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Non-small cell lung cancer: imaging features and post-therapy responses of untreated lymphadenopathy.
To study the imaging characteristics of untreated lymphadenopathy secondary to non-small cell lung cancer and determine the post-therapy responses of lymphadenopathy. We reviewed CT scans of 122 patients with histologically proven non-small cell lung cancer who had 134 untreated lymphadenopathy and were examined at our facility for suspected pulmonary metastases. We sought to determine the incidence and imaging characteristics of untreated lymphadenopathy, including qualitative and quantitative measurements of node size, shape, size ratio, margin, and internal architecture. The imaging responses of lymphadenopathy were determined. In 47 of 134 (35%) untreated lymphadenopathy, the following findings were noted: hypoattenuation, 29; intermediate density, 18; hyperattenuation, 6; air bronchogram, 7; halo sign, 7; and soft-tissue mass, 2. Of the remaining 81 untreated lymphadenopathy, 31 (38%) were hypoattenuating; 30 (37%) were hyperattenuating; and 19 (23%) were isosignal with adjacent muscle. After a median follow-up of 12 months (range, 3-46), metastatic spread was documented in 49 patients (40%) and was the only cause of death in four (4%). The size and shape ratios of the largest lymph node correlated with the pretreatment ratio and the size and shape ratios of the largest lymph nodes in the pathologic specimen correlated with the size and shape ratios in imaging. No abnormality in imaging studies detected metastatic spread of non-small cell lung cancer. Post-therapy scans showed no residual change in the size and shape ratios of lymphadenopathy in 87 patients (71%). This study demonstrates that untreated lymphadenopathy in patients with lung cancer is generally noted to be intermediate density and hyperattenuating or hyperintense on MRI. Large-scale studies are needed to confirm these findings. (A JCRP W1350-12).Just another WordPress.com site

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Monthly Archives: April 2014

So I’m finally back to complete the races I’ve signed up for (gulp! shiver) with two out of three finished, and the other one will be done today. I’m not thrilled that it was Spring Break and I didn’t even go to the Laconia race, but I am also aware that I didn’t want

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OData filter is not working in Google Sheets

I’m using Google Apps Script. I want to fetch data from OData and display it in Sheets. This is my script:
/**
* This is sample code for fetching data from odata and displaying it in sheet.
* Please modify it as per your requirement.
*/
function importData() {
const input = UrlFetchApp.fetch(”
+ ‘”odata.metadata”:””&$select=EmployeeId,EmployeeTitle,EmployeeDob’,’
+ ‘”odata.metadata”:mctor/Employees(select=$select)/Next($orderby=EmployeeDob desc)’
+ ‘}&$filter=EmployeeDob ge’+ new Date().toISOString()
);
const response = JSON.parse(input.getContentText());
Logger.log(response.d.results.value);
return SpreadsheetApp.openById(‘mySheetId’);
}

It fetches data and logs the response but the filter doesn’t work. Here’s the response:
[
{
“d”: {
“__metadata”: {
“type”: “Microsoft.ApiManagement/service/resources”
},
“results”: [
{
“EmployeeId”: “BPN
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