Application of a neural network to improve nodal staging accuracy with 18F-FDG PET in non-small cell lung cancer.
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.


CONCLUSIONS
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.
Vesselle
Hubert
H
Division of Nuclear Medicine, University of Washington, Seattle, Washington 98195, USA. vesselle@u.washington.edu
Turcotte
Eric
E
Wiens
Linda
L
Haynor
David
D
D000368:Aged; D001185:Artificial Intelligence; D002289:Carcinoma, Non-Small-Cell Lung / Q000000981:diagnostic imaging* / Q000473:pathology*; D015331:Cohort Studies; D005260:Female; D019788:Fluorodeoxyglucose F18; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging / Q000379:methods*; D016571:Neural Networks, Computer; D011877:Radionuclide Imaging; D019275:Radiopharmaceuticals; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18