Circular RNAs: Characterization, cellular roles, and applications.

Most circular RNAs are produced from the back-splicing of exons of precursor mRNAs. Recent technological advances have in part overcome problems with their circular conformation and sequence overlap with linear cognate mRNAs, allowing a better understanding of their cellular roles. Depending on their localization and specific interactions with DNA, RNA, and proteins, circular RNAs can modulate transcription and splicing, regulate stability and translation of cytoplasmic mRNAs, interfere with signaling pathways, and serve as templates for translation in different biological and pathophysiological contexts. Emerging applications of RNA circles to interfere with cellular processes, modulate immune responses, and direct translation into proteins shed new light on biomedical research. In this review, we discuss approaches used in circular RNA studies and the current understanding of their regulatory roles and potential applications.

SynergyX: a multi-modality mutual attention network for interpretable drug synergy prediction.

Discovering effective anti-tumor drug combinations is crucial for advancing cancer therapy. Taking full account of intricate biological interactions is highly important in accurately predicting drug synergy. However, the extremely limited prior knowledge poses great challenges in developing current computational methods. To address this, we introduce SynergyX, a multi-modality mutual attention network to improve anti-tumor drug synergy prediction. It dynamically captures cross-modal interactions, allowing for the modeling of complex biological networks and drug interactions. A convolution-augmented attention structure is adopted to integrate multi-omic data in this framework effectively. Compared with other state-of-the-art models, SynergyX demonstrates superior predictive accuracy in both the General Test and Blind Test and cross-dataset validation. By exhaustively screening combinations of approved drugs, SynergyX reveals its ability to identify promising drug combination candidates for potential lung cancer treatment. Another notable advantage lies in its multidimensional interpretability. Taking Sorafenib and Vorinostat as an example, SynergyX serves as a powerful tool for uncovering drug-gene interactions and deciphering cell selectivity mechanisms. In summary, SynergyX provides an illuminating and interpretable framework, poised to catalyze the expedition of drug synergy discovery and deepen our comprehension of rational combination therapy.

Complementary multi-modality molecular self-supervised learning via non-overlapping masking for property prediction.

Self-supervised learning plays an important role in molecular representation learning because labeled molecular data are usually limited in many tasks, such as chemical property prediction and virtual screening. However, most existing molecular pre-training methods focus on one modality of molecular data, and the complementary information of two important modalities, SMILES and graph, is not fully explored. In this study, we propose an effective multi-modality self-supervised learning framework for molecular SMILES and graph. Specifically, SMILES data and graph data are first tokenized so that they can be processed by a unified Transformer-based backbone network, which is trained by a masked reconstruction strategy. In addition, we introduce a specialized non-overlapping masking strategy to encourage fine-grained interaction between these two modalities. Experimental results show that our framework achieves state-of-the-art performance in a series of molecular property prediction tasks, and a detailed ablation study demonstrates efficacy of the multi-modality framework and the masking strategy.

MEG Evidence That Modality-Independent Conceptual Representations Contain Semantic and Visual Features.

The semantic knowledge stored in our brains can be accessed from different stimulus modalities. For example, a picture of a cat and the word "cat" both engage similar conceptual representations. While existing research has found evidence for modality-independent representations, their content remains unknown. Modality-independent representations could be semantic, or they might also contain perceptual features. We developed a novel approach combining word/picture cross-condition decoding with neural network classifiers that learned latent modality-independent representations from MEG data (25 human participants, 15 females, 10 males). We then compared these representations to models representing semantic, sensory, and orthographic features. Results show that modality-independent representations correlate both with semantic and visual representations. There was no evidence that these results were due to picture-specific visual features or orthographic features automatically activated by the stimuli presented in the experiment. These findings support the notion that modality-independent concepts contain both perceptual and semantic representations.

Identifying spatial domain by adapting transcriptomics with histology through contrastive learning.

