Skip to main content
main-content

07.08.2018 | Neuro

The diagnostic value of texture analysis in predicting WHO grades of meningiomas based on ADC maps: an attempt using decision tree and decision forest

Zeitschrift:
European Radiology
Autoren:
Yiping Lu, Li Liu, Shihai Luan, Ji Xiong, Daoying Geng, Bo Yin
Wichtige Hinweise
Yiping Lu and Li Liu contributed equally to this work.

Abstract

Objectives

The preoperative prediction of the WHO grade of a meningioma is important for further treatment plans. This study aimed to assess whether texture analysis (TA) based on apparent diffusion coefficient (ADC) maps could non-invasively classify meningiomas accurately using tree classifiers.

Methods

A pathology database was reviewed to identify meningioma patients who underwent tumour resection in our hospital with preoperative routine MRI scanning and diffusion-weighted imaging (DWI) between January 2011 and August 2017. A total of 152 meningioma patients with 421 preoperative ADC maps were included. Four categories of features, namely, clinical features, morphological features, average ADC values and texture features, were extracted. Three machine learning classifiers, namely, classic decision tree, conditional inference tree and decision forest, were built on these features from the training dataset. Then the performance of each classifier was evaluated and compared with the diagnosis made by two neuro-radiologists.

Results

The ADC value alone was unable to distinguish three WHO grades of meningiomas. The machine learning classifiers based on clinical, morphological features and ADC value could achieve equivalent diagnostic performance (accuracy = 62.96%) compared to two experienced neuro-radiologists (accuracy = 61.11% and 62.04%). Upon analysis, the decision forest that was built with 23 selected texture features and the ADC value from the training dataset achieved the best diagnostic performance in the testing dataset (kappa = 0.64, accuracy = 79.51%).

Conclusions

Decision forest with the ADC value and ADC map-based texture features is a promising multiclass classifier that could potentially provide more precise diagnosis and aid diagnosis in the near future.

Key Points

• A precise preoperative prediction of the WHO grade of a meningioma brings benefits to further treatment plans.
• Machine learning models based on clinical, morphological features and ADC value could achieve equivalent diagnostic performance compared to experienced neuroradiologists.
• The decision forest model built with 23 selected texture features and the ADC value achieved the best diagnostic performance (kappa = 0.64, accuracy = 79.51%).

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

★ PREMIUM-INHALT
e.Med Interdisziplinär

Mit e.Med Interdisziplinär erhalten Sie Zugang zu allen CME-Fortbildungen und Fachzeitschriften auf SpringerMedizin.de. Zusätzlich können Sie eine Zeitschrift Ihrer Wahl in gedruckter Form beziehen – ohne Aufpreis.

Nicht verpassen: e.Med bis 22. Oktober 100 € günstiger.

Weitere Produktempfehlungen anzeigen
Literatur
Über diesen Artikel
  1. Das kostenlose Testabonnement läuft nach 14 Tagen automatisch und formlos aus. Dieses Abonnement kann nur einmal getestet werden.

Neu im Fachgebiet Radiologie

Meistgelesene Bücher aus der Radiologie

2016 | Buch

Medizinische Fremdkörper in der Bildgebung

Thorax, Abdomen, Gefäße und Kinder

Dieses einzigartige Buch enthält ca. 1.600 hochwertige radiologische Abbildungen und Fotos iatrogen eingebrachter Fremdmaterialien im Röntgenbild und CT.

Herausgeber:
Dr. med. Daniela Kildal

2011 | Buch

Atlas Klinische Neuroradiologie des Gehirns

Radiologie lebt von Bildern! Der vorliegende Atlas trägt dieser Tatsache Rechnung. Sie finden zu jedem Krankheitsbild des Gehirns Referenzbilder zum Abgleichen mit eigenen Befunden.

Autoren:
Priv.-Doz. Dr. med. Jennifer Linn, Prof. Dr. med. Martin Wiesmann, Prof. Dr. med. Hartmut Brückmann

Mail Icon II Newsletter

Bestellen Sie unseren kostenlosen Newsletter Update Radiologie und bleiben Sie gut informiert – ganz bequem per eMail.

Bildnachweise