Current Management of Desmoid Tumors: A Review.

Importance: Desmoid tumor (DT) is a rare and locally aggressive monoclonal, fibroblastic proliferation characterized by a variable and often unpredictable clinical course. Previously, surgery was the standard primary treatment modality; however, within the past decade, a paradigm shift toward less-invasive management has been introduced and an effort to harmonize the strategy among clinicians has been made. To update the 2020 global evidence-based consensus guideline on the management of patients with DT, the Desmoid Tumor Working Group convened a 1-day consensus meeting in Milan, Italy, on June 30, 2023, under the auspices of the European Reference Network on Rare Adult Solid Cancers and Sarcoma Patient Advocacy Global Network, the Desmoid Foundation Italy, and the Desmoid Tumor Research Foundation. The meeting brought together over 90 adult and pediatric sarcoma experts from different disciplines as well as patients and patient advocates from around the world. Observations: The 2023 update of the global evidence-based consensus guideline focused on the positioning of local therapies alongside surgery and radiotherapy in the treatment algorithm as well as the positioning of the newest class of medical agents, such as γ-secretase inhibitors. Literature searches of MEDLINE and Embase databases were performed for English-language randomized clinical trials (RCTs) of systemic therapies to obtain data to support the consensus recommendations. Of the 18 full-text articles retrieved, only 4 articles met the inclusion criteria. The 2023 consensus guideline is informed by a number of new aspects, including data for local ablative therapies such as cryotherapy; other indications for surgery; and the γ-secretase inhibitor nirogacestat, the first representative of the newest class of medical agents and first approved drug for DT. Management of DT is complex and should be carried out exclusively in designated DT referral centers equipped with a multidisciplinary tumor board. Selection of the appropriate strategy should consider DT-related symptoms, associated risks, tumor location, disease morbidities, available treatment options, and preferences of individual patients. Conclusions and Relevance: The therapeutic armamentarium of DT therapy is continually expanding. It is imperative to carefully select the management strategy for each patient with DT to optimize tumor control and enhance quality of life.

Selective Intra-Arterial Doxorubicin Eluting Microsphere Embolization for Desmoid Fibromatosis: A Combined Prospective and Retrospective Study.

Desmoid fibromatoses (DFs) are locally aggressive tumors composed of monoclonal fibroblasts within an abundant extracellular matrix. Systemic doxorubicin treatment is effective, but toxic. We investigated arterial doxorubicin eluting embolization (DEE), an approach characterized by high drug concentrations in the tumor alongside limited systemic drug exposure. The primary and secondary endpoints were radiological response using MRI and RECIST 1.1, respectively. The study included 24 patients (median age, 24; interquartile range, 16-34 years). Data were collected prospectively for 9 patients and retrospectively for 15 patients. The most frequent tumor locations were chest/abdomen wall and neck/shoulder/axilla (29% each). Of 24 patients, 7 (24%) were treatment naïve, and 17 (71%) had received one or two prior treatments. Patients underwent a median of two treatments (range, 1-4), with a median of 49 mg (range, 8-75) doxorubicin/treatment. Efficacy outcomes were available for 23 patients. With a median follow-up of 8 months (interquartile range, 3-13), median tumor volumes decreased by 59% (interquartile range, 40-71%) and T2 signal intensity decreased by 36% (interquartile range, 19-55%). Of 23 patients, 9 (39%), 12 (52%), and 2 (9%) had a partial response, stable disease, and progressive disease, respectively. DEE was safe and well tolerated, with one reported grade 3-4 adverse event (cord injury). In conclusion, DEE was safe and achieved rapid clinical/volumetric responses in DFs.

Improving Cardiovascular Disease Prediction Using Automated Coronary Artery Calcium Scoring from Existing Chest CTs.

