Survival rate in nasopharyngeal carcinoma improved by high caseload volume: a nationwide population-based study in Taiwan

<p>Abstract</p> <p>Background</p> <p>Positive correlation between caseload and outcome has previously been validated for several procedures and cancer treatments. However, there is no information linking caseload and outcome of nasopharyngeal carcinoma (NPC) treatment. We used nationwide population-based data to examine the association between physician case volume and survival rates of patients with NPC.</p> <p>Methods</p> <p>Between 1998 and 2000, a total of 1225 patients were identified from the Taiwan National Health Insurance Research Database. Survival analysis, the Cox proportional hazards model, and propensity score were used to assess the relationship between 10-year survival rates and physician caseloads.</p> <p>Results</p> <p>As the caseload of individual physicians increased, unadjusted 10-year survival rates increased (<it>p </it>< 0.001). Using a Cox proportional hazard model, patients with NPC treated by high-volume physicians (caseload ≥ 35) had better survival rates (<it>p </it>= 0.001) after adjusting for comorbidities, hospital, and treatment modality. When analyzed by propensity score, the adjusted 10-year survival rate differed significantly between patients treated by high-volume physicians and patients treated by low/medium-volume physicians (75% <it>vs</it>. 61%; <it>p </it>< 0.001).</p> <p>Conclusions</p> <p>Our data confirm a positive volume-outcome relationship for NPC. After adjusting for differences in the case mix, our analysis found treatment of NPC by high-volume physicians improved 10-year survival rate.</p

Increased Risk of Vascular Events in Emergency Room Patients Discharged Home with Diagnosis of Dizziness or Vertigo: A 3-Year Follow-Up Study

BACKGROUND: Dizziness and vertigo symptoms are commonly seen in emergency room (ER). However, these patients are often discharged without a definite diagnosis. Conflicting data regarding the vascular event risk among the dizziness or vertigo patients have been reported. This study aims to determine the risk of developing stroke or cardiovascular events in ER patients discharged home with a diagnosis of dizziness or vertigo. METHODOLOGY: A total of 25,757 subjects with at least one ER visit in 2004 were identified. Of those, 1,118 patients were discharged home with a diagnosis of vertigo or dizziness. A Cox proportional hazard model was performed to compare the three-year vascular event-free survival rates between the dizziness/vertigo patients and those without dizziness/vertigo after adjusting for confounding and risk factors. RESULTS: We identified 52 (4.7%) vascular events in patients with dizziness/vertigo and 454 (1.8%) vascular events in patients without dizziness/vertigo. ER patients discharged home with a diagnosis of vertigo or dizziness had 2-fold (95% confidence interval [CI], 1.35-2.96; p<0.001) higher risk of stroke or cardiovascular events after adjusting for patient characteristics, co-morbidities, urbanization level of residence, individual socio-economic status, and initially taking medications after the onset of dizziness or vertigo during the first year. CONCLUSIONS: ER patients discharged home with a diagnosis of dizziness or vertigo were at a increased risk of developing subsequent vascular events than those without dizziness/vertigo after the onset of dizziness or vertigo. Further studies are warranted for developing better diagnostic and follow-up strategies in increased risk patients

The combined effect of individual and neighborhood socioeconomic status on cancer survival rates.

BACKGROUND: This population-based study investigated the relationship between individual and neighborhood socioeconomic status (SES) and mortality rates for major cancers in Taiwan. METHODS: A population-based follow-up study was conducted with 20,488 cancer patients diagnosed in 2002. Each patient was traced to death or for 5 years. The individual income-related insurance payment amount was used as a proxy measure of individual SES for patients. Neighborhood SES was defined by income, and neighborhoods were grouped as living in advantaged or disadvantaged areas. The Cox proportional hazards model was used to compare the death-free survival rates between the different SES groups after adjusting for possible confounding and risk factors. RESULTS: After adjusting for patient characteristics (age, gender, Charlson Comorbidity Index Score, urbanization, and area of residence), tumor extent, treatment modalities (operation and adjuvant therapy), and hospital characteristics (ownership and teaching level), colorectal cancer, and head and neck cancer patients under 65 years old with low individual SES in disadvantaged neighborhoods conferred a 1.5 to 2-fold higher risk of mortality, compared with patients with high individual SES in advantaged neighborhoods. A cross-level interaction effect was found in lung cancer and breast cancer. Lung cancer and breast cancer patients less than 65 years old with low SES in advantaged neighborhoods carried the highest risk of mortality. Prostate cancer patients aged 65 and above with low SES in disadvantaged neighborhoods incurred the highest risk of mortality. There was no association between SES and mortality for cervical cancer and pancreatic cancer. CONCLUSIONS: Our findings indicate that cancer patients with low individual SES have the highest risk of mortality even under a universal health-care system. Public health strategies and welfare policies must continue to focus on this vulnerable group

Increased Risk of Vascular Events in Emergency Room Patients Discharged Home with Diagnosis of Dizziness or Vertigo: A 3-Year Follow-Up Study

