Dataset Information

Endometrial cancer risk stratification using MRI radiomics: corroborating with choline metabolism.

ABSTRACT:

Background and purpose

Radiomics offers little explainability. This study aims to develop a radiomics model (Rad-Score) using diffusion-weighted imaging (DWI) to predict high-risk patients for nodal metastasis or recurrence in endometrial cancer (EC) and corroborate with choline metabolism.

Materials and methods

From August 2015 to July 2018, 356 EC patients were enrolled. Rad-Score was developed using LASSO regression in a training cohort (n = 287) and validated in an independent test cohort (n = 69). MR spectroscopy (MRS) was also used in 230 patients. Nuclear MRS measured choline metabolites in 70 tissue samples. The performance was compared against European Society for Medical Oncology (ESMO) risk groups. A P < .05 denoted statistical significance.

Results

Rad-Score achieved 71.1% accuracy in the training and 71.0% in the testing cohorts. Incorporating clinical parameters of age, tumor type, size, and grade, Rad-Signature reached accuracies of 73.2% in training and 75.4% in testing cohorts, closely matching the performance to the post-operatively based ESMO's 70.7% and 78.3%. Rad-Score was significantly associated with increased total choline levels on MRS (P = .034) and tissue levels (P = .019).

Conclusions

Development of a preoperative radiomics risk score, comparable to ESMO clinical standard and associated with altered choline metabolism, shows translational relevance for radiomics in high-risk EC patients.

Trial registration

This study was registered in ClinicalTrials.gov on 2015-08-01 with Identifier NCT02528864.

SUBMITTER: Lin Y

PROVIDER: S-EPMC11344325 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Publications

Endometrial cancer risk stratification using MRI radiomics: corroborating with choline metabolism.

Lin Yenpo Y Wu Ren-Chin RC Lin Yu-Chun YC Huang Yen-Ling YL Lin Chiao-Yun CY Lo Chi-Jen CJ Lu Hsin-Ying HY Lu Kuan-Ying KY Tsai Shang-Yueh SY Hsieh Ching-Yi CY Yang Lan-Yan LY Cheng Mei-Ling ML Chao Angel A Lai Chyong-Huey CH Lin Gigin G

Cancer imaging : the official publication of the International Cancer Imaging Society 20240824 1

<h4>Background and purpose</h4>Radiomics offers little explainability. This study aims to develop a radiomics model (Rad-Score) using diffusion-weighted imaging (DWI) to predict high-risk patients for nodal metastasis or recurrence in endometrial cancer (EC) and corroborate with choline metabolism.<h4>Materials and methods</h4>From August 2015 to July 2018, 356 EC patients were enrolled. Rad-Score was developed using LASSO regression in a training cohort (n = 287) and validated in an independent ...[more]

PMID: 39182135

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Project description:BackgroundIn endometrial cancer (EC), preoperative pelvic MRI is recommended for local staging, while final tumor stage and grade are established by surgery and pathology. MRI-based radiomic tumor profiling may aid in preoperative risk-stratification and support clinical treatment decisions in EC.PurposeTo develop MRI-based whole-volume tumor radiomic signatures for prediction of aggressive EC disease.Study typeRetrospective.PopulationA total of 138 women with histologically confirmed EC, divided into training (nT = 108) and validation cohorts (nV = 30).Field strength/sequenceAxial oblique T1 -weighted gradient echo volumetric interpolated breath-hold examination (VIBE) at 1.5T (71/138 patients) and DIXON VIBE at 3T (67/138 patients) at 2 minutes postcontrast injection.AssessmentPrimary tumors were manually segmented by two radiologists with 4 and 8 years' of experience. Radiomic tumor features were computed and used for prediction of surgicopathologically-verified deep (≥50%) myometrial invasion (DMI), lymph node metastases (LNM), advanced stage (FIGO III + IV), nonendometrioid (NE) histology, and high-grade endometrioid tumors (E3). Corresponding analyses were also conducted using radiomics extracted from the axial oblique image slice depicting the largest tumor area.Statistical testsLogistic least absolute shrinkage and selection operator (LASSO) was applied for radiomic modeling in the training cohort. The diagnostic performances of the radiomic signatures were evaluated by area under the receiver operating characteristic curve in the training (AUCT ) and validation (AUCV ) cohorts. Progression-free survival was assessed using the Kaplan-Meier and Cox proportional hazard model.ResultsThe whole-tumor radiomic signatures yielded AUCT /AUCV of 0.84/0.76 for predicting DMI, 0.73/0.72 for LNM, 0.71/0.68 for FIGO III + IV, 0.68/0.74 for NE histology, and 0.79/0.63 for high-grade (E3) tumor. Single-slice radiomics yielded comparable AUCT but significantly lower AUCV for LNM and FIGO III + IV (both P < 0.05). Tumor volume yielded comparable AUCT to the whole-tumor radiomic signatures for prediction of DMI, LNM, FIGO III + IV, and NE, but significantly lower AUCT for E3 tumors (P < 0.05). All of the whole-tumor radiomic signatures significantly predicted poor progression-free survival with hazard ratios of 4.6-9.8 (P < 0.05 for all).Data conclusionMRI-based whole-tumor radiomic signatures yield medium-to-high diagnostic performance for predicting aggressive EC disease. The signatures may aid in preoperative risk assessment and hence guide personalized treatment strategies in EC.Level of evidence4 TECHNICAL EFFICACY STAGE: 2.

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Dataset Information

Endometrial cancer risk stratification using MRI radiomics: corroborating with choline metabolism.

Background and purpose

Materials and methods

Results

Conclusions

Trial registration

Publications

Endometrial cancer risk stratification using MRI radiomics: corroborating with choline metabolism.

Similar Datasets

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