Ischemia-reperfusion injury in a salvaged penumbra: Longitudinal high-tesla perfusion magnetic resonance imaging in a rat model.

INTRODUCTION: Although ischemia-reperfusion (I/R) injury varies between cortical and subcortical regions, its effects on specific regions remain unclear. In this study, we used various magnetic resonance imaging (MRI) techniques to examine the spatiotemporal dynamics of I/R injury within the salvaged ischemic penumbra (IP) and reperfused ischemic core (IC) of a rodent model, with the aim of enhancing therapeutic strategies by elucidating these dynamics. MATERIALS AND METHODS: A total of 17 Sprague-Dawley rats were subjected to 1 h of transient middle cerebral artery occlusion with a suture model. MRI, including diffusion tensor imaging (DTI), T2-weighted imaging, perfusion-weighted imaging, and T1 mapping, was conducted at multiple time points for up to 5 days during the I/R phases. The spatiotemporal dynamics of blood-brain barrier (BBB) modifications were characterized through changes in T1 within the IP and IC regions and compared with mean diffusivity (MD), T2, and cerebral blood flow. RESULTS: During the I/R phases, the MD of the IC initially decreased, normalized after recanalization, decreased again at 24 h, and peaked on day 5. By contrast, the IP remained relatively stable. Both the IP and IC exhibited hyperperfusion, with the IP reaching its peak at 24 h, followed by resolution, whereas hyperperfusion was maintained in the IC until day 5. Despite hyperperfusion, the IP maintained an intact BBB, whereas the IC experienced persistent BBB leakage. At 24 h, the IC exhibited an increase in the T2 signal, corresponding to regions exhibiting BBB disruption at 5 days. CONCLUSIONS: Hyperperfusion and BBB impairment have distinct patterns in the IP and IC. Quantitative T1 mapping may serve as a supplementary tool for the early detection of malignant hyperemia accompanied by BBB leakage, aiding in precise interventions after recanalization. These findings underscore the value of MRI markers in monitoring ischemia-specific regions and customizing therapeutic strategies to improve patient outcomes.

Estimating the volume of penumbra in rodents using DTI and stack-based ensemble machine learning framework.

BACKGROUND: This study investigates the potential of diffusion tensor imaging (DTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion-diffusion mismatch (PDM), utilizing a stack-based ensemble machine learning (ML) approach with enhanced explainability. METHODS: Sixteen male rats were subjected to middle cerebral artery occlusion. The penumbra was identified using PDM at 30 and 90 min after occlusion. We used 11 DTI-derived metrics and 14 distance-based features to train five voxel-wise ML models. The model predictions were integrated using stack-based ensemble techniques. ML-estimated and PDM-defined PVs were compared to evaluate model performance through volume similarity assessment, the Pearson correlation analysis, and Bland-Altman analysis. Feature importance was determined for explainability. RESULTS: In the test rats, the ML-estimated median PV was 106.4 mL (interquartile range 44.6-157.3 mL), whereas the PDM-defined median PV was 102.0 mL (52.1-144.9 mL). These PVs had a volume similarity of 0.88 (0.79-0.96), a Pearson correlation coefficient of 0.93 (p < 0.001), and a Bland-Altman bias of 2.5 mL (2.4% of the mean PDM-defined PV), with 95% limits of agreement ranging from -44.9 to 49.9 mL. Among the features used for PV prediction, the mean diffusivity was the most important feature. CONCLUSIONS: Our study confirmed that PV can be estimated using DTI metrics with a stack-based ensemble ML approach, yielding results comparable to the volume defined by the standard PDM. The model explainability enhanced its clinical relevance. Human studies are warranted to validate our findings. RELEVANCE STATEMENT: The proposed DTI-based ML model can estimate PV without the need for contrast agent administration, offering a valuable option for patients with kidney dysfunction. It also can serve as an alternative if perfusion map interpretation fails in the clinical setting. KEY POINTS: • Penumbral volume can be estimated by DTI combined with stack-based ensemble ML. • Mean diffusivity was the most important feature used for predicting penumbral volume. • The proposed approach can be beneficial for patients with kidney dysfunction.

A prospective reappraisal of motor outcome prediction in patients with acute stroke by using atlas-based diffusion tensor imaging biomarkers.