Dizziness and vertigo symptoms are commonly seen in emergency room (ER). However, these patients are often discharged without a definite diagnosis. Conflicting data regarding the vascular event risk among the dizziness or vertigo patients have been reported. This study aims to determine the risk of developing stroke or cardiovascular events in ER patients discharged home with a diagnosis of dizziness or vertigo.A total of 25,757 subjects with at least one ER visit in 2004 were identified. Of those, 1,118 patients were discharged home with a diagnosis of vertigo or dizziness. A Cox proportional hazard model was performed to compare the three-year vascular event-free survival rates between the dizziness/vertigo patients and those without dizziness/vertigo after adjusting for confounding and risk factors.We identified 52 (4.7%) vascular events in patients with dizziness/vertigo and 454 (1.8%) vascular events in patients without dizziness/vertigo. ER patients discharged home with a diagnosis of vertigo or dizziness had 2-fold (95% confidence interval [CI], 1.35–2.96; pER patients discharged home with a diagnosis of dizziness or vertigo were at a increased risk of developing subsequent vascular events than those without dizziness/vertigo after the onset of dizziness or vertigo. Further studies are warranted for developing better diagnostic and follow-up strategies in increased risk patients.</p

Quantitative Measurement of Breast Tumors Using Intravoxel Incoherent Motion (IVIM) MR Images

Breast magnetic resonance imaging (MRI) is currently a widely used clinical examination tool. Recently, MR diffusion-related technologies, such as intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI), have been extensively studied by breast cancer researchers and gradually adopted in clinical practice. In this study, we explored automatic tumor detection by IVIM-DWI. We considered the acquired IVIM-DWI data as a hyperspectral image cube and used a well-known hyperspectral subpixel target detection technique: constrained energy minimization (CEM). Two extended CEM methods—kernel CEM (K-CEM) and iterative CEM (I-CEM)—were employed to detect breast tumors. The K-means and fuzzy C-means clustering algorithms were also evaluated. The quantitative measurement results were compared to dynamic contrast-enhanced T1-MR imaging as ground truth. All four methods were successful in detecting tumors for all the patients studied. The clustering methods were found to be faster, but the CEM methods demonstrated better performance according to both the Dice and Jaccard metrics. These unsupervised tumor detection methods have the advantage of potentially eliminating operator variability. The quantitative results can be measured by using ADC, signal attenuation slope, D*, D, and PF parameters to classify tumors of mass, non-mass, cyst, and fibroadenoma types

Development of U-Net Breast Density Segmentation Method for Fat-Sat MR Images Using Transfer Learning Based on Non-Fat-Sat Model.

To develop a U-net deep learning method for breast tissue segmentation on fat-sat T1-weighted (T1W) MRI using transfer learning (TL) from a model developed for non-fat-sat images. The training dataset (N = 126) was imaged on a 1.5&nbsp;T MR scanner, and the independent testing dataset (N = 40) was imaged on a 3&nbsp;T scanner, both using fat-sat T1W pulse sequence. Pre-contrast images acquired in the dynamic-contrast-enhanced (DCE) MRI sequence were used for analysis. All patients had unilateral cancer, and the segmentation was performed using the contralateral normal breast. The ground truth of breast and fibroglandular tissue (FGT) segmentation was generated using a template-based segmentation method with a clustering algorithm. The deep learning segmentation was performed using U-net models trained with and without TL, by using initial values of trainable parameters taken from the previous model for non-fat-sat images. The ground truth of each case was used to evaluate the segmentation performance of the U-net models by calculating the dice similarity coefficient (DSC) and the overall accuracy based on all pixels. Pearson's correlation was used to evaluate the correlation of breast volume and FGT volume between the U-net prediction output and the ground truth. In the training dataset, the evaluation was performed using tenfold cross-validation, and the mean DSC with and without TL was 0.97 vs. 0.95 for breast and 0.86 vs. 0.80 for FGT. When the final model developed with and without TL from the training dataset was applied to the testing dataset, the mean DSC was 0.89 vs. 0.83 for breast and 0.81 vs. 0.81 for FGT, respectively. Application of TL not only improved the DSC, but also decreased the required training case number. Lastly, there was a high correlation (R2 &gt; 0.90) for both the training and testing datasets between the U-net prediction output and ground truth for breast volume and FGT volume. U-net can be applied to perform breast tissue segmentation on fat-sat images, and TL is an efficient strategy to develop a specific model for each different dataset

Hazard ratios of individual SES for mortality in advantaged and disadvantaged neighborhoods^*.

<p>Abbreviation: Adjusted HR, adjusted hazard ratio; 95% CI, 95% confidence interval; SES, socioeconomic status.</p>*<p>Adjusted for the patients’ diagnosed age, gender, Charlson Comorbidity Index Score, tumor stage (local and locoregional versus distant metastasis), treatment modality (surgery versus without surgery), nonsurgical treatment (radiotherapy, chemotherapy, or chemoradiotherapy), and hospital characteristics (teaching level, and ownership).</p