A Multiparametric MRI-Based Radiomics Nomogram for Preoperative Prediction of Survival Stratification in Glioblastoma Patients With Standard Treatment

Exploring the prognostic value and biological pathways of transcriptomics and radiomics patterns in glioblastoma multiforme

Objectives

To develop a multi-omics prognostic model integrating transcriptomics and radiomics for predicting overall survival in patients with glioblastoma multiforme (GBM), and investigate the biological pathways of radiomics patterns.

Materials and methods

Transcription profiles of GBM patients and normal controls were used to obtain differentially expressed mRNAs and long non-coding RNAs (lncRNAs). Radiomics features were extracted from magnetic resonance imaging (MRI). Least absolute shrinkage and selection operator (LASSO) Cox regression was employed to select survival-associated features for the construction of transcriptomics and radiomics signatures. Genes associated with GBM prognosis were identified through the analysis of lncRNA-mRNA co-expression networks and Weighted Gene Co-expression Network Analysis (WGCNA), and their biological pathways were investigated using Genomes enrichment analysis. Transcriptomics, radiomics, and clinical data were integrated to evaluate the multi-omics prognostic model's performance.

Results

LASSO Cox regression yielded 21 survival-related features, including 19 transcriptomics features and 2 radiomics features. Based on transcriptomics and radiomics signature, GBM patients were classified as high-risk or low-risk. The genes obtained from the co-expression network screen were associated with microtubule binding, while those from the WGCNA screen were associated with growth factor receptor binding. In the training set, the AUC values for the multi-omics model and clinical model were 0.964 and 0.830, respectively, while in the validation set, they were 0.907 and 0.787. The multi-omics prognostic model outperformed the clinical prognostic model.

Conclusions

The co-expression network and WGCNA methods revealed genes associated with multiple biological pathways in GBM. The multi-omics prognostic model demonstrated excellent performance and indicated significant potential for clinical application.

Contrast-Enhanced CT-Based Deep Learning Radiomics Nomogram for the Survival Prediction in Gallbladder Cancer

RATIONALE AND OBJECTIVES

An accurate prognostic model is essential for the development of treatment strategies for gallbladder cancer (GBC). This study proposes an integrated model using clinical features, radiomics, and deep learning based on contrast-enhanced computed tomography (CT) images for survival prediction in patients with GBC after surgical resection.

METHODS

A total of 167 patients with GBC who underwent surgical resection at two medical institutions were retrospectively enrolled. After obtaining the pre-treatment CT images, the tumor lesions were manually segmented, and handcrafted radiomics features were extracted. A clinical prognostic signature and radiomics signature were built using machine learning algorithms based on the optimal clinical features or handcrafted radiomics features, respectively. Subsequently, a DenseNet121 model was employed for transfer learning on the radiomics image data and as the basis for the deep learning signature. Finally, we used logistic regression on the three signatures to obtain the unified multimodal model for comprehensive interpretation and analysis.

RESULTS

The integrated model performed better than the other models, exhibiting the highest area under the curve (AUC) of 0.870 in the test set, and the highest concordance index (C-index) of 0.736 in predicting patient survival rates. A Kaplan-Meier analysis demonstrated that patients in high-risk group had a lower survival probability compared to those in low-risk group (log-rank p < 0.05).

CONCLUSION

The nomogram is useful for predicting the survival of patients with GBC after surgical resection, helping in the identification of high-risk patients with poor prognosis and ultimately facilitating individualized management of patients with GBC.

Artificial Intelligence Imaging for Predicting High-risk Molecular Markers of Gliomas

Gliomas, the most prevalent primary malignant tumors of the central nervous system, present significant challenges in diagnosis and prognosis. The fifth edition of the World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS5) published in 2021, has emphasized the role of high-risk molecular markers in gliomas. These markers are crucial for enhancing glioma grading and influencing survival and prognosis. Noninvasive prediction of these high-risk molecular markers is vital. Genetic testing after biopsy, the current standard for determining molecular type, is invasive and time-consuming. Magnetic resonance imaging (MRI) offers a non-invasive alternative, providing structural and functional insights into gliomas. Advanced MRI methods can potentially reflect the pathological characteristics associated with glioma molecular markers; however, they struggle to fully represent gliomas' high heterogeneity. Artificial intelligence (AI) imaging, capable of processing vast medical image datasets, can extract critical molecular information. AI imaging thus emerges as a noninvasive and efficient method for identifying high-risk molecular markers in gliomas, a recent focus of research. This review presents a comprehensive analysis of AI imaging's role in predicting glioma high-risk molecular markers, highlighting challenges and future directions.

