SEARCH · Search PubMed

Results for “multimodality”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2Linked to original sources

PubMed1995

Genetic basis for multimodal relationship between apolipoprotein (a) size and lipoprotein (a) concentration in Mexican-Americans.

The reported general inverse relationship between apolipoprotein (a) (apo(a)) size and plasma lipoprotein (a) (Lp(a)) concentrations was further characterized using 927 samples taken from members of 42 Mexican-American families. When all samples were displayed in a scatter plot of apo(a) size versus natural log of Lp(a) concentration, the expected inverse relationship was observed (r2 = 0.24). However, the scatter plot revealed a multimodal pattern with at least two distinct modes of the inverse relationship between apo(a) size and Lp(a) concentration. Plots of 148 single-banded samples also showed the multimodal pattern, indicating that this pattern did not result from a difference between double- and single-banded samples. Also measured was the Lp(a) concentration associated with each of 508 apo(a) isoforms in samples from 254 double-banded phenotype individuals. These separated isoforms also showed the multimodal pattern. Using pedigree information, 29 different alleles were identified which were found in three or more family members. About 85% of variation in Lp(a) was explained by allele information, suggesting a genetic basis for the multimodal pattern. Thus, the data demonstrate at least two series of apo(a) alleles that have distinct relationships between apo(a) size and Lp(a) concentration.

Alleles↗

PubMed1986

The basis of Stroop interference involving the multimodal correlates of auditory pitch.

A pure auditory tone has a range of multimodal qualities that are determined by its pitch. A reaction-time task was used to demonstrate that subjects respond immediately and automatically to these qualities. Subjects were required to press one of two keys depending on which word, from a limited set, appeared on a microcomputer screen. The words were antonyms that represented multimodal stimulus qualities, and they were assigned to alternative responses so that the two words that shared the same response were correlated in the same way with pitch. As an incidental stimulus, either a 50 Hz tone or a 5500 Hz tone accompanied the presentation of each word. Subjects were found to respond more slowly when the multimodal qualities of the tone were incongruent with the qualities represented by the test word. When the stimulus-response mapping rules were changed, however, the Stroop effect did not occur; suggesting that a polarised semantic code of the incidental tone, that embraces its multimodal features, accesses the same semantic register as the equivalent code for the test word itself.

Auditory Perception↗

PubMed1982

Multimodality treatment of cancer.

Different and effective modalities are available for various cancers. However, early consideration is necessary to allow optimal integration. Failure to do this may compromise the cure potential for some tumours. The differing biology of tumours and the efficacy of various modalities dictates specific approaches for each. The principles of multimodality therapy can be considered together with the biological factors affecting the success and failure of each therapy tupe and this allows a multimodality approach to be based on careful planning. For many tumours, where effective systemic therapy exists, there are good reasons for commencing with a multimodality approach at the onset with adjuvant chemotherapy. Practical considerations dictate that surgeons must play a key role in the care of cancer patients. This in turn requires that they maintain a sound knowledge of multimodality therapy for the cancers that they treat.

Antineoplastic Agents↗

PubMed1993

Node status has prognostic significance in the multimodality therapy of diffuse, malignant mesothelioma.

PURPOSE: We studied a multimodality approach using extrapleural pneumonectomy, chemotherapy, and radiotherapy in patients with malignant pleural mesothelioma. PATIENTS AND METHODS: From 1980 to 1992, 52 selected patients, underwent treatment. Median age was 53 years (range, 33 to 69). Initial patient evaluation was performed by a multimodality team. Pathologic diagnosis was reviewed and confirmed before therapy. Patients with no medical contraindication and potentially resectable mesothelioma on computed tomography (CT) (magnetic resonance imaging [MRI] when it became available) received extrapleural pneumonectomy, cyclophosphamide, doxorubicin, and cisplatin (CAP) chemotherapy, and radiotherapy. RESULTS: Perioperative morbidity and mortality rates were 17% and 5.8%, respectively. The overall median survival duration is 16 months (range, 1 month to 8 years). The 32 patients with epithelial histologic variant had 1-, 2-, and 3-year survival rates of 77%, 50%, and 42%, respectively. Patients with mixed and sarcomatous cell disease had 1- and 2-year survival rates of 45% and 7.5%; no patient lived longer than 25 months (P < .01). At resection, positive regional mediastinal lymph nodes were found in 13. Positive lymph nodes were associated with poorer survival than were negative nodes (P < .01). Patients with epithelial variant and negative mediastinal lymph nodes had a survival rate of 45% at 5 years. CONCLUSION: Multimodality therapy including extrapleural pneumonectomy has acceptable morbidity and mortality for selected patients. Prolonged survival occurred in patients with epithelial histologic variant and negative mediastinal lymph nodes. These data provide a rationale for a revised staging system for malignant pleural mesothelioma; furthermore, they permit stratification of patients into groups likely to benefit from aggressive multimodality treatment.

