title: MDCT quantification is the dominant parameter in decision-making regarding chest tube drainage for stable patients with traumatic pneumothorax.
abstract: It is commonly believed that the size of a pneumothorax is an important determinant of treatment decision, in particular regarding whether chest tube drainage (CTD) is required. However, the volumetric quantification of pneumothoraces has not routinely been performed in clinics. In this paper, we introduced an automated computer-aided volumetry (CAV) scheme for quantification of volume of pneumothoraces in chest multi-detect CT (MDCT) images. Moreover, we investigated the impact of accurate volume of pneumothoraces in the improvement of the performance in decision-making regarding CTD in the management of traumatic pneumothoraces. For this purpose, an occurrence frequency map was calculated for quantitative analysis of the importance of each clinical parameter in the decision-making regarding CTD by a computer simulation of decision-making using a genetic algorithm (GA) and a support vector machine (SVM). A total of 14 clinical parameters, including volume of pneumothorax calculated by our CAV scheme, was collected as parameters available for decision-making. The results showed that volume was the dominant parameter in decision-making regarding CTD, with an occurrence frequency value of 1.00. The results also indicated that the inclusion of volume provided the best performance that was statistically significant compared to the other tests in which volume was excluded from the clinical parameters. This study provides the scientific evidence for the application of CAV scheme in MDCT volumetric quantification of pneumothoraces in the management of clinically stable chest trauma patients with traumatic pneumothorax.
authors.affiliation: Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA. Cai.Wenli@mgh.harvard.edu
authors.forename: Wenli
authors.initials: W
authors.lastname: Cai
authors.forename: June-Goo
authors.initials: JG
authors.lastname: Lee
authors.forename: Karim
authors.initials: K
authors.lastname: Fikry
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Yoshida
authors.forename: Robert
authors.initials: R
authors.lastname: Novelline
authors.forename: Marc
authors.initials: M
authors.lastname: de Moya
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D015505:Chest Tubes; D020000:Decision Support Systems, Clinical; D004322:Drainage; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011030:Pneumothorax / Q000175:diagnosis* / Q000601:surgery*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013898:Thoracic Injuries / Q000175:diagnosis* / Q000601:surgery*; D014057:Tomography, X-Ray Computed / Q000379:methods*; D055815:Young Adult