Magnetic resonance imaging and deoxyribonucleic acid methylation-based radiogenomic models for survival risk stratification of glioblastoma

Glioblastoma multiforme (GBM) is one of the deadliest tumours. This study aimed to construct radiogenomic prognostic models of glioblastoma overall survival (OS) based on magnetic resonance imaging (MRI) Gd-T1WI images and deoxyribonucleic acid (DNA) methylation-seq and to understand the related biological pathways. The ResNet3D-18 model was used to extract radiomic features, and Lasso-Cox regression analysis was utilized to establish the prognostic models. A nomogram was constructed by combining the radiogenomic features and clinicopathological variables. The DeLong test was performed to compare the area under the curve (AUC) of the models. We screened differentially expressed genes (DEGs) with original ribonucleic acid (RNA)-seq in risk stratification and used Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) annotations for functional enrichment analysis. For the 1-year OS models, the AUCs of the radiogenomic set, methylation set and deep learning set in the training cohort were 0.864, 0.804 and 0.787, and those in the validation cohort were 0.835, 0.768 and 0.651, respectively. The AUCs of the 0.5-, 1- and 2-year nomograms in the training cohort were 0.943, 0.861 and 0.871, and those in the validation cohort were 0.864, 0.885 and 0.805, respectively. A total of 245 DEGs were screened; functional enrichment analysis showed that these DEGs were associated with cell immunity. The survival risk-stratifying radiogenomic models for glioblastoma OS had high predictability and were associated with biological pathways related to cell immunity.

Deep learning-based radiomics model from pretreatment ADC to predict biochemical recurrence in advanced prostate cancer

Purpose

To develop deep-learning radiomics model for predicting biochemical recurrence (BCR) of advanced prostate cancer (PCa) based on pretreatment apparent diffusion coefficient (ADC) maps.

Methods

Data were collected retrospectively from 131 patients diagnosed with advanced PCa, randomly divided into training (n = 93) and test (n = 38) datasets. Pre-treatment ADC images were segmented using a pre-trained artificial intelligence (AI) model to identify suspicious PCa areas. Three models were constructed, including a clinical model, a conventional radiomics model and a deep-radiomics model. The receiver operating characteristic (ROC), precision-recall (PR) curve and decision curve analysis (DCA) were used to assess predictive performance in test dataset. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were employed to compare the performance enhancement of the deep-radiomics model in relation to the other two models.

Results

The deep-radiomics model exhibited a significantly higher area under the curve (AUC) of ROC than the other two (P = 0.033, 0.026), as well as PR curve (AUC difference 0.420, 0.432). The DCA curve demonstrated superior performance for the deep-radiomics model across all risk thresholds than the other two. Taking the clinical model as reference, the NRI and IDI was 0.508 and 0.679 for the deep-radiomics model with significant difference. Compared with the conventional radiomics model, the NRI and IDI was 0.149 and 0.164 for the deep-radiomics model without significant difference.

Conclusion

The deep-radiomics model exhibits promising potential in predicting BCR in advanced PCa, compared to both the clinical model and the conventional radiomics model.

Advancing precision prognostication in neuro-oncology: Machine learning models for data-driven personalized survival predictions in IDH-wildtype glioblastoma

Background

Glioblastoma (GBM) remains associated with a dismal prognoses despite standard therapies. While population-level survival statistics are established, generating individualized prognosis remains challenging. We aim to develop machine learning (ML) models that generate personalized survival predictions for GBM patients to enhance prognostication.

Methods

Adult patients with histologically confirmed IDH-wildtype GBM from the National Cancer Database (NCDB) were analyzed. ML models were developed with TabPFN, TabNet, XGBoost, LightGBM, and Random Forest algorithms to predict mortality at 6, 12, 18, and 24 months postdiagnosis. SHapley Additive exPlanations (SHAP) were employed to enhance the interpretability of the models. Models were primarily evaluated using the area under the receiver operating characteristic (AUROC) values, and the top-performing models indicated by the highest AUROCs for each outcome were deployed in a web application that was created for individualized predictions.

Results

A total of 7537 patients were retrieved from the NCDB. Performance evaluation revealed the top-performing models for each outcome were built using the TabPFN algorithm. The TabPFN models yielded mean AUROCs of 0.836, 0.78, 0.732, and 0.724 in predicting 6, 12, 18, and 24 month mortality, respectively.

