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PubMed2026

Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.

Cancer remains a major global health burden, with approximately 20 million new cases and 9.7 million cancer-related deaths reported globally in 2022. While advances in radiological imaging, molecular profiling, and clinical data have enhanced the interpretation of disease progression, the availability of multiple such modalities still does not meet the needs of a large patient population. This narrative review focuses on the role of multimodal artificial intelligence and machine learning in bridging the gap in interpreting heterogeneous modalities to improve risk prediction, prognostic assessment, and treatment decision-making in precision oncology. Multimodal frameworks such as Pathomic Fusion illustrate how complementary histopathological and genomic information can be integrated for cancer diagnosis and prognostic modeling. Multimodal models have demonstrated potential in virtual biopsy, cancer screening, prognostic prediction, radiotherapy planning, intraoperative guidance, and clinical-trial design using digital twins and synthetic control arms. The major limitations of incorporating multimodal artificial intelligence and machine learning in oncology include data heterogeneity, demographic or institutional biases, and reproducibility challenges that hinder translation. Accordingly, appropriate data-governance strategies, fairness audits, and privacy-preserving approaches such as federated learning should be considered where appropriate. Future progress will depend on the development of standardized benchmarking datasets, robust external validation, seamless integration with electronic health records and picture archiving and communication systems, and the implementation of explainable, secure, and clinically validated multimodal artificial intelligence frameworks that support precision oncology in routine clinical practice.

deep learning↗

PubMed2026

NeuroOmics-Net: An interpretable multimodal deep learning framework for Alzheimer's disease diagnosis and progression prediction using neuroimaging, EEG, and genomic data.

Accurate diagnosis and progression prediction of Alzheimer's disease (AD) remain challenging due to the heterogeneous nature of the disease, which involves structural brain degeneration, electrophysiological dysfunction, and molecular dysregulation. Most existing deep learning approaches rely on a single modality or limited multimodal combinations, thereby failing to capture the complex cross-domain interactions underlying AD progression. Furthermore, the scarcity of large-scale datasets containing synchronized neuroimaging, electrophysiological, and genomic measurements restricts the development of comprehensive multimodal diagnostic systems. To address these challenges, this study proposes NeuroOmics-Net, a multimodal deep learning framework for Alzheimer's disease analysis that integrates structural magnetic resonance imaging (sMRI), electroencephalography (EEG), and gene expression data. The proposed framework combines a Hierarchical Multi-View Encoder (HME) for modality-specific feature extraction, a Cross-Omics Attention Fusion (CAF) module for adaptive integration of complementary biomarkers, and a Disease Progression Graph Learning (DPGL) module for modeling progression-related relationships across biological domains. To facilitate cross-modal integration from independent cohorts, Regularized Canonical Correlation Analysis (RCCA) is employed to align heterogeneous feature representations within a shared latent space. Experiments were conducted using publicly available datasets from ADNI, PhysioNet, and GEO repositories comprising 1120 diagnosis-aligned samples. The proposed framework achieved 94.3% classification accuracy and an AUC of 0.975 for distinguishing normal controls (NC), mild cognitive impairment (MCI), and Alzheimer's disease subjects, while attaining 93.7% accuracy for predicting conversion from stable mild cognitive impairment (sMCI) to progressive mild cognitive impairment (pMCI). However, a fairness sensitivity analysis using stratified demographic reweighting revealed accuracy ranging from 90.8% (low-education, high-comorbidity proxy subgroup) to 96.1% (low-risk, high-reserve proxy subgroup), a demographic parity gap of 5.3 percentage points, indicating that overall accuracy reflects a performance ceiling in a relatively homogeneous research cohort rather than a realistic estimate for demographically diverse clinical populations. Comparative evaluations demonstrated consistent improvements over state-of-the-art unimodal and multimodal deep learning models. Interpretability analysis further identified clinically relevant biomarkers, including hippocampal and entorhinal atrophy, theta-alpha EEG alterations, and APOE-associated molecular pathways. Because sMRI, EEG, and gene expression data were sourced from separate, unpaired cohorts with no subjects possessing all three synchronized measurements, all reported cross-modal associations reflect population-level statistical correspondence across diagnosis-matched groups rather than within-subject physiological coupling; no claim of intra-individual causal cross-modal interaction is made. These findings demonstrate that NeuroOmics-Net provides an effective computer-aided framework for multimodal biomedical data processing and Alzheimer's disease analysis. By integrating neuroimaging, electrophysiological, and genomic information, the proposed approach enables accurate diagnosis, progression prediction, and biologically interpretable decision support for clinical and translational applications.

