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Despite the cerebellum's crucial role in brain functions, its early development, particularly in relation to the cerebrum, remains poorly understood. Here, we examine cerebellocortical connectivity using over 1,000 high-quality resting-state functional MRI scans of children from birth to 60 months. By mapping cerebellar topography with fine temporal detail for the first time, we show the hierarchical and contralateral organization of cerebellocortical connectivity from birth. We observe dynamic shifts in cerebellar network gradients, which become more focal with age while maintaining stable anchor points similar to adults, highlighting the cerebellum's evolving yet stable role in functional integration during early development. Our findings provide the first evidence of cerebellar connections to higher-order networks at birth, which generally strengthen with age, emphasizing the cerebellum's early role in cognitive processing beyond sensory and motor functions. Our study provides insights into early cerebellocortical interactions, reveals functional asymmetry and sexual dimorphism in cerebellar development, and lays the groundwork for future research on cerebellum-related disorders in children., Competing Interests: Competing Interests The authors declare that they have no competing financial interests.

Recent evidence indicates that the organization of the human neocortex is underpinned by smooth spatial gradients of functional connectivity (FC). These gradients provide crucial insight into the relationship between the brain's topographic organization and the texture of human cognition. However, no studies to date have charted how intrinsic FC gradient architecture develops across the entire human lifespan. In this work, we model developmental trajectories of the three primary gradients of FC using a large, high-quality, and temporally-dense functional MRI dataset spanning from birth to 100 years of age. The gradient axes, denoted as sensorimotor-association (SA), visual-somatosensory (VS), and modulation-representation (MR), encode crucial hierarchical organizing principles of the brain in development and aging. By tracking their evolution throughout the human lifespan, we provide the first ever comprehensive low-dimensional normative reference of global FC hierarchical architecture. We observe significant age-related changes in global network features, with global markers of hierarchical organization increasing from birth to early adulthood and decreasing thereafter. During infancy and early childhood, FC organization is shaped by primary sensory processing, dense short-range connectivity, and immature association and control hierarchies. Functional differentiation of transmodal systems supported by long-range coupling drives a convergence toward adult-like FC organization during late childhood, while adolescence and early adulthood are marked by the expansion and refinement of SA and MR hierarchies. While gradient topographies remain stable during late adulthood and aging, we observe decreases in global gradient measures of FC differentiation and complexity from 30 to 100 years. Examining cortical microstructure gradients alongside our functional gradients, we observed that structure-function gradient coupling undergoes differential lifespan trajectories across multiple gradient axes., Competing Interests: Competing Interests The authors declare that they have no competing financial interests.

Non-steroidal anti-inflammatory drugs (NSAIDs) reduced pleural adhesion in animal studies, but its effect on human had not been studied. A retrospective study was carried out for patients with solitary pulmonary nodules without a pre-operative tissue diagnosis positive for malignancy. The impact of the use of NSAIDs after stage one wedge resection was assessed by the degree of pleural adhesions encountered during second-stage, redo completion lobectomy. From April 2016 to March 2022, 50 consecutive patients meeting the inclusion criteria were included, and 44 patients were selected for analysis after exclusion (Treatment group with NSAID: N = 27; Control group without NSAID: N = 17). The preoperative characteristics and the final tumor pathologies were similar between the groups. The use of NSAID was significantly associated with lower risk of severe pleural adhesions and complete pleural symphysis (risk difference = -29%, p = 0.03). After controlling the effect of tumor size and chest drain duration, only the use of NSAID was statistically associated with the lowered risk of severe pleural adhesions and complete pleural symphysis. No statistically significant effects of NSAID on operative time (p = 0.86), blood loss (p = 0.72), and post-operative length of stay (p = 0.72) were demonstrated. In human, NSAIDs attenuated the formation of pleural adhesions after pleural disruptions. Physicians and surgeons should avoid the use of NSAIDs when pleural adhesion formation is the intended treatment outcome., (© 2023. Springer Nature Limited.)

