MRI-Based Radiomics Methods for Predicting Ki-67 Expression in Breast Cancer: A Systematic Review and Meta-analysis

Tabnak, Peyman; HajiEsmailPoor, Zanyar; Baradaran, Behzad; Pashazadeh, Fariba; Aghebati Maleki, Leili

doi:10.1016/j.acra.2023.10.010

Cited by 10 publications

(3 citation statements)

References 66 publications

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“…Thus, a careful evaluation of the quality of individual articles, facilitated by tools like RQS, becomes crucial to ensure the accuracy and applicability of meta-analytic outcomes in the field of radiomics and beyond. In a recently published meta-analysis on the diagnostic performance of MRI-based radiomics for predicting Ki-67 in breast cancer ( 24 ), the mean RQS of the included studies was around 6, significantly lower compared to our study. This suggests that the quality of the articles included in our meta-analysis was higher.…”

Section: Discussioncontrasting

confidence: 88%

CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis

Luo,

Zheng,

Zhang

et al. 2024

Front. Oncol.

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BackgroundRadiomics, an emerging field, presents a promising avenue for the accurate prediction of biomarkers in different solid cancers. Lung cancer remains a significant global health challenge, contributing substantially to cancer-related mortality. Accurate assessment of Ki-67, a marker reflecting cellular proliferation, is crucial for evaluating tumor aggressiveness and treatment responsiveness, particularly in non-small cell lung cancer (NSCLC).MethodsA systematic review and meta-analysis conducted following the preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA) guidelines. Two authors independently conducted a literature search until September 23, 2023, in PubMed, Embase, and Web of Science. The focus was on identifying radiomics studies that predict Ki-67 expression in lung cancer. We evaluated quality using both Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools. For statistical analysis in the meta-analysis, we used STATA 14.2 to assess sensitivity, specificity, heterogeneity, and diagnostic values.ResultsTen retrospective studies were pooled in the meta-analysis. The findings demonstrated that the use of computed tomography (CT) scan-based radiomics for predicting Ki-67 expression in lung cancer exhibited encouraging diagnostic performance. Pooled sensitivity, specificity, and area under the curve (AUC) in training cohorts were 0.78, 0.81, and 0.85, respectively. In validation cohorts, these values were 0.78, 0.70, and 0.81. Quality assessment using QUADAS-2 and RQS indicated generally acceptable study quality. Heterogeneity in training cohorts, attributed to factors like contrast-enhanced CT scans and specific Ki-67 thresholds, was observed. Notably, publication bias was detected in the training cohort, indicating that positive results are more likely to be published than non-significant or negative results. Thus, journals are encouraged to publish negative results as well.ConclusionIn summary, CT-based radiomics exhibit promise in predicting Ki-67 expression in lung cancer. While the results suggest potential clinical utility, additional research efforts should concentrate on enhancing diagnostic accuracy. This could pave the way for the integration of radiomics methods as a less invasive alternative to current procedures like biopsy and surgery in the assessment of Ki-67 expression.

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Section: Discussioncontrasting

confidence: 88%

CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis

Luo,

Zheng,

Zhang

et al. 2024

Front. Oncol.

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“…Recent systematic evaluations have revealed that the predictive performance of radiomics models in lung cancer is influenced by the inclusion of varying radiomics algorithms and clinical features ( 31 , 32 ), which explains the heterogeneity of the results based on meta-regression. This variation underpins the heterogeneity observed in our meta-analysis.…”

Section: Discussionmentioning

confidence: 99%

Predictive value of 18F-FDG PET/CT radiomics for EGFR mutation status in non-small cell lung cancer: a systematic review and meta-analysis

Ma,

Yang,

Wang

et al. 2024

Front. Oncol.

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ObjectiveThis study aimed to evaluate the value of 18F-FDG PET/CT radiomics in predicting EGFR gene mutations in non-small cell lung cancer by meta-analysis.MethodsThe PubMed, Embase, Cochrane Library, Web of Science, and CNKI databases were searched from the earliest available date to June 30, 2023. The meta-analysis was performed using the Stata 15.0 software. The methodological quality and risk of bias of included studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score criteria. The possible causes of heterogeneity were analyzed by meta-regression.ResultsA total of 17 studies involving 3763 non-small cell lung cancer patients were finally included. We analyzed 17 training cohorts and 10 validation cohorts independently. Within the training cohort, the application of 18F-FDG PET/CT radiomics in predicting EGFR mutations in NSCLC demonstrated a sensitivity of 0.76 (95% CI: 0.70-0.81) and a specificity of 0.78 (95% CI: 0.74-0.82), accompanied by a positive likelihood ratio of 3.5 (95% CI:3.0-4.2), a negative likelihood ratio of 0.31 (95% CI: 0.24-0.39), a diagnostic odds ratio of 11.0 (95% CI: 8.0-16.0), and an area under the curve (AUC) of 0.84 (95% CI: 0.80-0.87). In the validation cohort, the values included a sensitivity of 0.76 (95% CI: 0.67-0.83), a specificity of 0.75 (95% CI: 0.68-0.80), a positive likelihood ratio of 3.0 (95% CI:2.4-3.8), a negative likelihood ratio of 0.32 (95% CI: 0.24-0.44), a diagnostic odds ratio of 9 (95% CI: 6-15), and an AUC of 0.82 (95% CI: 0.78-0.85). The average Radiomics Quality Score (RQS) across studies was 10.47 ± 4.72. Meta-regression analysis identifies the application of deep learning and regions as sources of heterogeneity.Conclusion18F-FDG PET/CT radiomics may be useful in predicting mutation status of the EGFR gene in non-small cell lung cancer.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO, identifier CRD42022385364.

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“…Examples of DL-based radiomics classifiers applied to breast cancer imaging include algorithms for tumor [145] and lymph node malignancy assessment [98], pathologic markers evaluation [155], and treatment response prediction [90].…”

Section: Deep Learning-based Radiomics Classifiersmentioning

confidence: 99%

Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024

Carriero,

Groenhoff,

Vologina

et al. 2024

Diagnostics

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The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, prognosis prediction, and therapeutic response monitoring. Nevertheless, several challenges have limited the widespread adoption of AI techniques in clinical practice, emphasizing the importance of rigorous validation, interpretability, and technical considerations when implementing DL solutions. By examining fundamental concepts in DL techniques applied to medical imaging and synthesizing the latest advancements and trends, this narrative review aims to provide valuable and up-to-date insights for radiologists seeking to harness the power of AI in breast cancer care.

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MRI-Based Radiomics Methods for Predicting Ki-67 Expression in Breast Cancer: A Systematic Review and Meta-analysis

Cited by 10 publications

References 66 publications

CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis

CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis

Predictive value of 18F-FDG PET/CT radiomics for EGFR mutation status in non-small cell lung cancer: a systematic review and meta-analysis

Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024

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