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Utilization of radiomics to predict long-term outcome of magnetic resonance–guided focused ultrasound ablation therapy in adenomyosis

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

To develop and evaluate a T2 MR–based radiomics prediction model incorporating radiomics features and clinical parameters to predict the response to magnetic resonance–guided focused ultrasound surgery (MRgFUS) in patients with adenomyosis.

Materials and methods

Sixty-nine patients (mean age, 38.6 years; age range, 26–50 years) with adenomyosis treated by MRgFUS were reviewed and allocated to training (n = 48) and testing cohorts (n = 21). One thousand one hundred eighteen radiomics features were extracted from T2-weighted imaging before MRgFUS. The radiomics features’ dimension was reduced by Pearson correlation coefficient after normalization. Analysis of variance and logistical regression were used for feature selection by fivefold cross-validation in the training cohort, and the machine learning model was constructed for comparing the clinical model, radiomics model, and radiomics-clinical model which combined survived radiomics features and clinical parameters. The discrimination result of the model was obtained by bootstrap; receiver operating characteristic curve, area under the curve (AUC), and decision curve analyses were performed to illustrate the model performance in both the training and testing cohorts.

Results

Good response was achieved in 47 patients (68.1%) and failed in 22 patients (38.9%). The radiomics model comprised four selected features and demonstrated a degree of prediction capability of patients’ poor response to MRgFUS treatment. The radiomics-clinical model showed good discrimination, with an AUC of 0.81 (95% confidence interval, 0.592–0.975) in the testing cohort. The decision curve analysis also showed favorable performance of the radiomics-clinical model.

Conclusions

A prediction model composed of T2WI-based radiomics features and clinical parameters could be applied to guide the radiologist to evaluate MRgFUS for patients with adenomyosis who will achieve good response.

Key Points

• Magnetic resonance imaging–guided focused ultrasound surgery represents an alternative treatment for adenomyosis, but nearly one third of patients remain symptomatic 6 months after MRgFUS.

• Combining four radiomics features of T2-weighted MRI with eight clinical features further improves prediction of poor responders to MR-guided focused ultrasound treatment of uterine adenomyosis (AUC = 0.81 in the testing cohort).

• The radiomics model based on T2-weighted imaging combined with clinical parameters can help predict which patients are likely to have a good response to MRgFUS for adenomyosis.

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Abbreviations

L 3mm3DGLCMinverse variance :

T2 log sigma 3–0-mm 3D GLCM inverse variance

W HLHFOmedian :

T2 wavelet HLH first-order median

W HLLFOminimum :

T2 wavelet HLL first-order minimum

W LLHNGTDMstrength :

T2 wavelet LLH NGTDM strength

AUC:

Area under the ROC curve

DCA:

Decision curve analysis

GR:

Good responders

HIFU:

High-intensity focused ultrasound

MRgFUS:

Magnetic resonance–guided focused ultrasound surgery

NRS:

Numerical rating scales (to assess pain severity)

NSAIDs:

Non-steroidal anti-inflammatory drugs

PR:

Poor responders

ROC:

Receiver operating characteristic curve

SSS:

Symptom severity score

UFS-QOL:

Uterine fibroid symptom and quality of life

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Acknowledgments

This research is financially supported by the National Natural Science Foundation of China (81871400, 81671740, 81771816, and 61731009), the Shanghai Academic/Technology Research Leader Program (17XD1424200), and the Excellent Academic Leaders of Shanghai of the Shanghai Municipal Commission of Health (2017BR038).

Funding

This study has received funding from the National Natural Science Foundation of China, Shanghai Academic/Technology Research Leader Program, and Excellent Academic Leaders of Shanghai of the Shanghai Municipal Commission of Health.

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Author notes

  1. Zhicong Li and Jing Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, 100 Haining Rd, Shanghai, 200080, People’s Republic of China

    Zhicong Li, Xiaorui Yin, An Chen, Na Tang & Han Wang

  2. Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, 3663 N. Zhongshan Rd, Shanghai, 200062, People’s Republic of China

    Jing Zhang, Yang Song & Guang Yang

  3. Department of Radiology, Weill Cornell Medicine, 416 East 55th St, New York, NY, 10022, USA

    Xiaorui Yin & Martin R. Prince

Authors
  1. Zhicong Li
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  2. Jing Zhang
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Correspondence to Han Wang.

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The scientific guarantor of this publication is Han Wang.

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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

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• diagnostic or prognostic study/observational/performed at one institution

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Li, Z., Zhang, J., Song, Y. et al. Utilization of radiomics to predict long-term outcome of magnetic resonance–guided focused ultrasound ablation therapy in adenomyosis. Eur Radiol 31, 392–402 (2021). https://doi.org/10.1007/s00330-020-07076-1

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  • DOI: https://doi.org/10.1007/s00330-020-07076-1

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