Adult↗

PubMed1992

[Evaluation of radioimmunotherapy in the multimodality treatment of hepatocellular carcinoma (HCC)].

The evaluation of radioimmunotherapy using 131I-anti HCC isoferritin IgG antibody in the multimodality treatment of HCC was reported. Forty three patients with surgically verified unresectable HCC have been treated by radioimmunotherapy as a part of multimodality treatment during 1985-1990. The short-term responses and prolong survival were compared with that in control group of 39 patients with HCC receiving conventional multimodality treatment. The rates of tumor shrinkage, AFP level decline and second resection in radioimmunotherapy group were 67.4% (29/43), 69.6% (16/23) and 30.2% (13/43) respectively, significantly higher than those in control group 23.1% (15/39), 40.0% (8/20) and 10.3% (4/39) respectively. The 1, 3, 5-year survival rates were 61.5%, 40.4% and 35.5% in radioimmunotherapy group, however, in control group were 51.3%, 20.1% and 15.5%, respectively. The results suggested that radioimmunotherapy is one of modalities of choice, particularly for the treatment of unresectable HCC in the multimodality treatment regimen.

Adult↗

PubMed1992

[Multimodal evoked potentials and the blink reflex in patients with primary brainstem lesions].

In 17 patients with primary brainstem injury, out of 60 patients with severe head trauma, diagnostic and prognostic values of multimodal evoked potentials and blink reflex were evaluated in relation to clinical syndromes of the brainstem, the duration of coma and the outcome. Clinical classification of the brainstem syndromes according to Gerstenbrand and Rumpl was used for the evaluation of the clinical findings, the Innsbruck Coma Scale (ICS) for the evaluation of the coma level, and the Glasgow Outcome Scale (GOS) for the outcome. Analyses and measurements of the multimodal evoked potentials and blink reflex were used many times in the period of assessment (six months after the injury). The analysis of our results with multimodal evoked potentials and blink reflex revealed different correlation and sensitivity in relation to the clinical syndromes of the brainstem, the duration of coma and the outcome of the injury. The blink reflex and somatosensory evoked potentials had the best correlation and the greatest sensitivity, the auditory evoked potentials had somewhat, while the visual evoked potentials had none. Multimodal evoked potentials and blink reflex increase the specificity of the diagnosis of brainstem injury compared to clinical observation only, and improve prognostic reliability.

Adolescent↗

PubMedDate not supplied

Radiotherapy-centered multimodal treatment of unresectable pancreatic carcinoma.

Multimodal treatment procedures, including intraoperative and external beam radiotherapy, chemotherapy and hyperthermotherapy, used for treatment of unresectable pancreatic carcinoma for the past two years have been described. Among the ten progressive cases where multimodal treatment was applied, marked reduction in tumor mass was observed in three cases. The cases receiving such treatment reported prolonged survival, the median survival period being 250 days as compared with 85.4 days in the non-multimodal group. The conclusion is that optimal palliative effects can be achieved by sustained application of both intraoperative and postoperative radiotherapy, hyperthermia and other techniques of multimodal therapy in cases of unresectable pancreatic carcinoma.

Adult↗

PubMed1990

[Goals, results and limitations of multimodal tumor therapy].

Multimodal tumor therapy, or the employment of two or more treatment modalities in combination, had led to several advances in the cure or long-term survival of patients with various tumor types. The individual established indications for multimodal tumor therapy are listed and described. Other indications remain controversial or are under evaluation in prospective randomized studies. In certain tumor types no advantage from the use of multimodal treatment has been demonstrated as yet. Aside from the advantages of multimodal therapy, the problems involved and possible side effects, especially in regard to short-, mid- and long-term toxic effects are described.

Antineoplastic Combined Chemotherapy Protocols↗

PubMed1989

[Multimodality treatment of carcinoma of the pancreas].

