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PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗

PubMed2006

The multi-component model of working memory: explorations in experimental cognitive psychology.

There are a number of ways one can hope to describe and explain cognitive abilities, each of them contributing a unique and valuable perspective. Cognitive psychology tries to develop and test functional accounts of cognitive systems that explain the capacities and properties of cognitive abilities as revealed by empirical data gathered by a range of behavioral experimental paradigms. Much of the research in the cognitive psychology of working memory has been strongly influenced by the multi-component model of working memory [Baddeley AD, Hitch GJ (1974) Working memory. In: Recent advances in learning and motivation, Vol. 8 (Bower GA, ed), pp 47-90. New York: Academic Press; Baddeley AD (1986) Working memory. Oxford, UK: Clarendon Press; Baddeley A. Working memory: Thought and action. Oxford: Oxford University Press, in press]. By expanding the notion of a passive short-term memory to an active system that provides the basis for complex cognitive abilities, the model has opened up numerous questions and new lines of research. In this paper we present the current revision of the multi-component model that encompasses a central executive, two unimodal storage systems: a phonological loop and a visuospatial sketchpad, and a further component, a multimodal store capable of integrating information into unitary episodic representations, termed episodic buffer. We review recent empirical data within experimental cognitive psychology that has shaped the development of the multicomponent model and the understanding of the capacities and properties of working memory. Research based largely on dual-task experimental designs and on neuropsychological evidence has yielded valuable information about the fractionation of working memory into independent stores and processes, the nature of representations in individual stores, the mechanisms of their maintenance and manipulation, the way the components of working memory relate to each other, and the role they play in other cognitive abilities. With many questions still open and new issues emerging, we believe that the multicomponent model will continue to stimulate research while providing a comprehensive functional description of working memory.

Brain↗

PubMed2003

High-efficiency peptide analysis on monolithic multimode capillary columns: Pressure-assisted capillary electrochromatography/capillary electrophoresis coupled to UV and electrospray ionization-mass spectrometry.

High-efficiency peptide analysis using multimode pressure-assisted capillary electrochromatography/capillary electrophoresis (pCEC/pCE) monolithic polymeric columns and the separation of model peptide mixtures and protein digests by isocratic and gradient elution under an applied electric field with UV and electrospray ionization-mass spectrometry (ESI-MS) detection is demonstrated. Capillary multipurpose columns were prepared in silanized fused-silica capillaries of 50, 75, and 100 microm inner diameters by thermally induced in situ copolymerization of methacrylic monomers in the presence of n-propanol and formamide as porogens and azobisisobutyronitrile as initiator. N-Ethylbutylamine was used to modify the chromatographic surface of the monolith from neutral to cationic. Monolithic columns were termed as multipurpose or multimode columns because they showed mixed modes of separation mechanisms under different conditions. Anion-exchange separation ability in the liquid chromatography (LC) mode can be determined by the cationic chromatographic surface of the monolith. At acidic pH and high voltage across the column, the monolithic stationary phase provided conditions for predominantly capillary electrophoretic migration of peptides. At basic pH and electric field across the column, enhanced chromatographic retention of peptides on monolithic capillary column made CEC mechanisms of migration responsible for separation. The role of pressure, ionic strength, pH, and organic content of the mobile phase on chromatographic performance was investigated. High efficiencies (exceeding 300 000 plates/m) of the monolithic columns for peptide separations are shown using volatile and nonvolatile, acidic and basic buffers. Good reproducibility and robustness of isocratic and gradient elution pressure-assisted CEC/CE separations were achieved for both UV and ESI-MS detection. Manipulation of the electric field and gradient conditions allowed high-throughput analysis of complex peptide mixtures. A simple design of sheathless electrospray emitter provided effective and robust low dead volume interfacing of monolithic multimode columns with ESI-MS. Gradient elution pressure-assisted mixed-mode separation CE/CEC-ESI-MS mass fingerprinting and data-dependent pCE/pCEC-ESI-MS/MS analysis of a bovine serum albumin (BSA) tryptic digest in less than 5 min yielding high sequence coverage (73%) demonstrated the potential of the method.

Electrophoresis, Capillary↗

PubMed2026

AI-Based 3D Heterogeneous Network Model for Functional Prediction of Epigenetics.

