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Fababohnen (Vicia faba L.) nehmen in der Ökologischen Landwirtschaft als Protein- und N-Quelle eine wichtige Stellung in der Fruchtfolge ein (Lampkin 1994; Müller & von Fragstein und Niemsdorff 2006). Als ertragslimitierende Faktoren spielen neben Wasserknappheit auch Schaderreger wie Insekten, Pilze und verschiedene Viruserkrankungen (Cockbain 1983) eine wichtige Rolle. Virusbedingte Ertragseinbußen wurden von Schmidt (1984) in konventionellen Fababohnen Ostdeutschlands auf jährlich ca. 8% geschätzt. Unter den nicht-chemischen Verfahren zur Minderung von vektorvermittelten Viruserkrankungen in Ackerkulturen, mit zugleich potentieller Eignung für ökologische Anbauverhältnisse, gehört neben der Frühsaat (Heathcote & Gibbs 1962) auch die Anwendung von Strohmulch. Mulchen erzielte insbesondere bei nicht-persistenterÜbertragung durch Blattläuse virusreduzierende Effekte in Lupinen. (Jones 1994), Kartoffeln (Heimbach & al. 1998; Saucke & Döring 2004) und Raps (& al. 2002). Ziele der vorliegenden Arbeit bildeten die Anwendung von Strohmulch in Kombination mit Früh- und Spätsaat in einem faktoriellen Parzellenversuch im Ökologischen Anbau von Fababohnen hinsichtlich der Auswirkungen auf Blattlausbesiedelung, Virusinfektionen, Pflanzenentwicklung und Ertrag.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.