Cardiovascular disease (CVD) prediction models are widely used in modern medicine and are incorporated into prominent guidelines. Coronary artery calcium (CAC) is a marker of coronary atherosclerotic disease and has proven utility for predicting cardiovascular disease. Despite this, current guidelines recommend against including CAC scores in CVD prediction models due to the medical and financial costs of acquiring it, and the insufficient evidence concerning its ability to improve existing models. Modern machine learning models are capable of automatically extracting coronary calcium scores from existing chest computed tomography (CT) scans, negating these costs. To determine whether the inclusion of CAC scores, automatically extracted using a machine learning algorithm from chest CTs performed for any reason, improves the performance of the American Heart Association/American College of Cardiology 2013 pooled cohort equations (PCE). A retrospective cohort of patients with available chest CTs prior to an index date (2012) was used to compare the performance of the PCE model and an augmented-PCE model which utilizes the CT-based CAC scores on top of the existing model. The PCE and the augmented-PCE predictions were calculated as of an index date (2012) using data from the electronic health record and existing chest CTs. The performance of both models was evaluated by comparing their predictions to cardiovascular events that occurred during a 5-year follow-up period (until 2017). A total of 14,135 patients aged 40-79 years were included in the study, of whom 470 (3.3%) had documented CVD events during the follow-up. The augmented-PCE model showed a significant improvement in c-statistic (0.64 ≥ 0.69, Δ = 0.05, 95% CI: 0.03 to 0.06), sensitivity (53% ≥ 57%, Δ = 4.7%, 95% CI: 0-9.0%), specificity (67% ≥ 70%, Δ = 2.8%, 95% CI: 0.9-5.1%), in positive predictive value (5% ≥ 6%, Δ = 0.9%, 95% CI: 0.4 to 1.4%), negative predictive value (97.7% ≥ 97.9%, Δ = 0.3%, 95% CI: 0.1 to 0.5%), and in the categorical net reclassification index (7.4%, 95% CI: 2.4 to 12.1%). Automatically generated CAC scores from existing CTs can aid in CVD risk determination, improving model performance when used on top of existing predictors. Use of existing CTs avoids most pitfalls currently cited against the routine use of CAC in CVD predictions (e.g., additional radiation exposure), and thus affords a net gain in predictive accuracy.

Using Machine Learning to Identify Intravenous Contrast Phases on Computed Tomography.

PURPOSE: The purpose of the present work is to demonstrate the application of machine learning (ML) techniques to automatically identify the presence and physiologic phase of intravenous (IV) contrast in Computed Tomography (CT) scans of the Chest, Abdomen and Pelvis. MATERIALS AND METHODS: Training, testing and validation data were acquired from a dataset of 82,690 chest and abdomen CT examinations performed at 17 different institutions. Free text in DICOM metadata was utilized as weak labels for semi-supervised classification training. Contrast phase identification was approached as a classification task, using a 12-layer CNN and ResNet18 with four contrast-phase output. The model was reformulated to fit a regression task aimed to predict actual seconds from time of IV contrast administration to series image acquisition. Finally, transfer learning was used to optimize the model to predict contrast presence on CT Chest. RESULTS: By training based on labels inferred from noisy, free text DICOM information, contrast phase was predicted with 93.3% test accuracy (95% CI: 89.3%, 96.6%) . Regression analysis resulted in delineation of early vs late arterial phases and a nephrogenic phase in between the portal venous and delayed excretory phase. Transfer learning applied to Chest CT achieved an AUROC of 0.776 (95% CI: 0.721, 0.832) directly using the model trained for abdomen CT and 0.999 (95% CI: 0.998, 1.000) by fine-tuning. CONCLUSIONS: The presence and phase of contrast on CT examinations of the Abdomen-pelvis accurately and automatically be ascertained by a machine learning algorithm. Transfer learning applied to CT Chest achieves high precision with as little as 100 labeled samples.

Using machine learning algorithms to review computed tomography scans and assess risk for cardiovascular disease: Retrospective analysis from the National Lung Screening Trial (NLST).