Human biology and diseases are the result of constantly evolving processes within an intricately complex molecular network of interactions, such as epigenetic regulation. Epigenetics refers to heritable changes in gene expression that occur without alterations to the underlying DNA sequence. These changes, driven by mechanisms such as DNA methylation, histone modifications, and noncoding RNAs, play critical roles in regulating chromatin structure and gene activity. Epigenetic regulation offers valuable insights into biological systems, and when integrated with sophisticated analyses, it enables us to gain insights into gene regulation and cellular behavior. Here, we describe an artificial intelligence (AI)-based model that is capable of generating 3-dimensional (3D) heterogeneous network by integrating multimodal data for the functional prediction of epigenetic mechanisms, emphasizing its applications in medicine, developmental biology, and personalized therapeutics. Heterogeneous networks in biology are powerful tools for understanding the complex interactions and interdependencies within biological systems. Key advancements in AI and multiomics data integration have propelled this field, offering new insights into disease mechanisms, biomarker discovery, and therapeutic interventions.

Epigenesis, Genetic↗

PubMed2005

Bona fide stochastic resonance and multimodality in the stochastic Hodgkin-Huxley neuron.

The phenomena of stochastic resonance (SR) has attracted much attention in the studies of the excitable systems, in particular, the nervous systems under noise. Recently, an alternative SR condition, called the bona fide SR, was proposed for the bistable system under noise, based on the notion of the residence time distribution. As the forcing frequency increases, there exists an optimal resonant frequency. We study the SR in a stochastic Hodgkin-Huxley neuron, which has an inherent natural frequency in addition to the stochastic time scale. We have observed two resonant conditions; one between periodic forcing and natural frequencies, and the other between the periodic forcing and the stochastic frequencies. These resonance conditions show the bona fide stochastic resonance with multimodality. For comparison, we have studied the bona fide SR in the stochastic FitzHugh-Nagumo neuron, where, the multimodality is not observed. The differences in the resonance structure of two neuron models are understood in terms of differences in the phase portraits.

Action Potentials↗

PubMed2006

Model-based analysis of dispersion curves using chirplets.

Time-frequency representations, like the spectrogram or the scalogram, are widely used to characterize dispersive waves. The resulting energy distributions, however, suffer from the uncertainty principle, which complicates the allocation of energy to individual propagation modes (especially when the dispersion curves of these modes are close to each other in the time-frequency domain). This research applies the chirplet as a tool to analyze dispersive wave signals based on a dispersion model. The chirplet transform, a generalization of both the wavelet and the short-time Fourier transform, enables the extraction of components of a signal with a particular instantaneous frequency and group delay. An adaptive algorithm identifies frequency regions for which quantitative statements can be made about an individual mode's energy, and employs chirplets (locally adapted to a dispersion curve model) to extract the (proportional) energy distribution of that single mode from a multimode dispersive wave signal. The effectiveness of this algorithm is demonstrated on a multimode synthetic Lamb wave signal for which the ground-truth energy distribution is known for each mode. Finally, the robustness of this algorithm is demonstrated on real, experimentally measured Lamb wave signals by an adaption of a correlation technique developed in previous research.

Journal Article↗

PubMed2004

In vivo targeting of underglycosylated MUC-1 tumor antigen using a multimodal imaging probe.

One of the most difficult challenges of oncology is to improve methods for early tumor detection, which is crucial for the success of cancer therapy and greatly improves the survival rate. Underglycosylated mucin-1 antigen (uMUC-1) is one of the early hallmarks of tumorigenesis and is overexpressed and underglycosylated on almost all human epithelial cell adenocarcinomas as well as in nonepithelial cancer cell lines, as well as in hematological malignancies such as multiple myeloma, and some B-cell non-Hodgkin lymphomas. In this study, we designed, synthesized, and tested a novel multimodal imaging probe specifically recognizing in vivo uMUC-1 antigen in an animal model of human cancer. Furthermore, in vivo magnetic resonance- and near-infrared-imaging experiments on tumor-bearing animals showed specific accumulation of the probe in uMUC-1-positive tumors and virtually no signal in control tumors. We expect that this probe has a potential to greatly aid in screening prospective patients for early cancer detection and in monitoring the efficacy of drug therapy.

Amino Acid Sequence↗

PubMedDate not supplied

Integrating care for chronic pain patients.

Problems in the treatment of the chronic pain patient in a staff model HMO often lead to ineffective and expensive care. The authors support a multimodal approach, which includes medical, psychosocial, and behavioral elements. This treatment is offered in an individualized, integrated, and episodic fashion, which is particularly relevant to the multiple and extended needs of the chronic pain patient. Two cases are presented to illustrate the approach.

Adult↗

PubMed1987

Control of pain and other symptoms in advanced cancer.