Although surgical resection has been the mainstream treatment for carcinoma of the pancreas, the operative results have been so disappointing that most surgeons in western countries have given up performing the resectional procedure. On the contrary, Japanese surgeons have never abandoned their dream of surgical treatment as a cure for the disease. Therefore, more and more aggressive procedures have been performed. Our operative results have not so remarkably ameliorated, but we have become knowledgeable on the pathological features of the carcinoma and believe that the best procedure for carcinoma of the head of the pancreas is a pancreatoduodenectomy with extensive dissection of regional lymph nodes and retroperitoneal tissue, and that surgery itself can not cure the disease but multimodality treatment should be established. Two hundred cases with carcinoma of the pancreas in which cystadenocarcinoma and islet cell carcinoma were excluded, were encountered from 1969 to 1987 in our department. Of 200 cases, only 48 cases underwent resection. Resection was divided into curative and non-curative resection according to macroscopic findings and pathohistological examination of the resected specimen. In cases of curative resection group, the average survival period of cases which underwent multimodality treatment was much longer than that without any adjuvant treatment. However, in cases of noncurative resection group, average survival period of cases with multimodality treatment was almost the same as that without adjuvant therapy. Therefore, multimodality treatment should be applied for curatively resected cases in order to obtain better results. Radiation therapy, especially intraoperative radiation therapy is considered to be a promising alternative modality of extensive retroperitoneal dissection. Hepatic metastasis was found postoperatively in about 27 percent of the resected cases. It seems that this type of recurrence occurred by migration of malignant cells from the tumor into the portal vein due to operative manipulation during surgery. Therefore, intraoperative infusion of an anticancer agent through the portal vein is mandatory, and preoperative and postoperative adjuvant chemotherapy should be considered.

Combined Modality Therapy↗

PubMed1988

[Multimodal treatment of cancer of the biliary tract].

The incidence of cancer of the biliary tract has been recently increasing, but the results of treatment have been unsatisfactory. During the last 10 years and 10 months, 128 cases of carcinoma of the biliary tract, including 64 cases of the gallbladder and the bile duct, respectively, were admitted. Some 98 (86%) out of the 113 cases were resected, with a low curative surgery rate of 32.7%. The curative surgery even resulted in recurrence with a few long-term survivors, so multimodal treatment should be considered for all cases. In non-curative resection, the 2-year cumulative survival rate of gallbladder carcinoma was 25% with radiation and chemotherapy, compared to the group without such treatment, all of whom died within 2 years after surgery. In cancer of the bile duct, similar results were obtained, so multimodal treatment should be administered especially in non-curative resection cases. In 1985 Mizumoto's group reviewed 1614 cases of gallbladder carcinoma and bile duct carcinoma collected from 22 institutions in Japan. The resectability and curative rate have been increasing, and the survival rates of both groups have slightly increased. Multimodal treatment has involved radiation therapy in 13.6% and chemotherapy in 44.1% of the cases. A two-year cumulative survival rate increased in non-curative resection patients treated with multimodal treatment.

Bile Duct Neoplasms↗

PubMed1985

Multimodal frequency distribution analysis of peripheral nerves.

In morphometric studies of peripheral nerves, the statistical analysis of such data as axon diameters is complicated by the presence of multimodal distributions. Nerve fiber diameters, for example, cannot be analyzed by classical parametric tests, and such descriptive statistics as mean or variance lose much of their usefulness. The recent development of stochastic catastrophe models offers a new parametric tool with which to describe multimodal distributions. This paper describes our development of a PASCAL computer program that permitted the modelling, comparison and segmentation of multimodal distributions. The method is based on a description of the multimodal frequency distributions by probability density functions of the canonical exponential family.

Axons↗

PubMedDate not supplied

Multimodal treatment of stage IVa hepatocellular carcinoma.

BACKGROUND/AIMS: The effectiveness of multimodal therapy for Stage IVa hepatocellular carcinoma was investigated. MATERIAL AND METHODS: Between 1982 and 1994, 40 patients with primary Stage IVa tumors were treated in our clinical unit. RESULTS: The overall survival rate was 79.7% at 1 year, 37.0% at 3 years, and 20.8% at 5 years. However, the most successful multimodal therapy, a combination of hepatectomy, embolization, and ethanol injection, achieved significantly better results (92.9% at 1 year, 59.6% at 3 years, and 47.7% at 5 years). This improved survival could still be obtained by multimodal therapy, even when surgical resection was incomplete. CONCLUSION: These results suggest that multimodal therapy including hepatectomy can be recommended for improving the survival of patients with Stage IVa hepatocellular carcinoma.

