Machine-learning approach to predict molecular subgroups of medulloblastoma using multiparametric MRI-based tumor radiomics

Saju, Ann Christy; Chatterjee, Abhishek; Sahu, Arpita; Gupta, Tejpal; Krishnatry, Rahul; Mokal, Smruti; Sahay, Ayushi; Epari, Sridhar; Prasad, Maya; Chinnaswamy, Girish; Agarwal, Jai Prakash; Goda, Jayant Sastri

doi:10.1259/bjr.20211359

Cited by 9 publications

(26 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, there are relatively few works in which GLCM data is used for the development of AI models. Most of these models are used in radiology for the classification or prediction of different phenomena related to the presence of tumor tissue 49,50 . One of the rare approaches where GLCM is used in light microscopy analysis is our recent work in yeast cells exposed to sublethal doses of ethanol intended to induce low-level damage 51,52 .…”

Section: Discussionmentioning

confidence: 99%

Computational approaches for evaluating morphological changes in the corneal stroma associated with decellularization

Pantić

Cumic

Valjarević

et al. 2023

Preprint

View full text Add to dashboard Cite

Decellularized corneas offer a promising and sustainable source of replacement grafts, mimicking native tissue and reducing the risk of immune rejection post-transplantation. Despite great success in achieving acellular scaffolds, little consensus exists regarding the quality of the decellularized extracellular matrix (ECM). Metrics used to evaluate ECM performance are study-specific, subjective, and semi-quantitative. Thus, this work focused on developing a computational method to examine the effectiveness of corneal decellularization. We combined conventional semi-quantitative histological assessments and automated scaffold evaluations based on textual image analyses to assess decellularization efficiency. Our study highlights that it is possible to develop contemporary artificial intelligence (AI) models based on random forests and support vector machine algorithms, which can identify regions of interest in acellularized corneal stromal tissue with relatively high accuracy. These results provide a platform for developing AI biosensing systems for evaluating fine morphological changes in decellularized scaffolds crucial for evaluating their functionality.

show abstract

Section: Discussionmentioning

confidence: 99%

Computational approaches for evaluating morphological changes in the corneal stroma associated with decellularization

Pantić

Cumic

Valjarević

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Regarding applied MRI sequences, Iv et al used T1-weighted (T1W) and T2-weighted (T2W) sequences together for radiomic feature extraction (34). Chen et al and Saju et al used T1W contrast-enhanced and T2W sequences together (35,38). Yan et al combined T1W, T2W, fluid-attenuated inversion recovery (FLAIR) sequences, and apparent diffusion coefficient (ADC) values (36).…”

Section: Resultsmentioning

confidence: 99%

“…Chen et al did not use handcrafted radiomic features, instead applied a convolutional neural network (CNN) model for feature extraction (35). Four of the included studies used various cross-validation methods for validation (37, 38, 40, 41), while Yan et al used a separate test set (36).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Radiomics-based machine learning models for prediction of medulloblastoma subgroups: a systematic review and meta-analysis of the diagnostic test performance

et al. 2022

View full text Add to dashboard Cite

Background Medulloblastomas are a major cause of cancer-related mortality in the pediatric population. Four molecular groups have been identified, and these molecular groups drive risk stratification, prognostic modeling, and the development of novel treatment modalities. It has been demonstrated that radiomics-based machine learning (ML) models are effective at predicting the diagnosis, molecular class, and grades of CNS tumors. Purpose To assess radiomics-based ML models’ diagnostic performance in predicting medulloblastoma subgroups and the methodological quality of the studies. Material and Methods A comprehensive literature search was performed on PubMed; the last search was conducted on 1 May 2022. Studies that predicted all four medulloblastoma subgroups in patients with histopathologically confirmed medulloblastoma and reporting area under the curve (AUC) values were included in the study. The quality assessments were conducted according to the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM). A meta-analysis of radiomics-based ML studies’ diagnostic performance for the preoperative evaluation of medulloblastoma subgrouping was performed. Results Five studies were included in this meta-analysis. Regarding patient selection, two studies indicated an unclear risk of bias according to the QUADAS-2. The five studies had an average CLAIM score and compliance score of 23.2 and 0.57, respectively. The meta-analysis showed pooled AUCs of 0.88, 0.82, 0.83, and 0.88 for WNT, SHH, group 3, and group 4 for classification, respectively. Conclusion Radiomics-based ML studies have good classification performance in predicting medulloblastoma subgroups, with AUCs >0.80 in every subgroup. To be applied to clinical practice, they need methodological quality improvement and stability.