Conclusions

This study establishes ML models tailored to individual patients to enhance GBM prognostication. Future work should focus on external validation and dynamic updating as new data emerge.

Pretreatment MR-based radiomics in patients with glioblastoma: A systematic review and meta-analysis of prognostic endpoints

PURPOSE

Recent studies have shown promise of MR-based radiomics in predicting the survival of patients with untreated glioblastoma. This study aimed to comprehensively collate evidence to assess the prognostic value of radiomics in glioblastoma.

METHODS

PubMed-MEDLINE, Embase, and Web of Science were searched to find original articles investigating the prognostic value of MR-based radiomics in glioblastoma published up to July 14, 2023. Concordance indexes (C-indexes) and Cox proportional hazards ratios (HRs) of overall survival (OS) and progression-free survival (PFS) were pooled via random-effects modeling. For studies aimed at classifying long-term and short-term PFS, a hierarchical regression model was used to calculate pooled sensitivity and specificity. Between-study heterogeneity was assessed using the Higgin inconsistency index (I2). Subgroup regression analysis was performed to find potential factors contributing to heterogeneity. Publication bias was assessed via funnel plots and the Egger test.

RESULTS

Among 1371 abstracts, 18 and 17 studies were included for qualitative and quantitative data synthesis, respectively. Respective pooled C-indexes and HRs for OS were 0.65 (95 % confidence interval [CI], 0.58-0.72) and 2.88 (95 % CI, 2.28-3.64), whereas those for PFS were 0.61 (95 % CI, 0.55-0.66) and 2.78 (95 % CI, 1.91-4.03). Among 4 studies that predicted short-term PFS, the pooled sensitivity and specificity were 0.77 (95 % CI, 0.58-0.89) and 0.60 (95 % CI, 0.45-0.73), respectively. There was a substantial between-study heterogeneity among studies with the survival endpoint of OS C-index (n = 9, I2 = 83.8 %). Publication bias was not observed overall.

CONCLUSION

Pretreatment MR-based radiomics provided modest prognostic value in both OS and PFS in patients with glioblastoma.

Multi-parameter MRI based radiomics nomogram for predicting telomerase reverse transcriptase promoter mutation and prognosis in glioblastoma

Objective

To investigate the clinical utility of multi-parameter MRI-based radiomics nomogram for predicting telomerase reverse transcriptase (TERT) promoter mutation status and prognosis in adult glioblastoma (GBM).

Methods

We retrospectively analyzed MRI and pathological data of 152 GBM patients. A total of 2,832 radiomics features were extracted and filtered from preoperative MRI images. A radiomics nomogram was created on the basis of radiomics signature (rad-score) and clinical traits. The performance of the nomogram in TERT mutation identification was assessed using receiver operating characteristic (ROC) curve, calibration curves, and clinical decision curves. Pathologically confirmed TERT mutations and risk score-based TERT mutations were employed to assess patient prognosis, respectively.

Results

The random forest (RF) algorithm outperformed the other two algorithms, yielding the best diagnostic efficacy in differentiating TERT mutations, with area under the curve (AUC) values of 0.892 (95% CI: 0.828-0.956) and 0.824 (95% CI: 0.677-0.971) in the training set and validation sets, respectively. Furthermore, the predictive power of the radiomics nomogram constructed with the rad-score and clinical variables reached 0.916 (95%CI: 0.864, 0.968) in the training set and 0.880 (95%CI: 0.743, 1) in the validation set. Calibration curve and decision curve analysis findings further uphold the clinical application value of the radiomics nomogram. The overall survival of the high-risk subgroup was significantly shorter than that of the low-risk subgroup, which was consistent with the results of the pathologically confirmed TERT mutation group.

Conclusion

The radiomics nomogram could non-invasively provide promising insights for predicting TERT mutations and prognosis in GBM patients with excellent identification and calibration abilities.

Glioblastoma survival is associated with distinct proteomic alteration signatures post chemoirradiation in a large-scale proteomic panel

Background

Glioblastomas (GBM) are rapidly progressive, nearly uniformly fatal brain tumors. Proteomic analysis represents an opportunity for noninvasive GBM classification and biological understanding of treatment response.

Purpose

We analyzed differential proteomic expression pre vs. post completion of concurrent chemoirradiation (CRT) in patient serum samples to explore proteomic alterations and classify GBM by integrating clinical and proteomic parameters.