Humans↗

PubMed2025

Q RadFusion: Hybrid Quantum Classical Radiogenomic Framework for Breast Cancer Diagnosis.

BACKGROUND AND PURPOSE: Breast cancer remains the most common cancer in women worldwide, with early and accurate diagnosis critical for patient survival. Radiogenomics integrates imaging phenotypes with genomic profiles, offering a pathway to precision diagnostics. However, existing classical machine learning models often struggle with the high dimensionality and heterogeneity of multimodal data, leading to issues in calibration and reproducibility. This study presents Q RadFusion, a hybrid quantum-classical framework designed to enhance breast cancer diagnosis by fusing mammography and genomics data. METHODS: Q RadFusion was implemented on two publicly available datasets: CBIS-DDSM (2,600 curated mammography cases, TCIA) and TCGA-BRCA (1,000 genomic profiles, GDC). Imaging preprocessing included bias-field correction, segmentation, and harmonization, while genomic data underwent normalization and imputation. Feature selection was performed using the Quantum Approximate Optimization Algorithm (QAOA), and features were mapped into a quantum Hilbert space using Variational Quantum Circuits (VQC). For multimodal fusion, ResNet encoded mammography features, and a Transformer encoded genomic features. Patient-level and site-held-out splits were used for evaluation. RESULTS: Q RadFusion achieved an AUC of 0.96 and accuracy of 94%, outperforming baselines including CNN-LSTM, ResNet + XGBoost, and multimodal Transformers. Ablation studies confirmed the contribution of quantum components, with optimal performance observed at circuit depth, qubits, and QAOA layers. The model also demonstrated improved calibration and ~ 80% fewer parameters compared to deep fusion networks. CONCLUSION: Q RadFusion demonstrates that hybrid quantum-classical radiogenomic integration can deliver accurate, reproducible, and clinically meaningful diagnostic support for breast cancer, with strong potential for future clinical translation.

Breast Cancer↗

PubMed2003

Evaluation of neuronavigation in lesional and non-lesional epilepsy surgery.

OBJECTIVE: For many patients, surgery for intractable epilepsy provides not only freedom or substantial relief from seizures, but also functional improvement and increased quality of life. Precise intraoperative localization of the underlying structural and functional processes is crucial in this regard. The aim of this study was to clinically evaluate whether neuronavigation leads to an improvement in the precision and safety of epilepsy surgery. In this paper, we also attempt to assess the navigation workstation as a platform for the integration of multimodal information (multimodal information guidance). PATIENTS: Out of a series of 223 epilepsy surgery procedures, 140 were performed with the aid of neuronavigation. Patient and surgical data were prospectively collected. METHODS: We used the neuronavigation device as a common platform to merge complementary information modalities. Correlation of anatomic and structural details with functional information contributed to the surgical script in non-lesional and localization-related epilepsy surgery. At least two different information modalities contributed to planning and surgical guidance in every patient. Immediately following the operative procedure, the surgeon answered a set of questions on the reasons for the application of neuronavigation, and the efficiency and safety of navigation. Detailed analysis of the location of the operative procedure, histopathological findings and outcome was performed. RESULTS: The main benefits of neuronavigation in epilepsy surgery were precision of targeting even in small and deep-seated targets, safe manipulation in critical brain areas, accurate placement of electrodes, and correlation of electro-clinical information modalities with underlying structures. Furthermore, navigation provided individual tailoring of craniotomy and corticotomy. It was less reliable for verification of resection boundaries in the case of underlying glioma. Neuronavigational localization and its combination with image fusion and functional investigations greatly improved discussion within the epilepsy surgery team. CONCLUSION: The neuronavigation concept proved its value in epilepsy surgery by linking anatomic, pathologic and functional data of the individual patient. Enhanced by the integration of multimodal information, neuronavigation significantly improved the available treatment options.

