Radiomic signature as a predictive factor for lymph node metastasis in early-stage cervical cancer.

Adult Aged Area Under Curve Contrast Media / pharmacology Decision Making Female Humans Lymph Nodes / pathology Lymphatic Metastasis / diagnostic imaging Magnetic Resonance Imaging Middle Aged Neoplasm Metastasis Prognosis ROC Curve Retrospective Studies Risk Factors Support Vector Machine Uterine Cervical Neoplasms / diagnostic imaging

Journal

Journal of magnetic resonance imaging : JMRI

ISSN: 1522-2586

Titre abrégé: J Magn Reson Imaging

Pays: United States

ID NLM: 9105850

Informations de publication

Date de publication:
01 2019

Historique:

received: 08 03 2018

accepted: 12 05 2018

pubmed: 14 8 2018

medline: 24 3 2020

entrez: 14 8 2018

Statut: ppublish

Résumé

Lymph node metastasis (LNM) is the principal risk factor for poor outcomes in early-stage cervical cancer. Radiomics may offer a noninvasive way for predicting the stage of LNM. To evaluate a radiomic signature of LN involvement based on sagittal T Retrospective. In all, 143 patients were randomly divided into two primary and validation cohorts with 100 patients in the primary cohort and 43 patients in the validation cohort. T The gold standard of LN status was based on histologic results. A radiologist with 10 years of experience used the ITK-SNAP software for 3D manual segmentation. A senior radiologist with 15 years of experience validated all segmentations. The area under the receiver operating characteristics curve (ROC AUC), classification accuracy, sensitivity, and specificity were used between LNM and non-LNM groups. A total of 970 radiomic features and seven clinical characteristics were extracted. Minimum redundancy / maximum relevance and support vector machine algorithms were applied to select features and construct a radiomic signature. The Mann-Whitney U-test and the chi-square test were used to test the performance of clinical characteristics and potential prognostic outcomes. The results were used to assess the quantitative discrimination performance of the SVM-based radiomic signature. The radiomic signatures allowed good discrimination between LNM and non-LNM groups. The ROC AUC was 0.753 (95% confidence interval [CI], 0.656-0.850) in the primary cohort and 0.754 (95% CI, 0584-0.924) in the validation cohort. A multiple-sequence MRI radiomic signature can be used as a noninvasive biomarker for preoperative assessment of LN status and potentially influence the therapeutic decision-making in early-stage cervical cancer patients. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:304-310.

Sections du résumé

BACKGROUND

Lymph node metastasis (LNM) is the principal risk factor for poor outcomes in early-stage cervical cancer. Radiomics may offer a noninvasive way for predicting the stage of LNM.

PURPOSE

To evaluate a radiomic signature of LN involvement based on sagittal T

STUDY TYPE

Retrospective.

POPULATION

In all, 143 patients were randomly divided into two primary and validation cohorts with 100 patients in the primary cohort and 43 patients in the validation cohort.

FIELD STRENGTH/SEQUENCE

ASSESSMENT

The gold standard of LN status was based on histologic results. A radiologist with 10 years of experience used the ITK-SNAP software for 3D manual segmentation. A senior radiologist with 15 years of experience validated all segmentations. The area under the receiver operating characteristics curve (ROC AUC), classification accuracy, sensitivity, and specificity were used between LNM and non-LNM groups.

STATISTICAL TESTS

A total of 970 radiomic features and seven clinical characteristics were extracted. Minimum redundancy / maximum relevance and support vector machine algorithms were applied to select features and construct a radiomic signature. The Mann-Whitney U-test and the chi-square test were used to test the performance of clinical characteristics and potential prognostic outcomes. The results were used to assess the quantitative discrimination performance of the SVM-based radiomic signature.

RESULTS

The radiomic signatures allowed good discrimination between LNM and non-LNM groups. The ROC AUC was 0.753 (95% confidence interval [CI], 0.656-0.850) in the primary cohort and 0.754 (95% CI, 0584-0.924) in the validation cohort.

DATA CONCLUSIONS

A multiple-sequence MRI radiomic signature can be used as a noninvasive biomarker for preoperative assessment of LN status and potentially influence the therapeutic decision-making in early-stage cervical cancer patients.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:304-310.

Identifiants

DOI: 10.1002/jmri.26209 PMID: 30102438

pubmed: 30102438

doi: 10.1002/jmri.26209

doi:

Substances chimiques

Contrast Media 0

Types de publication

Journal Article Validation Study

Langues

eng