BACKGROUND: Diffusion tensor imaging (DTI) biomarkers can be used to quantify microstructural changes in the cerebral white matter (WM) following injury. OBJECTIVES: This prospective single-center study aimed to evaluate whether atlas-based DTI-derived metrics obtained within 1 week after stroke can predict the motor outcome at 3 months. METHODS: Forty patients with small acute stroke (2-7 days after onset) involving the corticospinal tract were included. Each patient underwent magnetic resonance imaging (MRI) within 1 week and at 3 months after stroke, and the changes based on DTI-derived metrics were compared by performing WM tract atlas-based quantitative analysis. RESULTS: A total of 40 patients were included, with median age 63.5 years and a majority of males (72.5%). Patients were classified into good-prognosis group (mRS 0-2, n = 27) and poor-prognosis group (mRS 3-5, n = 13) by outcome. The median (25th-75th percentile) of MD (0.7 (0.6-0.7) vs. 0.7 (0.7-0.8); p = 0.049) and AD (0.6 (0.5, 0.7) vs. 0.7 (0.6, 0.8); p = 0.023) ratios within 1 week were significantly lower in the poor-prognosis group compared to the good-prognosis group. The ROC curve of the combined DTI-derived metrics model showed comparable Youden index (65.5% vs. 58.4%-65.4%) and higher specificity (96.3% vs. 69.2%-88.5%) compared to clinical indexes. The area under the ROC curve of the combined DTI-derived metrics model is comparable to those of the clinical indexes (all p > 0.1) and higher than those of the individual DTI-derived metrics parameters. CONCLUSIONS: Atlas-based DTI-derived metrics at acute stage provide objective information for prognosis prediction of patients with ischemic or lacunar stroke.

Thalamocortical coherence predicts persistent postconcussive symptoms.

The pathogenetic mechanism of persistent post-concussive symptoms (PCS) following concussion remains unclear. Thalamic damage is known to play a role in PCS prolongation while the evidence and biomarkers that trigger persistent PCS have never been elucidated. We collected longitudinal neuroimaging and behavior data from patients and rodents after concussion, complemented with rodents' histological staining data, to unravel the early biomarkers of persistent PCS. Diffusion tensor imaging (DTI) were acquired to investigated the thalamic damage, while quantitative thalamocortical coherence was derived through resting-state functional MRI for evaluating thalamocortical functioning and predicting long-term behavioral outcome. Patients with prolonged symptoms showed abnormal DTI-derived indices at the boundaries of bilateral thalami (peri-thalamic regions). Both patients and rats with persistent symptoms demonstrated enhanced thalamocortical coherence between different thalamocortical circuits, which disrupted thalamocortical multifunctionality. In rodents, the persistent DTI abnormalities were validated in thalamic reticular nucleus (TRN) through immunohistochemistry, and correlated with enhanced thalamocortical coherence. Strong predictive power of these coherence biomarkers for long-term PCS was also validated using another patient cohort. Postconcussive events may begin with persistent TRN injury, followed by disrupted thalamocortical coherence and prolonged PCS. Functional MRI-based coherence measures can be surrogate biomarkers for early prediction of long-term PCS.

Automatic segmentation and radiomic texture analysis for osteoporosis screening using chest low-dose computed tomography.

OBJECTIVE: This study developed a diagnostic tool combining machine learning (ML) segmentation and radiomic texture analysis (RTA) for bone density screening using chest low-dose computed tomography (LDCT). METHODS: A total of 197 patients who underwent LDCT followed by dual-energy X-ray absorptiometry were analyzed. First, an autosegmentation model was trained using LDCT to delineate the thoracic vertebral body (VB). Second, a two-level classifier was developed using radiomic features extracted from VBs for the hierarchical pairwise classification of each patient's bone status. All the patients were initially classified as either normal or abnormal, and all patients with abnormal bone density were then subdivided into an osteopenia group and an osteoporosis group. The performance of the classifier was evaluated through fivefold cross-validation. RESULTS: The model for automated VB segmentation achieved a Sorenson-Dice coefficient of 0.87 ± 0.01. Furthermore, the area under the receiver operating characteristic curve scores for the two-level classifier were 0.96 ± 0.01 for detecting abnormal bone density (accuracy = 0.91 ± 0.02; sensitivity = 0.93 ± 0.03; specificity = 0.89 ± 0.03) and 0.98 ± 0.01 for distinguishing osteoporosis (accuracy = 0.94 ± 0.02; sensitivity = 0.95 ± 0.03; specificity = 0.93 ± 0.03). The testing prediction accuracy levels for the first- and second-level classifiers were 0.92 ± 0.04 and 0.94 ± 0.05, respectively. The overall testing prediction accuracy of our method was 0.90 ± 0.05. CONCLUSION: The combination of ML segmentation and RTA for automated bone density prediction based on LDCT scans is a feasible approach that could be valuable for osteoporosis screening during lung cancer screening. KEY POINTS: • This study developed an automatic diagnostic tool combining machine learning-based segmentation and radiomic texture analysis for bone density screening using chest low-dose computed tomography. • The developed method enables opportunistic screening without quantitative computed tomography or a dedicated phantom. • The developed method could be integrated into the current clinical workflow and used as an adjunct for opportunistic screening or for patients who are ineligible for screening with dual-energy X-ray absorptiometry.