Adolescent↗

PubMed1991

Computer applications in radiology.

Computer applications in radiology are evolving rapidly, tied to incremental improvements in hardware, software, and methods. In computer hardware, the emergence of dramatically improved graphic and computational performance for engineering workstations enables their use for visualization. Major changes in networking, storage, and display technology play a major role in influencing applications. The use of three-dimensional digitizers to perform localization of real three-dimensional points in conjunction with images and the rendering of objects using rapid prototyping methods, such as stereolithography, were recently reported. Major software advances have taken place through the availability of applications packages that are operated with menu-driven or point-and-click user interfaces, data flow languages, or complete turnkey applications. Imaging methods including CT, MR imaging, digital radiography, biomagnetism, and optical range sensing, which take advantage of advanced computer technology, are new this year. Image processing for multimodality fusion or image registration, visualization, reconstruction, and quantification of images, have been reported at a wide variety of conferences and in key publications. New computer methods to fabricate custom orthopaedic implants, and to improve imaging technology assessment were introduced.

Brain↗

PubMed1997

Normal fusion for three-dimensional integrated visualization of SPECT and magnetic resonance brain images.

UNLABELLED: Multimodality visualization aims at efficiently presenting integrated information obtained from different modalities, usually combining a functional modality (SPECT, PET, functional magnetic resonance imaging) with an anatomical modality [CT, magnetic resonance imaging (MRI)]. This paper presents a technique for three-dimensional integrated visualization of SPECT and magnetic resonance brain images, where MRI is used as a framework of reference for the display of the SPECT data. METHODS: A novel technique for three-dimensional integrated visualization of functional and anatomical information, called normal fusion, is presented. With this technique, local functional information is projected onto an anatomic structure. RESULTS: The normal fusion technique is applied to three cases of SPECT/MRI integration. The results are presented, discussed and evaluated for clinical relevance. CONCLUSION: The results for three-dimensional integrated display of SPECT and MR brain images indicate that the normal fusion technique provides a potentially comprehensive and diagnostically valuable presentation of cerebral blood perfusion in relation to the anatomy of the brain.

Autistic Disorder↗

PubMed1997

Imaging green fluorescent protein fusion proteins in Saccharomyces cerevisiae.

Tagging expressed proteins with the green fluorescent protein (GFP) from Aequorea victoria [1] is a highly specific and sensitive technique for studying the intracellular dynamics of proteins and organelles. We have developed, as a probe, a fusion protein of the carboxyl terminus of dynein and GFP (dynein-GFP), which fluorescently labels the astral microtubules of the budding yeast Saccharomyces cerevisiae. This paper describes the modifications to our multimode microscope imaging system [2,3], the acquisition of three-dimensional (3-D) data sets and the computer processing methods we have developed to obtain time-lapse recordings of fluorescent astral microtubule dynamics and nuclear movements over the complete duration of the 90-120 minute yeast cell cycle. This required low excitation light intensity to prevent GFP photobleaching and phototoxicity, efficient light collection by the microscope optics, a cooled charge-coupled device (CCD) camera with high quantum efficiency, and image reconstruction from serial optical sections through the 6 micron-wide yeast cell to see most or all of the astral molecules. Methods are also described for combining fluorescent images of the microtubules labeled with dynein-GFP with high resolution differential interference contrast (DIC) images of nuclear and cellular morphology [4], and fluorescent images of the chromosomes stained with 4,6-diamidino-2-phenylindole (DAPI) [5].

Dyneins↗

PubMed2005

Multimodal genetic diagnosis of solid variant alveolar rhabdomyosarcoma.