Recent advances in spatial transcriptomics have enabled measurements of gene expression at cell/spot resolution meanwhile retaining both the spatial information and the histology images of the tissues. Accurately identifying the spatial domains of spots is a vital step for various downstream tasks in spatial transcriptomics analysis. To remove noises in gene expression, several methods have been developed to combine histopathological images for data analysis of spatial transcriptomics. However, these methods either use the image only for the spatial relations for spots, or individually learn the embeddings of the gene expression and image without fully coupling the information. Here, we propose a novel method ConGI to accurately exploit spatial domains by adapting gene expression with histopathological images through contrastive learning. Specifically, we designed three contrastive loss functions within and between two modalities (the gene expression and image data) to learn the common representations. The learned representations are then used to cluster the spatial domains on both tumor and normal spatial transcriptomics datasets. ConGI was shown to outperform existing methods for the spatial domain identification. In addition, the learned representations have also been shown powerful for various downstream tasks, including trajectory inference, clustering, and visualization.

Adaptive risk-aware sharable and individual subspace learning for cancer survival analysis with multi-modality data.

Biomedical multi-modality data (also named multi-omics data) refer to data that span different types and derive from multiple sources in clinical practices (e.g. gene sequences, proteomics and histopathological images), which can provide comprehensive perspectives for cancers and generally improve the performance of survival models. However, the performance improvement of multi-modality survival models may be hindered by two key issues as follows: (1) how to learn and fuse modality-sharable and modality-individual representations from multi-modality data; (2) how to explore the potential risk-aware characteristics in each risk subgroup, which is beneficial to risk stratification and prognosis evaluation. Additionally, learning-based survival models generally refer to numerous hyper-parameters, which requires time-consuming parameter setting and might result in a suboptimal solution. In this paper, we propose an adaptive risk-aware sharable and individual subspace learning method for cancer survival analysis. The proposed method jointly learns sharable and individual subspaces from multi-modality data, whereas two auxiliary terms (i.e. intra-modality complementarity and inter-modality incoherence) are developed to preserve the complementary and distinctive properties of each modality. Moreover, it equips with a grouping co-expression constraint for obtaining risk-aware representation and preserving local consistency. Furthermore, an adaptive-weighted strategy is employed to efficiently estimate crucial parameters during the training stage. Experimental results on three public datasets demonstrate the superiority of our proposed model.

scMHNN: a novel hypergraph neural network for integrative analysis of single-cell epigenomic, transcriptomic and proteomic data.

Technological advances have now made it possible to simultaneously profile the changes of epigenomic, transcriptomic and proteomic at the single cell level, allowing a more unified view of cellular phenotypes and heterogeneities. However, current computational tools for single-cell multi-omics data integration are mainly tailored for bi-modality data, so new tools are urgently needed to integrate tri-modality data with complex associations. To this end, we develop scMHNN to integrate single-cell multi-omics data based on hypergraph neural network. After modeling the complex data associations among various modalities, scMHNN performs message passing process on the multi-omics hypergraph, which can capture the high-order data relationships and integrate the multiple heterogeneous features. Followingly, scMHNN learns discriminative cell representation via a dual-contrastive loss in self-supervised manner. Based on the pretrained hypergraph encoder, we further introduce the pre-training and fine-tuning paradigm, which allows more accurate cell-type annotation with only a small number of labeled cells as reference. Benchmarking results on real and simulated single-cell tri-modality datasets indicate that scMHNN outperforms other competing methods on both cell clustering and cell-type annotation tasks. In addition, we also demonstrate scMHNN facilitates various downstream tasks, such as cell marker detection and enrichment analysis.

Matching single cells across modalities with contrastive learning and optimal transport.

Understanding the interactions between the biomolecules that govern cellular behaviors remains an emergent question in biology. Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening new avenues for understanding cellular complexity and heterogeneity. Still, the resulting multimodal single-cell datasets present unique challenges arising from the high dimensionality and multiple sources of acquisition noise. Computational methods able to match cells across different modalities offer an appealing alternative towards this goal. In this work, we propose MatchCLOT, a novel method for modality matching inspired by recent promising developments in contrastive learning and optimal transport. MatchCLOT uses contrastive learning to learn a common representation between two modalities and applies entropic optimal transport as an approximate maximum weight bipartite matching algorithm. Our model obtains state-of-the-art performance on two curated benchmarking datasets and an independent test dataset, improving the top scoring method by 26.1% while preserving the underlying biological structure of the multimodal data. Importantly, MatchCLOT offers high gains in computational time and memory that, in contrast to existing methods, allows it to scale well with the number of cells. As single-cell datasets become increasingly large, MatchCLOT offers an accurate and efficient solution to the problem of modality matching.