Cephalometric analysis relies on accurate detection of craniomaxillofacial (CMF) landmarks from cone-beam computed tomography (CBCT) images. However, due to the complexity of CMF bony structures, it is difficult to localize landmarks efficiently and accurately. In this paper, we propose a deep learning framework to tackle this challenge by jointly digitalizing 105 CMF landmarks on CBCT images. By explicitly learning the local geometrical relationships between the landmarks, our approach extends Mask R-CNN for end-to-end prediction of landmark locations. Specifically, we first apply a detection network on a down-sampled 3D image to leverage global contextual information to predict the approximate locations of the landmarks. We subsequently leverage local information provided by higher-resolution image patches to refine the landmark locations. On patients with varying non-syndromic jaw deformities, our method achieves an average detection accuracy of 1.38± 0.95mm, outperforming a related state-of-the-art method.

Brain functional connectivity network (FCN) based on resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to identify neuropsychiatric disorders such as autism spectrum disorder (ASD). Most existing FCN-based methods only estimate the correlation between brain regions of interest (ROIs), without exploring more informative higher-level interactions among multiple ROIs which could be beneficial to disease diagnosis. To fully explore the discriminative information provided by different brain networks, a cluster-based multi-view high-order FCN (Ho-FCN) framework is proposed in this paper. Specifically, we first group the functional connectivity (FC) time series into different clusters and compute the multi-order central moment series for the FC time series in each cluster. Then we utilize the correlation of central moment series between different clusters to reveal the high-order FC relationships among multiple ROIs. In addition, to address the phase mismatch issue in conventional FCNs, we also adopt the central moments of the correlation time series as the temporal-invariance features to capture the dynamic characteristics of low-order dynamic FCN (Lo-D-FCN). Experimentalresults on the ABIDE dataset validate that: 1) the proposed multi-view Ho-FCNs is able to explore rich discriminative information for ASD diagnosis; 2) the phase mismatch issue can be well circumvented by using central moments; and 3) the combination of different types of FCNs can significantly improve the diagnostic accuracy of ASD (86.2%).

Objective: To estimate a patient-specific reference bone shape model for a patient with craniomaxillofacial (CMF) defects due to facial trauma., Methods: We proposed an automatic facial bone shape estimation framework using pre-traumatic conventional portrait photos and post-traumatic head computed tomography (CT) scans via a 3D face reconstruction and a deformable shape model. Specifically, a three-dimensional (3D) face was first reconstructed from the patient's pre-traumatic portrait photos. Second, a correlation model between the skin and bone surfaces was constructed using a sparse representation based on the CT images of training normal subjects. Third, by feeding the reconstructed 3D face into the correlation model, an initial reference shape model was generated. In addition, we refined the initial estimation by applying non-rigid surface matching between the initially estimated shape and the patient's post-traumatic bone based on the adaptive-focus deformable shape model (AFDSM). Furthermore, a statistical shape model, built from the training normal subjects, was utilized to constrain the deformation process to avoid overfitting., Results and Conclusion: The proposed method was evaluated using both synthetic and real patient data. Experimental results show that the patient's abnormal facial bony structure can be recovered using our method, and the estimated reference shape model is considered clinically acceptable by an experienced CMF surgeon., Significance: The proposed method is more suitable to the complex CMF defects for CMF reconstructive surgical planning.

Antisocial behavior (ASB) is believed to have neural substrates; however, the association between ASB and functional brain networks remains unclear. The temporal variability of the functional connectivity (or dynamic FC) derived from resting-state functional MRI has been suggested as a useful metric for studying abnormal behaviors including ASB. This is the first study using low-frequency fluctuations of the dynamic FC to unravel potential system-level neural correlates with ASB. Specifically, we individually associated the dynamic FC patterns with the ASB scores (measured by Antisocial Process Screening Device) of the male offenders (age: 23.29 ± 3.36 years) based on machine learning. Results showed that the dynamic FCs were associated with individual ASB scores. Moreover, we found that it was mainly the inter-network dynamic FCs that were negatively associated with the ASB severity. Three major high-order cognitive functional networks and the sensorimotor network were found to be more associated with ASB. We further found that impaired behavior in the ASB subjects was mainly associated with decreased FC dynamics in these networks, which may explain why ASB subjects usually have impaired executive control and emotional processing functions. Our study shows that temporal variation of the FC could be a promising tool for ASB assessment, treatment, and prevention., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Fast and automated image quality assessment (IQA) of diffusion MR images is crucial for making timely decisions for rescans. However, learning a model for this task is challenging as the number of annotated data is limited and the annotation labels might not always be correct. As a remedy, we will introduce in this paper an automatic image quality assessment (IQA) method based on hierarchical non-local residual networks for pediatric diffusion MR images. Our IQA is performed in three sequential stages, i.e., 1) slice-wise IQA, where a nonlocal residual network is first pre-trained to annotate each slice with an initial quality rating (i.e., pass/questionable/fail), which is subsequently refined via iterative semi-supervised learning and slice self-training; 2) volume-wise IQA, which agglomerates the features extracted from the slices of a volume, and uses a nonlocal network to annotate the quality rating for each volume via iterative volume self-training; and 3) subject-wise IQA, which ensembles the volumetric IQA results to determine the overall image quality pertaining to a subject. Experimental results demonstrate that our method, trained using only samples of modest size, exhibits great generalizability, and is capable of conducting rapid hierarchical IQA with near-perfect accuracy.