BACKGROUND: The National Lung Screening Trial (NLST) demonstrated that annual screening with low dose CT in high-risk population was associated with reduction in lung cancer mortality. Nonetheless, the leading cause of mortality in the study was from cardiovascular diseases. PURPOSE: To determine whether the used machine learning automatic algorithms assessing coronary calcium score (CCS), level of liver steatosis and emphysema percentage in the lungs are good predictors of cardiovascular disease (CVD) mortality and incidence when applied on low dose CT scans. MATERIALS AND METHODS: Three fully automated machine learning algorithms were used to assess CCS, level of liver steatosis and emphysema percentage in the lung. The algorithms were used on low-dose computed tomography scans acquired from 12,332 participants in NLST. RESULTS: In a multivariate analysis, association between the three algorithm scores and CVD mortality have shown an OR of 1.72 (p = 0.003), 2.62 (p < 0.0001) for CCS scores of 101-400 and above 400 respectively, and an OR of 1.12 (p = 0.044) for level of liver steatosis. Similar results were shown for the incidence of CVD, OR of 1.96 (p < 0.0001), 4.94 (p < 0.0001) for CCS scores of 101-400 and above 400 respectively. Also, emphysema percentage demonstrated an OR of 0.89 (p < 0.0001). Similar results are shown for univariate analyses of the algorithms. CONCLUSION: The three automated machine learning algorithms could help physicians to assess the incidence and risk of CVD mortality in this specific population. Application of these algorithms to existing LDCT scans can provide valuable health care information and assist in future research.

PEGylated Liposomal Methyl Prednisolone Succinate does not Induce Infusion Reactions in Patients: A Correlation Between in Vitro Immunological and in Vivo Clinical Studies.

PEGylated nanomedicines are known to induce infusion reactions (IRs) that in some cases can be life-threatening. Herein, we report a case study in which a patient with rare mediastinal and intracardiac IgG4-related sclerosing disease received 8 treatments of intravenously administered PEGylated liposomal methylprednisolone-succinate (NSSL-MPS). Due to the ethical requirements to reduce IRs, the patient received a cocktail of premedication including low dose of steroids, acetaminophen and H2 blockers before each infusion. The treatment was well-tolerated in that IRs, complement activation, anti-PEG antibodies and accelerated blood clearance of the PEGylated drug were not detected. Prior to the clinical study, an in vitro panel of assays utilizing blood of healthy donors was used to determine the potential of a PEGylated drug to activate complement system, elicit pro-inflammatory cytokines, damage erythrocytes and affect various components of the blood coagulation system. The overall findings of the in vitro panel were negative and correlated with the results observed in the clinical phase.

Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization.

Methods for identifying patients at high risk for osteoporotic fractures, including dual-energy X-ray absorptiometry (DXA)1,2 and risk predictors like the Fracture Risk Assessment Tool (FRAX)3-6, are underutilized. We assessed the feasibility of automatic, opportunistic fracture risk evaluation based on routine abdomen or chest computed tomography (CT) scans. A CT-based predictor was created using three automatically generated bone imaging biomarkers (vertebral compression fractures (VCFs), simulated DXA T-scores and lumbar trabecular density) and CT metadata of age and sex. A cohort of 48,227 individuals (51.8% women) aged 50-90 with available CTs before 2012 (index date) were assessed for 5-year fracture risk using FRAX with no bone mineral density (BMD) input (FRAXnb) and the CT-based predictor. Predictions were compared to outcomes of major osteoporotic fractures and hip fractures during 2012-2017 (follow-up period). Compared with FRAXnb, the major osteoporotic fracture CT-based predictor presented better receiver operating characteristic area under curve (AUC), sensitivity and positive predictive value (PPV) (+1.9%, +2.4% and +0.7%, respectively). The AUC, sensitivity and PPV measures of the hip fracture CT-based predictor were noninferior to FRAXnb at a noninferiority margin of 1%. When FRAXnb inputs are not available, the initial evaluation of fracture risk can be done completely automatically based on a single abdomen or chest CT, which is often available for screening candidates7,8.