Multi-modality attribute learning-based method for drug-protein interaction prediction based on deep neural network.

Identification of active candidate compounds for target proteins, also called drug-protein interaction (DPI) prediction, is an essential but time-consuming and expensive step, which leads to fostering the development of drug discovery. In recent years, deep network-based learning methods were frequently proposed in DPIs due to their powerful capability of feature representation. However, the performance of existing DPI methods is still limited by insufficiently labeled pharmacological data and neglected intermolecular information. Therefore, overcoming these difficulties to perfect the performance of DPIs is an urgent challenge for researchers. In this article, we designed an innovative 'multi-modality attributes' learning-based framework for DPIs with molecular transformer and graph convolutional networks, termed, multi-modality attributes (MMA)-DPI. Specifically, intermolecular sub-structural information and chemical semantic representations were extracted through an augmented transformer module from biomedical data. A tri-layer graph convolutional neural network module was applied to associate the neighbor topology information and learn the condensed dimensional features by aggregating a heterogeneous network that contains multiple biological representations of drugs, proteins, diseases and side effects. Then, the learned representations were taken as the input of a fully connected neural network module to further integrate them in molecular and topological space. Finally, the attribute representations were fused with adaptive learning weights to calculate the interaction score for the DPIs tasks. MMA-DPI was evaluated in different experimental conditions and the results demonstrate that the proposed method achieved higher performance than existing state-of-the-art frameworks.

Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning.

Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer's disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging's use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

Single-Cell Alternative Splicing Analysis with Expedition Reveals Splicing Dynamics during Neuron Differentiation.

Alternative splicing (AS) generates isoform diversity for cellular identity and homeostasis in multicellular life. Although AS variation has been observed among single cells, little is known about the biological or evolutionary significance of such variation. We developed Expedition, a computational framework consisting of outrigger, a de novo splice graph transversal algorithm to detect AS; anchor, a Bayesian approach to assign modalities; and bonvoyage, a visualization tool using non-negative matrix factorization to display modality changes. Applying Expedition to single pluripotent stem cells undergoing neuronal differentiation, we discover that up to 20% of AS exons exhibit bimodality. Bimodal exons are flanked by more conserved intronic sequences harboring distinct cis-regulatory motifs, constitute much of cell-type-specific splicing, are highly dynamic during cellular transitions, preserve reading frame, and reveal intricacy of cell states invisible to conventional gene expression analysis. Systematic AS characterization in single cells redefines our understanding of AS complexity in cell biology.

Distinct Patterns of Connectivity between Brain Regions Underlie the Intra-Modal and Cross-Modal Value-Driven Modulations of the Visual Cortex.

Past reward associations may be signaled from different sensory modalities; however, it remains unclear how different types of reward-associated stimuli modulate sensory perception. In this human fMRI study (female and male participants), a visual target was simultaneously presented with either an intra- (visual) or a cross-modal (auditory) cue that was previously associated with rewards. We hypothesized that, depending on the sensory modality of the cues, distinct neural mechanisms underlie the value-driven modulation of visual processing. Using a multivariate approach, we confirmed that reward-associated cues enhanced the target representation in early visual areas and identified the brain valuation regions. Then, using an effective connectivity analysis, we tested three possible patterns of connectivity that could underlie the modulation of the visual cortex: a direct pathway from the frontal valuation areas to the visual areas, a mediated pathway through the attention-related areas, and a mediated pathway that additionally involved sensory association areas. We found evidence for the third model demonstrating that the reward-related information in both sensory modalities is communicated across the valuation and attention-related brain regions. Additionally, the superior temporal areas were recruited when reward was cued cross-modally. The strongest dissociation between the intra- and cross-modal reward-driven effects was observed at the level of the feedforward and feedback connections of the visual cortex estimated from the winning model. These results suggest that, in the presence of previously rewarded stimuli from different sensory modalities, a combination of domain-general and domain-specific mechanisms are recruited across the brain to adjust the visual perception.SIGNIFICANCE STATEMENT Reward has a profound effect on perception, but it is not known whether shared or disparate mechanisms underlie the reward-driven effects across sensory modalities. In this human fMRI study, we examined the reward-driven modulation of the visual cortex by visual (intra-modal) and auditory (cross-modal) reward-associated cues. Using a model-based approach to identify the most plausible pattern of inter-regional effective connectivity, we found that higher-order areas involved in the valuation and attentional processing were recruited by both types of rewards. However, the pattern of connectivity between these areas and the early visual cortex was distinct between the intra- and cross-modal rewards. This evidence suggests that, to effectively adapt to the environment, reward signals may recruit both domain-general and domain-specific mechanisms.