The most common types of rhabdomyosarcoma (RMS) are alveolar RMS (ARMS), which are characterized by the specific translocation t(2;13)(q35;q14) or its rarer variant, t(1;13)(p36;q14), producing the fusion genes PAX3-FKHR and PAX7-FKHR, respectively, and embryonal RMS (ERMS), which is characterized by multiple numeric chromosome changes. A solid variant of ARMS that is morphologically indistinguishable from ERMS has been described recently. We present two cases with an initial histopathologic diagnosis of ERMS in which the combined findings by cytogenetic, reverse-transcriptase polymerase chain reaction (RT-PCR), and comparative genomic hybridization (CGH) analyses demonstrate that both tumors were in fact the solid variant of ARMS. The cytogenetic analysis of patient 1 revealed a t(2;13)(q35;q14) and the RT-PCR study detected the corresponding PAX3-FKHR chimeric transcript. In patient 2, the cytogenetic finding of multiple trisomies was compatible with the initial histopathologic diagnosis of ERMS, but the finding of a PAX7-FKHR fusion transcript by RT-PCR pointed to the diagnosis of ARMS. Interestingly, the CGH findings of this case reconciled the molecular and cytogenetic data by detecting, in addition to the trisomies, amplification of chromosomal bands 1p36 and 13q14, where the PAX7 and FKHR genes are located, respectively. Our data indicate that this multimodal genetic analysis could be important for the differential diagnosis of these tumors. Furthermore, our findings and previous studies indicate that there are no apparent genetic differences between solid variant and typical ARMS.

Base Sequence↗

PubMed2006

Multimodal fusion imaging ensemble for targeted sentinel lymph node management: initial results of an innovative promising approach for anatomically difficult lymphatic drainage in different tumour entities.

PURPOSE: There are situations where exact identification and localisation of sentinel lymph nodes (SLNs) are very difficult using lymphoscintigraphy, a hand-held gamma probe and vital dye, either a priori or a posteriori. We developed a new method using a simultaneous injection of two lymphotropic agents for exact topographical tomographic localisation and biopsy of draining SLNs. The purpose of this prospective pilot study was to investigate the feasibility and efficacy of this method ensemble. METHODS: Fourteen patients with different tumour entities were enrolled. A mixture of (99m)Tc-nanocolloid and a dissolved superparamagnetic iron oxide was injected interstitially. Dynamic, sequential static lymphoscintigraphy and SPECT served as pathfinders. MR imaging was performed 2 h after injection. SPECT, contrast MRI and, if necessary, CT scan data sets were fused and evaluated with special regard to the topographical location of SLNs. The day after injection, nine patients underwent SLN biopsy and, in the presence of SLN metastasis, an elective lymph node dissection. RESULTS: Twenty-five SLNs were localised in the 14 patients examined. A 100% fusion correlation was achieved in all patients. The anatomical sites of SLNs detected during surgery showed 100% agreement with those localised on the multimodal fusion images. SLNs could be excised in 11/14 patients, six of whom had nodal metastasis. CONCLUSION: Our novel approach of multimodal fusion imaging for targeted SLN management in primary tumours with lymphatic drainage to anatomically difficult regions enables SLN biopsy even in patients with lymphatic drainage to obscure regions. Currently, we are testing its validity in larger patient groups and other tumour entities.

Adult↗

PubMed1997

Integration of functional and anatomical brain images.

This article concerns the integration of functional and anatomical volumetric brain images. Integration consists of two steps: matching or registration, where the images are brought into spatial agreement, and fusion or simultaneous display where the registered multimodal image information is presented in an integrated fashion. Approaches to register multiple images are divided into extrinsic methods based on artificial markers, and intrinsic matching methods based solely on the patient related image data. The various methods are compared by a number of characteristics, which leads to a clear preference for one class of intrinsic methods, viz. voxel-based matching. Furthermore, two- and three-dimensional techniques to display multimodality image information are outlined.

Brain↗

PubMed2026

An Integrative Morphological and Genomic Analysis With a Refined Fluorescence In Situ Hybridization (FISH) Threshold and Novel Kinase Fusions in a Large Asian Cohort of Spitzoid Neoplasms.

