Multimodal Combination Research Articles

A manual controlled theranostic nanoplatform with real-time photoacoustic quantification of drug release for chemophotothermal therapy

The development of intelligent nanodrug delivery systems that can visually guide the on-demand quantitative control of drug release has received extensive attention. Herein, two chemotherapeutic drugs, gallic acid and 5-fluorouracil, and Fe(III) were selected to prepare nanomedicine GF-Fe via polyphenol-metal self-assembly and infinite coordination of drug-metal. GF-Fe has good biocompatibility, photothermal properties and photoacoustic (PA) signals. When deferoxamine (DFO) was artificially applied and interacted with GF-Fe, GF-Fe began to disassemble, gallic acid and 5-fluorouracil were gradually released, while the PA signal of the nanomedicine decayed synchronously. Based on this, the relationship between the intensity of the PA signal and the drug release amount was established, so as to realize the precise quantitative control of the drug release in real-time under the guidance of PA imaging. Besides, the combined effect of the two therapeutic drugs in combination with photothermal therapy (PTT) can improve the therapeutic effect, resulting in significant superadditiveness. This nanoplatform constructed by facile synthesis provided good clinical translation potential for the implementation of precise multimodal combination therapy strategies for tumors.

Multi-Armed Anti-CD40-Mediated Dual Drug Delivery System Based on Mesoporous Silica/Au Nanorod Nanocomposites for Multimodality Imaging and Combination Therapy

Multi-Armed Anti-CD40-Mediated Dual Drug Delivery System Based on Mesoporous Silica/Au Nanorod Nanocomposites for Multimodality Imaging and Combination Therapy

CNAMD Corpus: A Chinese Natural Audiovisual Multimodal Database of Conversations for Social Interactive Agents

Impressive progress has been made in developing companion Socially Interactive Agents (SIAs) that provide companionship and reduce loneliness. However, recent works focus on analyzing multimodal feedback in Answer part but ignore Question part. Furthermore, research on SIAs is primarily based on English, which poses a challenge for Chinese SIAs because of cultural differences between English and Chinese. Therefore, we introduce a Chinese Natural Audiovisual Multimodal Database (CNAMD) corpus, the first and largest freely available Chinese multimodal database for multi-person interaction, containing 48 hours of videos and annotations across eight modalities. Using CNAMD, we analyze the characteristics of vocal-verbal, audio, behavioral, and multimodal combinations during questioning, test the performance of six baselines on three tasks, and propose improvements for processing daily Chinese data. The present findings will help designers consider Chinese customs and language when designing Chinese SIAs, making them more suitable for the Chinese cultural context and users.

Hyaluronic acid-based supramolecular nanomedicine with optimized ratio of oxaliplatin/chlorin e6 for combined chemotherapy and O2-economized photodynamic therapy.

Dual- or multi-modality combination therapy has become one of the most effective strategies to overcome drug resistance in cancer therapy, and the optimized ratio of the therapeutic agents working on the tumor greatly affects the therapeutic outcomes. However, the absence of a facile method to optimize the ratio of therapeutic agents in nanomedicine has, at least in part, impaired the clinical potential of combination therapy. Herein, a new cucurbit[7]uril (CB[7])-conjugated hyaluronic acid (HA) based nanomedicine was developed, in which both chlorin e6 (Ce6) and oxaliplatin (OX) were co-loaded non-covalently at an optimized ratio via facile host-guest complexation, for optimal, combined photodynamic therapy (PDT)/chemotherapy. To maximize the therapeutic efficacy, a mitochondrial respiration inhibitor, atovaquone (Ato), was also loaded into the nanomedicine to limit consumption of oxygen by the solid tumor, sparing oxygen for more efficient PDT. Additionally, HA on the surface of nanomedicine allowed targeted delivery to cancer cells with over-expressed CD44 receptors (such as CT26 cell lines). Thus, this supramolecular nanomedicine platform with an optimal ratio of photosensitizer and chemotherapeutic agent not only provides an important new tool for enhanced PDT/chemotherapy of solid tumors, but also offers a CB[7]-based host-guest complexation strategy to facilely optimize the ratio of therapeutic agents for multi-modality nanomedicine. STATEMENT OF SIGNIFICANCE: Chemotherapy remains the most common modality for cancer treatment in clinical practice. Combination therapy by co-delivery of two or more therapeutic agents has been recognized as one of the most effective strategies to improve therapeutic outcome of cancer treatment. However, the ratio of loaded drugs could not be facilely optimized, which may greatly affect the combination efficiency and overall therapeutic outcome. Herein, we developed a hyaluronic acid based supramolecular nanomedicine with facile method to optimize the ratio of two therapeutic agents for improved therapeutic outcome. This supramolecular nanomedicine not only provides an important new tool for enhanced photodynamic therapy/chemotherapy of solid tumors, but also offers insights in using macrocyclic molecule-based host-guest complexation to facilely optimize the ratio of therapeutic agents in multi-modality nanomedicine.