Simulating Dual-Energy X-Ray Absorptiometry in CT Using Deep-Learning Segmentation Cascade.

PURPOSE: Osteoporosis is an underdiagnosed condition despite effective screening modalities. Dual-energy x-ray absorptiometry (DEXA) screening, although recommended in clinical guidelines, remains markedly underutilized. In contrast to DEXA, CT utilization is high and presents a valuable data source for opportunistic osteoporosis screening. The purpose of this study was to describe a method to simulate lumbar DEXA scores from routinely acquired CT studies using a machine-learning algorithm. METHODS: Between January 2010 and September 2014, 610 CT studies of the abdomen and pelvis were used to develop spinal column and L1 to L4 multiclass segmentation. DEXA simulation training and validation used 1,843 pairs of CT studies accompanied by DEXA results obtained within a 6-month interval from the same individual. Machine learning-based regression was used to determine correlation between calculated grade (on the basis of vertebrae L1-L4) and DEXA t score. RESULTS: Analysis of the t score equivalent, generated by the algorithm, revealed true positives in 1,144 patients, false positives in 92 patients, true negatives in 245 patients, and false negatives in 212 patients, resulting in an accuracy of 82%. Sensitivity for the detection of osteoporosis or osteopenia was 84.4% (95% confidence interval, 82.3%-86.2%), and specificity was 72.7% (95% confidence interval, 67.7%-77.2%). CONCLUSIONS: The presented algorithm can identify osteoporosis and osteopenia with a high degree of accuracy (82%) and a small proportion of false positives. Efforts to cull greater information using machine-learning algorithms from pre-existing data have the potential to have a marked impact on population health efforts such as bone mineral density screening for osteoporosis, in which gaps in screening currently exist.

Doxorubicin-Eluting Intra-Arterial Therapy for Pediatric Extra-Abdominal Desmoid Fibromatoses: A Promising Approach for a Perplexing Disease.

Systemic doxorubicin is effective for desmoid fibromatosis (DF), but its use is limited by dose-dependent cardiotoxicity. A protocol of selective intra-arterial doxorubicin drug-eluting embolization (DEE) was designed to maximize target tissue efficacy of doxorubicin, while minimizing systemic exposure. Four children with recurrent or refractory DF were treated between 2014 and 2017. Tumor volumes were reduced by 54%-97% over a follow-up interval of 6-32 months. A single patient experienced transient lower extremity paresthesia (Common Terminology Criteria for Adverse Events grade I). Further investigation is needed to better establish these promising results for doxorubicin DEE in DF treatment.

Malignancy Detection on Mammography Using Dual Deep Convolutional Neural Networks and Genetically Discovered False Color Input Enhancement.

Breast cancer is the most prevalent malignancy in the US and the third highest cause of cancer-related mortality worldwide. Regular mammography screening has been attributed with doubling the rate of early cancer detection over the past three decades, yet estimates of mammographic accuracy in the hands of experienced radiologists remain suboptimal with sensitivity ranging from 62 to 87% and specificity from 75 to 91%. Advances in machine learning (ML) in recent years have demonstrated capabilities of image analysis which often surpass those of human observers. Here we present two novel techniques to address inherent challenges in the application of ML to the domain of mammography. We describe the use of genetic search of image enhancement methods, leading us to the use of a novel form of false color enhancement through contrast limited adaptive histogram equalization (CLAHE), as a method to optimize mammographic feature representation. We also utilize dual deep convolutional neural networks at different scales, for classification of full mammogram images and derivative patches combined with a random forest gating network as a novel architectural solution capable of discerning malignancy with a specificity of 0.91 and a specificity of 0.80. To our knowledge, this represents the first automatic stand-alone mammography malignancy detection algorithm with sensitivity and specificity performance similar to that of expert radiologists.