Differentiating atypical Spitz tumors (ASTs) from true Spitz melanomas (SMs) and conventional melanomas with spitzoid features (MSFs) remains a formidable diagnostic challenge. Because current molecular epidemiological data are overwhelmingly derived from Caucasian cohorts, the genomic landscape of Asian populations remains largely unexplored. To elucidate the molecular progression landscape and refine the diagnostic criteria, we performed a comprehensive multimodal analysis-integrating histomorphology, immunohistochemistry, multiprobe fluorescence in situ hybridization (FISH), and targeted RNA/DNA-based next-generation sequencing (NGS)-on a cohort of 140 spitzoid neoplasms. This cohort, comprising 126 ASTs, 8 SMs, and 6 MSFs, represents the largest Asian cohort to date. Malignant phenotype strongly correlated with lesional asymmetry, deep atypical mitoses, a sheet-like growth pattern, diffuse preferentially expressed antigen of melanoma positivity, and significant loss of p16 expression (64.3% in SM/MSF vs 9.5% in ASTs; P < .0001). Building upon the established melanoma FISH criteria, we optimized a prognostic threshold of ≥2 FISH abnormalities specifically tailored for spitzoid neoplasms. We demonstrated that isolated single chromosomal aberrations (particularly MYB loss) are relatively stable events that are frequent in indolent ASTs, whereas our refined ≥2 threshold yielded 100% sensitivity and 92.5% specificity for predicting regional lymph node metastasis/local recurrence. Molecularly, NGS identified mutually exclusive initiating driver alterations (comprising kinase fusions and HRAS mutations) in 89.9% of true Spitz neoplasms, a remarkably high prevalence suggesting a distinct genetic background in Asian populations. We also characterized 5 entirely novel kinase fusions (ZNF24::ROS1, PCBP1::ROS1, NUMA1::RET, CBWD1::ALK, and TPR::NTRK1). Furthermore, NGS definitively segregated true Spitz neoplasms from morphological mimics (MSF), which lacked fusions and were driven by canonical genomic alterations of the conventional melanoma pathway. Integrating these genomic landscapes validated a stepwise progression model. Although isolated kinase fusions drove indolent ASTs, malignant SM invariably harbored concurrent pathogenic secondary alterations, demonstrating a profound reliance on CDKN2A/B, TP53, and CDK4 aberrations. Ultimately, we propose an integrated diagnostic algorithm combining morphological evaluation, the refined FISH threshold, and comprehensive NGS profiling, providing a precise, evidence-based framework for pathway classification and clinical management of spitzoid neoplasms.

fluorescence in situ hybridization↗

PubMed2003

Multimodal image fusion in ultrasound-based neuronavigation: improving overview and interpretation by integrating preoperative MRI with intraoperative 3D ultrasound.

OBJECTIVE: We have investigated alternative ways to integrate intraoperative 3D ultrasound images and preoperative MR images in the same 3D scene for visualizing brain shift and improving overview and interpretation in ultrasound-based neuronavigation. MATERIALS AND METHODS: A Multi-Modal Volume Visualizer (MMVV) was developed that can read data exported from the SonoWand neuronavigation system and reconstruct the spatial relationship between the volumes available at any given time during an operation, thus enabling the exploration of new ways to fuse pre- and intraoperative data for planning, guidance and therapy control. In addition, the mismatch between MRI volumes registered to the patient and intraoperative ultrasound acquired from the dura was qualified. RESULTS: The results show that image fusion of intraoperative ultrasound images in combination with preoperative MRI will make perception of available information easier by providing updated (real-time) image information and an extended overview of the operating field during surgery. This approach will assess the degree of anatomical changes during surgery and give the surgeon an understanding of how identical structures are imaged using the different imaging modalities. The present study showed that in 50% of the cases there were indications of brain shift even before the surgical procedure had started. CONCLUSIONS: We believe that image fusion between intraoperative 3D ultrasound and preoperative MRI might improve the quality of the surgical procedure and hence also improve the patient outcome.

Brain↗

PubMed1995

Correlative morphologic and functional imaging for diagnosis, staging and follow up in AIDS: an overview.

The immunocompromised patient can be affected by different opportunistic infections and tumors, that can involve all organ systems, and particularly, the central nervous system, the respiratory system, the gastrointestinal tract. The extreme variability of AIDS presentations requires a specific preparation and cooperation between the different diagnostic imaging specialists and a close collaboration with the clinicians. With AIDS, different morphologic and functional imaging techniques can be used for detection of disease sites, assessment of the extent of the disease and monitoring of disease changes over time and response to treatment. The complexity of AIDS presentations is such that full integration of the complementary information obtained with different techniques could be most useful. Image registration (coregistration, fusion) indicates approaches where precise spatial cross-references are obtained, in order to combine the information acquired with different imaging modalities, in particular by blending morphologic CT/MRI data with functional SPET/PET data. In this paper, an overview of the developments in the field of "image fusion" follows some comments on the results of morphologic and functional assessment of brain, chest and abdominal diseases in the course of AIDS, as illustrative examples of the potential benefits of multimodality image correlation.