Aged↗

PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗

PubMed2026

Impact of a multimodal prehabilitation program on postoperative cognitive dysfunction: a single-center randomized controlled trial.

BACKGROUND: Postoperative cognitive dysfunction (POCD) is a frequent complication after cardiac surgery. Exercise-based prehabilitation may enhance functional reserve and reduce vulnerability to perioperative cerebral insults. We hypothesized that multimodal prehabilitation reduces POCD 3 months after cardiac surgery. METHODS: This prespecified substudy of a single-center randomized controlled trial (NCT03466606) included patients aged ≥50 years undergoing elective coronary artery bypass grafting and/or valve surgery. Participants were randomized 1:1 to 4-6 weeks of multimodal prehabilitation (exercise training, nutritional support, and psychological support) or standard preoperative care. Cognitive function was assessed at baseline and 3 months postoperatively using an age- and education-adjusted neuropsychological battery. POCD was defined as performance ≥1.5 standard deviations below normative values in at least 2 cognitive tests, excluding the Mini-Mental State Examination. Logistic regression analyses were performed to evaluate factors associated with POCD. RESULTS: Of 160 participants screened from the parent trial, 134 met eligibility criteria for the substudy and were randomized; 116 completed 3-month follow-up (prehabilitation n = 53; control n = 63). POCD occurred in 29 patients (25%), including 15/53 (28%) in the prehabilitation group and 14/63 (22%) in controls (odds ratio [OR] 1.37, 95% confidence interval [CI] 0.54-3.50, P = 0.52). In multivariable analysis, preoperative cognitive impairment was independently associated with POCD (OR 13.28, 95% CI 4.06-43.41, P < 0.001), whereas prehabilitation was not (OR 1.09, 95% CI 0.35-3.45, P = 0.877). Higher physical activity levels at 3 months were associated with lower odds of POCD (OR 0.97, 95% CI 0.95-1.00, P = 0.047). CONCLUSIONS: In this randomized controlled trial, a 4-6-week multimodal prehabilitation program did not reduce postoperative cognitive dysfunction 3 months after cardiac surgery. Although the intervention did not achieve measurable cognitive protection, the observed association between postoperative physical activity levels and postoperative cognitive dysfunction warrants further investigation.

Humans↗

PubMed2025

Foundation model based multimodal transformer framework for survival analysis in HER2 stratified breast cancer.

Objective. To improve survival prediction for HER2-positive breast cancer by integrating histopathological, molecular, and clinical data using a multimodal transformer framework.Approach. We propose a multimodal transformer framework for breast cancer survival prediction using HER2 stratified (SurvMBC), a foundation model-enhanced architecture that fuses three data modalities: whole-slide images, clinical narratives, and molecular features. Tumor microenvironment features are extracted using a pathology language and image pre-training (PLIP), clinical narratives are processed with BioBERT, and miRNA expression plus DNA methylation data are embedded using Gen2Vec. These representations are integrated through a cross-modal transformer with attention mechanisms for survival prediction.Main results. The model was evaluated on 1,095 HER2-positive breast cancer patients from The Cancer Genome Atlas. SurvMBC achieved a concordance index (C-index) of 0.857 (95% CI: 0.834, 0.880), a low integrated Brier score, and a strong inverse negative binomial log-likelihood. Risk stratification based on model outputs significantly separated high- and low-risk groups (log-rankp< 0.01) and showed strong associations with tumor stage, grade, and hormone receptor status (allp< 0.05).Significance. SurvMBC demonstrates the effectiveness of multimodal fusion in addressing tumor heterogeneity and improving prognostic accuracy. The attention-based integration enables context-aware learning of survival-relevant features across modalities, supporting individualized risk stratification and risk-adaptive treatment planning for HER2 stratified breast cancer patients.

Breast Neoplasms↗

PubMed2025

Multimodal deep learning for immunotherapy response prediction and biomarker discovery in non-small cell lung cancer.