In the patient with advanced cancer, effective symptom control requires a multidisciplinary, multimodal approach. Considerations involved in the control of pain can serve as a model for management of other symptoms, such as anorexia and dyspnea, as well.

Analgesics↗

PubMed1997

Patient specific color texture mapping of CT-based anatomical surface models utilizing cryosection data.

In traditional medical imaging modalities, color and texture information can add considerable information for diagnostics. Presently, multimodal images of a patient are unregistered and referenced independent of each other, or registered and fused into a single hybrid volume. Doctors and other medical professionals need to be able to visualize and interrogate, on a per-patient basis, a wide variety of 2D and 3D data representations that can be created from non-invasive imaging modalities, such as MRI and CT. In addition, any colorization that may be applied to the image data is strictly based on tissue density, radiation emission, or magnetic signature, and not on any physiological foundation. In order for "true-to-life" color information to be incorporated with non-invasive imaging techniques, and for it to be of consistent quality across the entire body, a single whole-body cryosection specimen with associated medical image data is needed. The National Library of Medicine's Visible Human Project offers just such a specimen. Using the full-body medical image data along with the cryosection images of the Visible Human subject, a set of color lookup tables for all visually well defined structures and organs can be created. As a result, patient-specific colorization based on real tissue color and characteristics can be incorporated into traditional intensity-based imaging modalities. The primary goal of this work has been to create CT color lookup tables for all visually well-defined structures in the Visible Human male cryosection data set. The subsequent goal has been to develop a method for stripping textures from a volumetric data set for polygonal models and non-uniform rational B-spline (NURBS) models, also generated from the volumetric data.

Color↗

PubMed1998

Relationships among cerebral perfusion pressure, autoregulation, and transcranial Doppler waveform: a modeling study.

OBJECT: The aim of this study was to analyze how the main values extrapolated from the transcranial Doppler (TCD) waveform (systolic, mean, and diastolic velocity; velocity peak-to-peak amplitude; and pulsatility index [PI]) are affected by changes in intracranial pressure (ICP), systemic arterial pressure (SAP), autoregulation, and intracranial compliance. METHODS: The analysis was performed using a mathematical model of the intracranial dynamics. This model includes a passive middle cerebral artery, the biomechanics of large and small pial arteries subjected to autoregulatory mechanisms, a collapsing venous cerebrovascular bed, the cerebrospinal fluid circulation, and the ICP-volume relationship. The results indicate that there are approximately three distinct zones characterized by different relationships between cerebral perfusion pressure (CPP) and velocity parameters in patients with preserved autoregulation. In the central autoregulatory zone (CPP > 70 mm Hg) the mean velocity does not change with decreasing CPP, whereas the PI and velocity peak-to-peak amplitude increase moderately. In a second zone (CPP between 4045 and 70 mm Hg), in which vasodilation of small pial arteries becomes maximal, the mean velocity starts to decrease, whereas the PI and velocity amplitude continue to increase. In the third zone, in which autoregulation is completely exhausted (CPP < 40 mm Hg), arterioles behave passively, mean velocity and velocity amplitude decline abruptly, and the PI exhibits a disproportionate rise. Moreover, this rise is quite independent of whether CPP is reduced by increasing ICP or reducing mean SAP. In contrast, in patients with defective autoregulation, the mean velocity and velocity amplitude decrease linearly with decreasing CPP, but the PI still increases in a way similar to that observed in patients with preserved autoregulation. CONCLUSIONS: The information contained in the TCD waveform is affected by many factors, including ICP, SAP, autoregulation. and intracranial compliance. Model results indicate that only a comparative analysis of the concomitant changes in ultrasonographic quantities during multimodality monitoring may permit the assessment of several aspects of intracranial dynamics (cerebral blood flow changes, vascular pulsatility, ICP changes, intracranial compliance, CPP, and autoregulation).

Arteries↗

PubMed2005

A hierarchy of associations in hippocampo-cortical systems: cognitive maps and navigation strategies.