Identification of Differentially Expressed Genes in Different Glioblastoma Regions and Their Association with Cancer Stem Cell Development and Temozolomide Response.

The molecular heterogeneity of gene expression profiles of glioblastoma multiforme (GBM) are the most important prognostic factors for tumor recurrence and drug resistance. Thus, the aim of this study was to identify potential target genes related to temozolomide (TMZ) resistance and GBM recurrence. The genomic data of patients with GBM from The Cancer Genome Atlas (TCGA; 154 primary and 13 recurrent tumors) and a local cohort (29 primary and 4 recurrent tumors), samples from different tumor regions from a local cohort (29 tumor and 25 peritumoral regions), and Gene Expression Omnibus data (GSE84465, single-cell RNA sequencing; 3589 cells) were included in this study. Critical gene signatures were identified based an analysis of differentially expressed genes (DEGs). DEGs were further used to evaluate gene enrichment levels among primary and recurrent GBMs and different tumor regions through gene set enrichment analysis. Protein-protein interactions (PPIs) were incorporated into gene regulatory networks to identify the affected metabolic pathways. The enrichment levels of 135 genes were identified in the peritumoral regions as being risk signatures for tumor recurrence. Fourteen genes (DVL1, PRKACB, ARRB1, APC, MAPK9, CAMK2A, PRKCB, CACNA1A, ERBB4, RASGRF1, NF1, RPS6KA2, MAPK8IP2, and PPM1A) derived from the PPI network of 135 genes were upregulated and involved in the regulation of cancer stem cell (CSC) development and relevant signaling pathways (Notch, Hedgehog, Wnt, and MAPK). The single-cell data analysis results indicated that 14 key genes were mainly expressed in oligodendrocyte progenitor cells, which could produce a CSC niche in the peritumoral region. The enrichment levels of 336 genes were identified as biomarkers for evaluating TMZ resistance in the solid tumor region. Eleven genes (ARID5A, CDC42EP3, CDKN1A, FLT3, JUNB, MAP2K3, MYBPC2, RGS14, RNASEK, TBC1D30, and TXNDC11) derived from the PPI network of 336 genes were upregulated and may be associated with a high risk of TMZ resistance; these genes were identified in both the TCGA and local cohorts. Furthermore, the expression patterns of ARID5A, CDKN1A, and MAP2K3 were identical to the gene signatures of TMZ-resistant cell lines. The identified enrichment levels of the two gene sets expressed in tumor and peritumoral regions are potentially helpful for evaluating TMZ resistance in GBM. Moreover, these key genes could be used as biomarkers, potentially providing new molecular strategies for GBM treatment.

Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer.

Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for patients with non-small-cell lung cancer (NSCLC). We proposed a machine learning-based model for feature selection and prediction of EGFR and KRAS mutations in patients with NSCLC by including the least number of the most semantic radiomics features. We included a cohort of 161 patients from 211 patients with NSCLC from The Cancer Imaging Archive (TCIA) and analyzed 161 low-dose computed tomography (LDCT) images for detecting EGFR and KRAS mutations. A total of 851 radiomics features, which were classified into 9 categories, were obtained through manual segmentation and radiomics feature extraction from LDCT. We evaluated our models using a validation set consisting of 18 patients derived from the same TCIA dataset. The results showed that the genetic algorithm plus XGBoost classifier exhibited the most favorable performance, with an accuracy of 0.836 and 0.86 for detecting EGFR and KRAS mutations, respectively. We demonstrated that a noninvasive machine learning-based model including the least number of the most semantic radiomics signatures could robustly predict EGFR and KRAS mutations in patients with NSCLC.

