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Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma.

ABSTRACT: Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in isocitrate dehydrogenase (IDH)-wildtype GBM patients, by combining conventional and deep learning methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. Support vector machine (SVM) classifiers were trained on the radiomic features in the discovery cohort (n = 404) to categorize patient groups of high-risk (OS < 6 months) vs all, and low-risk (OS ≥ 18 months) vs all. The trained radiomic model was independently tested in the replication cohort (n = 112) and a patient-wise survival prediction index was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed area under the ROC curves (AUCs) of 0.78 (95% CI 0.70-0.85)/0.75 (95% CI 0.64-0.79) and 0.75 (95% CI 0.65-0.84)/0.63 (95% CI 0.52-0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95% CI 0.6-0.7) for clinical data improving to 0.75 (95% CI 0.72-0.79) for the combination of all omics. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM.

SUBMITTER: Fathi Kazerooni A

PROVIDER: S-EPMC9130299 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma.

Fathi Kazerooni Anahita A Saxena Sanjay S Toorens Erik E Tu Danni D Bashyam Vishnu V Akbari Hamed H Mamourian Elizabeth E Sako Chiharu C Koumenis Costas C Verginadis Ioannis I Verma Ragini R Shinohara Russell T RT Desai Arati S AS Lustig Robert A RA Brem Steven S Mohan Suyash S Bagley Stephen J SJ Ganguly Tapan T O'Rourke Donald M DM Bakas Spyridon S Nasrallah MacLean P MP Davatzikos Christos C

Scientific reports 20220524 1

Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in isocitrate dehydrogenase (IDH)-wildtype GBM patients, by combining conventional and deep learning methods. Conventional radiomics and deep learning features were extracted from pre-operati ...[more]

PMID: 35610333

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Project description:PurposeThis study aimed to develop a radiomics signature for the preoperative prognosis prediction of isocitrate dehydrogenase (IDH)-wild-type glioblastoma (GBM) patients and to provide personalized assistance in the clinical decision-making for different patients.Materials and methodsA total of 142 IDH-wild-type GBM patients classified using the new classification criteria of WHO 2021 from two centers were included in the study and randomly divided into a training set and a test set. Firstly, their clinical characteristics were screened using univariate Cox regression. Then, the radiomics features were extracted from the tumor and peritumoral edema areas on their contrast-enhanced T1-weighted image (CE-T1WI), T2-weighted image (T2WI), and T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) magnetic resonance imaging (MRI) images. Subsequently, inter- and intra-class correlation coefficient (ICC) analysis, Spearman's correlation analysis, univariate Cox, and the least absolute shrinkage and selection operator (LASSO) Cox regression were used step by step for feature selection and the construction of a radiomics signature. The combined model was established by integrating the selected clinical factors. Kaplan-Meier analysis was performed for the validation of the discrimination ability of the model, and the C-index was used to evaluate consistency in the prediction. Finally, a Radiomics + Clinical nomogram was generated for personalized prognosis analysis and then validated using the calibration curve.ResultsAnalysis of the clinical characteristics resulted in the screening of four risk factors. The combination of ICC, Spearman's correlation, and univariate and LASSO Cox resulted in the selection of eight radiomics features, which made up the radiomics signature. Both the radiomics and combined models can significantly stratify high- and low-risk patients (p < 0.001 and p < 0.05 for the training and test sets, respectively) and obtained good prediction consistency (C-index = 0.74-0.86). The calibration plots exhibited good agreement in both 1- and 2-year survival between the prediction of the model and the actual observation.ConclusionRadiomics is an independent preoperative non-invasive prognostic tool for patients who were newly classified as having IDH-wild-type GBM. The constructed nomogram, which combined radiomics features with clinical factors, can predict the overall survival (OS) of IDH-wild-type GBM patients and could be a new supplement to treatment guidelines.

