A Systematic Review of the Current Status and Quality of Radiomics for Glioma Differential Diagnosis

Brancato, Valentina; Cerrone, Marco; Lavitrano, Marialuisa; Salvatore, Marco; Cavaliere, Carlo

doi:10.3390/cancers14112731

Cited by 16 publications

(11 citation statements)

References 72 publications

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“…Additionally, none of the included studies met the high level of evidence criteria, as all were retrospective and lacked cost-effectiveness analyses. Our analysis also showed low scores in biologic correlations (9.09%) and open science/data (14.77%), which are similar to the limitations observed in radiomics models used for other purposes ( 44 – 46 ).…”

Section: Discussionsupporting

confidence: 76%

Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study

Jiang,

Zhao,

Liu

et al. 2023

Front. Oncol.

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BackgroundMachine learning radiomics models are increasingly being used to predict gastric cancer prognoses. However, the methodological quality of these models has not been evaluated. Therefore, this study aimed to evaluate the methodological quality of radiomics studies in predicting the prognosis of gastric cancer, summarize their methodological characteristics and performance.MethodsThe PubMed and Embase databases were searched for radiomics studies used to predict the prognosis of gastric cancer published in last 5 years. The characteristics of the studies and the performance of the models were extracted from the eligible full texts. The methodological quality, reporting completeness and risk of bias of the included studies were evaluated using the RQS, TRIPOD and PROBAST. The discrimination ability scores of the models were also compared.ResultsOut of 283 identified records, 22 studies met the inclusion criteria. The study endpoints included survival time, treatment response, and recurrence, with reported discriminations ranging between 0.610 and 0.878 in the validation dataset. The mean overall RQS value was 15.32 ± 3.20 (range: 9 to 21). The mean adhered items of the 35 item of TRIPOD checklist was 20.45 ± 1.83. The PROBAST showed all included studies were at high risk of bias.ConclusionThe current methodological quality of gastric cancer radiomics studies is insufficient. Large and reasonable sample, prospective, multicenter and rigorously designed studies are required to improve the quality of radiomics models for gastric cancer prediction.Study registrationThis protocol was prospectively registered in the Open Science Framework Registry (https://osf.io/ja52b).

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Section: Discussionsupporting

confidence: 76%

Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study

Jiang,

Zhao,

Liu

et al. 2023

Front. Oncol.

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“…Peripheral edema and peritumor in gliomas are two distinct concepts, the peritumor is typically an area within a specified radius around the tumor, whereas the peripheral edema around a glioma is irregular and frequently dispersed along the cerebral gyrus. Previous research has shown that the degree of peritumoral edema increases with the pathological grade and aggressiveness of glioma ( 14 ). It was demonstrated that individuals with severe edema (>10 mm) had mean or overall survival rates that were more than 50% lower than those with mild edema ( 8 , 9 , 44 ).…”

Section: Discussionmentioning

confidence: 99%

MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study

Tan,

Sui,

Wang

et al. 2024

Front. Oncol.

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BackgroundAccurate preoperative prediction of glioma is crucial for developing individualized treatment decisions and assessing prognosis. In this study, we aimed to establish and evaluate the value of integrated models by incorporating the intratumoral and peritumoral features from conventional MRI and clinical characteristics in the prediction of glioma grade.MethodsA total of 213 glioma patients from two centers were included in the retrospective analysis, among which, 132 patients were classified as the training cohort and internal validation set, and the remaining 81 patients were zoned as the independent external testing cohort. A total of 7728 features were extracted from MRI sequences and various volumes of interest (VOIs). After feature selection, 30 radiomic models depended on five sets of machine learning classifiers, different MRI sequences, and four different combinations of predictive feature sources, including features from the intratumoral region only, features from the peritumoral edema region only, features from the fusion area including intratumoral and peritumoral edema region (VOI-fusion), and features from the intratumoral region with the addition of features from peritumoral edema region (feature-fusion), were established to select the optimal model. A nomogram based on the clinical parameter and optimal radiomic model was constructed for predicting glioma grade in clinical practice.ResultsThe intratumoral radiomic models based on contrast-enhanced T1-weighted and T2-flair sequences outperformed those based on a single MRI sequence. Moreover, the internal validation and independent external test underscored that the XGBoost machine learning classifier, incorporating features extracted from VOI-fusion, showed superior predictive efficiency in differentiating between low-grade gliomas (LGG) and high-grade gliomas (HGG), with an AUC of 0.805 in the external test. The radiomic models of VOI-fusion yielded higher prediction efficiency than those of feature-fusion. Additionally, the developed nomogram presented an optimal predictive efficacy with an AUC of 0.825 in the testing cohort.ConclusionThis study systematically investigated the effect of intratumoral and peritumoral radiomics to predict glioma grading with conventional MRI. The optimal model was the XGBoost classifier coupled radiomic model based on VOI-fusion. The radiomic models that depended on VOI-fusion outperformed those that depended on feature-fusion, suggesting that peritumoral features should be rationally utilized in radiomic studies.

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“…In other fields of medicine, especially in oncology, radiomic analyses using statistical learning methods by analysing ultrasound [53,54], computed tomography [55][56][57], magnetic resonance [58,59], or positron emission tomography [60] images are very useful for differentiating between cancer and inflammation [61].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma

et al. 2023

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Anterior segment optical coherence tomography (AS-OCT) allows the explore not only the anterior chamber but also the front part of the vitreous cavity. Our cross-sectional single-centre study investigated whether AS-OCT can distinguish between vitreous involvement due to vitreoretinal lymphoma (VRL) and vitritis in uveitis. We studied AS-OCT images from 28 patients (11 with biopsy-proven VRL and 17 with differential diagnosis uveitis) using publicly available radiomics software written in MATLAB. Patients were divided into two balanced groups: training and testing. Overall, 3260/3705 (88%) AS-OCT images met our defined quality criteria, making them eligible for analysis. We studied five different sets of grey-level samplings (16, 32, 64, 128, and 256 levels), finding that 128 grey levels performed the best. We selected the five most effective radiomic features ranked by the ability to predict the class (VRL or uveitis). We built a classification model using the xgboost python function; through our model, 87% of eyes were correctly diagnosed as VRL or uveitis, regardless of exam technique or lens status. Areas under the receiver operating characteristic curves (AUC) in the 128 grey-level model were 0.95 [CI 0.94, 0.96] and 0.84 for training and testing datasets, respectively. This preliminary retrospective study highlights how AS-OCT can support ophthalmologists when there is clinical suspicion of VRL.

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A Systematic Review of the Current Status and Quality of Radiomics for Glioma Differential Diagnosis

Cited by 16 publications

References 72 publications

Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study

Methodological quality of radiomic-based prognostic studies in gastric cancer: a cross-sectional study

MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study

Artificial Intelligence-Assisted Processing of Anterior Segment OCT Images in the Diagnosis of Vitreoretinal Lymphoma

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