AIDS-Related Opportunistic Infections↗

PubMed2001

[Physical and methodological aspects of multimodality imaging and principles of treatment planning in 3D conformal radiotherapy].

The recent evolutions of the imaging modalities, the dose calculation models, the linear accelerators and the portal imaging permit to improve the quality of the conformal radiation therapy treatment planning. With DICOM protocols, the acquired imaging data coming from different modalities are treated by performant image fusion algorithms and yield more precise target volumes and organs at risk. The transformation of the clinical target volumes (CTV) to planning target volumes (PTV) can be realised using advanced probabilistic techniques based on clinical experience. The treatment plans evaluation is based on the dose volume histograms. Their precision and clinical relevance are improved by the multi-modality imaging and the advanced dose calculation models. The introduction of the inverse planning systems permitting to realise modulated intensity radiation therapy generates highly conformal dose distributions. All the previously cited complex techniques require the application of rigorous quality assurance programs.

Dose Fractionation, Radiation↗

PubMed2004

Structure and membrane interaction of the internal fusion peptide of avian sarcoma leukosis virus.

The structure and membrane interaction of the internal fusion peptide (IFP) fragment of the avian sarcoma and leucosis virus (ASLV) envelope glycoprotein was studied by an array of biophysical methods. The peptide was found to induce lipid mixing of vesicles more strongly than the fusion peptide derived from the N-terminal fusion peptide of influenza virus (HA2-FP). It was observed that the helical structure was enhanced in association with the model membranes, particularly in the N-terminal portion of the peptide. According to the infrared study, the peptide inserted into the membrane in an oblique orientation, but less deeply than the influenza HA2-FP. Analysis of NMR data in sodium dodecyl sulfate micelle suspension revealed that Pro13 of the peptide was located near the micelle-water interface. A type II beta-turn was deduced from NMR data for the peptide in aqueous medium, demonstrating a conformational flexibility of the IFP in analogy to the N-terminal FP such as that of gp41. A loose and multimodal self-assembly was deduced from the rhodamine fluorescence self-quenching experiments for the peptide bound to the membrane bilayer. Oligomerization of the peptide and its variants can also be observed in the electrophoretic experiments, suggesting a property in common with other N-terminal FP of class I fusion proteins.

Avian Sarcoma Viruses↗

PubMed2003

Multimodality monitoring of transcranial electric motor and somatosensory-evoked potentials during surgical correction of spinal deformity in patients with cerebral palsy and other neuromuscular disorders.

STUDY DESIGN: This prospective, descriptive study determined the reliability of transcranial electric motor and posterior tibial nerve somatosensory-evoked potentials in children with neuromuscular scoliosis. OBJECTIVE: To assess the applicability of transcranial electric motor and posterior tibial nerve somatosensory-evoked potentials during surgical correction of neuromuscular scoliosis, particularly with cerebral palsy-related deformity. SUMMARY OF BACKGROUND DATA: During corrective spinal surgery for neuromuscular scoliosis, intraoperative multimodality spinal cord monitoring is recommended. There exist conflicting, retrospective studies regarding the reliability of spinal cord monitoring in patients with neuromuscular scoliosis. METHODS: Transcranial electric motor potentials and posterior tibial nerve somatosensory-evoked potentials were monitored in all patients presenting for spinal fusion between 2000 and 2001. Anesthesia was standardized for all patients. RESULTS: There were 68 patients subdivided into two subject groups. Group I consisted of 39 patients with neuromuscular scoliosis associated with cerebral palsy, and Group II consisted of 29 children with neuromuscular scoliosis due to a disease process other than cerebral palsy. Five of the 68 patients had significant amplitude changes in 1 or both monitoring methods during surgery relative to baseline. Of these, one had permanent neurologic deficit despite standard intervention. Somatosensory-evoked potentials were monitored successfully in 82% of the cerebral palsy and 86% of the noncerebral palsy patients. Transcranial electric motor-evoked potentials, on the other hand, were monitorable in 63% of patients with mild or moderate degrees of cerebral palsy and 39% of those with severe involvement. Eighty-six percent of those with noncerebral palsy-related neuromuscular scoliosis had recordable motor-evoked potentials at baseline. CONCLUSION: Both transcranial electric motor and posterior tibial nerve somatosensory-evoked potentials can be monitored reliably in most patients with neuromuscular scoliosis. Those with severe cerebral palsy present the greatest challenge to successful neurophysiologic monitoring.