Materials and methods

82 patients with GBM were clinically annotated and serum samples obtained pre- and post-CRT. Serum samples were then screened using the aptamer-based SOMAScan® proteomic assay. Significant traits from uni- and multivariate Cox models for overall survival (OS) were designated independent prognostic factors and principal component analysis (PCA) was carried out. Differential expression of protein signals was calculated using paired t-tests, with KOBAS used to identify associated KEGG pathways. GSEA pre-ranked analysis was employed on the overall list of differentially expressed proteins (DEPs) against the MSigDB Hallmark, GO Biological Process, and Reactome databases with weighted gene correlation network analysis (WGCNA) and Enrichr used to validate pathway hits internally.

Results

3 clinical clusters of patients with differential survival were identified. 389 significantly DEPs pre vs. post-treatment were identified, including 284 upregulated and 105 downregulated, representing several pathways relevant to cancer metabolism and progression. The lowest survival group (median OS 13.2 months) was associated with DEPs affiliated with proliferative pathways and exhibiting distinct oppositional response including with respect to radiation therapy related pathways, as compared to better-performing groups (intermediate, median OS 22.4 months; highest, median OS 28.7 months). Opposite signaling patterns across multiple analyses in several pathways (notably fatty acid metabolism, NOTCH, TNFα via NF-κB, Myc target V1 signaling, UV response, unfolded protein response, peroxisome, and interferon response) were distinct between clinical survival groups and supported by WGCNA. 23 proteins were statistically signficant for OS with 5 (NETO2, CST7, SEMA6D, CBLN4, NPS) supported by KM.

Conclusion

Distinct proteomic alterations with hallmarks of cancer, including progression, resistance, stemness, and invasion, were identified in serum samples obtained from GBM patients pre vs. post CRT and corresponded with clinical survival. The proteome can potentially be employed for glioma classification and biological interrogation of cancer pathways.

Discovery of novel thyrointegrin αvβ3 antagonist fb-PMT (NP751) in the management of human glioblastoma multiforme

Background

Thyrointegrin αvβ3 receptors are unique molecular cancer therapeutic targets because of their overexpression on cancer and rapidly dividing blood vessel cells compared and quiescent on normal cells. A macromolecule, TriAzole Tetraiodothyroacetic acid (TAT) conjugated to polyethylene glycol with a lipophilic 4-fluorobenyl group (fb-PMT and NP751), interacts with high affinity (0.21 nM) and specificity with the thyrointegrin αvβ3 receptors on the cell surface without nuclear translocation in contrast to the non-polymer conjugated TAT.

Methods

The following in vitro assays were carried out to evaluate NP751 including binding affinity to different integrins, transthyretin (TTR)-binding affinity, glioblastoma multiforme (GBM) cell adhesion, proliferation assays, nuclear translocations, chorioallantoic membrane model of angiogenesis, and microarray for molecular mechanisms. Additionally, in vivo studies were carried out to evaluate the anticancer efficacy of NP751, its biodistribution, and brain GBM tumor versus plasma levels kinetics.

Results

NP751 demonstrated a broad spectrum of antiangiogenesis and anticancer efficacy in experimental models of angiogenesis and xenografts of human GBM cells. Tumor growth and cancer cells' viability were markedly decreased (by > 90%; P < .001) in fb-PMT-treated U87-luc or 3 different primary human GBM xenograft-bearing mice based on tumor in vivo imaging system (IVIS) imaging and histopathological examination, without relapse upon treatment discontinuation. Additionally, it effectively transports across the blood-brain barrier via its high-affinity binding to plasma TTR with high retention in brain tumors. NP751-induced effects on gene expression support the model of molecular interference at multiple key pathways essential for GBM tumor progression and vascularization.

Conclusions

fb-PMT is a potent thyrointegrin αvβ3 antagonist with potential impact on GBM tumor progression.

Artificial intelligence in multi-parametric magnetic resonance imaging: A review

Multi-parametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting the deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, and super-resolution. The current availability of increasing computational power and fast-improving AI algorithms have empowered numerous computer-based systems for applying mpMRI to disease diagnosis, imaging-guided radiotherapy, patient risk and overall survival time prediction, and the development of advanced quantitative imaging technology for magnetic resonance fingerprinting. However, the wide application of these developed systems in the clinic is still limited by a number of factors, including robustness, reliability, and interpretability. This survey aims to provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the general concepts, main application scenarios, and remaining challenges of AI in mpMRI. This article is protected by copyright. All rights reserved.