A new strategy for immunotherapy of microsatellite-stable (MSS)-type advanced colorectal cancer: Multi-pathway combination therapy with PD-1/PD-L1 inhibitors.

Colorectal cancer (CRC) is a frequent gastrointestinal malignancy with high rates of morbidity and mortality; 85% of these tumours are proficient mismatch repair (pMMR)-microsatellite instability-low (MSI-L)/microsatellite stable (MSS) CRC known as 'cold' tumours that are resistant to immunosuppressive drugs. Monotherapy with programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) inhibitors is ineffective for treating MSS CRC, making immunotherapy for MSS CRC a bottleneck. Recent studies have found that the multi-pathway regimens combined with PD-1/PD-L1 inhibitors can enhance the efficacy of anti-PD-1/PD-L1 in MSS CRC by increasing the number of CD8+ T cells, upregulating PD-L1 expression and improving the tumour microenvironment. This paper reviews the research progress of PD-1/PD-L1 inhibitors in combination with cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitors, oncolytic virus, intestinal flora, antiangiogenic agents, chemotherapy, radiotherapy and epigenetic drugs for the treatment of pMMR-MSI-L/MSS CRC.

Multi-modality artificial intelligence in digital pathology.

In common medical procedures, the time-consuming and expensive nature of obtaining test results plagues doctors and patients. Digital pathology research allows using computational technologies to manage data, presenting an opportunity to improve the efficiency of diagnosis and treatment. Artificial intelligence (AI) has a great advantage in the data analytics phase. Extensive research has shown that AI algorithms can produce more up-to-date and standardized conclusions for whole slide images. In conjunction with the development of high-throughput sequencing technologies, algorithms can integrate and analyze data from multiple modalities to explore the correspondence between morphological features and gene expression. This review investigates using the most popular image data, hematoxylin-eosin stained tissue slide images, to find a strategic solution for the imbalance of healthcare resources. The article focuses on the role that the development of deep learning technology has in assisting doctors' work and discusses the opportunities and challenges of AI.

ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data.

Breast cancer patients often have recurrence and metastasis after surgery. Predicting the risk of recurrence and metastasis for a breast cancer patient is essential for the development of precision treatment. In this study, we proposed a novel multi-modal deep learning prediction model by integrating hematoxylin & eosin (H&E)-stained histopathological images, clinical information and gene expression data. Specifically, we segmented tumor regions in H&E into image blocks (256 × 256 pixels) and encoded each image block into a 1D feature vector using a deep neural network. Then, the attention module scored each area of the H&E-stained images and combined image features with clinical and gene expression data to predict the risk of recurrence and metastasis for each patient. To test the model, we downloaded all 196 breast cancer samples from the Cancer Genome Atlas with clinical, gene expression and H&E information simultaneously available. The samples were then divided into the training and testing sets with a ratio of 7: 3, in which the distributions of the samples were kept between the two datasets by hierarchical sampling. The multi-modal model achieved an area-under-the-curve value of 0.75 on the testing set better than those based solely on H&E image, sequencing data and clinical data, respectively. This study might have clinical significance in identifying high-risk breast cancer patients, who may benefit from postoperative adjuvant treatment.

ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug-protein interaction prediction.