Project description:ImportanceThe prognosis of overall survival (OS) in patients with glioblastoma (GBM) may depend on the underlying structural connectivity of the brain.ObjectiveTo examine the association between white matter tracts affected by GBM and patients' OS by means of a new tract density index (TDI).Design, setting, and participantsThis prognostic study in patients with a histopathologic diagnosis of GBM examined a discovery cohort of 112 patients who underwent surgery between February 1, 2015, and November 30, 2020 (follow-up to May 31, 2023), in Italy and 70 patients in a replicative cohort (n = 70) who underwent surgery between September 1, 2012, and November 30, 2015 (follow-up to May 31, 2023), in Germany. Statistical analyses were performed from June 1, 2021, to May 31, 2023. Thirteen and 12 patients were excluded from the discovery and the replicative sets, respectively, because of magnetic resonance imaging artifacts.ExposureThe density of white matter tracts encompassing GBM.Main outcomes and measuresCorrelation, linear regression, Cox proportional hazards regression, Kaplan-Meier, and prediction analysis were used to assess the association between the TDI and OS. Results were compared with common prognostic factors of GBM, including age, performance status, O6-methylguanine-DNA methyltransferase methylation, and extent of surgery.ResultsIn the discovery cohort (n = 99; mean [SD] age, 62.2 [11.5] years; 29 female [29.3%]; 70 male [70.7%]), the TDI was significantly correlated with OS (r = -0.34; P < .001). This association was more stable compared with other prognostic factors. The TDI showed a significant regression pattern (Cox: hazard ratio, 0.28 [95% CI, 0.02-0.55; P = .04]; linear: t = -2.366; P = .02). and a significant Kaplan-Meier stratification of patients as having lower or higher OS based on the TDI (log-rank test = 4.52; P = .03). Results were confirmed in the replicative cohort (n = 58; mean [SD] age, 58.5 [11.1] years, 14 female [24.1%]; 44 male [75.9%]). High (24-month cutoff) and low (18-month cutoff) OS was predicted based on the TDI computed in the discovery cohort (accuracy = 87%).Conclusions and relevanceIn this study, GBMs encompassing regions with low white matter tract density were associated with longer OS. These findings indicate that the TDI is a reliable presurgical outcome predictor that may be considered in clinical trials and clinical practice. These findings support a framework in which the outcome of GBM depends on the patient's brain organization.

Project description:PurposeThis study explores integrating clinical features with radiomic and dosiomic characteristics into AI models to enhance the prediction accuracy of radiation dermatitis (RD) in breast cancer patients undergoing volumetric modulated arc therapy (VMAT).Materials and methodsThis study involved a retrospective analysis of 120 breast cancer patients treated with VMAT at Kaohsiung Veterans General Hospital from 2018 to 2023. Patient data included CT images, radiation doses, Dose-Volume Histogram (DVH) data, and clinical information. Using a Treatment Planning System (TPS), we segmented CT images into Regions of Interest (ROIs) to extract radiomic and dosiomic features, focusing on intensity, shape, texture, and dose distribution characteristics. Features significantly associated with the development of RD were identified using ANOVA and LASSO regression (p-value < 0.05). These features were then employed to train and evaluate Logistic Regression (LR) and Random Forest (RF) models, using tenfold cross-validation to ensure robust assessment of model efficacy.ResultsIn this study, 102 out of 120 VMAT-treated breast cancer patients were included in the detailed analysis. Thirty-two percent of these patients developed Grade 2+ RD. Age and BMI were identified as significant clinical predictors. Through feature selection, we narrowed down the vast pool of radiomic and dosiomic data to 689 features, distributed across 10 feature subsets for model construction. In the LR model, the J subset, comprising DVH, Radiomics, and Dosiomics features, demonstrated the highest predictive performance with an AUC of 0.82. The RF model showed that subset I, which includes clinical, radiomic, and dosiomic features, achieved the best predictive accuracy with an AUC of 0.83. These results emphasize that integrating radiomic and dosiomic features significantly enhances the prediction of Grade 2+ RD.ConclusionIntegrating clinical, radiomic, and dosiomic characteristics into AI models significantly improves the prediction of Grade 2+ RD risk in breast cancer patients post-VMAT. The RF model analysis demonstrates that a comprehensive feature set maximizes predictive efficacy, marking a promising step towards utilizing AI in radiation therapy risk assessment and enhancing patient care outcomes.

Dataset Information

Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma.

Publications

Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma.

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