Predicting Malignancy Risk of Screen-Detected Lung Nodules–Mean Diameter or Volume Journal Articles uri icon

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  • OBJECTIVE: In lung cancer screening practice low-dose computed tomography, diameter, and volumetric measurement have been used in the management of screen-detected lung nodules. The aim of this study was to compare the performance of nodule malignancy risk prediction tools using diameter or volume and between computer-aided detection (CAD) and radiologist measurements. METHODS: Multivariable logistic regression models were prepared by using data from two multicenter lung cancer screening trials. For model development and validation, baseline low-dose computed tomography scans from the Pan-Canadian Early Detection of Lung Cancer Study and a subset of National Lung Screening Trial (NLST) scans with lung nodules 3 mm or more in mean diameter were analyzed by using the CIRRUS Lung Screening Workstation (Radboud University Medical Center, Nijmegen, the Netherlands). In the NLST sample, nodules with cancer had been matched on the basis of size to nodules without cancer. RESULTS: Both CAD-based mean diameter and volume models showed excellent discrimination and calibration, with similar areas under the receiver operating characteristic curves of 0.947. The two CAD models had predictive performance similar to that of the radiologist-based model. In the NLST validation data, the CAD mean diameter and volume models also demonstrated excellent discrimination: areas under the curve of 0.810 and 0.821, respectively. These performance statistics are similar to those of the Pan-Canadian Early Detection of Lung Cancer Study malignancy probability model with use of these data and radiologist-measured maximum diameter. CONCLUSION: Either CAD-based nodule diameter or volume can be used to assist in predicting a nodule's malignancy risk.


  • Tammemagi, Martin
  • Ritchie, Alex J
  • Atkar-Khattra, Sukhinder
  • Dougherty, Brendan
  • Sanghera, Calvin
  • Mayo, John R
  • Yuan, Ren
  • Manos, Daria
  • McWilliams, Annette M
  • Schmidt, Heidi
  • Gingras, Michel
  • Pasian, Sergio
  • Stewart, Lori
  • Tsai, Scott
  • Seely, Jean M
  • Burrowes, Paul
  • Bhatia, Rick
  • Haider, Ehsan
  • Boylan, Colm
  • Jacobs, Colin
  • van Ginneken, Bram
  • Tsao, Ming-Sound
  • Lam, Stephen

publication date

  • February 2019

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