OBJECTIVE: Immunotherapy has emerged as a promising treatment for advanced non-small cell lung cancer (NSCLC), but accurately predicting which patients will benefit from it remains a major clinical challenge. To address this, we aim to develop a novel multimodal method, DeepAFM, that integrates histopathology, genomic features, and clinical information to predict patient responses to anti-PD-(L)1 immunotherapy. MATERIALS AND METHODS: A total of 93 patients with advanced NSCLC were included in this study. Histopathological whole-slide images were processed using a self-supervised VQVAE2 for representation learning. PCA and K-means clustering were then applied for dimensionality reduction and feature grouping. Key regions of interest were visualized through permutation importance evaluation and color-coding techniques. The extracted histopathological features, along with genomic alterations and clinical variables, were integrated into the DeepAFM multimodal prediction model. RESULTS: The DeepAFM achieved a high predictive performance with an area under the curve (AUC) of 0.77 (95% confidence interval: 0.69-1.00). Attention-based heatmaps revealed that the model could identify critical pathological patterns, genomic mutations, and clinical indicators associated with patient responses to immunotherapy. DISCUSSION: The integration of multimodal data enabled the model to capture complex interactions among pathology, genomics, and clinical characteristics, enhancing the interpretability and predictive power of immunotherapy response prediction. The visualization techniques facilitated the identification of biologically meaningful features and potential biomarkers. CONCLUSION: This study demonstrates the effectiveness of the DeepAFM in predicting responses to immunotherapy in advanced NSCLC. The approach not only improves prediction accuracy but also provides valuable insights for personalized treatment strategies and biomarker discovery.

Humans↗

PubMed2026

Deep learning-based multimodal pathogenomics integration for precision cancer prognosis.

BACKGROUND: Recent studies have revealed valuable prognostic insights in haematoxylin and eosin (H&E)-stained histological sections and transcriptomic profiles, suggesting potential applications in machine learning. However, existing methods lack sufficient intra- and inter-modal interactions, and face challenges in clinical validation due to incomplete multimodal data. METHODS: We proposed PathoGems (PathoGenomics-based integrative survival prediction), a weakly-supervised, interpretable multimodal learning framework that integrates histology and genomic profiles for precise cancer prognosis prediction. To evaluate the robustness of PathoGems, we initially curated a dataset of 1965 cases across four cohorts from The Cancer Genome Atlas (TCGA), including breast, colorectal, glioblastoma, and esophageal cancers. For external validation, PathoGems was further evaluated on four independent cohorts, consisting of 76 breast cancer and 41 esophageal squamous cell carcinoma cases from Zhejiang Cancer Hospital, as well as 102 colorectal cancer and 58 glioblastoma cases from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). RESULTS: PathoGems effectively stratified patients into favorable and unfavorable risk groups, revealing significant differences in histological patterns, genomic features, and overall survival (log-rank test, p < 0.05). Moreover, the model’s predictions are further supported by visualization and transcriptomic analysis, enhancing interpretability and reliability. CONCLUSIONS: By fusing histological and clinicogenomic multimodal models, PathoGems will provide a solid foundation for developing an innovative tool that aids clinicians in making informed decisions and selection personalized treatment strategies for cancer patients.

Humans↗

PubMed2026

Multimodal Deep Learning and Foundation Models for Early Detection and Forecasting of Plant Diseases.

Plant diseases destroy 20-40% of global food production annually, posing a critical threat to food security for a projected population of 9.7 billion by 2050. Conventional diagnostic approaches relying on expert visual assessment are slow, costly, and unsuitable for modern agricultural scales. While deep convolutional neural networks demonstrated early promise, single-modality, image-centric systems consistently fail under real-world field conditions characterized by variable lighting, co-occurring infections, and cultivar diversity. This review synthesizes a decade of progress across four interconnected frontiers: the evolution of deep learning architectures for plant disease detection; the adaptation of foundation models including CLIP, SAM, and DINOv2 to agricultural contexts; the development of multimodal fusion frameworks integrating imagery, environmental, genomic, and hyperspectral data; and the transition from static disease diagnosis to descriptive comparison of reported metrics, which suggested that multimodal approaches frequently reported improved diagnostic performance relative to corresponding single-modality baselines, although direct cross-study comparison was limited by methodological heterogeneity. A systematic review following PRISMA guidelines identifies eligible comparative studies. Descriptive comparison of reported performance metrics across these studies indicated that multimodal approaches generally achieved higher accuracy and sensitivity than single-modality models, particularly for pre-symptomatic disease detection. Eight critical research gaps are identified, including the absence of a unified agricultural foundation model and limited climate-aware forecasting under non-stationary climate projections. A structured research agenda is proposed to accelerate translation from laboratory performance to globally equitable, field-deployable crop protection systems.

convolutional neural networks↗