Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1075
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3195
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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Treatment response assessment in lung cancer is crucial in the management strategy and outcome of patients. Accurate treatment response assessment can guide the treating physicians and improve patient survival. Anatomic and metabolic tumor response assessments have been evaluated extensively, showing a positive impact in the management of these patients. F-FDG PET/CT provides early and more specific treatment response assessments, preceding anatomic changes in these tumors. Familiarity with the different treatment response assessment algorithms, criteria, time intervals, imaging pitfalls is essential for treating physicians and nuclear radiologists to provide accurate response assessments. Artificial Intelligence is being more frequently explored for this purpose and can assist physicians in providing prompt and accurate treatment response assessments.
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http://dx.doi.org/10.1053/j.semnuclmed.2022.04.001 | DOI Listing |