During the first years of life, the human brain undergoes dynamic spatially-heterogeneous changes, invo- lving differentiation of neuronal types, dendritic arbori- zation, axonal ingrowth, outgrowth and retraction, synaptogenesis, and myelination. To better quantify these changes, this article presents a method for probing tissue microarchitecture by characterizing water diffusion in a spectrum of length scales, factoring out the effects of intra-voxel orientation heterogeneity. Our method is based on the spherical means of the diffusion signal, computed over gradient directions for a set of diffusion weightings (i.e., b -values). We decompose the spherical mean profile at each voxel into a spherical mean spectrum (SMS), which essentially encodes the fractions of spin packets undergoing fine- to coarse-scale diffusion proce- sses, characterizing restricted and hindered diffusion stemming respectively from intra- and extra-cellular water compartments. From the SMS, multiple orientation distribution invariant indices can be computed, allowing for example the quantification of neurite density, microscopic fractional anisotropy ( μ FA), per-axon axial/radial diffusivity, and free/restricted isotropic diffusivity. We show that these indices can be computed for the developing brain for greater sensitivity and specificity to development related changes in tissue microstructure. Also, we demonstrate that our method, called spherical mean spectrum imaging (SMSI), is fast, accurate, and can overcome the biases associated with other state-of-the-art microstructure models.

Accurate segmentation of organs at risk (OARs) from head and neck (H&N) CT images is crucial for effective H&N cancer radiotherapy. However, the existing deep learning methods are often not trained in an end-to-end fashion, i.e., they independently predetermine the regions of target organs before organ segmentation, causing limited information sharing between related tasks and thus leading to suboptimal segmentation results. Furthermore, when conventional segmentation network is used to segment all the OARs simultaneously, the results often favor big OARs over small OARs. Thus, the existing methods often train a specific model for each OAR, ignoring the correlation between different segmentation tasks. To address these issues, we propose a new multi-view spatial aggregation framework for joint localization and segmentation of multiple OARs using H&N CT images. The core of our framework is a proposed region-of-interest (ROI)-based fine-grained representation convolutional neural network (CNN), which is used to generate multi-OAR probability maps from each 2D view (i.e., axial, coronal, and sagittal view) of CT images. Specifically, our ROI-based fine-grained representation CNN (1) unifies the OARs localization and segmentation tasks and trains them in an end-to-end fashion, and (2) improves the segmentation results of various-sized OARs via a novel ROI-based fine-grained representation. Our multi-view spatial aggregation framework then spatially aggregates and assembles the generated multi-view multi-OAR probability maps to segment all the OARs simultaneously. We evaluate our framework using two sets of H&N CT images and achieve competitive and highly robust segmentation performance for OARs of various sizes.

In this paper, we introduce an image quality assessment (IQA) method for pediatric T1- and T2-weighted MR images. IQA is first performed slice-wise using a nonlocal residual neural network (NR-Net) and then volume-wise by agglomerating the slice QA results using random forest. Our method requires only a small amount of quality-annotated images for training and is designed to be robust to annotation noise that might occur due to rater errors and the inevitable mix of good and bad slices in an image volume. Using a small set of quality-assessed images, we pre-train NR-Net to annotate each image slice with an initial quality rating (i.e., pass, questionable, fail), which we then refine by semi-supervised learning and iterative self-training. Experimental results demonstrate that our method, trained using only samples of modest size, exhibit great generalizability, capable of real-time (milliseconds per volume) large-scale IQA with nearperfect accuracy.