Pasireotide for malignant insulinoma.

UNLABELLED: Malignant insulinoma usually has a poor prognosis, as no efficient medical treatment is available. The somatostatin analogs octreotide and lanreotide have limited ability to control the hypoglycemic events. Pasireotide is a multi-receptor targeted somatostatin-analog with improved affinity for SSTR5. There is to date no reported treatment experience with this drug in such tumors. CASE DESCRIPTION: A 72-year-old patient with a G2 stage IV insulinoma, who underwent excision of the primary pancreatic tumor and multiple hepatic metastases, required further treatment for recurrent hypoglycemic events. The glycemic control achieved with pasireotide LAR was better compared with lanreotide and everolimus. However, none of these treatments showed tumor anti-proliferative effects. CONCLUSIONS: Pasireotide monthly injections achieved improved glycemic control in a patient with malignant insulinoma and recurrent hypoglycemic events compared with other medical treatments.

Noncirrhotic Extrahepatic Portosystemic Shunt Causing Adult-Onset Encephalopathy Treated with Endovascular Closure.

A 54-year-old woman presented with a six-month history of episodic confusion and progressive ataxia. A comprehensive metabolic panel was notable for elevated values of alkaline phosphatase (161 U/L), total bilirubin (1.5 mg/dL), and serum ammonia of 300 umol/L (normal range 9-47). Hepatitis panel, relevant serological tests, tumor markers (CA-19-9, CEA), and urea cycle enzyme studies were unrevealing. Lactulose and rifaximin therapy failed to normalize serum ammonia levels. Imaging revealed a structural vascular abnormality communicating between an enlarged inferior mesenteric vein and the left renal vein, measuring 16 mm in greatest diameter. The diagnosis of congenital extrahepatic portosystemic shunt was made and endovascular shunt closure was performed using a 22 mm Amplatzer II vascular plug. Within a day, serum ammonia levels normalized. Lactulose and rifaximin were discontinued, and confusion and ataxia resolved.

Long-term outcomes comparing surgery to embolization-ablation for treatment of solitary HCC<7 cm.

BACKGROUND: Resection has been the standard of care for patients with solitary hepatocellular carcinoma (HCC). Transarterial embolization and percutaneous ablation are alternative therapies often reserved for suboptimal surgical candidates. Here we compare long-term outcomes of patients with solitary HCC treated with resection versus combined embo-ablation. METHODS: We previously reported a retrospective comparison of resection and embo-ablation in 73 patients with solitary HCC<7 cm after a median follow-up of 23 months. This study represents long-term updated follow-up over a median of 134 months. RESULTS: There was no difference in survival among Okuda I patients who underwent resection versus embo-ablation (66 vs 58 months, p=.39). There was no difference between the groups in the rate of distant intrahepatic (p=.35) or metastatic progression (p=.48). Surgical patients experienced more complications (p=.004), longer hospitalizations (p<.001), and were more likely to require hospital readmission within 30 days of discharge (p=.03). CONCLUSION: Over a median follow up of more than 10 years, we found no significant difference in overall survival of Okuda 1 patients with solitary HCC<7 cm who underwent surgical resection versus embo-ablation. Our data suggest that there may be a greater role for primary embo-ablation in the treatment of potentially resectable solitary HCC.

The interventional radiologist's perspective on lung biopsy.

Percutaneous image-guided lung biopsies are commonly performed by radiologists. Here, we review common indications and contraindications for the procedure, image guidance and tissue retrieval options, and other special considerations.

Immune responses to Pneumocystis murina are robust in healthy mice but largely absent in CD40 ligand-deficient mice.