In this letter we describe a hippocampo-cortical model of spatial processing and navigation based on a cascade of increasingly complex associative processes that are also relevant for other hippocampal functions such as episodic memory. Associative learning of different types and the related pattern encoding-recognition take place at three successive levels: (1) an object location level, which computes the landmarks from merged multimodal sensory inputs in the parahippocampal cortices; (2) a subject location level, which computes place fields by combination of local views and movement-related information in the entorhinal cortex; and (3) a spatiotemporal level, which computes place transitions from contiguous place fields in the CA3-CA1 region, which form building blocks for learning temporospatial sequences. At the cell population level, superficial entorhinal place cells encode spatial, context-independent maps as landscapes of activity; populations of transition cells in the CA3-CA1 region encode context-dependent maps as sequences of transitions, which form graphs in prefrontal-parietal cortices. The model was tested on a robot moving in a real environment; these tests produced results that could help to interpret biological data. Two different goal-oriented navigation strategies were displayed depending on the type of map used by the system. Thanks to its multilevel, multimodal integration and behavioral implementation, the model suggests functional interpretations for largely unaccounted structural differences between hippocampo-cortical systems. Further, spatiotemporal information, a common denominator shared by several brain structures, could serve as a cognitive processing frame and a functional link, for example, during spatial navigation and episodic memory, as suggested by the applications of the model to other domains, temporal sequence learning and imitation in particular.

Action Potentials↗

PubMed2006

Biological parametric mapping: A statistical toolbox for multimodality brain image analysis.

In recent years, multiple brain MR imaging modalities have emerged; however, analysis methodologies have mainly remained modality-specific. In addition, when comparing across imaging modalities, most researchers have been forced to rely on simple region-of-interest type analyses, which do not allow the voxel-by-voxel comparisons necessary to answer more sophisticated neuroscience questions. To overcome these limitations, we developed a toolbox for multimodal image analysis called biological parametric mapping (BPM), based on a voxel-wise use of the general linear model. The BPM toolbox incorporates information obtained from other modalities as regressors in a voxel-wise analysis, thereby permitting investigation of more sophisticated hypotheses. The BPM toolbox has been developed in Matlab with a user-friendly interface for performing analyses, including voxel-wise multimodal correlation, ANCOVA, and multiple regression. It has a high degree of integration with the SPM (statistical parametric mapping) software relying on it for visualization and statistical inference. Furthermore, statistical inference for a correlation field, rather than a widely used T-field, has been implemented in the correlation analysis for more accurate results. An example with in vivo data is presented, demonstrating the potential of the BPM methodology as a tool for multimodal image analysis.

Brain↗

PubMed2000

The episodic buffer: a new component of working memory?

In 1974, Baddeley and Hitch proposed a three-component model of working memory. Over the years, this has been successful in giving an integrated account not only of data from normal adults, but also neuropsychological, developmental and neuroimaging data. There are, however, a number of phenomena that are not readily captured by the original model. These are outlined here and a fourth component to the model, the episodic buffer, is proposed. It comprises a limited capacity system that provides temporary storage of information held in a multimodal code, which is capable of binding information from the subsidiary systems, and from long-term memory, into a unitary episodic representation. Conscious awareness is assumed to be the principal mode of retrieval from the buffer. The revised model differs from the old principally in focussing attention on the processes of integrating information, rather than on the isolation of the subsystems. In doing so, it provides a better basis for tackling the more complex aspects of executive control in working memory.

Journal Article↗

PubMedDate not supplied

Linguistic adaptations during spoken and multimodal error resolution.

Fragile error handling in recognition-based systems is a major problem that degrades their performance, frustrates users, and limits commercial potential. The aim of the present research was to analyze the types and magnitude of linguistic adaptation that occur during spoken and multimodal human-computer error resolution. A semiautomatic simulation method with a novel error-generation capability was used to collect samples of users' spoken and pen-based input immediately before and after recognition errors, and at different spiral depths in terms of the number of repetitions needed to resolve an error. When correcting persistent recognition errors, results revealed that users adapt their speech and language in three qualitatively different ways. First, they increase linguistic contrast through alternation of input modes and lexical content over repeated correction attempts. Second, when correcting with verbatim speech, they increase hyperarticulation by lengthening speech segments and pauses, and increasing the use of final falling contours. Third, when they hyperarticulate, users simultaneously suppress linguistic variability in their speech signal's amplitude and fundamental frequency. These findings are discussed from the perspective of enhancement of linguistic intelligibility. Implications are also discussed for corroboration and generalization of the Computer-elicited Hyperarticulate Adaptation Model (CHAM), and for improved error handling capabilities in next-generation spoken language and multimodal systems.

Computer Simulation↗

PubMed1988

Malignant mesothelioma of the pleura. A prospective therapeutic study of 132 patients from 1981-1985.