Compared to computed tomography (CT), magnetic resonance imaging (MRI) delineation of craniomaxillofacial (CMF) bony structures can avoid harmful radiation exposure. However, bony boundaries are blurry in MRI, and structural information needs to be borrowed from CT during the training. This is challenging since paired MRI-CT data are typically scarce. In this paper, we propose to make full use of unpaired data, which are typically abundant, along with a single paired MRI-CT data to construct a one-shot generative adversarial model for automated MRI segmentation of CMF bony structures. Our model consists of a cross-modality image synthesis sub-network, which learns the mapping between CT and MRI, and an MRI segmentation sub-network. These two sub-networks are trained jointly in an end-to-end manner. Moreover, in the training phase, a neighbor-based anchoring method is proposed to reduce the ambiguity problem inherent in cross-modality synthesis, and a feature-matching-based semantic consistency constraint is proposed to encourage segmentation-oriented MRI synthesis. Experimental results demonstrate the superiority of our method both qualitatively and quantitatively in comparison with the state-of-the-art MRI segmentation methods.

The fusion of complementary information contained in multi-modality data [e.g., magnetic resonance imaging (MRI), positron emission tomography (PET), and genetic data] has advanced the progress of automated Alzheimer's disease (AD) diagnosis. However, multi-modality based AD diagnostic models are often hindered by the missing data, i.e., not all the subjects have complete multi-modality data. One simple solution used by many previous studies is to discard samples with missing modalities. However, this significantly reduces the number of training samples, thus leading to a sub-optimal classification model. Furthermore, when building the classification model, most existing methods simply concatenate features from different modalities into a single feature vector without considering their underlying associations. As features from different modalities are often closely related (e.g., MRI and PET features are extracted from the same brain region), utilizing their inter-modality associations may improve the robustness of the diagnostic model. To this end, we propose a novel latent representation learning method for multi-modality based AD diagnosis. Specifically, we use all the available samples (including samples with incomplete modality data) to learn a latent representation space. Within this space, we not only use samples with complete multi-modality data to learn a common latent representation, but also use samples with incomplete multi-modality data to learn independent modality-specific latent representations. We then project the latent representations to the label space for AD diagnosis. We perform experiments using 737 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and the experimental results verify the effectiveness of our proposed method.

Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their pipelines, implementations, as well as source codes. We further discuss limitations and possible future directions. We hope the dataset in iSeg-2017 and this review article could provide insights into methodological development for the community.

In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive Impairment (MCI), from normal aging subjects. Multimodality neuroimaging data such as MRI and PET provide valuable insights into brain abnormalities, while genetic data such as single nucleotide polymorphism (SNP) provide information about a patient's AD risk factors. When these data are used together, the accuracy of AD diagnosis may be improved. However, these data are heterogeneous (e.g., with different data distributions), and have different number of samples (e.g., with far less number of PET samples than the number of MRI or SNPs). Thus, learning an effective model using these data is challenging. To this end, we present a novel three-stage deep feature learning and fusion framework, where deep neural network is trained stage-wise. Each stage of the network learns feature representations for different combinations of modalities, via effective training using the maximum number of available samples. Specifically, in the first stage, we learn latent representations (i.e., high-level features) for each modality independently, so that the heterogeneity among modalities can be partially addressed, and high-level features from different modalities can be combined in the next stage. In the second stage, we learn joint latent features for each pair of modality combination by using the high-level features learned from the first stage. In the third stage, we learn the diagnostic labels by fusing the learned joint latent features from the second stage. To further increase the number of samples during training, we also use data at multiple scanning time points for each training subject in the dataset. We evaluate the proposed framework using Alzheimer's disease neuroimaging initiative (ADNI) dataset for AD diagnosis, and the experimental results show that the proposed framework outperforms other state-of-the-art methods., (© 2018 Wiley Periodicals, Inc.)

