Entwicklungen in der Analyse von Tumorheterogenität in computertomografischen Aufnahmen

Zusammenfassung

Lange beschränkte sich die bildmorphologische Beurteilung von Tumoren auf deren Größe einschließlich der Größendynamik, das Kontrastierungsmuster sowie deren visuell fassbarem Verhalten zum umliegenden Gewebe. In der letzten Dekade rückten zunehmend weitere Aspekte der Bildauswertung von in der klinischen Routine angefertigten Bilddatensätzen in den Fokus, welche dank stetig steigender Prozessorleistung und entsprechend neuer Anwendungsmöglichkeiten erstmals in größerem Umfang analysiert werden konnten. Begriffe wie Tumorheterogenität, „Machine Learning“ und „Big Data“ fanden sich immer häufiger in den Überschriften der Publikationen. Es ist gemeinhin anerkannt, dass Tumoren biologisch in der überwiegenden Zahl der Fälle keine homogene Masse darstellen, sondern sowohl auf makroskopischer als auch auf mikroskopischer und genetischer Ebene heterogene Gewebe darstellen. Diese histopathologischen und immunhistochemischen Erkenntnisse mit Tumorarealen unterschiedlicher Zelldichte, Angioneogenese und nekrotischen Anteilen sollten ein entsprechend quantifizierbares Korrelat in den bildgebenden Verfahren aufweisen. Die Heterogenitätsanalyse von Geweben in computertomografischen Datensätzen findet entsprechend in der Onkologie ein breites Anwendungsspektrum, aber auch nicht maligne Erkrankungen stellen einen möglichen Anwendungsbereich für diese Art der Bilddatenauswertung dar. Der nachfolgende Artikel soll eine Übersicht über bereits erfolgte Auswertungen von CT-Datensätzen bei verschiedenen Tumorentitäten und nichtonkologischen Fragestellungen liefern und die Herausforderungen für die weitere Anwendung von Heterogenitätsanalysen aufzeigen.

Ein wichtiges Ziel stellt hierbei die Identifikation von möglichen bildgebenden Biomarkern zur Therapieresponseevaluation dar, um mit entsprechenden Rückschlüssen die Fortführung oder Umstellung der therapeutischen Maßnahmen zu untermauern.

Abstract

For a long time, the morphological assessment of tumours in imaging data was restricted to the evaluation of their size including the size dynamics, the attenuation pattern as well as their visually comprehensible behavior to the surrounding tissue. In the last decade, different aspects of image analysis of data sets produced in the clinical routine came into focus, which could be analyzed for the first time on a larger scale thanks to constantly increasing processor performance and new applications. Terms such as tumour heterogeneity, “machine learning” and “big data” were increasingly found in the headlines of the publications. It is generally accepted that tumours rather do not represent a biologically homogenous mass in the vast majority of cases, than a heterogeneous tissue both on a macroscopic as on a microscopic and genetic level. These histopathological and immunohistochemical findings with tumour areas of different cell density, angioneogenesis and necrotic proportions should have a correspondingly quantifiable correlate in the imaging datasets. The heterogeneity analysis of tissues in computed tomography images accordingly finds a wide range of applications in oncology, but also non-malignant diseases represent a possible area of application for this type of image data analysis. The following article is intended to provide an overview of already performed evaluations of CT datasets regarding tissue heterogeneity in various tumour entities and non-oncological issues and identify the challenges for the further applications of heterogeneity analysis.

An important goal is the identification of possible imaging biomarkers for therapeutic response evaluation in order to underpin the continuation or conversion of the therapeutic strategies with appropriate conclusions.

Schlüsselwörter

Tumorheterogenität - Texturanalyse - Computertomografie - bildgebende Biomarker

Key words

tumour heterogeneity - texture analysis - computed tomography - imaging biomarker

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