Added Value of Rescue Devices in Intra-Arterial Thrombectomy: When Should We Apply Them?

Introduction: Recent trials have demonstrated the superior efficacy of mechanical thrombectomy over other medical treatments for acute ischemic stroke; however, not every large vessel occlusion (LVO) can be recanalized using a single thrombectomy device. Rescue devices were proved to increase the reperfusion rate, but the efficacy is unclear. Objective: In this retrospective study, we evaluated the efficacy of rescue therapy in different locations of LVO. Methods: We analyzed the outcomes of mechanical thrombectomy from a prospective registry of consecutive 82 patients in Taipei Medical University Hospital. The reperfusion rate and the functional outcome were compared in patients who received first-line therapy only and patients who need rescue therapy. Results: An 84.1% reperfusion rate was achieved in our cohort. We applied first-line stent retriever (SR) treatment in 6 patients, among which 4 (66.6%) achieved successful reperfusion. We applied a direct-aspiration first-pass technique (ADAPT) as the first-line treatment in 76 patients, among which 46 (60.5%) achieved successful reperfusion. Successful reperfusion could not be achieved in 30 cases (39.5%); therefore, we applied a second-line rescue SR for 28 patients, and reperfusion was established in 18 (64.3%) of them. These results revealed that the LVO in anterior circulation has a higher chance to respond to SR rescue therapy than posterior circulation lesions (68 vs. 33.3%, P < 0.001). Patients who received only first-line therapy exhibited significantly better functional outcomes than those who were also treated with rescue SR therapy (41.2 vs. 16.7%, P = 0.001). In addition, patients with LVO in the anterior circulation were found to have a higher probability of achieving functional independence than patients with posterior circulation lesions (10.7 vs. 0.0%, P < 0.001). The adjusted multivariate analysis revealed that successful reperfusion and treatment type (first-line or rescue therapy) were significantly related to a modified Rankin Scale (mRS) score at 90 days. Conclusion: This study reveals that rescue SR therapy improves the reperfusion rate. Patients who require rescue SR therapy have a lower likelihood of functional independence. LVO in the anterior circulation responds better to rescue SR therapy and results in better functional outcomes than posterior circulation lesions.

Diffusion Tensor Imaging of the Spinal Cord.

Spinal cord often is regarded as one of the last territories in the central nervous system where diffusion tensor imaging (DTI) can be used to probe white matter architecture. This article reviews current progress in spinal cord DTI, starting with anatomic properties and technical challenges that make spinal cord DTI a difficult task. Several possibilities offered by advanced pulse sequences that might overcome the difficulties are addressed, with associated trade-offs and limitations. Potential clinical assistance also is discussed in various spinal cord pathologies, such as myelopathy due to external compression, spinal cord tumors, acute ischemia, traumatic injury, and so forth.

Computer-aided diagnosis of isocitrate dehydrogenase genotypes in glioblastomas from radiomic patterns.

World Health Organization tumor classifications of the central nervous system differentiate glioblastoma multiforme (GBM) into wild-type (WT) and mutant isocitrate dehydrogenase (IDH) genotypes. This study proposes a noninvasive computer-aided diagnosis to interpret the status of IDH in glioblastomas from transformed magnetic resonance imaging patterns. The collected image database was composed of 32 WT and 7 mutant IDH cases. For each image, a ranklet transformation which changed the original pixel values into relative coefficients was 1st applied to reduce the effects of different scanning parameters and machines on the underlying patterns. Extracting various textural features from the transformed ranklet images and combining them in a logistic regression classifier allowed an IDH prediction. We achieved an accuracy of 90%, a sensitivity of 57%, and a specificity of 97%. Four of the selected textural features in the classifier (homogeneity, difference entropy, information measure of correlation, and inverse difference normalized) were significant (P < .05), and the other 2 were close to being significant (P = .06). The proposed computer-aided diagnosis system based on radiomic textural features from ranklet-transformed images using relative rankings of pixel values as intensity-invariant coefficients is a promising noninvasive solution to provide recommendations about the IDH status in GBM across different healthcare institutions.

Transcatheter arterial chemoembolization improves the resectability of malignant breast phyllodes tumor with angiosarcoma component: a case report.