Discriminative methods commonly produce models with relatively good generalization abilities. However, this advantage is challenged in real-world applications (e.g., medical image analysis problems), in which there often exist outlier data points (sample-outliers) and noises in the predictor values (feature-noises). Methods robust to both types of these deviations are somewhat overlooked in the literature. We further argue that denoising can be more effective, if we learn the model using all the available labeled and unlabeled samples, as the intrinsic geometry of the sample manifold can be better constructed using more data points. In this paper, we propose a semi-supervised robust discriminative classification method based on the least-squares formulation of linear discriminant analysis to detect sample-outliers and feature-noises simultaneously, using both labeled training and unlabeled testing data. We conduct several experiments on a synthetic, some benchmark semi-supervised learning, and two brain neurodegenerative disease diagnosis datasets (for Parkinson's and Alzheimer's diseases). Specifically for the application of neurodegenerative diseases diagnosis, incorporating robust machine learning methods can be of great benefit, due to the noisy nature of neuroimaging data. Our results show that our method outperforms the baseline and several state-of-the-art methods, in terms of both accuracy and the area under the ROC curve.

Brain-wide and genome-wide association (BW-GWA) study is presented in this paper to identify the associations between the brain imaging phenotypes (i.e., regional volumetric measures) and the genetic variants [i.e., single nucleotide polymorphism (SNP)] in Alzheimer's disease (AD). The main challenges of this study include the data heterogeneity, complex phenotype-genotype associations, high-dimensional data (e.g., thousands of SNPs), and the existence of phenotype outliers. Previous BW-GWA studies, while addressing some of these challenges, did not consider the diagnostic label information in their formulations, thus limiting their clinical applicability. To address these issues, we present a novel joint projection and sparse regression model to discover the associations between the phenotypes and genotypes. Specifically, to alleviate the negative influence of data heterogeneity, we first map the genotypes into an intermediate imaging-phenotype-like space. Then, to better reveal the complex phenotype-genotype associations, we project both the mapped genotypes and the original imaging phenotypes into a diagnostic-label-guided joint feature space, where the intraclass projected points are constrained to be close to each other. In addition, we use l 2,1 -norm minimization on both the regression loss function and the transformation coefficient matrices, to reduce the effect of phenotype outliers and also to encourage sparse feature selections of both the genotypes and phenotypes. We evaluate our method using AD neuroimaging initiative dataset, and the results show that our proposed method outperforms several state-of-the-art methods in term of the average root-mean-square error of genome-to-phenotype predictions. Besides, the associated SNPs and brain regions identified in this study have also been shown in the previous AD-related studies, thus verifying the effectiveness and potential of our proposed method in AD pathogenesis study.

Autism spectrum disorder (ASD) is a wide range of disabilities that cause life-long cognitive impairment and social, communication, and behavioral challenges. Early diagnosis and medical intervention are important for improving the life quality of autistic patients. However, in the current practice, diagnosis often has to be delayed until the behavioral symptoms become evident during childhood. In this study, we demonstrate the feasibility of using machine learning techniques for identifying high-risk ASD infants at as early as six months after birth. This is based on the observation that ASD-induced abnormalities in white matter (WM) tracts and whole-brain connectivity have already started to appear within 24 months after birth. In particular, we propose a novel multikernel support vector machine classification framework by using the connectivity features gathered from WM connectivity networks, which are generated via multiscale regions of interest (ROIs) and multiple diffusion statistics such as fractional anisotropy, mean diffusivity, and average fiber length. Our proposed framework achieves an accuracy of 76% and an area of 0.80 under the receiver operating characteristic curve (AUC), in comparison to the accuracy of 70% and the AUC of 70% provided by the best single-parameter single-scale network. The improvement in accuracy is mainly due to the complementary information provided by multiparameter multiscale networks. In addition, our framework also provides the potential imaging connectomic markers and an objective means for early ASD diagnosis., (© 2015 Wiley Periodicals, Inc.)

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images). Experimental results show very promising performance of our proposed MLPD method.

