Frances R. Anderson

Application of support vector machines to breast cancer screening using mammogram and clinical history data

The objectives of this paper are to discuss: (1) the development and testing of a new Evolutionary Programming (EP) method to optimally configure Support Vector Machine (SVM) parameters for facilitating the diagnosis of breast cancer; (2) evaluation of EP derived learning machines when the number of BI-RADS and clinical history discriminators are reduced from 16 to 7; (3) establishing system performance for several SVM kernels in addition to the EP/Adaptive Boosting (EP/AB) hybrid using the Digital Database for Screening Mammography, University of South Florida (DDSM USF) and Duke data sets; and (4) obtaining a preliminary evaluation of the measurement of SVM learning machine inter-institutional generalization capability using BI-RADS data. Measuring performance of the SVM designs and EP/AB hybrid against these objectives will provide quantative evidence that the software packages described can generalize to larger patient data sets from different institutions. Most iterative methods currently in use to optimize learning machine parameters are time consuming processes, which sometimes yield sub-optimal values resulting in performance degradation. SVMs are new machine intelligence paradigms, which use the Structural Risk Minimization (SRM) concept to develop learning machines. These learning machines can always be trained to provide global minima, given that the machine parameters are optimally computed. In addition, several system performance studies are described which include EP derived SVM performance as a function of: (a) population and generation size as well as a method for generating initial populations and (b) iteratively derived versus EP derived learning machine parameters. Finally, the authors describe a set of experiments providing preliminary evidence that both the EP/AB hybrid and SVM Computer Aided Diagnostic C++ software packages will work across a large population of patients, based on a data set of approximately 2,500 samples from five different institutions.

Breast cancer computer aided diagnosis (CAD) using a recently developed SVM/GRNN Oracle hybrid

Carcinoma of the breast is second only to lung cancer as a tumor-related cause of death in women. For 2003, it has been reported that 211,300 new cases and 39,800 deaths occurred just in the US. It has been proposed, however, that the mortality from breast cancer could be decreased by up to 25% if all women in appropriate age groups were screened regularly. Currently, the method of choice for the early detection of breast cancer is mammography, due to its general widespread availability, low cost, speed, and non-invasiveness. At the same time, while mammography is sensitive to the detection of breast cancer, its positive predictive value (PPV) is low, resulting in costly, invasive biopsies that are only 15%-34% likely to reveal malignancy at histological examination. This paper explores the use of a newly designed support vector machine (SVM)/generalized regression neural network (GRNN) Oracle hybrid and evaluates its performance as an interpretive aid to radiologists. The authors demonstrate that this hybrid has the potential to improve both the specificity and PPV of screen film mammography at 95-100% sensitivity, and consistently produce partial A/sub Z/ values (defined as average specificity over the top 10% of the ROC curve) of grater than 50% using a data set of /spl sim/2000 lesions from four different hospitals. As expected, initial experiments demonstrated that combining age and mass margin (AgeMM) that provided the most accurate diagnostic performance. Secondly, the value of the crossover constant, CR = 0.6, provided the best A/sub Z/, while CR = 0.8 resulted in the most accurate partial A/sub Z/ specificity, and PPV at the lower sensitivities. Finally, the results of the GRNN oracle output were essentially the same as those of the SVM suggesting that the SVMs, as anticipated, had optimized the diagnostic performance. Practically, this means that at 100% sensitivity (which means no cancerous lesions are misdiagnosed) and using a crossover constant of 0.8, approximately 454 biopsies is avoided using this SVM/GRNN oracle diagnostic aid when compared to the circumstance where all 1979 samples were biopsied.

Applying support vector machines to breast cancer diagnosis using screen film mammogram data

This paper explores the use of different support vector machines (SVM) kernels, and combinations of kernels, to ascertain the diagnostic accuracy of a screen film mammogram data set containing /spl cong/ 2500 samples from five different institutions. This research has demonstrated that: (1) specificity improves, on the average, of about 4% at 100% sensitivity and about 18%, on the average, at 98% sensitivity. This means that approximately 52 and 134 women would not have to undergo biopsy, at 100% and 98% sensitivity, when compared to the case of every women being biopsied, which would be necessary to identify all cancers in the absence of a computer aided diagnostic (CAD) process, (2) positive predictive value (PPV) at these same values of sensitivity are much better, ranging from 48% to 51 % as sensitivity is decreased from 100 to 98%. Finally, the average specificity over the top 10% or the ROC curve (which is the average specificity between 90-100% sensitivity) is about 30%. This means that, on the average, 440 women would not have to undergo biopsy, when compared to the case of all women being biopsied.

Data fusion of several support-vector-machine breast-cancer diagnostic paradigms using a GRNN oracle

Breast cancer is second to lung cancer as a tumor-related cause of death in women. For 2003, it was reported that 211,300 new cases and 39,800 deaths would occur in the US. It has been proposed that breast cancer mortality could be decreased by 25% if women in appropriate age groups were screened regularly. Currently, the preferred method for breast cancer screening is mammography, due to its widespread availability, low cost, speed, and non-invasiveness. At the same time, while mammography is sensitive to the detection of breast cancer, its positive predictive value (PPV) is low, resulting in costly, invasive biopsies that are only 15-34% likely to reveal malignancy at histologic examination. This paper explores the use of a newly designed Support Vector Machine (SVM)/Generalized Regression Neural Network (GRNN) Oracle hybrid and evaluates the hybrid’s performance as an interpretive aid to radiologists. The authors demonstrate that this hybrid has the potential to (1) improve both specificity and PPV of screen film mammography at 95-100% sensitivity, and (2) consistently produce partial AZ values (defined as average specificity over the top 10% of the ROC curve) of greater than 30%, using a data set of ~2500 lesions from five different hospitals and/or institutions.

