Radiomics Analysis of Multiparametric PET/MRI for N- and M-Staging in Patients with Primary Cervical Cancer

Abstract

Purpose The aim of this study was to investigate the potential of multiparametric ¹⁸F-FDG PET/MR imaging as a platform for radiomics analysis and machine learning algorithms based on primary cervical cancers to predict N- and M-stage in patients.

Materials and Methods A total of 30 patients with histopathological confirmation of primary and untreated cervical cancer were prospectively enrolled for a multiparametric ¹⁸F-FDG PET/MR examination, comprising a dedicated protocol for imaging of the female pelvis. The primary tumor in the uterine cervix was manually segmented on post-contrast T1-weighted images. Quantitative features were extracted from the segmented tumors using the Radiomic Image Processing Toolbox for the R software environment for statistical computing and graphics. 45 different image features were calculated from non-enhanced as well as post-contrast T1-weighted TSE images, T2-weighted TSE images, the ADC map, the parametric Ktrans, Kep, Ve and iAUC maps and PET images, respectively. Statistical analysis and modeling was performed using Python 3.5 and the scikit-learn software machine learning library for the Python programming language.

Results Prediction of M-stage was superior when compared to N-stage. Prediction of M-stage using SVM with SVM-RFE as feature selection obtained the highest performance providing sensitivity of 91 % and specificity of 92 %. Using receiver operating characteristic (ROC) analysis of the pooled predictions, the area under the curve (AUC) was 0.97. Prediction of N-stage using RBF-SVM with MIFS as feature selection reached sensitivity of 83 %, specificity of 67 % and an AUC of 0.82.

Conclusion M- and N-stage can be predicted based on isolated radiomics analyses of the primary tumor in cervical cancers, thus serving as a template for noninvasive tumor phenotyping and patient stratification using high-dimensional feature vectors extracted from multiparametric PET/MRI data.

Key points: 

• Radiomics analysis based on multiparametric PET/MRI enables prediction of the metastatic status of cervical cancers

• Prediction of M-stage is superior to N-stage

• Multiparametric PET/MRI displays a valuable platform for radiomics analyses 

Zusammenfassung

Zielsetzung Ziel dieser Studie war die Evaluierung des prädiktiven Potenzials der Radiomics-Analyse zur Bestimmung des N- und M-Stadiums des primären Zervixkarzinoms anhand multiparametrischer ¹⁸F-FDG-PET/MRT-Bildgebung.

Material und Methoden 30 Patientinnen mit einem histologisch gesicherten, primären und therapienaiven Zervixkarzinom unterzogen sich einer multiparametrischen ¹⁸F-FDG-PET/MRT-Untersuchung unter Verwendung eines dedizierten Untersuchungsprotokolls des weiblichen Beckens. Nach Segmentierung der Primärtumoren wurden quantitative Bildparameter mittels der Radiomic-Image-Processing-Toolbox bestimmt. Insgesamt wurden 45 verschiedene quantitative Bildmerkmale jeweils anhand der T2-gewichteten TSE-Sequenzen, der nativen und kontrastmittelgestützten T1-gewichteten TSE-Sequenzen, der ADC-Map, verschiedenen Perfusionsparametern (Ktrans, Kep, Ve and iAUC) und den ¹⁸F-FDG-PET-Datensätzen für jeden Tumor extrahiert. Die statistische Analyse zur Bestimmung des N- und M-Stadiums erfolgte unter der Verwendung der Python 3.5 und Scikit-learn-Software-Bibliothek für maschinelles Lernen.

Ergebnisse Insgesamt zeigte sich eine höhere Genauigkeit zur Prädiktion des korrekten M-Stadiums im Vergleich zum N-Stadium. Zur Prädiktion des korrekten M-Stadiums zeigten sich unter der Verwendung von SVM und SVM-RFE zur Feature-Auswahl die besten Ergebnisse mit einer Sensitivität von 91 %, einer Spezifität von 92 % und einer Fläche unter der Kurve (AUC) von 0,97. Die höchste Genauigkeit für die Bestimmung des N-Stadiums erfolgte unter der Verwendung von RBF-SVM und MIFS zur Feature-Auswahl mit einer Sensitivität von 83 %, einer Spezifität von 67 % und einer Fläche unter der Kurve (AUC) von 0,82.

Schlussfolgerung Die Radiomics-Analyse von multiparametrischen PET/MR-Datensätzen ermöglicht eine präzise Prädiktion des M- und N-Stadiums von Patientinnen mit primärem Zervixkarzinom und könnte damit supportiv zur nichtinvasiven Tumor-Phänotypisierung und Patientenstratifizierung eingesetzt werden.

Kernaussagen: 

• Die Radiomics-Analyse der multiparametrischen PET/MRT ermöglicht die Prädiktion des Metastasierungsstatus des Zervixkarzinoms.

• Die Prädiktion des M-Stadiums ist der Prädiktion des N-Stadiums überlegen.

• Die multiparametrische PET/MRT bietet eine valide Plattform für Radiomics-Analysen.

Key words

machine learning - multiparametric PET/MRI - cervical cancer - radiomics analysis

Publication History

Received: 28 August 2019

Accepted: 05 January 2020

Article published online:
07 February 2024

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