BACKGROUND: A giant phyllodes tumor of the breast is a rare fibroepithelial lesion, and its treatment is controversial. Many case reports have reported performing skin graft reconstruction after tumor excision. Chest wall resection may be required if the tumor has invaded the chest muscle layer. We speculated that transcatheter arterial chemoembolization (TACE) can improve the resectability of malignant phyllodes tumor of the breast without requiring skin grafting. The English literature contains only one case report similar to our experience. CASE PRESENTATION: We report a rare case of a 51-year-old woman who had a giant malignant phyllodes tumor with heterologous sarcomatous differentiation in her right breast. The tumor was 19.43 × 12.98 × 21.47 cm. Whole-body computed tomography (CT) and bone scan did not reveal distant metastasis. Chest magnetic resonance imaging showed chest wall tumor invasion. Considering that skin defects after mastectomy can be extensive, we administered four courses of chemoembolization in the 5 weeks before surgery (30 mg of epirubicin and embozene microspheres [400, 500, and 700 µm]/week). Each process was well tolerated, with no serious complications. Only fever and local pain at the tumor site were noted, and these symptoms resolved with time. The follow-up CT scan showed a 45% reduction in tumor volume. Therefore, simple mastectomy was performed without skin grafting reconstruction. Wound healing was satisfactory, and the patient was discharged 1 week after surgery. Pathological and immunohistochemistry (IHC) findings showed a malignant phyllodes tumor with an angiosarcoma component. Because of tumor invasion of the chest wall, we recommended the patient receive radiotherapy, but she refused. Two months after surgery, recurrence of the malignant phyllodes tumor with right axillary lymph node involvement and lung metastasis was confirmed. CONCLUSION: Initial surgical resection of giant phyllodes tumors is often challenging. For initial presentation with unresectable giant phyllodes tumor, we recommend to perform TACE prior to surgery. In our patient, preoperative TACE was effective and safe. If the tumor has invaded the chest wall, early radiotherapy after surgery may be recommended for preventing recurrence.

First-Line A Direct Aspiration First-Pass Technique vs. First-Line Stent Retriever for Acute Ischemic Stroke Therapy: A Meta-Analysis.

Background: Recent trials have proved the efficacy of mechanical thrombectomy over medical treatment for patients with acute ischemic stroke, with the balance of equivalent rates of adverse events. Stent retrievers were applied predominantly in most trials; however, the role of other thrombectomy devices has not been well validated. A direct aspiration first-pass technique (ADAPT) is proposed to be a faster thrombectomy technique than the stent retriever technique. This meta-analysis investigated and compared the efficacy and adverse events of first-line ADAPT with those of first-line stent retrievers in patients with acute ischemic stroke. Methods: A structured search was conducted comprehensively. A total of 1623 papers were found, and 4 articles were included in our meta-analysis. The Critical Appraisal Skills Programme tools were applied to evaluate the quality of studies. The primary outcome was defined as the proportion of patients with the Thrombolysis in Cerebral Ischemia (TICI) scale of 2b/3 at the end of all procedures. Secondary outcomes were the proportion of patients with functional independence (modified Rankin scale of 0-2) at the third month, the proportion of patients with the Thrombolysis in Cerebral Ischemia (TICI) scale of 2b/3 by primary chosen device, and the proportion of patients who received rescue therapies. Safety outcomes were the symptomatic intracranial hemorrhage (sICH) rate and the mortality rate within 3 months. Results: One randomized controlled trial, one prospective cohort study, and two retrospective cohort studies were included. No significant difference between these 2 strategies of management were observed in the primary outcome (TICI scale at the end of all procedures, odds ratio [OR] = 0.78), two secondary outcomes (functional independence at the third month, OR = 1.16; TICI scale by primary chosen device, OR = 1.25), and all safety outcomes (sICH rate, OR = 1.56; mortality rate, OR = 0.91). The proportion of patients who received rescue therapies was higher in the first-line ADAPT group (OR = 0.64). Conclusions: Among first-line thrombectomy devices for patients with ischemic stroke, ADAPT with the latest thrombosuction system was as efficient and safe as stent retrievers.

Machine Learning-Based Radiomics for Molecular Subtyping of Gliomas.

