Computer-aided detection and automated CT volumetry of pulmonary nodules.
With use of multislice computed tomography (MSCT), small pulmonary nodules are being detected in vast numbers, constituting the majority of all noncalcified lung nodules. Although the prevalence of lung cancers among such lesions in lung cancer screening populations is low, their isolation may contribute to increased patient survival. Computer-aided diagnosis (CAD) has emerged as a diverse set of diagnostic tools to handle the large number of images in MSCT datasets and most importantly, includes automated detection and volumetry of pulmonary nodules. Current CAD systems can significantly enhance experienced radiologists' performance and outweigh human limitations in identifying small lesions and manually measuring their diameters, augment observer consistency in the interpretation of such examinations and may thus help to detect significantly higher rates of early malignomas and give more precise estimates on chemotherapy response than can radiologists alone. In this review, we give an overview of current CAD in lung nodule detection and volumetry and discuss their relative merits and limitations.
Marten
Katharina
K
Department of Radiology, Klinikum rechts der Isar, Technical University Munich, Ismaningerstr. 22, 81675, Munich, Germany. Katharina.marten@roe.med.tu-muenchen.de
Engelke
Christoph
C
D000328:Adult; D000368:Aged; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis*; D008297:Male; D008875:Middle Aged; D009362:Neoplasm Metastasis / Q000175:diagnosis*; D015588:Observer Variation; D015203:Reproducibility of Results; D003074:Solitary Pulmonary Nodule / Q000175:diagnosis*; D014057:Tomography, X-Ray Computed / Q000379:methods