show abstract

“…Recently, some studies have used a single MRI sequence or ADC values to construct prediction models for the prediction of MB molecular subtypes ( Iv et al, 2019 ; Gonçalves et al, 2022 ; Saju et al, 2022 ). However, the results are still unstable, and there are fewer reports on constructing prediction models based on multiparametric MRI combined with clinical parameters for the prediction of MB molecular subtype.…”

Section: Introductionmentioning

confidence: 99%

Preoperative prediction of sonic hedgehog and group 4 molecular subtypes of pediatric medulloblastoma based on radiomics of multiparametric MRI combined with clinical parameters

Wang

Qin

et al. 2023

Front. Neurosci.

View full text Add to dashboard Cite

PurposeTo construct a machine learning model based on radiomics of multiparametric magnetic resonance imaging (MRI) combined with clinical parameters for predicting Sonic Hedgehog (SHH) and Group 4 (G4) molecular subtypes of pediatric medulloblastoma (MB).MethodsThe preoperative MRI images and clinical data of 95 patients with MB were retrospectively analyzed, including 47 cases of SHH subtype and 48 cases of G4 subtype. Radiomic features were extracted from T1-weighted imaging (T1), contrast-enhanced T1 weighted imaging (T1c), T2-weighted imaging (T2), T2 fluid-attenuated inversion recovery imaging (T2FLAIR), and apparent diffusion coefficient (ADC) maps, using variance thresholding, SelectKBest, and Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms. The optimal features were filtered using LASSO regression, and a logistic regression (LR) algorithm was used to build a machine learning model. The receiver operator characteristic (ROC) curve was plotted to evaluate the prediction accuracy, and verified by its calibration, decision and nomogram. The Delong test was used to compare the differences between different models.ResultsA total of 17 optimal features, with non-redundancy and high correlation, were selected from 7,045 radiomics features, and used to build an LR model. The model showed a classification accuracy with an under the curve (AUC) of 0.960 (95% CI: 0.871−1.000) in the training cohort and 0.751 (95% CI: 0.587−0.915) in the testing cohort, respectively. The location of the tumor, pathological type, and hydrocephalus status of the two subtypes of patients differed significantly (p < 0.05). When combining radiomics features and clinical parameters to construct the combined prediction model, the AUC improved to 0.965 (95% CI: 0.898−1.000) in the training cohort and 0.849 (95% CI: 0.695−1.000) in the testing cohort, respectively. There was a significant difference in the prediction accuracy, as measured by AUC, between the testing cohorts of the two prediction models, which was confirmed by Delong’s test (p = 0.0144). Decision curves and nomogram further validate that the combined model can achieve net benefits in clinical work.ConclusionThe combined prediction model, constructed based on radiomics of multiparametric MRI and clinical parameters can potentially provide a non-invasive clinical approach to predict SHH and G4 molecular subtypes of MB preoperatively.

show abstract

Machine-learning approach to predict molecular subgroups of medulloblastoma using multiparametric MRI-based tumor radiomics

Cited by 9 publications

References 18 publications

Computational approaches for evaluating morphological changes in the corneal stroma associated with decellularization

Computational approaches for evaluating morphological changes in the corneal stroma associated with decellularization

Radiomics-based machine learning models for prediction of medulloblastoma subgroups: a systematic review and meta-analysis of the diagnostic test performance

Preoperative prediction of sonic hedgehog and group 4 molecular subtypes of pediatric medulloblastoma based on radiomics of multiparametric MRI combined with clinical parameters

Contact Info

Product

Resources

About