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Do serum biomarkers really measure breast cancer?

Jesneck JL, Mukherjee S, Yurkovetsky Z, Clyde M, Marks JR, Lokshin AE, Lo JY - BMC Cancer (2009)

Bottom Line: Because of significant noise in the data set, we applied iterated Bayesian model averaging to account for model selection uncertainty and to improve generalization performance.Overall, the selected serum proteins showed moderate ability for detecting lesions.However, they are probably more indicative of secondary effects such as inflammation rather than specific for malignancy.

View Article: PubMed Central - HTML - PubMed

Affiliation: Duke Advanced Imaging Labs, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27710, USA. JonathanL_Jesneck@dfci.harvard.edu

ABSTRACT

Background: Because screening mammography for breast cancer is less effective for premenopausal women, we investigated the feasibility of a diagnostic blood test using serum proteins.

Methods: This study used a set of 98 serum proteins and chose diagnostically relevant subsets via various feature-selection techniques. Because of significant noise in the data set, we applied iterated Bayesian model averaging to account for model selection uncertainty and to improve generalization performance. We assessed generalization performance using leave-one-out cross-validation (LOOCV) and receiver operating characteristic (ROC) curve analysis.

Results: The classifiers were able to distinguish normal tissue from breast cancer with a classification performance of AUC = 0.82 +/- 0.04 with the proteins MIF, MMP-9, and MPO. The classifiers distinguished normal tissue from benign lesions similarly at AUC = 0.80 +/- 0.05. However, the serum proteins of benign and malignant lesions were indistinguishable (AUC = 0.55 +/- 0.06). The classification tasks of normal vs. cancer and normal vs. benign selected the same top feature: MIF, which suggests that the biomarkers indicated inflammatory response rather than cancer.

Conclusion: Overall, the selected serum proteins showed moderate ability for detecting lesions. However, they are probably more indicative of secondary effects such as inflammation rather than specific for malignancy.

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Related in: MedlinePlus

ROC curves showing the classification performance of statistical models using the serum protein levels. The models were run with a 70% train and 30% test split of the data set (A-C) and also with leave-one-out cross-validation (LOOCV) (D-F). The classifiers performed similarly, with moderate classification results for normal vs. malignant or benign lesions (A, B, D, E) and poor classification results for malignant vs. benign lesions (C, F).
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Figure 1: ROC curves showing the classification performance of statistical models using the serum protein levels. The models were run with a 70% train and 30% test split of the data set (A-C) and also with leave-one-out cross-validation (LOOCV) (D-F). The classifiers performed similarly, with moderate classification results for normal vs. malignant or benign lesions (A, B, D, E) and poor classification results for malignant vs. benign lesions (C, F).

Mentions: The classifiers achieved moderate classification performance for both normal vs. malignant (AUC = 0.77 ± 0.02 on the test set, and AUC = 0.80 ± 0.02 for LOOCV) and normal vs. benign (AUC = 0.75 ± 0.03 on the test set, and AUC = 0.77 ± 0.02 for LOOCV), but very poor performance for malignant vs. benign tumors (AUC = 0.57 ± 0.05 on the test set, and AUC = 0.53 ± 0.03 for LOOCV). The classification performance is shown as ROC curves in Figure 1. Whereas the ROC curves show the classification performance over the entire range of prediction thresholds, we also considered the threshold of Pr(Yi = 1/β) = 0.5 in particular. For this threshold, Table 3 shows the classification error. The models performed similarly, with approximately 20 false negatives and 10 false positives. All classifiers were run with leave-one-out cross-validation (LOOCV). The classifiers chose the best subsets of proteins for classification.


Do serum biomarkers really measure breast cancer?

Jesneck JL, Mukherjee S, Yurkovetsky Z, Clyde M, Marks JR, Lokshin AE, Lo JY - BMC Cancer (2009)

ROC curves showing the classification performance of statistical models using the serum protein levels. The models were run with a 70% train and 30% test split of the data set (A-C) and also with leave-one-out cross-validation (LOOCV) (D-F). The classifiers performed similarly, with moderate classification results for normal vs. malignant or benign lesions (A, B, D, E) and poor classification results for malignant vs. benign lesions (C, F).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2696469&req=5

Figure 1: ROC curves showing the classification performance of statistical models using the serum protein levels. The models were run with a 70% train and 30% test split of the data set (A-C) and also with leave-one-out cross-validation (LOOCV) (D-F). The classifiers performed similarly, with moderate classification results for normal vs. malignant or benign lesions (A, B, D, E) and poor classification results for malignant vs. benign lesions (C, F).
Mentions: The classifiers achieved moderate classification performance for both normal vs. malignant (AUC = 0.77 ± 0.02 on the test set, and AUC = 0.80 ± 0.02 for LOOCV) and normal vs. benign (AUC = 0.75 ± 0.03 on the test set, and AUC = 0.77 ± 0.02 for LOOCV), but very poor performance for malignant vs. benign tumors (AUC = 0.57 ± 0.05 on the test set, and AUC = 0.53 ± 0.03 for LOOCV). The classification performance is shown as ROC curves in Figure 1. Whereas the ROC curves show the classification performance over the entire range of prediction thresholds, we also considered the threshold of Pr(Yi = 1/β) = 0.5 in particular. For this threshold, Table 3 shows the classification error. The models performed similarly, with approximately 20 false negatives and 10 false positives. All classifiers were run with leave-one-out cross-validation (LOOCV). The classifiers chose the best subsets of proteins for classification.

Bottom Line: Because of significant noise in the data set, we applied iterated Bayesian model averaging to account for model selection uncertainty and to improve generalization performance.Overall, the selected serum proteins showed moderate ability for detecting lesions.However, they are probably more indicative of secondary effects such as inflammation rather than specific for malignancy.

View Article: PubMed Central - HTML - PubMed

Affiliation: Duke Advanced Imaging Labs, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27710, USA. JonathanL_Jesneck@dfci.harvard.edu

ABSTRACT

Background: Because screening mammography for breast cancer is less effective for premenopausal women, we investigated the feasibility of a diagnostic blood test using serum proteins.

Methods: This study used a set of 98 serum proteins and chose diagnostically relevant subsets via various feature-selection techniques. Because of significant noise in the data set, we applied iterated Bayesian model averaging to account for model selection uncertainty and to improve generalization performance. We assessed generalization performance using leave-one-out cross-validation (LOOCV) and receiver operating characteristic (ROC) curve analysis.

Results: The classifiers were able to distinguish normal tissue from breast cancer with a classification performance of AUC = 0.82 +/- 0.04 with the proteins MIF, MMP-9, and MPO. The classifiers distinguished normal tissue from benign lesions similarly at AUC = 0.80 +/- 0.05. However, the serum proteins of benign and malignant lesions were indistinguishable (AUC = 0.55 +/- 0.06). The classification tasks of normal vs. cancer and normal vs. benign selected the same top feature: MIF, which suggests that the biomarkers indicated inflammatory response rather than cancer.

Conclusion: Overall, the selected serum proteins showed moderate ability for detecting lesions. However, they are probably more indicative of secondary effects such as inflammation rather than specific for malignancy.

Show MeSH
Related in: MedlinePlus