Adolescent↗

PubMed2004

Hypotension-induced loss of intraoperative monitoring data during surgical correction of scheuermann kyphosis: a case report.

STUDY DESIGN: Presentation of a case report of Scheuermann kyphosis surgical correction. OBJECTIVE: To describe a scenario where both neurogenic mixed evoked potentials and somatosensory-evoked potentials were lost due solely to hypotension before any correction of a kyphotic spinal deformity was performed. SUMMARY OF BACKGROUND DATA: Multimodality intraoperative neurophysiologic monitoring of the spinal cord has become widely utilized during surgical correction of scoliotic and kyphotic deformities. Most spinal surgeries also benefit from a state of hypotension to minimize blood loss, but unchecked and persistent hypotension may lead to inadequate perfusion to the spinal cord, resulting in spinal cord dysfunction noted by diminution of neuromonitoring data. METHODS: An 18-year-old boy with a 95 degrees Scheuermann kyphosis underwent a posterior spinal fusion for correction of his deformity. Intraoperative neurophysiologic monitoring consisting of neurogenic mixed evoked potentials and somatosensory-evoked potentials were performed throughout surgery. RESULTS: After placement of segmental pedicle screw fixation points and multiple osteotomies, before any instrumented correction of the deformity, all lower extremity neuromonitoring data were acutely lost. The surgeon was immediately warned of the data loss, with the mean arterial pressure noted to be 50 mm Hg. The mean arterial pressure was raised with the use of epinephrine bolus and dopamine infusion. Subsequently, all lower extremity neuromonitoring data returned. A Stagnara wake-up test was performed, which the patient passed, and the surgical correction was performed with his pressure maintained on a dopamine infusion. He awakened without neurologic deficits and had an uneventful recovery. CONCLUSIONS: Although a state of mild hypotension may be beneficial to limit blood loss during spinal deformity corrective surgery, acute and/or prolonged hypotension may jeopardize spinal cord vascularity and should be avoided especially during surgical treatment of high-risk deformities such as kyphosis. Early warning by multimodality physiologic neuromonitoring appears to be a useful method to alert surgeons of the potentially devastating problem of hypotension-induced spinal cord dysfunction and allows immediate corrective actions.

Adolescent↗

PubMedDate not supplied

New avenues in 3-D computerised (stereopathological) imaging of breast cancer (review).

Multimodal methods of three-dimensional (3-D) imaging of breast cancer are described. These involve scanning confocal microscopy, using 50 MHz acoustic or near-infrared images, four-view (tetrahedral) radiography and x-ray projection microscopy. Computerised volume data from these techniques can be used to produce three-dimensional images of tissue ranging from 500 microns to approximately 4 mm in thickness. Preliminary findings indicate that stereoscopic images or 3-D computerised reconstructions are capable of advancing the understanding of the structure of ductal carcinoma in situ, lesions simulating microinvasive breast carcinoma, surgical clearance of high-grade calcifying ductal carcinoma in situ, and the 3-D growth patterns of invasive forms of breast carcinoma. In the future computerised image fusion techniques seem likely to be able to take advantage of multimodal imaging of breast cancers, thus correcting primary imaging artefacts, improving robustness, and combining complementary information. In addition, the use of computerised tetrahedral radiography may change the intraoperative assessment of breast cancers, which mostly depend at present upon subsequent laboratory procedures that take days to perform.

Breast Neoplasms↗