When a minute seemslike a millennium

Surgery is a stressful experience for patients and their families. The increased use of ambulatory surgical services, coupled with changes in health care economics, requires nurses to design innovative ways of addressing the emotional needs of patients and families, while not compromising standards of care or becoming frustrated with the fast-paced world of ambulatory surgery. This article presents ways to help patients and their families throughout the preoperative experience.

Engaging Parents in Reproductive Health Education: Lessons Learned Implementing a Parent Designed, Peer-Led Educational Model for Parents of Preteens

Engaging and supporting parents to provide sexuality education to their children is successful when parents take ownership of the intervention. The purpose of this article is to illustrate the lessons learned from implementing a parent-designed, parent-led sexuality education curriculum for parents of preteens (10–14 year olds). The parents developed the methods to disseminate their expertise, experience, and strategies they discovered that worked to enhance family communication and education around sexuality. Specific lessons learned include how the parents’ created an educational experience; using parents as recruiters and peer educators; and ensuring fidelity of the parent led design.

Breast cancer classification improvements using a new kernel function with evolutionary-programming-configured support vector machines

Mammography is an effective tool for the early detection of breast cancer; however, most women referred for biopsy based on mammographic findings do not, in fact, have cancer. This study is part of an ongoing effort to reduce the number of benign cases referred for biopsy by developing tools to aid physicians in classifying suspicious lesions. Specifically, this study examines the use of an Evolutionary Programming (EP)-derived Support Vector Machine (SVM) with a modified radial basis function (RBF) kernel, and compares this with results using a normal Gaussian radial basis function kernel. Results demonstrate that the modified kernel can provide moderate performance improvements; however, due to its ability to create a more complex decision surface, this kernel can easily begin to memorize the training data resulting in a loss of generalization ability. Nonetheless, these methods could reduce the number of benign cases referred for biopsy by over half, while missing less than 5% of malignancies. Future work will focus on methods to improve the EP process to preserve SVMs which generalize well.

Improving the predictive value of mammography using a specialized evolutionary programming hybrid and fitness functions

Mammography is an effective tool for the early detection of breast cancer; however, most women referred for biopsy based on mammographic findings do not, have cancer. This study is part of an ongoing effort to reduce the number of benign cases referred for biopsy by developing tools to aid physicians in classifying suspicious lesions. Specifically, this study examines the use of an Evolutionary Programming (EP)/Adaptive Boosting (AB) hybrid, specifically modified to focus on improving the performance of computer-assisted diagnostic (CAD) tools at high specificity levels (missing few or no cancers). An EP/AB hybrid developed by the authors and used in previous studies was modified with two new fitness functions: 1) a function which favored networks with the high PPV values at thresholds corresponding to high sensitivities and 2) a function which favored networks with the highest partial ROC Az (normalized area about 90% sensitivity). The modified hybrid with specialized fitness functions was evaluated using k-fold cross-validation against two real-word mammogram data sets. Results indicate that the number of benign cases referred for biopsy might be reduced by over a third, while missing no cancers. If sensitivity is allowed to decrease to 97% (missing 3% of the cancers), the number of spared biopsies could be raised to over half.

Applying knowledge engineering and representation methods to improve support vector machine and multivariate probabilistic neural network CAD performance

Achieving consistent and correct database cases is crucial to the correct evaluation of any computer-assisted diagnostic (CAD) paradigm. This paper describes the application of artificial intelligence (AI), knowledge engineering (KE) and knowledge representation (KR) to a data set of ≈2500 cases from six separate hospitals, with the objective of removing/reducing inconsistent outlier data. Several support vector machine (SVM) kernels were used to measure diagnostic performance of the original and a “cleaned” data set. Specifically, KE and ER principles were applied to the two data sets which were re-examined with respect to the environment and agents. One data set was found to contain 25 non-characterizable sets. The other data set contained 180 non-characterizable sets. CAD system performance was measured with both the original and “cleaned” data sets using two SVM kernels as well as a multivariate probabilistic neural network (PNN). Results demonstrated: (i) a 10% average improvement in overall Az and (ii) approximately a 50% average improvement in partial Az.

Integrating knowledge representation/engineering, the multivariant PNN, and machine learning to improve breast cancer diagnosis

Breast cancer is second only to lung cancer as a tumor-related cause of death in women. Currently, the method of choice for the early detection of breast cancer is mammography. While sensitive to the detection of breast cancer, its positive predictive value (PPV) is low. One of the main deterrents to achieving high computer aided diagnostic (CAD) accuracy is carelessly developed databases. These “noisy” data sets have always appeared to disrupt learning agents from learning correctly. A new statistical method for cleaning data sets was developed that improves the performance of CAD systems. Initial research efforts showed the following: PLS Az value improved by 8.79% and partial Az improved by 49.71%. The K-PLS Az value at Sigma 4.1 improved by 9.18% and the partial Az by 43.47%. The K-PLS at Sigma 3.6 (best fit sigma with this data set) Az value improved by 9.24% and the partial Az by 44.29%. With larger data sets, the ROC curves potentially could look much better than they do now. The Az value for K-PLS (0.892565) is better than PLS, PNN, and most SVMs. The SVM-rbf kernel was the only agent that out performed the K-PLS with an Az value of 0.895362. However, K-PLS runs much faster and appears to be just as accurate as the SVM-rbf kernel.