MOTIVATION: Effective computational methods to predict drug-protein interactions (DPIs) are vital for drug discovery in reducing the time and cost of drug development. Recent DPI prediction methods mainly exploit graph data composed of multiple kinds of connections among drugs and proteins. Each node in the graph usually has topological structures with multiple scales formed by its first-order neighbors and multi-order neighbors. However, most of the previous methods do not consider the topological structures of multi-order neighbors. In addition, deep integration of the multi-modality similarities of drugs and proteins is also a challenging task. RESULTS: We propose a model called ALDPI to adaptively learn the multi-scale topologies and multi-modality similarities with various significance levels. We first construct a drug-protein heterogeneous graph, which is composed of the interactions and the similarities with multiple modalities among drugs and proteins. An adaptive graph learning module is then designed to learn important kinds of connections in heterogeneous graph and generate new topology graphs. A module based on graph convolutional autoencoders is established to learn multiple representations, which imply the node attributes and multiple-scale topologies composed of one-order and multi-order neighbors, respectively. We also design an attention mechanism at neighbor topology level to distinguish the importance of these representations. Finally, since each similarity modality has its specific features, we construct a multi-layer convolutional neural network-based module to learn and fuse multi-modality features to obtain the attribute representation of each drug-protein node pair. Comprehensive experimental results show ALDPI's superior performance over six state-of-the-art methods. The results of recall rates of top-ranked candidates and case studies on five drugs further demonstrate the ability of ALDPI to discover potential drug-related protein candidates. CONTACT: zhang@hlju.edu.cn.

Advancing Genetic Testing in Kidney Diseases: Report From a National Kidney Foundation Working Group.

About 37 million people in the United States have chronic kidney disease, a disease that encompasses diseases of multiple causes. About 10% or more of kidney diseases in adults and about 70% of selected chronic kidney diseases in children are expected to be explained by genetic causes. Despite the advances in genetic testing and an increasing understanding of the genetic bases of certain kidney diseases, genetic testing in nephrology lags behind other medical fields. More understanding of the benefits and logistics of genetic testing is needed to advance the implementation of genetic testing in chronic kidney diseases. Accordingly, the National Kidney Foundation convened a Working Group of experts with diverse expertise in genetics, nephrology, and allied fields to develop recommendations for genetic testing for monogenic disorders and to identify genetic risk factors for oligogenic and polygenic causes of kidney diseases. Algorithms for clinical decision making on genetic testing and a road map for advancing genetic testing in kidney diseases were generated. An important aspect of this initiative was the use of a modified Delphi process to reach group consensus on the recommendations. The recommendations and resources described herein provide support to nephrologists and allied health professionals to advance the use of genetic testing for diagnosis and screening of kidney diseases.

Outcomes of Integrated Home Dialysis Care: Results From the Canadian Organ Replacement Register.

RATIONALE & OBJECTIVE: The integrated home dialysis model proposes the initiation of kidney replacement therapy (KRT) with peritoneal dialysis (PD) and a timely transition to home hemodialysis (HHD) after PD ends. We compared the outcomes of patients transitioning from PD to HHD with those initiating KRT with HHD. STUDY DESIGN: Observational analysis of the Canadian Organ Replacement Register (CORR). SETTINGS & PARTICIPANTS: All patients who initiated PD or HHD within the first 90 days of KRT between 2005 and 2018. EXPOSURE: Patients transitioning from PD to HHD (PD+HHD group) versus patients initiating KRT with HHD (HHD group). OUTCOME: (1) A composite of all-cause mortality and modality transfer (to in-center hemodialysis or PD for 90 days) and (2) all hospitalizations (considered as recurrent events). ANALYTICAL APPROACH: A propensity score analysis for which PD+HHD patients were matched 1:1 to (1) incident HHD patients ("incident-match" analysis) or (2) HHD patients with a KRT vintage at least equivalent to the vintage of PD+HHD patients at the transition time ("vintage-matched" analysis). Cause-specific hazards models (composite outcome) and shared frailty models (hospitalization) were used to compare groups. RESULTS: Among 63,327 individuals in the CORR, 163 PD+HHD patients (median of 1.9 years in PD) and 711 HHD patients were identified. In the incident-match analysis, compared to the HHD patients, the PD+HHD group had a similar risk of the composite outcome (HR, 0.88 [95% CI, 0.58-1.32]) and hospitalizations (HR, 1.04 [95% CI, 0.76-1.41]). In the vintage-match analysis, PD+HHD patients had a lower hazard for the composite outcome (HR, 0.61 [95% CI, 0.40-0.94]) but a similar hospitalization risk (HR, 0.85 [95% CI, 0.59-1.24]). LIMITATIONS: Risk of survivor bias in the PD+HHD cohort and residual confounding. CONCLUSIONS: Controlling for KRT vintage, the patients transitioning from PD to HHD had better clinical outcomes than the incident HHD patients. These data support the use of integrated home dialysis for patients initiating home-based KRT. PLAIN-LANGUAGE SUMMARY: The integrated home dialysis model proposes the initiation of dialysis with peritoneal dialysis (PD) and subsequent transition to home hemodialysis (HHD) once PD is no longer feasible. It allows patients to benefit from initial lifestyle advantages of PD and to continue home-based treatments after its termination. However, some patients may prefer to initiate dialysis with HHD from the outset. In this study, we compared the long-term clinical outcomes of both approaches using a large Canadian dialysis register. We found that both options led to a similar risk of hospitalization. In contrast, the PD-to-HHD model led to improved survival when controlling for the duration of kidney failure.

