Exploring breast cancer response prediction to neoadjuvant systemic therapy using MRI-based radiomics: A systematic review

Granzier, R.; Nijnatten, Thiemo J A van; Woodruff, Henry C.; Smidt, Marjolein L.; Lobbes, Marc

doi:10.1016/j.ejrad.2019.108736

Cited by 75 publications

(54 citation statements)

References 54 publications

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“…It could help identifying high-quality results among the large number of publications in this field as well as issues limiting their value and applicability. The average RQS of the articles included in our systematic review was low (6.96, [22,51,52]. Therefore, the problems affecting radiomics studies and limiting the RQS score seem to be general and not restricted to a specific application.…”

Section: Discussionmentioning

confidence: 89%

Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis

et al. 2021

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Purpose To systematically review and evaluate the methodological quality of studies using radiomics for diagnostic and predictive purposes in patients with intracranial meningioma. To perform a meta-analysis of machine learning studies for the prediction of intracranial meningioma grading from pre-operative brain MRI. Methods Articles published from the year 2000 on radiomics and machine learning applications in brain imaging of meningioma patients were included. Their methodological quality was assessed by three readers with the radiomics quality score, using the intra-class correlation coefficient (ICC) to evaluate inter-reader reproducibility. A meta-analysis of machine learning studies for the preoperative evaluation of meningioma grading was performed and their risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool. Results In all, 23 studies were included in the systematic review, 8 of which were suitable for the meta-analysis. Total (possible range, −8 to 36) and percentage radiomics quality scores were respectively 6.96 ± 4.86 and 19 ± 13% with a moderate to good inter-reader reproducibility (ICC = 0.75, 95% confidence intervals, 95%CI = 0.54–0.88). The meta-analysis showed an overall AUC of 0.88 (95%CI = 0.84–0.93) with a standard error of 0.02. Conclusions Machine learning and radiomics have been proposed for multiple applications in the imaging of meningiomas, with promising results for preoperative lesion grading. However, future studies with adequate standardization and higher methodological quality are required prior to their introduction in clinical practice.

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Section: Discussionmentioning

confidence: 89%

Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis

et al. 2021

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“…To our knowledge, this is the first review performed to provide an overview of radiomics in predicting tumor response in patients with GI cancer. Other reviews on radiomics have been published with regard to patients suffering from head and neck cancer, lung cancer, and breast cancer [13,[79][80][81]. The study of Granzier et al focused on radiomics predicting tumor response in patients with breast cancer [79].…”

Section: Discussionmentioning

confidence: 99%

Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment

Wesdorp

Hellingman

Jansma

et al. 2020

Eur J Nucl Med Mol Imaging

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Purpose Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients with gastrointestinal tumors. Methods A systematic review was conducted, according to PRISMA guidelines. The protocol was prospectively registered (PROSPERO: CRD42019128408). PubMed, Embase, and Cochrane databases were searched. Original studies reporting on the value of radiomics in predicting response to treatment in patients with a gastrointestinal tumor were included. A narrative synthesis of results was conducted. Results were stratified by tumor type. Quality assessment of included studies was performed, according to the radiomics quality score. Results The comprehensive literature search identified 1360 unique studies, of which 60 articles were included for analysis. In 37 studies, radiomics models and individual radiomic features showed good predictive performance for response to treatment (area under the curve or accuracy > 0.75). Various strategies to construct predictive models were used. Internal validation of predictive models was often performed, while the majority of studies lacked external validation. None of the studies reported predictive models implemented in clinical practice. Conclusion Radiomics is increasingly used to predict response to treatment in patients suffering from gastrointestinal cancer. This review demonstrates its great potential to help predict response to treatment and improve patient selection and early adjustment of treatment strategy in a non-invasive manner.

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“…In breast cancers, MRI-based radiomics has been evaluated in the prediction of response to neoadjuvant therapy [39][40][41], prediction of sentinel lymph node metastasis [42], and recognition of molecular subtypes [43]. Moreover, ultrasound-based radiomics has been applied in the prediction of axillary lymph node metastasis [12] and the differential diagnosis between TNBC and fibroadenoma [44].…”

Section: Discussionmentioning

confidence: 99%

A Nomogram Based on Radiomics with Mammography Texture Analysis for the Prognostic Prediction in Patients with Triple-Negative Breast Cancer

Jiang

Zou

Sun

et al. 2020

Contrast Media & Molecular Imaging

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Objectives. To develop and validate a radiomics-based nomogram with texture features from mammography for the prognostic prediction in patients with early-stage triple-negative breast cancer (TNBC). Methods. The study included 200 consecutive patients with TNBC (training cohort: n = 133, validation cohort: n = 67). A total of 136 mammography-derived textural features were extracted, and LASSO (least absolute shrinkage and selection operator) was applied to select features for building the radiomics score (Rad-score). After univariate and multivariate logistic regression, a radiomics-based nomogram was constructed with independent prognostic factors. The discrimination and calibration power were assessed, and further the clinical applicability of the nomograms was evaluated. Results. Among the 136 mammography-derived textural features, fourteen were used to build the Rad-score after LASSO regression. A radiomics nomogram that incorporates Rad-score and pN stage was constructed. This nomogram achieved a C-index of 0.873 (95% CI: 0.758–0.989) for predicting iDFS (invasive disease-free survival), which outperformed the clinical model. Moreover, it is feasible to stratify patients into high-risk and low-risk groups based on the optimal cut-off point of Rad-score. The validations of the nomogram confirmed favorable discrimination and considerable predictive efficiency. Conclusions. The radiomics nomogram that incorporates Rad-score and pN stage exhibited favorable performance in the prediction of iDFS in patients with early-stage TNBCs.

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Exploring breast cancer response prediction to neoadjuvant systemic therapy using MRI-based radiomics: A systematic review

Cited by 75 publications

References 54 publications

Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis

Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis

Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment

A Nomogram Based on Radiomics with Mammography Texture Analysis for the Prognostic Prediction in Patients with Triple-Negative Breast Cancer

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