We report a case of a large right atrial hemangioma masquerading as a clinical presentation of obstructive sleep apnea syndrome (OSAS) in a 57-year-old man, who was wrongfully treated with nocturnal continuous positive airway pressure (CPAP) prior to surgical consultation. The exact diagnosis was made during the investigation for his cardiac arrhythmia. A large right atrial tumor obstructing the tricuspid valve intermittently was noted on cardiac echocardiography. His symptoms became worse when the patient was lying flat. Tumor excision under cardiopulmonary bypass was carried out, which confirmed the preoperative finding of cardiac hemangioma. The patient underwent uneventful recovery postoperatively and the symptoms of OSAS settled after surgery. To our knowledge, this is the first reported case of right atrial tumor masquerading as a clinical presentation of OSAS.

Background: Video-assisted thoracoscopic surgery (VATS) major lung resection for lung cancer has been an important part of thoracic surgical training program in our institution. In this study, we compared the results of VATS major lung resection performed by surgical trainees with those performed by experienced thoracic surgeons with specialist interest in VATS., Methods: From January 2002 to October 2006, the clinical data of 111 consecutive patients scheduled for VATS major lung resection were prospectively entered into the computerized clinical management system of the local health authority; these include patient demographics, comorbidity, operating time, postoperative complications, and outcome. We retrospectively compared the data of patients who were operated on by trainees with those who were operated on by experienced VATS surgeons., Results: One hundred and eleven patients with clinical stage I and II lung cancer underwent VATS major lung resection. Fifty-one (46%) of the procedures were performed by consultant surgeons and 60 VATS lung resections (54%) were performed by supervised trainees. Patients' demography and risk factors were comparable between the two groups. Trainees spent more time in performing the operation as compared with experienced VATS surgeons (mean operating time 162 minutes, p = 0.01). There was no significant difference in intraoperative or postoperative complications and outcomes between the two groups., Conclusions: Video-assisted thoracic surgery major lung resection for early stage nonsmall-cell lung cancer can be taught to residents who work under the supervision of experienced VATS surgeons. Video-assisted thoracic surgery major lung resection for lung cancer should be an integral part of thoracic surgical training program.

Objective: Studies in other surgical specialties have suggested that pre-emptive wound infiltration using a local anesthetic may reduce post-operative pain. We report the first randomized trial to assess the use of pre-emptive local anesthesia in video-assisted thoracic surgery (VATS)., Method: Thirty-one consecutive patients undergoing bilateral needlescopic VATS sympathectomy for palmar hyperhidrosis were studied prospectively. Each patient acted as their own control. For each patient, one side was randomized to receive 10ml 0.5% bupivicaine injected to the port sites before incision, and the contralateral control side to receive 10ml saline. Pain severity on a visual analog scale (VAS) was recorded for each chest side at 4h, 1 day and 7 days following surgery. All patients were blinded to the results of randomization throughout the study., Results: Follow up was complete for all patients. At 7 days after surgery, wound pain was significantly reduced by pre-emptive local anesthesia, with 10 (62.5%) of the 16 patients having residual pain reporting less pain on the pre-treated side (p=0.039). There was a trend for reduced pain on the pre-treated side at the other time points. Pain reduction by pre-emptive local anesthesia was not correlated with any demographic or clinical variable. Chest wall paresthesia distinct from localized wound pain was noted by six patients (19.4%), but was not reduced by pre-emptive local anesthesia. Overall, the post-operative discomforts felt by the patients after needlescopic VATS were mild, and did not cause significant functional disturbances., Conclusion: Pre-emptive wound infiltration with a local anesthetic may reduce post-operative wound pain in needlescopic VATS procedures.