Outcomes of patients in nasopharyngeal adenoid cystic carcinoma in the IMRT era: a single-center experience.

OBJECTIVE: Nasopharyngeal adenoid cystic carcinoma (NACC) is a rare malignancy with special biological features. Controversies exist regarding the treatment approach and prognostic factors in the IMRT era. This study aimed to evaluate the long-term outcomes and management approaches in NACC. METHODS: Fifty patients with NACC at our institution between 2010 and 2020 were reviewed. Sixteen patients received primary radiotherapy (RT), and 34 patients underwent primary surgery. RESULTS: Between January 2010 and October 2020, a total of 50 patients with pathologically proven NACC were included in our analysis. The median follow-up time was 58.5 months (range: 6.0-151.0 months). The 5-year overall survival rate (OS) and progression-free survival rate (PFS) were 83.9% and 67.5%, respectively. The 5-year OS rates of patients whose primary treatment was surgery and RT were 90.0% and 67.3%, respectively (log-rank P = 0.028). The 5-year PFS rates of patients whose primary treatment was surgery or RT were 80.8% and 40.7%, respectively (log-rank P = 0.024). Multivariate analyses showed that nerve invasion and the pattern of primary treatment were independent factors associated with PFS. CONCLUSIONS: Due to the relative insensitivity to radiation, primary surgery seemed to provide a better chance of disease control and improved survival in NACC. Meanwhile, postoperative radiotherapy should be performed for advanced stage or residual tumours. Cranial nerve invasion and treatment pattern might be important factors affecting the prognosis of patients with NACC.

Trimodal therapy vs radical cystectomy in patients with muscle-invasive bladder cancer: a systematic review and meta-analysis of comparative studies.

OBJECTIVE: To perform a systematic review and meta-analysis of trials comparing trimodal therapy (TMT) and radical cystectomy (RC), evaluating differences in terms of oncological outcomes, quality of life, and costs. MATERIALS AND METHODS: In July 2023, a literature search of multiple databases was conducted to identify studies analysing patients with cT2-4 N any M0 muscle-invasive bladder cancer (MIBC; Patients) receiving TMT (Intervention) compared to RC (Comparison), to evaluate survival outcomes, recurrence rates, costs, and quality of life (Outcomes). The primary outcome was overall survival (OS). Secondary outcomes were cancer-specific survival (CSS) and metastasis-free survival (MFS). Hazard ratios (HRs) were used to analyse survival outcomes according to different treatment modalities and odds ratios were used to evaluate the likelihood of receiving each type of treatment according to T stage. RESULTS: No significant difference in terms of OS was observed between RC and TMT (HR 1.07, 95% confidence interval [CI] 0.81-1.4; P = 0.6), even when analysing radiation therapy regimens ≥60 Gy (HR 1.02, 95% CI 0.69-1.52; P = 0.9). No significant difference was observed in CSS (HR 1.12, 95% CI 0.79-1.57, P = 0.5) or MFS (HR 0.88, 95% CI 0.66-1.16; P = 0.3). The mean cost of TMT was significantly higher than that of RC ($289 142 vs $148 757; P < 0.001), with greater effectiveness in terms of cost per quality-adjusted life-year. TMT ensured significantly higher general quality-of-life scores. CONCLUSION: Trimodal therapy appeared to yield comparable oncological outcomes to RC concerning OS, CSS and MFS, while providing superior patient quality of life and cost effectiveness.