Dataset Information

Magnetic resonance imaging and ultrasound for prediction of residual tumor size in early breast cancer within the ADAPT subtrials.

ABSTRACT:

Background

Prediction of histological tumor size by post-neoadjuvant therapy (NAT) ultrasound and magnetic resonance imaging (MRI) was evaluated in different breast cancer subtypes.

Methods

Imaging was performed after 12-week NAT in patients enrolled into three neoadjuvant WSG ADAPT subtrials. Imaging performance was analyzed for prediction of residual tumor measuring ≤10 mm and summarized using positive (PPV) and negative (NPV) predictive values.

Results

A total of 248 and 588 patients had MRI and ultrasound, respectively. Tumor size was over- or underestimated by < 10 mm in 4.4% and 21.8% of patients by MRI and in 10.2% and 15.8% by ultrasound. Overall, NPV (proportion of correctly predicted tumor size ≤10 mm) of MRI and ultrasound was 0.92 and 0.83; PPV (correctly predicted tumor size > 10 mm) was 0.52 and 0.61. MRI demonstrated a higher NPV and lower PPV than ultrasound in hormone receptor (HR)-positive/human epidermal growth factor receptor 2 (HER2)-positive and in HR-/HER2+ tumors. Both methods had a comparable NPV and PPV in HR-/HER2- tumors.

Conclusions

In HR+/HER2+ and HR-/HER2+ breast cancer, MRI is less likely than ultrasound to underestimate while ultrasound is associated with a lower risk to overestimate tumor size. These findings may help to select the most optimal imaging approach for planning surgery after NAT.

Trial registration

Clinicaltrials.gov , NCT01815242 (registered on March 21, 2013), NCT01817452 (registered on March 25, 2013), and NCT01779206 (registered on January 30, 2013).

SUBMITTER: Graeser M

PROVIDER: S-EPMC7977310 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Publications

Magnetic resonance imaging and ultrasound for prediction of residual tumor size in early breast cancer within the ADAPT subtrials.

Graeser Monika M Schrading Simone S Gluz Oleg O Strobel Kevin K Herzog Christopher C Umutlu Lale L Frydrychowicz Alex A Rjosk-Dendorfer Dorothea D Würstlein Rachel R Culemann Ralph R Eulenburg Christine C Adams Jascha J Nitzsche Henrik H Prange Anna A Kümmel Sherko S Grischke Eva-Maria EM Forstbauer Helmut H Braun Michael M Potenberg Jochem J von Schumann Raquel R Aktas Bahriye B Kolberg-Liedtke Cornelia C Harbeck Nadia N Kuhl Christiane K CK Nitz Ulrike U

Breast cancer research : BCR 20210318 1

<h4>Background</h4>Prediction of histological tumor size by post-neoadjuvant therapy (NAT) ultrasound and magnetic resonance imaging (MRI) was evaluated in different breast cancer subtypes.<h4>Methods</h4>Imaging was performed after 12-week NAT in patients enrolled into three neoadjuvant WSG ADAPT subtrials. Imaging performance was analyzed for prediction of residual tumor measuring ≤10 mm and summarized using positive (PPV) and negative (NPV) predictive values.<h4>Results</h4>A total of 248 and ...[more]

PMID: 33736679

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Project description:ObjectivesTo develop and evaluate magnetic resonance imaging (MRI)-based radiomics models for predicting residual myoma regrowth within 1 year after high-intensity focused ultrasound (HIFU) ablation of uterine myomas.MethodsA retrospective analysis of residual myoma regrowth within 1 year was performed on 428 myomas in 339 patients who were diagnosed with uterine myoma and treated with HIFU ablation in two hospital centers. In total, 851 radiomics features were extracted from T2-weighted images (T2WI) obtained 1 day after HIFU ablation, and the least absolute shrinkage and selection operator in the training cohort (n = 243) was employed to select radiomics features. Support vector machines were adopted to develop radiomics, clinicoradiological and combined radiomics-clinical models to predict residual myoma regrowth, defined as an increase in residual myoma volume of > 10% between that at day 1 post HIFU and that at follow-up MRI within 1 year. These models were validated in both internal (n = 81) and external (n = 104) test cohorts. The predictive performance and clinical application of these models were assessed using receiver-operating-characteristics-curve analysis, the area under the curve (AUC) and decision-curve analysis.ResultsThe AUCs of the T2WI-based radiomics prediction model in the internal and external test cohorts were 0.834 (95% CI, 0.747-0.920) and 0.801 (95% CI, 0.712-0.889), respectively, and those of the clinicoradiological model were 0.888 (95% CI, 0.816-0.960) and 0.912 (95% CI, 0.851-0.973), respectively. The combined model had better predictive performance than either the radiomics or the clinicoradiological model, with AUC values of 0.922 (95% CI, 0.857-0.987) and 0.930 (95% CI, 0.880-0.980) in the internal and external test cohorts, respectively. Decision-curve analysis also indicated that application of the combined model has clinical value, this model achieving more net benefits than the other two models.ConclusionT2WI-based radiomics features can predict effectively the occurrence of residual myoma regrowth within 1 year after HIFU ablation of uterine myomas, which serves as an accurate and convenient reference for clinical decision-making. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Dataset Information

Magnetic resonance imaging and ultrasound for prediction of residual tumor size in early breast cancer within the ADAPT subtrials.

Background

Methods

Results

Conclusions

Trial registration

Publications

Magnetic resonance imaging and ultrasound for prediction of residual tumor size in early breast cancer within the ADAPT subtrials.

Similar Datasets

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