Leptomeningeal metastasis in primary uterine cervical cancer: a rare case and review of the literature

Objectives. Leptomeningeal metastasis (LM) of primary uterine cervical cancer is rare and treatment options are limited. In this case report and literature review, we aimed to present a patient with cervical cancer with LM and discuss previously reported cases in the literature. Case presentation. Our case was a 58-year-old patient who was initially diagnosed with metastatic primary uterine cervical cancer and treated with chemotherapy and chemoradiotherapy. During follow-up, she developed neurological symptoms, and LM was detected in the craniospinal regions. Cerebrospinal fluid cytology examination has confirmed metastatic disease. The patient was treated with concurrent intrathecal methotrexate and whole-brain radiotherapy (WBRT). A good clinical and cytological response was obtained. However, while intrathecal methotrexate was being continued after WBRT, she succumbed to hematological toxicity before the radiological response could be evaluated. Conclusions. LM is an extremely rare and catastrophic distant spread pattern in patients with cervical cancer. In the literature, a total of 26 patients were reported up to date. Median survival after detection of LM was nine weeks, including our case. Multimodal treatment combinations such as systemic and intrathecal chemotherapy and radiotherapy (RT) were used. However, most of these reports did not have detailed information about toxicity. Despite the combined use of aggressive treatment modalities, patients have limited survival and very high risks of hematologic toxicity. Concurrent use of intrathecal chemotherapy and radiotherapy should be avoided due to increased risk of morbidity.

Neurokinin-1 receptor antagonists for the prevention of postoperative nausea and vomiting.

Despite the availability of several classes of antiemetics, postoperative nausea and vomiting (PONV) remains a substantial burden for patients following surgery, resulting in patient dissatisfaction and prolonged stays in post-anesthesia care units and ultimately increasing the cost of care. Enhanced recovery protocols and PONV management guidelines are now centered on the assessment of the individual patient's risk for developing PONV, as well as multimodal prophylaxis using antiemetics targeting different mechanisms of action. Over the last two decades, the neurokinin-1 receptor (NK1R) has emerged as a therapeutic target for the management of PONV. This review of the literature explains the role of the NK1R and its ligand-substance P-in vomiting, describes the pharmacologic and pharmacokinetic properties of NK1R antagonists (NK1RAs) and summarizes the clinical evidence supporting NK1RAs for PONV prophylaxis in patients undergoing surgery. In particular, we discuss the therapeutic application of NK1RA in PONV prophylaxis protocols owing to their advantages over other antiemetic classes in efficacy, duration of efficacy, safety, pharmacology, and ease of administration. Future studies will be aimed at further investigating the efficacy and safety of NK1RA-based multimodal combinations, particularly among vulnerable populations (e.g., children and elderly).

Numerical and experimental investigations on the propagation property of a mid-infrared 7 × 1 multimode fiber combiner.

Mid-infrared fiber combiners have great potential in power and spectral combination. However, studies on mid-infrared transmission optical field distributions using these combiners are limited. In this study, we designed and fabricated a 7 × 1 multimode fiber combiner based on sulfur-based glass fibers and observed approximately 80% per-port transmission efficiency at 4.778 µm wavelength. We investigated the propagation properties of the prepared combiners and explored the effects of transmission wavelength, output fiber length, and fusion deviation on the transmitted optical field and beam quality factor M2. Additionally, we assessed the effect of coupling on the excitation mode and spectral combination of the mid-infrared fiber combiner for multiple light sources. Our results provide an in-depth understanding of the propagation properties of the mid-infrared multimode fiber combiners, which may find applications in high-beam-quality laser devices.

Prediction of cognitive performance differences in older age from multimodal neuroimaging data.