Objective: The pain following thoracic surgery and trauma is often refractory to conventional analgesic strategies. However, it shares key characteristics with neuropathic pain which gabapentin, an anticonvulsant, has been proven to effectively treat. To our knowledge, this is the first prospective study assessing the use of gabapentin in cardiothoracic surgery patients., Methods: Gabapentin was prescribed to 60 consecutive out-patients with refractory pain persisting at four weeks or more after thoracic surgery or trauma. Follow-up of 45 patients (75%) was performed for a median of 21 months (range: 12-28), and clinical data collected prospectively. The mean age of these patients was 51.6 years (range 22-83). Of these 45 patients, 22 had received video-assisted thoracic surgery (VATS), 8 had received thoracotomy, 3 had received median sternotomy, and 12 were treated for blunt chest trauma., Results: The mean duration of pre-treatment refractory pain was 5.76 months (range 1-62). The mean duration of gabapentin use was 21.9 weeks (range 1-68). No deaths or major complications were encountered. Minor side effects-mostly somnolence and dizziness-occurred in 18 patients (40.0%), causing 3 patients (6.7%) to discontinue gabapentin. Overall, 33 patients (73.3%) noted reduction of pain. Chest wall paresthesia distinguishable from wound pain was relieved in 24 (75.0%) of 32 affected patients. Severe initial pain was significantly correlated with pain relief using gabapentin (p=0.009). No other demographical or clinical variable correlated with benefit or side effects. Satisfaction with gabapentin use was expressed by 40 patients (88.9%). Side effects were not a source of dissatisfaction in any patient., Conclusions: Gabapentin appears safe and well tolerated when used for persistent post-operative and post-traumatic pain in thoracic surgery patients, although minor side effects do occur. Gabapentin may relieve refractory chest wall pain in some of these patients, particularly those with more severe pain. Further studies are warranted to define the role of gabapentin in cardiothoracic surgical practice.

Objective: Chest wall paresthesia is a reported sequela of thoracotomy and Video-Assisted Thoracic Surgery (VATS) which is distinct from wound pain. Although needlescopic VATS confers less post-operative pain and better cosmesis, the incidence of paresthesia after needlescopic VATS has not been quantified., Methods: For homogeneity of the patient cohort, we studied 50 patients who received bilateral needlescopic VATS sympathectomy (T2-T4 excision) for palmar hyperhidrosis using 2 or 3 mm instruments during a 36-month period at a single institute. A standard questionnaire was administered by telephone interview, with 34 patents responding (68.0%). The median post-operative observation time was 16.5 months (range: 10-40 months). Collected data were compared with a historical group who received conventional VATS using 10 mm ports., Results: Paresthetic discomfort distinguishable from wound pain was described by 17 patients (50.0%). The most common descriptions were of 'bloating' (41.2%), 'pins and needles' (35.3%), or 'numbness' (23.5%) in the chest wall. The paresthesia resolved in less than two months in 12 patients (70.6%), but was still felt for over 12 months in three patients (17.6%). Post-operative paresthesia and pain did not impact on patient satisfaction with the surgery, whereas compensatory hyperhidrosis in 24 patients (70.6%) did (P=0.001). The rates and characteristics of the paresthesia following needlescopic VATS are similar to those observed after conventional VATS., Conclusions: Chest wall paresthesia affects a significant but previously overlooked proportion of patients following needlescopic VATS, but has minimal impact on post-operative satisfaction. Needlescopic VATS offers no apparent advantage over conventional VATS with regard to paresthesia.

Coronary artery aneurysm requiring surgery is rare. We report a case of a ruptured giant coronary artery aneurysm due to Kawasaki vasculitis which presented with cardiac arrest and was successfully treated by emergency coronary artery bypass grafting. The controversies surrounding the management of this disease are also discussed.

Objective: Thoracic injuries are among the most severe forms of trauma and also a leading cause of morbidity and mortality. Video Assisted Thoracic Surgery (VATS) has recently provided an alternative method to simultaneously diagnose and manage patients sustaining chest injuries. We analyze our experience with VATS in the setting of thoracic trauma detailing indications for exploration, procedures performed and results of surgery., Methods: A 6-year single institution review of patients undergoing VATS due to injuries sustained from both blunt and penetrating chest trauma at a Level I trauma center and university teaching hospital. Comparisons were made between groups of blunt and penetrating trauma as to Injury Severity Score (ISS), presence of extra-thoracic injuries, initial thoracostomy drainage and length of postoperative stay., Results: VATS was successfully performed in 19 consecutive patients without conversion to thoracotomy. Indications for exploration included acute hemorrhage, retained hemothorax, suspected diaphragmatic injuries (DI), suspected cardiac injury, intra-thoracic foreign body, persistent airleak and chronic empyema. Procedures performed consisted of evacuation of retained hemothorax, hemostasis of intra-thoracic bleeders, repair of DI, wedge lung resections and decortication. Mean postoperative length of stay was 5.86 days. There were no morbidities. One patient with severe intra-abdominal injuries expired on the first postoperative day., Conclusion: In hemodynamically stable patients with thoracic injuries, VATS provides an accurate assessment of intra-thoracic organ injury and can be utilized to definitively and effectively manage injuries sustained as a result of blunt or penetrating thoracic trauma. VATS should be used with caution in patients sustaining severe and life threatening intra-abdominal injuries.

