies retrospectively annotated cancerous lesions onto mpMRI identified in whole-mount sections from RPS, three studies used a backward projection of histological prostate biopsy information, and one study used a combined cohort of both approaches. All studies cross-validated their data sets; only four used a test set and one a multisite validation scheme. Performance metrics for lesion detection ranged from 87.9% to 92% at a threshold specificity of 50%. The lesion classification accuracy of the algorithms was comparable to that of the Prostate Imaging-Reporting and Data System. CONCLUSIONS For an algorithm to be implemented into radiological workflows and to be clinically applicable, it must be trained with a ground truth labeling that reflects histopathological information for the entire prostate and it must be externally validated. Lesion detection and classification performance metrics are promising but require prospective implementation and external validation for clinical significance. PATIENT SUMMARY We reviewed the literature for studies on prostate cancer detection and classification using magnetic resonance imaging (MRI) and artificial intelligence algorithms. The main application is in supporting radiologists in interpreting MRI scans and improving the diagnostic performance, so that fewer unnecessary biopsies are carried out

A Machine Learning Framework Reduces the Manual Workload for Systematic Reviews of the Diagnostic Performance of Prostate Magnetic Resonance Imaging

Prostate magnetic resonance imaging has become the imaging standard for prostate cancer in various clinical settings, with interpretation standardized according to the Prostate Imaging Reporting and Data System (PI-RADS). Each year, hundreds of scientific studies that report on the diagnostic performance of PI-RADS are published. To keep up with this ever-increasing evidence base, systematic reviews and meta-analyses are essential. As systematic reviews are highly resource-intensive, we investigated whether a machine learning framework can reduce the manual workload and speed up the screening process (title and abstract). We used search results from a living systematic review of the diagnostic performance of PI-RADS (1585 studies, of which 482 were potentially eligible after screening). A naïve Bayesian classifier was implemented in an active learning environment for classification of the titles and abstracts. Our outcome variable was the percentage of studies that can be excluded after 95% of relevant studies have been identified by the classifier (work saved over sampling: WSS@95%). In simulation runs of the entire screening process (controlling for classifier initiation and the frequency of classifier updating), we obtained a WSS@95% value of 28% (standard error of the mean ±0.1%). Applied prospectively, our classification framework would translate into a significant reduction in manual screening effort. Patient summary: Systematic reviews of scientific evidence are labor-intensive and take a lot of time. For example, many studies on prostate cancer diagnosis via MRI (magnetic resonance imaging) are published every year. We describe the use of machine learning to reduce the manual workload in screening search results. For a review of MRI for prostate cancer diagnosis, this approach reduced the screening workload by about 28%

Enhancing Radiation Dose Efficiency in Prospective ECG-Triggered Coronary CT Angiography Using Calcium-Scoring CT

Background: This study investigates whether the scan length adjustment of prospectively ECG-triggered coronary CT angiography (CCTA) using calcium-scoring CT (CAS-CT) images can reduce overall radiation doses. Methods: A retrospective analysis was conducted on 182 patients who underwent CAS-CT and prospectively ECG-triggered CCTA using a second-generation Dual-Source CT scanner. CCTA planning was based on CAS-CT images, for which simulated scout view planning was performed for comparison. Effective doses were compared between two scenarios: Scenario 1—CAS-CT-derived CCTA + CAS-CT and Scenario 2—scout-view-derived CCTA without CAS-CT. Dose differences were further analyzed with respect to scan mode and body mass index. Results: Planning CCTA using CAS-CT led to a shorter scan length than planning via scout view (114.3 ± 9.7 mm vs. 133.7 ± 13.2 mm, p p n = 182). Notably, Scenario 1 resulted in a significantly lower radiation dose for sequential scans and obese patients. Only high-pitch spiral CCTA showed dose reduction in Scenario 2. Conclusions: Using CAS-CT for planning prospectively ECG-triggered CCTA reduced the overall radiation dose administered compared to scout view planning without CAS-CT, except for high-pitch spiral CCTA, where a slightly opposite effect was observed

Ex Vivo Fluorescence Confocal Microscopy of MRI-Guided Targeted Prostate Biopsies for Rapid Detection of Clinically Significant Carcinomas—A Feasibility Study

Background: MRI-guided prostate biopsies from visible tumor-specific lesions (TBx) can be used to diagnose clinically significant carcinomas (csPCa) requiring treatment more selectively than conventional systematic biopsies (SBx). Ex vivo fluorescence confocal microscopy (FCM) is a novel technique that can be used to examine TBx prior to conventional histologic workup. Methods: TBx from 150 patients were examined with FCM on the day of collection. Preliminary findings were reported within 2 h of collection. The results were statistically compared with the final histology. Results: 27/40 (68%) of the csPCa were already recognized in the intraday FCM in accordance with the results of conventional histology. Even non-significant carcinomas (cisPCa) of the intermediate and high-risk groups (serum prostate-specific antigen (PSA) > 10 or 20 ng/mL) according to conventional risk stratifications were reliably detectable. In contrast, small foci of cisPCa were often not detected or were difficult to distinguish from reactive changes. Conclusion: The rapid reporting of preliminary FCM findings helps to reduce the psychological stress on patients, and can improve the clinical management of csPCa. Additional SBx can be avoided in individual cases, leading to lower rates of complications and scarring in the future surgical area. Additional staging examinations can be arranged without losing time. FCM represents a promising basis for future AI-based diagnostic algorithms