Medical Radiology News said:
July 17, 2012 at 5:59 pm
New Method For Identifying Lung Nodules
Main Category: Lung Cancer
Pulmonary nodules are common, but few studies of lung nodule identification and clinical evaluation have been performed in community settings. Researchers from Kaiser Permanente Southern California identified 7,112 patients who had one or more nodules by using existing information within the electronic medical record.
Their study presented in the August 2012 issue of the International Association for the Study of Lung Cancer’s (IASLC) Journal of Thoracic Oncology, showed how researchers developed and implemented a new method for identifying lung nodules in community-based settings.
The researchers used a combination of ICD-9 codes, CPT codes and an algorithm for natural language processing (NLP) to classify the nodules. This automated method had a 96 percent sensitivity and 86 percent specificity compared to clinician review.
The authors suggest that the automated process, “could be used to study the incidence and prevalence of lung nodules in large populations, with the caveat that approximately 13 percent of cases identified by the automated method would not meet our definition of one or more nodules (e.g., be false-positives).”
Since this study favored sensitivity over specificity, the authors advise that the method “could be used as a sensitive first step to be followed by more specific review of radiology transcripts or actual imaging studies.”
As screening programs for lung cancer have proven to be beneficial in specific high-risk populations, this study also provides useful information for the study of screen-detected nodules.
August 14, 2014 at 7:16 am
Say, you got a nice blog article.Really thank you! Want more.
financial association
August 11, 2014 at 9:56 am
Thanks for sharing, this is a fantastic article post.Really looking forward to read more. Great.
enseГ±anza
August 9, 2014 at 7:32 am
Thank you ever so for you blog post.Really looking forward to read more. Cool.
forensic engineer columbia sc
August 8, 2014 at 5:58 am
Very good blog article.Much thanks again. Great.
Affiliate Cash Craze Review
August 7, 2014 at 1:53 pm
Awesome post.Really thank you! Keep writing.
Bucuresti
August 7, 2014 at 7:03 am
Im grateful for the post.Really looking forward to read more. Fantastic.
annuaire federation sophrologie coaching
August 6, 2014 at 4:02 am
Say, you got a nice blog post.Much thanks again. Cool.
access control cards
August 5, 2014 at 6:32 am
I loved your article.Really looking forward to read more. Fantastic.
Car Rental in Cabo
August 4, 2014 at 3:11 am
Really appreciate you sharing this blog article.Really looking forward to read more. Really Cool.
Airports Hotels
August 1, 2014 at 11:20 am
Great, thanks for sharing this post.Much thanks again. Cool.
web manager
July 30, 2014 at 8:55 am
A round of applause for your blog article.Much thanks again. Really Great.
best 10 inch tablet
June 26, 2014 at 6:48 pm
Say, you got a nice blog post.Thanks Again. Want more.
fiverrr23Jz
May 11, 2014 at 10:07 pm
Enjoyed every bit of your blog post. Want more.
July 17, 2012 at 5:59 pm
New Method For Identifying Lung Nodules
Main Category: Lung Cancer
Pulmonary nodules are common, but few studies of lung nodule identification and clinical evaluation have been performed in community settings. Researchers from Kaiser Permanente Southern California identified 7,112 patients who had one or more nodules by using existing information within the electronic medical record.
Their study presented in the August 2012 issue of the International Association for the Study of Lung Cancer’s (IASLC) Journal of Thoracic Oncology, showed how researchers developed and implemented a new method for identifying lung nodules in community-based settings.
The researchers used a combination of ICD-9 codes, CPT codes and an algorithm for natural language processing (NLP) to classify the nodules. This automated method had a 96 percent sensitivity and 86 percent specificity compared to clinician review.
The authors suggest that the automated process, “could be used to study the incidence and prevalence of lung nodules in large populations, with the caveat that approximately 13 percent of cases identified by the automated method would not meet our definition of one or more nodules (e.g., be false-positives).”
Since this study favored sensitivity over specificity, the authors advise that the method “could be used as a sensitive first step to be followed by more specific review of radiology transcripts or actual imaging studies.”
As screening programs for lung cancer have proven to be beneficial in specific high-risk populations, this study also provides useful information for the study of screen-detected nodules.