Differences in brain structure and functional and structural network architecture have been found to partly explain cognitive performance differences in older ages. Thus, they may serve as potential markers for these differences. Initial unimodal studies, however, have reported mixed prediction results of selective cognitive variables based on these brain features using machine learning (ML). Thus, the aim of the current study was to investigate the general validity of cognitive performance prediction from imaging data in healthy older adults. In particular, the focus was with examining whether (1) multimodal information, i.e., region-wise grey matter volume (GMV), resting-state functional connectivity (RSFC), and structural connectivity (SC) estimates, may improve predictability of cognitive targets, (2) predictability differences arise for global cognition and distinct cognitive profiles, and (3) results generalize across different ML approaches in 594 healthy older adults (age range: 55-85years) from the 1000BRAINS study. Prediction potential was examined for each modalityand all multimodal combinations, with and without confound (i.e., age, education, and sex) regression across different analytic options, i.e., variations in algorithms, feature sets, and multimodal approaches (i.e., concatenation vs. stacking). Results showed that prediction performance differed considerably between deconfounding strategies. In the absence of demographic confounder control, successful prediction of cognitive performance could be observed across analytic choices. Combination of different modalities tended to marginally improve predictability of cognitive performance compared to single modalities. Importantly, all previously described effects vanished in the strict confounder control condition. Despite a small trend for a multimodal benefit, developing a biomarker for cognitive aging remains challenging.

A Stepwise Targeting and Antibacterial Strategy by Leukocyte Membrane-Based Conjugated Oligomer Nanoparticles.

Bacterial infection poses an enormous threat to human life and health. The inability of drugs to be effectively delivered to the site of infection and the development of bacterial resistance make the treatment process more difficult. Herein, a stepwise targeted biomimetic nanoparticle (NPs@M-P) with inflammatory tendency and Gram-negative bacterial targeting was designed, which can achieve efficient antibacterial activity under near-infrared triggering. Leukocyte membranes and targeted molecules (PMB) are used to deliver NPs to the surface of Gram-negative bacteria. The heat and ROS released by NPs@M-P can efficiently kill Gram-negative bacteria under low-power near-infrared light. Thus, this multimodal combination therapy strategy has broad promise in fighting bacterial infection and avoiding drug resistance.

Multimodal communication development in semiwild chimpanzees

Human language is characterized by the integration of multiple signal modalities, including speech, facial and gestural signals. While language likely has deep evolutionary roots that are shared with some of our closest living relatives, studies of great ape communication have largely focused on each modality separately, thus hindering insights into the origins of its multimodal nature. Studying when multimodal signals emerge during great ape ontogeny can inform about both the proximate and ultimate mechanisms underlying their communication systems, shedding light on potential evolutionary continuity between humans and other apes. To this end, the current study investigated developmental patterns of multimodal signal production by 28 semiwild chimpanzees, Pan troglodytes, ranging in age from infancy to early adolescence. We examined the production of facial expressions, gestures and vocalizations across a range of behavioural contexts, both when produced separately and as part of multimodal signal combinations (henceforth multimodal). Overall, we found that while unimodal signals were produced consistently more often than multimodal combinations across all ages and contexts, the frequency of multimodal combinations increased significantly in older individuals and most within the aggression and play contexts, where the costs of signalling ambiguity may be higher. Furthermore, older individuals were more likely to produce a multimodal than a unimodal signal and, again, especially in aggressive contexts. Variation in production of individual signal modalities across ages and contexts are also presented and discussed. Overall, evidence that multimodality increases with age in chimpanzees is consistent with patterns of developing communicative complexity in human infancy, revealing apparent evolutionary continuity. Findings from this study contribute novel insights into the evolution and development of multimodality and highlight the importance of adopting a multimodal approach in the comparative study of primate communication.

Oxygen-Vacancy-Rich Piezoelectric BiO2- x Nanosheets for Augmented Piezocatalytic, Sonothermal, and Enzymatic Therapies.