Between March 1981 and February 1985, 93 out of 132 patients with a histologically confirmed diagnosis of malignant pleural mesothelioma were eligible for therapy and were prospectively assigned to receive either combined therapy or best supportive care, according to their personal preferences. Fifty-seven patients underwent multimodal therapy including surgical resection where possible, polychemotherapy, and radiation therapy in case of partial remission. Thirty-six patients received maximal supportive care only, as did 39 patients who were not eligible for treatment. The median survival was 13 months for treated patients compared to 7 for those receiving best supportive care and 5 for patients not amenable to treatment. Median progress-free survival was 6, 2, and 1 month respectively. Surgical resection did not prolong life expectancy within the treated group. In view of significant differences in the distribution of various cofactors over the two study groups, stepwise Cox model analyses were performed. Prognostic nontreatment variables related to prolonged survival were: good performance status, stage I and II, absence of chest pain, age below 50 years, and epithelial histology. Although in the Cox model analyses the survival improvement of patients being treated could be greatly attributed to other cofactors, multimodal treatment showed some prolongation of life expectancy.

Adult↗

PubMed2026

Beyond Morphology: Reframing Lymph-Node Metastasis Prediction Through Clonal Ecology-Decades-Long Genomic Instability and Polyclonal-to-Monoclonal Transitions as the Missing Dimension in Cancer.

Recent whole-genome, lineage-tracing, single-cell, and spatial studies have reshaped our understanding of tumor evolution, revealing that cancers can arise from polyclonal populations, undergo decades-long genomic instability before clinical detection, and progress through dynamic changes in subclonal composition, cellular state, and ecological organization. These findings challenge the assumption underlying morphology-based prediction models that metastatic risk can be inferred from static histological features alone. Here, we revisit lymph-node metastasis prediction in colorectal cancer through clonal ecology, integrating computational pathology with evolutionary oncology. Drawing on the subclonal switchboard model proposed in 2012 and subsequent artificial intelligence (AI)-enabled approaches for tracking dominant and dormant subclones, we synthesize evidence that metastatic potential reflects clonal ancestry, evolutionary timing, spatial niche architecture, cellular plasticity, intercellular interactions, dormancy, and treatment-driven shifts in subclonal fitness. We define five complementary methodological pillars for operationalizing clonal ecology: single-cell transcriptomics for resolving rare subclones, evolutionary trajectories, and adaptive cell states; lineage tracing and phylogenetics for reconstructing clonal ancestry and divergence; spatial transcriptomics and genomics for mapping subclonal geography and tumor-stromal-immune interactions; longitudinal liquid biopsy surveillance for monitoring residual disease, clonal turnover, and emerging resistance; and AI-enabled multimodal integration for connecting histopathology, genomics, spatial biology, and longitudinal data into predictive ecological-state models. Multiple-instance learning and pathology foundation models provide scalable computational foundations for evolution-aware prediction. Translationally, dormant subclones represent actionable reservoirs of recurrence. A longitudinal clinical and experimental study of KMT2A-rearranged acute myeloid leukemia further supports central predictions of the subclonal switchboard framework by demonstrating treatment-associated shifts in subclonal dominance, persistence of cryptic adaptive programs, and ecological rewiring during resistance and relapse. We propose clonal ecology as a measurable dimension for extending morphology-driven prediction toward integrative models that anticipate evolutionary transitions, identify therapeutic windows, and proactively constrain adaptive tumor ecosystems before resistant or metastatic subclones achieve clinical dominance.

Humans↗

PubMed2006

Q-ball reconstruction of multimodal fiber orientations using the spherical harmonic basis.

Diffusion tensor imaging (DTI) accurately delineates white matter pathways when the Gaussian model of diffusion is valid. However, DTI yields erroneous results when diffusion takes on a more complex distribution, as is the case in the brain when fiber tracts cross. High angular resolution diffusion imaging (HARDI) overcomes this limitation of DTI by more fully characterizing the angular dependence of intravoxel diffusion. Among the various HARDI methods that have been proposed, QBI offers advantages such as linearity, model independence, and relatively easy implementation. In this work, reconstruction of the q-ball orientation distribution function (ODF) is reformulated in terms of spherical harmonic basis functions, yielding an analytic solution with useful properties of a frequency domain representation. The harmonic basis is parsimonious for typical b-values, which enables the ODF to be synthesized from a relatively small number of noisy measurements and thus brings the technique closer to clinical feasibility from the standpoint of total imaging time. The proposed method is assessed using Monte Carlo computer simulations and compared with conventional q-ball reconstruction using spherical RBFs. In vivo results from 3T whole-brain HARDI of adult volunteers are also provided to verify the underlying mathematical theory.

Adult↗