UNLABELLED: We proposed to train a back-propagation artificial neural network (aNN) on a cohort of surgically proven non-small cell lung cancers (NSCLCs) and compare its accuracy with that of a trained (18)F-FDG PET reader. We plan to show that an aNN trained on (18)F-FDG PET- and CT-derived data is more accurate in predicting the true surgicopathologic nodal stage than a human reader. METHODS: One hundred thirty-three NSCLC patients with surgically proven N status treated at the University of Washington Medical Center or the Veterans Affairs Puget Sound Health Care System between February 1998 and September 2002 were used as inputs for the creation of an aNN. From CT of the thorax and (18)F-FDG PET (neck to pelvis) performed before surgery, we extracted the primary tumor size and uptake (maximum pixel SUV [maxSUV]), normal lung and mediastinal uptake, and nodal uptake (maxSUV). Using the same 133 cases, the same output (surgical N status, N(0) to N(3)), and the same software configuration settings, scenarios were created to assess which input parameters were most influential in creating an optimal aNN. To compute this optimal aNN, cases were split randomly 100 times into a training subset of 103 cases and a testing subset of 30 cases having the same proportion of N(0), N(1), N(2), and N(3) cases. N status predicted by the aNN was compared with the proven surgical N status to calculate the aNN accuracy. The N status readings from (18)F-FDG PET were also compared with the surgical N status for the same cases to determine (18)F-FDG PET accuracy. RESULTS: Statistical tests demonstrate that the best aNN accuracy is achieved by using N(1)-N(2)- N(3) nodal maxSUV divided by background uptake, the primary tumor size, and primary tumor maxSUV as inputs. The aNN correctly predicted the N stage in 87.3% of the testing cases compared with 73.5% for the (18)F-FDG PET expert reader. Accuracy of the aNN increased to 94.8% (PET, 89.4%) when comparing N(0) + N(1) with N(2) or N(3) status and to 94.9% (PET, 91.9%) when comparing N(0) + N(1) with N(2) + N(3) status. CONCLUSION: A back-propagation aNN can be trained to predict hilar and mediastinal nodal involvement with greater accuracy than an expert (18)F-FDG PET reader. Such a tool could be used to improve clinical interpretations and for clinical training.
UNLABELLED: We proposed to train a back-propagation artificial neural network (aNN) on a cohort of surgically proven non-small cell lung cancers (NSCLCs) and compare its accuracy with that of a trained (18)F-FDG PET reader. We plan to show that an aNN trained on (18)F-FDG PET- and CT-derived data is more accurate in predicting the true surgicopathologic nodal stage than a human reader. METHODS: One hundred thirty-three NSCLCpatients with surgically proven N status treated at the University of Washington Medical Center or the Veterans Affairs Puget Sound Health Care System between February 1998 and September 2002 were used as inputs for the creation of an aNN. From CT of the thorax and (18)F-FDG PET (neck to pelvis) performed before surgery, we extracted the primary tumor size and uptake (maximum pixel SUV [maxSUV]), normal lung and mediastinal uptake, and nodal uptake (maxSUV). Using the same 133 cases, the same output (surgical N status, N(0) to N(3)), and the same software configuration settings, scenarios were created to assess which input parameters were most influential in creating an optimal aNN. To compute this optimal aNN, cases were split randomly 100 times into a training subset of 103 cases and a testing subset of 30 cases having the same proportion of N(0), N(1), N(2), and N(3) cases. N status predicted by the aNN was compared with the proven surgical N status to calculate the aNN accuracy. The N status readings from (18)F-FDG PET were also compared with the surgical N status for the same cases to determine (18)F-FDG PET accuracy. RESULTS: Statistical tests demonstrate that the best aNN accuracy is achieved by using N(1)-N(2)- N(3) nodal maxSUV divided by background uptake, the primary tumor size, and primary tumor maxSUV as inputs. The aNN correctly predicted the N stage in 87.3% of the testing cases compared with 73.5% for the (18)F-FDG PET expert reader. Accuracy of the aNN increased to 94.8% (PET, 89.4%) when comparing N(0) + N(1) with N(2) or N(3) status and to 94.9% (PET, 91.9%) when comparing N(0) + N(1) with N(2) + N(3) status. CONCLUSION: A back-propagation aNN can be trained to predict hilar and mediastinal nodal involvement with greater accuracy than an expert (18)F-FDG PET reader. Such a tool could be used to improve clinical interpretations and for clinical training.
Authors: Maja Guberina; Ken Herrmann; Christoph Pöttgen; Nika Guberina; Hubertus Hautzel; Thomas Gauler; Till Ploenes; Lale Umutlu; Axel Wetter; Dirk Theegarten; Clemens Aigner; Wilfried E E Eberhardt; Martin Metzenmacher; Marcel Wiesweg; Martin Schuler; Rüdiger Karpf-Wissel; Alina Santiago Garcia; Kaid Darwiche; Martin Stuschke Journal: Sci Rep Date: 2022-10-20 Impact factor: 4.996
Authors: Farhood Farjah; David K Madtes; Douglas E Wood; David R Flum; Megan E Zadworny; Rachel Waworuntu; Billanna Hwang; Michael S Mulligan Journal: J Thorac Cardiovasc Surg Date: 2015-08-06 Impact factor: 5.209
Authors: Silvia Taralli; Valentina Scolozzi; Luca Boldrini; Jacopo Lenkowicz; Armando Pelliccioni; Margherita Lorusso; Ola Attieh; Sara Ricciardi; Francesco Carleo; Giuseppe Cardillo; Maria Lucia Calcagni Journal: Front Med (Lausanne) Date: 2021-04-22