Piezocatalytic therapy is a new-emerging reactive oxygen species (ROS)-enabled therapeutic strategy that relies on built-in electric field and energy-band bending of piezoelectric materials activated by ultrasound (US) irradiation. Despite becoming a hot topic, material development and mechanism exploration are still underway. Herein, as-synthesized oxygen-vacancy-rich BiO2- x nanosheets (NSs) demonstrate outstanding piezoelectric properties. Under US, a piezo-potential of 0.25V for BiO2- x NSs is sufficient to tilt the conduction band to be more negative than the redox potentials of O2 /• O2 - , • O2 - /H2 O2 , and H2 O2 /• OH, which initiates a cascade reaction for ROS generation. Moreover, the BiO2- x NSs exhibit peroxidase and oxidase-like activities to augment ROS production, especially in the H2 O2 -overexpressed tumor microenvironment. Density functional theory calculations show that the generated oxygen vacancies in BiO2- x NSs are favorable for H2 O2 adsorption and increasing the carrier density to produce ROS. Furthermore, the quick movement of electrons enables an excellent sonothermal effect, for example, rapid rise in temperature to nearly 65°C upon US with low power (1.2Wcm-2 ) and short time (96s). Therefore, this system realizes a multimode synergistic combination of piezocatalytic, enzymatic, and sonothermal therapies, providing a new direction for defect engineering-optimized piezoelectric materials for tumor therapy.

Privacy-preserving activity recognition using multimodal sensors in smart office

Developing a human activity recognition (HAR) system for employees is essential to incorporate intelligence into smart office environments, enabling various human-centered applications to enhance employees’ well-being. Although remarkable progress has been made for the HAR in the smart office, several issues still exist, including lacking a privacy-preserving and unobtrusive method and demanding enhanced generalization performance across users. Therefore, a novel privacy-preserving HAR method based on multimodal sensors is investigated in this study, containing an infrared array sensor, a sensing chair, and a triaxial accelerometer built in a smartphone. The effectiveness of different multimodal combinations is examined. Moreover, a deep learning model is developed for multimodal data fusion to enhance the generalization performances across users. The model contains a residual 3D convolutional neural network (CNN) and 1D CNN for learning spatial–temporal feature representation of different modalities. Additionally, external memory units and an adaptive decision fusion operation are utilized for multimodal data fusion. Finally, extensive experiments are conducted to examine the performance of the proposed model using a self-collected dataset and the leave-one-subject-out cross-validation approach. The results verify the effectiveness of the proposed model.

Assessing the Value of Multimodal Interfaces: A Study on Human-Machine Interaction in Weld Inspection Workstations.

Multimodal user interfaces promise natural and intuitive human-machine interactions. However, is the extra effort for the development of a complex multisensor system justified, or can users also be satisfied with only one input modality? This study investigates interactions in an industrial weld inspection workstation. Three unimodal interfaces, including spatial interaction with buttons augmented on a workpiece or a worktable, and speech commands, were tested individually and in a multimodal combination. Within the unimodal conditions, users preferred the augmented worktable, but overall, the interindividual usage of all input technologies in the multimodal condition was ranked best. Our findings indicate that the implementation and the use of multiple input modalities is valuable and that it is difficult to predict the usability of individual input modalities for complex systems.

Effects of Bilingual Videos in National Innovation Project in College English Listening Teaching Under the Influences of Multi-modal Theory

Multi-modal discourse refers to the phenomenon of using multiple senses such as auditory, visual, and tactile senses to communicate through multiple means and symbolic resources such as language, images, sounds, and actions. This paper reviews the theory of multi-modal discourse and studies the application of multi-modal discourse in English teaching at home and abroad, and conducts an empirical study of a new university English listening teaching mode in a multi-modal context by using comparative analysis method, taking Y University as an example. Secondly, through comparative analysis, one control group and four experimental groups were set up , the relevant materials of this experiment are all from the impression Yue platform, and video, audio, Chinese subtitles and English subtitles were used as variables, and through analyzing the experimental results, it was found that the reasonable use of multi-modal combinations can significantly improve students’ listening comprehension, and the use of audio-visual combinations and English subtitles is more beneficial to students’ listening The experimental results showed that the use of multi-modal combinations can significantly improve students’ listening comprehension, and the use of audio-visual combinations and English subtitles is more beneficial to students' listening comprehension.

Radioimmunotherapy in colorectal cancer treatment: present and future.

