R K Orr1. 1. Department of Medical Education (Surgery), Spartanburg Regional Medical Center, 101 E. Wood Ave., Spartanburg, SC 29303, USA.
Abstract
BACKGROUND: The purpose of this study was to develop a simplified method for standardized categorization of patients with abnormal mammograms by incorporating quantitative risk assessment. METHODS AND PATIENTS: A prospective collection of 1288 outpatient referrals to a surgeon for abnormal mammograms, 185 (14.4%) with malignancy, was studied. Artificial neural network (ANN) and logistic regression (LR) models were developed and compared with the surgeon's clinical impression. The first 490 patients were used as the training set for each model. The ANN and LR were tested on the remaining patients, who were divided into 2 consecutive groups. The main outcome measures were (1) the accuracy (receiver operating characteristic [ROC] curve analysis) of biopsy recommendations based on the surgeon's impression and created by the 2 models and (2) the percentage of cancers that were falsely categorized as benign by the surgeon or the 2 models. RESULTS: Despite the fact that the surgeon's clinical impression showed good discrimination (area under ROC = 0.86), 13 of 708 cases (1.8%) thought to be benign by the surgeon proved to be carcinomas. The neural network (but not the LR model) was statistically superior to the surgeon's impression (ANN: ROC = 0.89, P =.004; LR: ROC = 0.86). Additionally, the computerized models were able to quantitate risk. Those patients predicted to be "benign" by the network (n = 391) had only a 0.5% risk of intraductal carcinoma and no invasive carcinoma, whereas 47% of those patients in the highest risk quartile had cancer. Both computerized models predicted a need for biopsy in 11 of 13 of the lesions (85%) missed by the surgeon's impression. Each model missed only 2 cases of intraductal carcinoma in young women. CONCLUSIONS: Computerized risk stratification models, used in routine practice, may help surgeons with decision making. The use of either model helps quantitate risk, thereby facilitating discussions with patients, and may reduce the number of "missed" cancers.
BACKGROUND: The purpose of this study was to develop a simplified method for standardized categorization of patients with abnormal mammograms by incorporating quantitative risk assessment. METHODS AND PATIENTS: A prospective collection of 1288 outpatient referrals to a surgeon for abnormal mammograms, 185 (14.4%) with malignancy, was studied. Artificial neural network (ANN) and logistic regression (LR) models were developed and compared with the surgeon's clinical impression. The first 490 patients were used as the training set for each model. The ANN and LR were tested on the remaining patients, who were divided into 2 consecutive groups. The main outcome measures were (1) the accuracy (receiver operating characteristic [ROC] curve analysis) of biopsy recommendations based on the surgeon's impression and created by the 2 models and (2) the percentage of cancers that were falsely categorized as benign by the surgeon or the 2 models. RESULTS: Despite the fact that the surgeon's clinical impression showed good discrimination (area under ROC = 0.86), 13 of 708 cases (1.8%) thought to be benign by the surgeon proved to be carcinomas. The neural network (but not the LR model) was statistically superior to the surgeon's impression (ANN: ROC = 0.89, P =.004; LR: ROC = 0.86). Additionally, the computerized models were able to quantitate risk. Those patients predicted to be "benign" by the network (n = 391) had only a 0.5% risk of intraductal carcinoma and no invasive carcinoma, whereas 47% of those patients in the highest risk quartile had cancer. Both computerized models predicted a need for biopsy in 11 of 13 of the lesions (85%) missed by the surgeon's impression. Each model missed only 2 cases of intraductal carcinoma in young women. CONCLUSIONS: Computerized risk stratification models, used in routine practice, may help surgeons with decision making. The use of either model helps quantitate risk, thereby facilitating discussions with patients, and may reduce the number of "missed" cancers.
Authors: Turgay Ayer; Oguzhan Alagoz; Jagpreet Chhatwal; Jude W Shavlik; Charles E Kahn; Elizabeth S Burnside Journal: Cancer Date: 2010-07-15 Impact factor: 6.860