A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility

Huang, Meng-Lin; Ren, Jing; Jin, Zhengyu; Li, Xinyu; Y, He; Li, Yuan; Xue, Huadan

doi:10.1186/s13244-023-01464-z

Cited by 12 publications

(4 citation statements)

References 86 publications

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“…Most published meta-analyses on the application of machine learning in ovarian cancer focus on the diagnosis and prediction of ovarian cancer; however, there are some differences in specific research methods, evaluation tools, and presentation of results. Huang et al [ 57 ] reviewed the application of computed tomography and magnetic resonance imaging radiomics in ovarian cancer, achieving promising results in differential diagnosis and prognosis prediction. Other studies [ 58 , 59 ] have summarized artificial intelligence methods for gynecological malignant tumors, emphasizing that variable selection, machine learning methods, and end point selection can all influence model performance.…”

Section: Discussionmentioning

confidence: 99%

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

Wang,

Chang,

Liu

et al. 2024

J Med Internet Res

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Background Machine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate. Objective This study aims to systematically review relevant literature on the predictive value of machine learning for platinum-based chemotherapy responses in patients with ovarian cancer. Methods Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched the PubMed, Embase, Web of Science, and Cochrane databases for relevant studies on predictive models for platinum-based therapies for the treatment of ovarian cancer published before April 26, 2023. The Prediction Model Risk of Bias Assessment tool was used to evaluate the risk of bias in the included articles. Concordance index (C-index), sensitivity, and specificity were used to evaluate the performance of the prediction models to investigate the predictive value of machine learning for platinum chemotherapy responses in patients with ovarian cancer. Results A total of 1749 articles were examined, and 19 of them involving 39 models were eligible for this study. The most commonly used modeling methods were logistic regression (16/39, 41%), Extreme Gradient Boosting (4/39, 10%), and support vector machine (4/39, 10%). The training cohort reported C-index in 39 predictive models, with a pooled value of 0.806; the validation cohort reported C-index in 12 predictive models, with a pooled value of 0.831. Support vector machine performed well in both the training and validation cohorts, with a C-index of 0.942 and 0.879, respectively. The pooled sensitivity was 0.890, and the pooled specificity was 0.790 in the training cohort. Conclusions Machine learning can effectively predict how patients with ovarian cancer respond to platinum-based chemotherapy and may provide a reference for the development or updating of subsequent scoring systems.

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Section: Discussionmentioning

confidence: 99%

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

Wang,

Chang,

Liu

et al. 2024

J Med Internet Res

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“…Similarly, a review focusing on ovarian imaging, which included 63 studies, found a median RQS of 6, corresponding to 30.6% of the total RQS, indicating lower scoring ( 53 ). Another meta-analysis covering 57 ovarian cancer studies reported an average RQS of 30.7%, which is also considered unsatisfactory ( 54 ). Reviews in other areas of radiomics, such as prostate cancer ( 55 ), meningiomas ( 56 ), nasopharyngeal carcinoma ( 57 ), and cardiovascular fields ( 58 ), have also highlighted concerns regarding the quality of machine learning and radiomics research, underscoring the general insufficiency in the scientific rigor of radiomics studies ( 59 ).…”

Section: Discussionmentioning

confidence: 99%

Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis

Kong,

Zhang

2024

Front. Neurol.

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BackgroundPre-treatment prediction of reperfusion and long-term prognosis in acute ischemic stroke (AIS) patients is crucial for effective treatment and decision-making. Recent studies have demonstrated that the inclusion of radiomics data can improve the performance of predictive models. This paper reviews published studies focused on radiomics-based prediction of reperfusion and long-term prognosis in AIS patients.MethodsWe systematically searched PubMed, Web of Science, and Cochrane databases up to September 9, 2023, for studies on radiomics-based prediction of AIS patient outcomes. The methodological quality of the included studies was evaluated using the phase classification criteria, the radiomics quality scoring (RQS) tool, and the Prediction model Risk Of Bias Assessment Tool (PROBAST). Two separate meta-analyses were performed of these studies that predict long-term prognosis and reperfusion in AIS patients.ResultsSixteen studies with sample sizes ranging from 67 to 3,001 were identified. Ten studies were classified as phase II, and the remaining were categorized as phase 0 (n = 2), phase I (n = 1), and phase III (n = 3). The mean RQS score of all studies was 39.41%, ranging from 5.56 to 75%. Most studies (87.5%, 14/16) were at high risk of bias due to their retrospective design. The remaining two studies were categorized as low risk and unclear risk, respectively. The pooled area under the curve (AUC) was 0.88 [95% confidence interval (CI) 0.84–0.92] for predicting the long-term prognosis and 0.80 (95% CI 0.74–0.86) for predicting reperfusion in AIS.ConclusionRadiomics has the potential to predict immediate reperfusion and long-term outcomes in AIS patients. Further external validation and evaluation within the clinical workflow can facilitate personalized treatment for AIS patients. This systematic review provides valuable insights for optimizing radiomics prediction systems for both reperfusion and long-term outcomes in AIS patients.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023461671, identifier CRD42023461671.

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“…The whole OC volume was assessed during texture analysis to differentiate epithelial OCs in previous studies [ 18 – 21 ]. However, no studies have investigated the potential of MRI texture analysis for discriminating CCC from EC [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Magnetic resonance imaging-based radiomics analysis of the differential diagnosis of ovarian clear cell carcinoma and endometrioid carcinoma: a retrospective study

Takeyama,

Sasaki,

Ueda

et al. 2024

Jpn J Radiol

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Purpose To retrospectively evaluate the diagnostic potential of magnetic resonance imaging (MRI)-based features and radiomics analysis (RA)-based features for discriminating ovarian clear cell carcinoma (CCC) from endometrioid carcinoma (EC). Materials and methods Thirty-five patients with 40 ECs and 42 patients with 43 CCCs who underwent pretherapeutic MRI examinations between 2011 and 2022 were enrolled. MRI-based features of the two groups were compared. RA-based features were extracted from the whole tumor volume on T2-weighted images (T2WI), contrast-enhanced T1-weighted images (cT1WI), and apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation method was performed to select features. Logistic regression analysis was conducted to construct the discriminating models. Receiver operating characteristic curve (ROC) analyses were performed to predict CCC. Results Four features with the highest absolute value of the LASSO algorithm were selected for the MRI-based, RA-based, and combined models: the ADC value, absence of thickening of the uterine endometrium, absence of peritoneal dissemination, and growth pattern of the solid component for the MRI-based model; Gray-Level Run Length Matrix (GLRLM) Long Run Low Gray-Level Emphasis (LRLGLE) on T2WI, spherical disproportion and Gray-Level Size Zone Matrix (GLSZM), Large Zone High Gray-Level Emphasis (LZHGE) on cT1WI, and GLSZM Normalized Gray-Level Nonuniformity (NGLN) on ADC map for the RA-based model; and the ADC value, spherical disproportion and GLSZM_LZHGE on cT1WI, and GLSZM_NGLN on ADC map for the combined model. Area under the ROC curves of those models were 0.895, 0.910, and 0.956. The diagnostic performance of the combined model was significantly superior (p = 0.02) to that of the MRI-based model. No significant differences were observed between the combined and RA-based models. Conclusion Conventional MRI-based analysis can effectively distinguish CCC from EC. The combination of RA-based features with MRI-based features may assist in differentiating between the two diseases.

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A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility

Cited by 12 publications

References 86 publications

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis

Magnetic resonance imaging-based radiomics analysis of the differential diagnosis of ovarian clear cell carcinoma and endometrioid carcinoma: a retrospective study

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