Colorectal cancer (CRC) is a deadly form of cancer worldwide. Patients with locally advanced rectal cancer and metastatic CRC have a poor long-term prognosis, and rational and effective treatment remains a major challenge. Common treatments include multi-modal combinations of surgery, radiotherapy, and chemotherapy; however, recurrence and metastasis rates remain high. The combination of radiotherapy and immunotherapy (radioimmunotherapy [RIT]) may offer new solutions to this problem, but its prospects remain uncertain. This review aimed to summarize the current applications of radiotherapy and immunotherapy, elaborate on the underlying mechanisms, and systematically review the preliminary results of RIT-related clinical trials for CRC. Studies have identified several key predictors of RIT efficacy. Summarily, rational RIT regimens can improve the outcomes of some patients with CRC, but current study designs have limitations. Further studies on RIT should focus on including larger sample sizes and optimizing the combination therapy regimen based on underlying influencing factors.

Radiomics Analysis of Breast Lesions in Combination with Coronal Plane of ABVS and Strain Elastography.

Breast cancer is the most common tumor globally. Automated Breast Volume Scanner (ABVS) and strain elastography (SE) can provide more useful breast information. The use of radiomics combined with ABVS and SE images to predict breast cancer has become a new focus. Therefore, this study developed and validated a radiomics analysis of breast lesions in combination with coronal plane of ABVS and SE to improve the differential diagnosis of benign and malignant breast diseases. 620 pathologically confirmed breast lesions from January 2017 to August 2021 were retrospectively analyzed and randomly divided into a training set (n=434) and a validation set (n=186). Radiomic features of the lesions were extracted from ABVS, B-ultrasound, and strain elastography (SE) images, respectively. These were then filtered by Gradient Boosted Decision Tree (GBDT) and multiple logistic regression. The ABVS model is based on coronal plane features for the breast, B+SE model is based on features of B-ultrasound and SE, and the multimodal model is based on features of three examinations. The evaluation of the predicted performance of the three models used the receiver operating characteristic (ROC) and decision curve analysis (DCA). The area under the curve, accuracy, specificity, and sensitivity of the multimodal model in the training set are 0.975 (95% CI:0.959-0.991),93.78%, 92.02%, and 96.49%, respectively, and 0.946 (95% CI:0.913 -0.978), 87.63%, 83.93%, and 93.24% in the validation set, respectively. The multimodal model outperformed the ABVS model and B+SE model in both the training (P < 0.001, P = 0.002, respectively) and validation sets (P < 0.001, P = 0.034, respectively). Radiomics from the coronal plane of the breast lesion provide valuable information for identification. A multimodal model combination with radiomics from ABVS, B-ultrasound, and SE could improve the diagnostic efficacy of breast masses.

Deep Learning Network with Spatial Attention Module for Detecting Acute Bilirubin Encephalopathy in Newborns Based on Multimodal MRI.

Acute bilirubin encephalopathy (ABE) is a significant cause of neonatal mortality and disability. Early detection and treatment of ABE can prevent the further development of ABE and its long-term complications. Due to the limited classification ability of single-modal magnetic resonance imaging (MRI), this study aimed to validate the classification performance of a new deep learning model based on multimodal MRI images. Additionally, the study evaluated the effect of a spatial attention module (SAM) on improving the model's diagnostic performance in distinguishing ABE. This study enrolled a total of 97 neonates diagnosed with ABE and 80 neonates diagnosed with hyperbilirubinemia (HB, non-ABE). Each patient underwent three types of multimodal imaging, which included T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and an apparent diffusion coefficient (ADC) map. A multimodal MRI classification model based on the ResNet18 network with spatial attention modules was built to distinguish ABE from non-ABE. All combinations of the three types of images were used as inputs to test the model's classification performance, and we also analyzed the prediction performance of models with SAMs through comparative experiments. The results indicated that the diagnostic performance of the multimodal image combination was better than any single-modal image, and the combination of T1WI and T2WI achieved the best classification performance (accuracy = 0.808 ± 0.069, area under the curve = 0.808 ± 0.057). The ADC images performed the worst among the three modalities' images. Adding spatial attention modules significantly improved the model's classification performance. Our experiment showed that a multimodal image classification network with spatial attention modules significantly improved the accuracy of ABE classification.

Multifunctional Soft Stackable Robots by Netting-Rolling-Splicing Pneumatic Artificial Muscles.