Pneumocystis is a pathogen of immunocompromised hosts but can also infect healthy hosts, in whom infection is rapidly controlled and cleared. Microarray methods were used to examine differential gene expression in the lungs of C57BL/6 and CD40 ligand knockout (CD40L-KO) mice over time following exposure to Pneumocystis murina. Immunocompetent C57BL/6 mice, which control and clear infection efficiently, showed a robust response to infection characterized by the up-regulation of 349 primarily immune response-associated genes. Temporal changes in the expression of these genes identified an early (Week 2), primarily innate response, which waned before the infection was controlled; this was followed by primarily adaptive immune responses that peaked at Week 5, which coincided with clearance of the infection. In conjunction with the latter, there was an increased expression of B cell-associated (Ig) genes at Week 6 that persisted through 11 weeks. In contrast, CD40L-KO mice, which are highly susceptible to developing severe Pneumocystis pneumonia, showed essentially no up-regulation of immune response-associated genes at Days 35-75. Immunohistochemical staining supported these observations by demonstrating an increase in CD4+, CD68+, and CD19+ cells in C57BL/6 but not CD40L-KO mice. Thus, the healthy host demonstrates a robust, biphasic response to infection by Pneumocystis; CD40L is an essential upstream regulator of the adaptive immune responses that efficiently control infection and prevent development of progressive pneumonia.

IL-13Ralpha2 and IL-10 coordinately suppress airway inflammation, airway-hyperreactivity, and fibrosis in mice.

Development of persistent Th2 responses in asthma and chronic helminth infections are a major health concern. IL-10 has been identified as a critical regulator of Th2 immunity, but mechanisms for controlling Th2 effector function remain unclear. IL-10 also has paradoxical effects on Th2-associated pathology, with IL-10 deficiency resulting in increased Th2-driven inflammation but also reduced airway hyperreactivity (AHR), mucus hypersecretion, and fibrosis. We demonstrate that increased IL-13 receptor alpha 2 (IL-13Ralpha2) expression is responsible for the reduced AHR, mucus production, and fibrosis in BALB/c IL-10(-/-) mice. Using models of allergic asthma and chronic helminth infection, we demonstrate that IL-10 and IL-13Ralpha2 coordinately suppress Th2-mediated inflammation and pathology, respectively. Although IL-10 was identified as the dominant antiinflammatory mediator, studies with double IL-10/IL-13Ralpha2-deficient mice illustrate an indispensable role for IL-13Ralpha2 in the suppression of AHR, mucus production, and fibrosis. Thus, IL-10 and IL-13Ralpha2 are both required to control chronic Th2-driven pathological responses.

The impact of gown-use requirement on hand hygiene compliance.

BACKGROUND: Hand hygiene compliance remains low, despite its effectiveness in preventing infections. Gowns are routinely used to reduce dissemination of organisms within hospitals. Use of gowns is time consuming and costly, and their effectiveness, compared with that of hand hygiene alone, is debated. Among the arguments supporting a gown-use requirement is the potential to boost awareness of infection control, leading to improved hand hygiene compliance. METHODS: Hand hygiene compliance was recorded in a 14-month crossover trial comparing compliance at 2 intensive care units during periods with and without a gown-use requirement. RESULTS: During 170 h of observation, 1619 hand hygiene opportunities were recorded. Compliance was 10% before care was given and 36% after care was given. Compliance with glove and gown use was 62% and 63%, respectively. After-care hand hygiene compliance for nurses, physicians, and therapists was 40%, 38%, and 22%, respectively. Compliance after patient contact, body fluid contact, and other in-room activity was 42%, 48%, and 22%, respectively. For periods with and without a gown-use requirement, overall after-care compliance (37% vs. 34%) and rates by personnel and activity type were similar. In the subgroup of patients on contact precautions, hand hygiene compliance during the period with a gown-use requirement versus the period without a gown-use requirement was 11% versus 10% (P=.85) before care was given and 45% versus 39% (P=.09) after care was given. In this subgroup, after adjustment for type of in-room activity, medical personnel, intensive care unit, and observer, the predicted after-care hand hygiene compliance during periods with and without a gown-use requirement was 48% versus 41% (P=.02). CONCLUSIONS: The hypothesis that a gown-use requirement might improve hand hygiene compliance in the intensive care unit could not be confirmed. In the subgroup of patients on contact precautions, improvement in hand hygiene compliance associated with the gown-use requirement was small and did not affect precare rates.