Purpose: The new classification announced by the World Health Organization in 2016 recognized five molecular subtypes of diffuse gliomas based on isocitrate dehydrogenase (IDH) and 1p/19q genotypes in addition to histologic phenotypes. We aim to determine whether clinical MRI can stratify these molecular subtypes to benefit the diagnosis and monitoring of gliomas.Experimental Design: The data from 456 subjects with gliomas were obtained from The Cancer Imaging Archive. Overall, 214 subjects, including 106 cases of glioblastomas and 108 cases of lower grade gliomas with preoperative MRI, survival data, histology, IDH, and 1p/19q status were included. We proposed a three-level machine-learning model based on multimodal MR radiomics to classify glioma subtypes. An independent dataset with 70 glioma subjects was further collected to verify the model performance.Results: The IDH and 1p/19q status of gliomas can be classified by radiomics and machine-learning approaches, with areas under ROC curves between 0.922 and 0.975 and accuracies between 87.7% and 96.1% estimated on the training dataset. The test on the validation dataset showed a comparable model performance with that on the training dataset, suggesting the efficacy of the trained classifiers. The classification of 5 molecular subtypes solely based on the MR phenotypes achieved an 81.8% accuracy, and a higher accuracy of 89.2% could be achieved if the histology diagnosis is available.Conclusions: The MR radiomics-based method provides a reliable alternative to determine the histology and molecular subtypes of gliomas. Clin Cancer Res; 24(18); 4429-36. ©2018 AACR.

Secondary pallidonigral degeneration mimicking recurrent acute stroke in clinical presentation and magnetic resonance imaging: a case report.

BACKGROUND: Secondary pallidonigral transneuronal degeneration after a remote primary cerebral infarct can mimic recurrent stroke at clinical presentation. We describe a patient with secondary pallidonigral degeneration following a previous putaminal infarct, which was diagnosed through diffusion-weighted (DWI) and T2-weighted imaging (T2WI). CASE PRESENTATION: A 64-year-old man complained of an acute relapse of right-lower-limb weakness following a cerebral infarction 2 months before presentation. Recurrent cerebral stroke was initially diagnosed in the emergency room. DWI of the brain revealed a subacute to chronic infarct in the left putamen and new acute cytotoxic edema in the left substantia nigra (SN) and globus pallidus while T2WI also showed hyperintensity in the same regions. The SN was outside the aforementioned middle cerebral arterial territory, which includes the putamen. These findings are compatible with the diagnosis of acute pallidonigral injury secondary to striatal infarction. The patient had fully recovered from his right-lower-limb weakness after 1 month. CONCLUSIONS: Secondary pallidonigral degeneration may mimic recurrent stroke. DWI along with T2WI facilitates elucidation of this clinicopathological entity, and thus unnecessary treatment can be avoided.

Recurrent nonconvulsive status epilepticus in a patient with progressive left hemispheric leukoencephalopathy after a remote viral meningoencephalitis.

Nonconvulsive status epilepticus (NCSE), defined as changes in behavior and/or mental processes from baseline with continuous epileptiform discharges, remains a diagnostic and treatment challenge. Here, we present a 68-year-old female who developed 3 episodes of NCSE 10 years after a viral meningoencephalitis which gradually progressed to left hemispheric leukoencephalopathy. In this case, we hypothesize that immune-mediated mechanisms and perhaps genetic predisposition played a role in epileptogenesis, and these will be discussed. This article is part of a Special Issue entitled "Status Epilepticus".

Bilateral neuralgic amyotrophy presenting with left vocal cord and phrenic nerve paralysis.

This article reports the difference between neuralgic amyotrophy and neuropathy caused by chemotherapy and radiation treatment which manifested with severe shoulder pain followed by marked weakness of bilateral upper arms and involvement of cranial nerves. A 62-year-old man presented with acute severe neuropathic pain at the left shoulder, bilateral shoulder weakness, hoarseness of voice from vocal cord palsy, and respiratory insufficiency from left diaphragm palsy, which all occurred sequentially over a 1-month period. The diagnosis of neuralgic amyotrophy was supported and differentiated from tumor-induced and radiation-induced neuropathy by clinical presentation, electrophysiologic and imaging studies. Unlike previous reports of the onset of neuralgic amyotrophy being associated with initiation of radiation treatment in cancer patients, this report demonstrates that neuralgic amyotrophy can occur at any point of the malignant disease process after radiation and chemotherapy.