Soft robots equipped with multifunctionalities have been increasingly needed for secure, adaptive, and autonomous functioning in unknown and unpredictable environments. Robotic stacking is a promising solution to increase the functional diversity of soft robots, which are required for safe human-machine interactions and adapting in unstructured environments. However, most existing multifunctional soft robots have a limited number of functions or have not fully shown the superiority of the robotic stacking method. In this study, we present a novel robotic stacking strategy, Netting-Rolling-Splicing (NRS) stacking, based on a dimensional raising method via 2D-to-3D rolling-and-splicing of netted stackable pneumatic artificial muscles to quickly and efficiently fabricate multifunctional soft robots based on the same, simple, and cost-effective elements. To demonstrate it, we developed a TriUnit robot that can crawl 0.46 ± 0.022 body length per second (BL/s) and climb 0.11 BL/s, and can carry a 3 kg payload while climbing. Also, the TriUnit can be used to achieve novel omnidirectional pipe climbing including rotating climbing, and conduct bionic swallowing-and-regurgitating, multi-degree-of-freedom manipulation based on their multimodal combinations. Apart from these, steady rolling, with a speed of 0.19 BL/s, can be achieved by using a pentagon unit. Furthermore, we applied the TriUnit pipe climbing robot in panoramic shooting and cargo transferring to demonstrate the robot's adaptability for different tasks. The NRS stacking-driven soft robot here has demonstrated the best overall performance among existing stackable soft robots, representing a new and effective way for building multifunctional and multimodal soft robots in a cost-effective and efficient way.

Abstract 5373: Autoencoder-based multimodal prediction of survival for non-small cell lung cancer

Abstract Most non-small cell lung cancer (NSCLC) prognosis prediction approaches use one data type and do not take advantage of the large amount of multimodal data available. To evaluate and explore the benefits of multimodal data integration, we present a combined feature selection and denoising autoencoder pipeline for NSCLC survival prediction and survival subtype identification using microRNA (miRNA), mRNA, DNA methylation, long non-coding RNA (lncRNA) and clinical data. Survival performance for both lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) patients was compared across modality combinations, data integration time and training data types. Multimodal data combinations outperformed single data modalities, with the early integration of all data modalities achieving concordance indexes (C-indexes) of 0.67 (±.04) and 0.63 (±.02) for LUAD and LUSC, respectively versus corresponding C-index of 0.64 (±.02) and 0.59 (±.03) for the best single cell modality (clinical). Notably, combining just lncRNA and clinical data facilitated effective survival discrimination, with C-indexes of 0.69 (±.03) for LUAD and 0.62 (±.03) for LUSC. Overall, higher performance was achieved by using a single denoising autoencoder for all biological data (early integration) and by training on both LUSC and LUAD patient data together. Two survival subtypes (log rank test p-value=1e-9) were identified, with 991 differentially expressed transcripts in the poorer survival group. Our analysis shows the value of multimodal data integration for predicting NSCLC progression, with especially good performance using the combination of lncRNA and clinical data. Early integration of biological data, with an initial linear feature selection technique and a denoising autoencoder for dimensionality reduction, showed effective survival performance and survival subtype identification. Further research is underway to expand analysis to different cancer types and data modalities and extract more biological interpretability from autoencoder models. Citation Format: Jacob G. Ellen, Etai Jacob, Nikos Nikolaou, Natasha Markuzon. Autoencoder-based multimodal prediction of survival for non-small cell lung cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5373.

Hollow mesoporous organosilica nanoparticles reduced graphene oxide based nanosystem for multimodal image-guided photothermal/photodynamic/chemo combinational therapy triggered by near-infrared.

Developing a nanosystem that can perform multimodal imaging-guided combination therapy is highly desirable but challenging. In this study, we introduced multifunctional nanoparticles (NPs) consisting of graphene oxide-grafted hollow mesoporous organosilica loaded with the drug doxorubicin (DOX) and photosensitizers tetraphenylporphyrin (TPP). These NPs were encapsulated by thermosensitive liposomes that release their contents once the temperature exceeds a certain threshold. Metal oxide NPs grown on the graphene oxide (GO) surface served multiple roles, including enhancing photothermal efficiency, acting as contrast agents to improve magnetic resonance imaging, increasing the sensitivity and specificity of photoacoustic imaging, and catalysing hydrogen peroxide for the generation of reactive oxygen species (ROS). When locally injected, the HMONs-rNGO@Fe3 O4 /MnOx@FA/DOX/TPP NPs effectively enriched in subcutaneous Hela cell tumour of mice. The photothermal/photodynamic/chemo combination therapy triggered by near-infrared (NIR) successfully suppressed the tumour without noticeable side effects. This study presented a unique approach to develop multimodal imaging-guided combination therapy for cancer.