High-Throughput GoMiner, an 'industrial-strength' integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of Common Variable Immune Deficiency (CVID).

BACKGROUND: We previously developed GoMiner, an application that organizes lists of 'interesting' genes (for example, under-and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. The original version of GoMiner was oriented toward visualization and interpretation of the results from a single microarray (or other high-throughput experimental platform), using a graphical user interface. Although that version can be used to examine the results from a number of microarrays one at a time, that is a rather tedious task, and original GoMiner includes no apparatus for obtaining a global picture of results from an experiment that consists of multiple microarrays. We wanted to provide a computational resource that automates the analysis of multiple microarrays and then integrates the results across all of them in useful exportable output files and visualizations. RESULTS: We now introduce a new tool, High-Throughput GoMiner, that has those capabilities and a number of others: It (i) efficiently performs the computationally-intensive task of automated batch processing of an arbitrary number of microarrays, (ii) produces a human-or computer-readable report that rank-orders the multiple microarray results according to the number of significant GO categories, (iii) integrates the multiple microarray results by providing organized, global clustered image map visualizations of the relationships of significant GO categories, (iv) provides a fast form of 'false discovery rate' multiple comparisons calculation, and (v) provides annotations and visualizations for relating transcription factor binding sites to genes and GO categories. CONCLUSION: High-Throughput GoMiner achieves the desired goal of providing a computational resource that automates the analysis of multiple microarrays and integrates results across all of the microarrays. For illustration, we show an application of this new tool to the interpretation of altered gene expression patterns in Common Variable Immune Deficiency (CVID). High-Throughput GoMiner will be useful in a wide range of applications, including the study of time-courses, evaluation of multiple drug treatments, comparison of multiple gene knock-outs or knock-downs, and screening of large numbers of chemical derivatives generated from a promising lead compound.

The healthcare experiences of Arab Israeli women in a reformed healthcare system.

UNLABELLED: Arab Israeli women are subject to unique social stresses deriving from their status as part of an ethno-political minority and from their position as women in a patriarchal community. Collectively, their health profiles rate poorly in comparison to Jewish Israeli women or to women in the vast majority of developed countries. OBJECTIVES: To examine the experiences of Arab Israeli women in the contemporary Israeli healthcare system, following implementation of the National Health Insurance Law (NHIL). METHODS: The study combined quantitative and qualitative research methodologies. A telephone survey utilizing a structured questionnaire was conducted during August-September 1998 among a random national sample of 849 women, with a response rate of 83%. Between the months of January and July of 2000, qualitative data was attained via participant-observation, long and short semi-structured interviews, and focus groups in one large Muslim Arab Israeli village. FINDINGS: Arab Israeli women in the national survey reported poorer self-assessed health, lower rates of care by a woman primary care physician, lower satisfaction ratings for primary care physicians and more frequently foregoing medical care than did native or immigrant Jewish Israeli women. Three major factors contributing to Arab Israeli women's healthcare experiences were elucidated by the qualitative study: (1) the threat of physical and social exposure (2) difficulties in communicating with male physicians and (3) the stifling effect of family politics and surveillance on healthcare. DISCUSSION: We discuss our findings in relation to structural changes associated with the recent reform of the Israeli health care system. We conclude by suggesting policy measures for better adapting primary care services to the needs of Arab Israeli women, and note the relevance to other systems that aim to provide service to cultural and ethno-political minorities, in which healthcare delivery is shaped by unique local circumstances.