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La Radiologia medica
Feb, 2023;128 (2): 242-251.

Multisequence magnetic resonance imaging-based radiomics models for the prediction of microsatellite instability in endometrial cancer.

Xiao-Li Song, Hong-Jian Luo, Jia-Liang Ren, Ping Yin, Ying Liu, Jinliang Niu, Nan Hong
Author Information
  1. Xiao-Li Song: Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
  2. Hong-Jian Luo: Department of Radiology, Peking University People's Hospital, Beijing, China.
  3. Jia-Liang Ren: Department of Pharmaceuticals Diagnosics, GE Healthcare, Beijing, China.
  4. Ping Yin: Department of Radiology, Peking University People's Hospital, Beijing, China.
  5. Ying Liu: Department of Radiology, Peking University People's Hospital, Beijing, China.
  6. Jinliang Niu: Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
  7. Nan Hong: Department of Radiology, Peking University People's Hospital, Beijing, China. hongnan1968@163.com.
PMID: 36656410 DOI: 10.1007/s11547-023-01590-0

Abstract

PURPOSE: To evaluate the performance of multisequence magnetic resonance imaging (MRI)-based radiomics models in the assessment of microsatellite instability (MSI) status in endometrial cancer (EC).
MATERIALS AND METHODS: This retrospective multicentre study included 338 EC patients with available MSI status and preoperative MRI scans, divided into training (37 MSI, 123 microsatellite stability [MSS]), internal validation (15 MSI, 52 MSS), and external validation cohorts (30 MSI, 81 MSS). Radiomics features were extracted from T2-weighted images, diffusion-weighted images, and contrast-enhanced T1-weighted images. The ComBat harmonisation method was applied to remove intrascanner variability. The Boruta wrapper algorithm was used for key feature selection. Three classification algorithms, logistic regression (LR), random forest (RF), and support vector machine (SVM), were applied to build the radiomics models. The area under the receiver operating characteristic curve (AUC) was calculated to compare the diagnostic performance of the models. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the models.
RESULTS: Among the 1980 features, Boruta finally selected nine radiomics features. A higher MSI prediction performance was achieved after running the ComBat harmonisation method. The SVM algorithm had the best performance, with AUCs of 0.921, 0.903, and 0.937 in the training, internal validation, and external validation cohorts, respectively. The DCA results showed that the SVM algorithm achieved higher net benefits than the other classifiers over a threshold range of 0.581-0.783.
CONCLUSION: The multisequence MRI-based radiomics models showed promise in preoperatively predicting the MSI status in EC in this multicentre setting.

Keywords

Endometrial neoplasms Magnetic resonance imaging Microsatellite instability Radiomics

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MeSH Term

Humans
Female
Microsatellite Instability
Magnetic Resonance Imaging
Retrospective Studies
ROC Curve
Endometrial Neoplasms
Multicenter Study Journal Article

Word Cloud

Created with Highcharts 10.0.0MSImodelsradiomicsperformance0resonancemicrosatelliteinstabilityECfeaturesimagesalgorithmSVMmultisequencemagneticimagingMRIendometrialcancermulticentrestatustraininginternal validationMSSexternalvalidationcohortsRadiomicsComBatharmonisationmethodBorutacurveDCAhigherpredictionachievedshowedPURPOSE:evaluate-basedassessmentstatus inMATERIALSANDMETHODS:retrospectivestudyincluded338patientsavailablepreoperativescansdivided37123stability[MSS]15523081extractedT2-weighteddiffusion-weightedcontrast-enhancedT1-weightedappliedremoveintrascannervariabilitywrapperusedkeyfeatureselectionThreeclassificationalgorithmslogisticregressionLRrandomforestRFsupportvectormachinewere appliedbuildareareceiveroperatingcharacteristicAUCcalculatedcomparediagnosticDecisionanalysisconducteddetermineclinicalusefulnessRESULTS:Among1980finallyselectedninerunningbestAUCs921903937respectivelyresultsnetbenefitsclassifiersthresholdrange581-0783CONCLUSION:MRI-basedpromisepreoperativelypredictingsettingMultisequenceimaging-basedEndometrialneoplasmsMagneticMicrosatellite

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