Background: We determined the feasibility, safety, and short-term efficacy of bronchoscopic placement of a one-way endobronchial valve in selected bronchopulmonary segments as an alternative to surgical lung volume reduction., Methods: A total of 21 patients with incapacitating emphysema who underwent this procedure were studied. All patients had placement of the endobronchial valves into the most emphysematous lung segments. We recorded any major complications or deaths attributed to the procedure and analyzed (1) improvements in the spirometric and functional parameters and quality of life and (2) the radiologic changes compared with the baseline data at 30 and 90 days., Results: A total of 20 patients had complete follow-up data. There was no mortality in the group studied. The forced expiratory volume at 1 second, forced expiratory volume at 1 second (percentage of predicted), forced vital capacity, and forced vital capacity (percentage of predicted) all improved significantly at 90 days (0.73 +/- 0.26 L vs 0.92 +/- 0.34 L [P =.009]; 33.3% +/- 11.9% vs 42.2% +/- 15.0% [P =.006]; 1.94 +/- 0.62 L vs 2.25 +/- 0.61 L [P =.015]; and 63.3% +/- 17.6% vs 73.9% +/- 17.1% [P =.012], respectively). The 6-minute walking distance improved at 30 and 90 days (251.6 +/- 100.2 m vs 306.3 +/- 112.3 m and 322.3 +/- 129.7 m; P =.012 and P =.003). The results of the 36-Item Short-Form Health Survey and the St George Respiratory Questionnaire showed significant improvements at 90 days. The Medical Research Council dyspnea grade also improved significantly at 30 and at 90 days (P =.006 and P =.003, respectively)., Conclusions: Endobronchial valve placement is a safe procedure, with significant short-term improvements in functional status, quality of life, and relief of dyspnea in selected patients with emphysema. A larger study with long-term follow-up is therefore warranted.

Objective: On-pump beating heart coronary artery surgery provides the opportunity to examine the isolated effect of cardiopulmonary bypass. This prospective randomized study compares the early clinical outcomes and inflammatory response of patients undergoing elective on-pump and off-pump beating heart coronary artery bypass grafting., Method and Patients: Thirty-seven consecutive patients undergoing elective coronary artery bypass grafting were recruited from a pool of 73 patients, with 19 patients randomized to on-pump beating heart surgery and 18 patients to off-pump coronary bypass surgery. Intraoperative events and postoperative outcomes were recorded. Plasma levels of interleukin-6, interleukin-8, and interleukin-10, tumor necrosis factor-alpha, and vascular cell adhesion molecule-1 were measured before the operation, intraoperatively, after the operation, and 4, 24, and 48 hours thereafter., Results: There was no significant difference in clinical outcomes between the 2 groups. The operating time was longer and consumption of platelets was greater for the on-pump beating heart group. There was no postoperative mortality or major complication in either group. There was significant elevation in the levels of interleukin-6, interleukin-8, and interleukin-10 and tumor necrosis factor-alpha during and immediately after the operations in the on-pump beating heart group when compared with the off-pump group. Levels of interleukin-8 (P =.01) and tumor necrosis factor-alpha (P =.0004) remained significantly elevated 4 hours after the operation in the on-pump beating heart group. The level of vascular adhesion molecule dropped significantly during the operation but was elevated 4 hours (P =.026) after the operation in the on-pump beating heart group., Conclusion: The use of cardiopulmonary bypass alone without global myocardial ischemia secondary to aortic crossclamping and cardioplegic cardiac arrest can trigger intense inflammatory responses.

We report the case of a 64-year-old woman who presented with massive hemoptysis. She was found to be bleeding from a pulmonary arteriovenous malformation in the right middle lobe, which had a peculiar blood supply from the right internal mammary artery. Video-assisted thoracic surgery lobectomy was successfully performed for this condition. Limitations of embolization as a treatment modality for this condition are discussed.