The Performance of Machine Learning for Prediction of H3K27 M Mutation in Midline Gliomas: A Systematic Review and Meta-Analysis

Habibi, Mohammad Amin; Aghaei, Fateme; Tajabadi, Zohreh; Mirjani, Mohammad Sina; Minaee, Poriya; Eazi, SeyedMohammad

doi:10.1016/j.wneu.2023.11.061

Cited by 7 publications

(2 citation statements)

References 36 publications

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“…Recently, many meta-analyses on radiomics-based methods have been published to investigate the overall diagnostic performance of the available studies in the field. This will help in obtaining a standpoint regarding the current radiomics methods for predicting biomarkers in cancers ( 23 – 25 ). Thus, this study aims to provide a meta-analysis of the radiomics studies for predicting Ki-67 expression in lung cancer for the first time and evaluate their quality as well.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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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“…In children with FS, EEG can help identify those at risk for developing epilepsy or RFS and has been proposed to predict RFS in children with FS. [9][10][11] The pathophysiology of RFS is not well understood, but abnormal electrical activity in the brain is believed to play a role. Abnormalities detected on EEG have been associated with an increased risk of RFS.…”

Section: Introductionmentioning

confidence: 99%

The Role of Electroencephalography in the Prediction of Relapse of Febrile Convulsion in Patients Aged Between 6 to 60 Months: A Prospective Cohort Study

Amouzadeh,

Mollamohammadi,

Razavi

et al. 2024

Journal of Pediatric Neurology

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Febrile seizures (FS) are the most common type of seizure in pediatrics, and patients with FS can present with recurrent febrile seizures (RFS). Some clinical factors have been known to predict RFS, but there is no consensus regarding electroencephalography (EEG). We aimed to determine the ability of EEG to predict RFS. This prospective cohort study was done in children aged between 6 and 60 months. The EEG was performed on patients 2 weeks after the FS, and the patients were followed up for 6 months. Two hundred patients were enrolled. Recurrence of febrile convulsion occurred in 27% of the patients. A higher risk of recurrent seizures was seen in females compared with males (p = 0.04, hazard ratio [HR] = 1.83), children with a family history of seizure (p = 0.011, HR = 2), people with fever-to-seizure interval <1 hour (p = 0.009, HR = 6.1), as well as 1 to 24 hours interval between fever and seizure (p = 0.033, HR = 2.15), aEnd patients with abnormal EEG findings compared with normal EEG (p = 0.001, HR = 4.45). There was no significant relationship between RFS and other studied factors. This study showed the ability of EEG to predict RFS, and the results showed a higher likelihood of recurrence occurring in subjects with abnormal rather than normal EEG.

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Prediction of the treatment response and local failure of patients with brain metastasis treated with stereotactic radiosurgery using machine learning: A systematic review and meta-analysis

Habibi,

Rashidi,

Habibzadeh

et al. 2024

Neurosurg Rev

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The Performance of Machine Learning for Prediction of H3K27 M Mutation in Midline Gliomas: A Systematic Review and Meta-Analysis

Cited by 7 publications

References 36 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

The Role of Electroencephalography in the Prediction of Relapse of Febrile Convulsion in Patients Aged Between 6 to 60 Months: A Prospective Cohort Study

Prediction of the treatment response and local failure of patients with brain metastasis treated with stereotactic radiosurgery using machine learning: A systematic review and meta-analysis

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