Purpose:To assess the performance of computer-extracted dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging kinetic and morphologic features in the differentiation of invasive versus noninvasive breast lesions and metastatic versus nonmetastatic breast lesions. Materials and Methods:In this institutional review board-approved HIPAA-compliant study, in which the requirement for informed patient consent was waived, breast MR images were retrospectively collected. The images had been obtained with a 1.5-T MR unit by using a gadodiamide-enhanced T1-weighted spoiled gradient-recalled acquisition in the steady state sequence. The breast MR imaging database contained 132 benign, 71 ductal carcinoma in situ (DCIS), and 150 invasive ductal carcinoma (IDC) lesions. Fifty-four IDC lesions were associated with metastasis-positive lymph nodes (LNs), and 64 IDC lesions were associated with negative LNs. Lesion segmentation and extraction of morphologic and kinetic features were automatically performed by a laboratory-developed computer workstation. Features were fi rst selected by using stepwise linear discriminant analysis and then merged by using Bayesian neural networks. Lesion classifi cation performance was assessed with receiver operating characteristic analysis. Results: Conclusion:Computer-aided diagnosis of breast DCE MR imagingdepicted lesions was extended from the task of discriminating between malignant and benign lesions to the prognostic tasks of distinguishing between noninvasive and invasive lesions and discriminating between metastatic and nonmetastatic lesions, yielding MR imaging-based prognostic markers.q RSNA, 2010 Supplemental material: http://radiology.rsna.org/lookup/ suppl

Purpose: In this preliminary study, recently developed unsupervised nonlinear dimension reduction ͑DR͒ and data representation techniques were applied to computer-extracted breast lesion feature spaces across three separate imaging modalities: Ultrasound ͑U.S.͒ with 1126 cases, dynamic contrast enhanced magnetic resonance imaging with 356 cases, and full-field digital mammography with 245 cases. Two methods for nonlinear DR were explored: Laplacian eigenmaps ͓M. Belkin and P. Niyogi, "Laplacian eigenmaps for dimensionality reduction and data representation," Neural Comput. 15, 1373-1396 ͑2003͔͒ and t-distributed stochastic neighbor embedding ͑t-SNE͒ ͓L. van der Maaten and G. Hinton, "Visualizing data using t-SNE," J. Mach. Learn. Res. 9, 2579-2605 ͑2008͔͒. Methods: These methods attempt to map originally high dimensional feature spaces to more human interpretable lower dimensional spaces while preserving both local and global information. The properties of these methods as applied to breast computer-aided diagnosis ͑CADx͒ were evaluated in the context of malignancy classification performance as well as in the visual inspection of the sparseness within the two-dimensional and three-dimensional mappings. Classification performance was estimated by using the reduced dimension mapped feature output as input into both linear and nonlinear classifiers: Markov chain Monte Carlo based Bayesian artificial neural network ͑MCMC-BANN͒ and linear discriminant analysis. The new techniques were compared to previously developed breast CADx methodologies, including automatic relevance determination and linear stepwise ͑LSW͒ feature selection, as well as a linear DR method based on principal component analysis. Using ROC analysis and 0.632+ bootstrap validation, 95% empirical confidence intervals were computed for the each classifier's AUC performance. Results: In the large U.S. data set, sample high performance results include, AUC 0.632+ = 0.88 with 95% empirical bootstrap interval ͓0.787;0.895͔ for 13 ARD selected features and AUC 0.632+ = 0.87 with interval ͓0.817;0.906͔ for four LSW selected features compared to 4D t-SNE mapping ͑from the original 81D feature space͒ giving AUC 0.632+ = 0.90 with interval ͓0.847;0.919͔, all using the MCMC-BANN. Conclusions: Preliminary results appear to indicate capability for the new methods to match or exceed classification performance of current advanced breast lesion CADx algorithms. While not appropriate as a complete replacement of feature selection in CADx problems, DR techniques offer a complementary approach, which can aid elucidation of additional properties associated with the data. Specifically, the new techniques were shown to possess the added benefit of delivering sparse lower dimensional representations for visual interpretation, revealing intricate data structure of the feature space.

Background. Neutrophil-lymphocyte ratio (NLR) is a measure of systemic inflammation that appears prognostic in localized and advanced non-small cell lung cancer (NSCLC). Increased systemic inflammation portends a poorer prognosis in cancer patients. We hypothesized that low NLR at diagnosis is associated with improved overall survival (OS) in locally advanced NSCLC (LANSCLC) patients. Patients and Methods. Records from 276 patients with stage IIIA and IIIB NSCLC treated with definitive chemoradiation with or without surgery between 2000 and 2010 with adequate data were retrospectively reviewed. Baseline demographic data and pretreatment peripheral blood absolute neutrophil and lymphocyte counts were collected. Patients were grouped into quartiles based on NLR. OS was estimated using the KaplanMeier method. The log-rank test was used to compare mortality between groups. A linear test-for-trend was used for the NLR

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A multi-parametric computer-aided diagnosis (CADx) scheme that combines information from T1-weighted DCE-MRI and T2-weighted MRI was investigated using a database of 110 malignant and 86 benign breast lesions. Automatic lesion segmentation was performed, and three categories of lesion features (geometric, T1-weighted DCE, and T2-weighted) were automatically extracted. Stepwise feature selection was performed considering only geometric features, only T1-weighted DCE features, only T2-weighted features, and all features. Features were merged with Bayesian artificial neural networks, and diagnostic performance was evaluated by ROC analysis. With leave-one-lesion-out cross-validation, an AUC value of 0.77 ± 0.03 was achieved with T2-weighted-only features, indicating high diagnostic value of information in T2-weighted images. AUC values of 0.79 ± 0.03 and 0.80 ± 0.03 were obtained for geometric-only features and T1-weighted DCE-only features, respectively. When all features were considered, an AUC value of 0.85 ± 0.03 was achieved. We observed p-values of 0.0006, 0.023, and 0.0014 between the {geometric-only, T1-weighted DCE-only, and T2-weighted-only features} and all features conditions, respectively. When ranked, the p-values satisfied the Holm-Bonferroni multiple-comparison test; thus, the improvement of multi-parametric CADx was statistically significant. A CADx scheme that combines information from T1-weighted DCE and T2-weighted MRI may be advantageous over conventional T1-weighted DCE-MRI CADx.

The purpose of this study is to investigate whether computerized analysis using three-class Bayesian artificial neural network (BANN) feature selection and classification can characterize tumor grades (grade 1, grade 2 and grade 3) of breast lesions for prognostic classification on DCE-MRI. A database of 26 IDC grade 1 lesions, 86 IDC grade 2 lesions and 58 IDC grade 3 lesions was collected. The computer automatically segmented the lesions, and kinetic and morphological lesion features were automatically extracted. The discrimination tasks-grade 1 versus grade 3, grade 2 versus grade 3, and grade 1 versus grade 2 lesions-were investigated.Step-wise feature selection was conducted by three-class BANNs. Classification was performed with threeclass BANNs using leave-one-lesion-out cross-validation to yield computerestimated probabilities of being grade 3 lesion, grade 2 lesion and grade 1 lesion. Two-class ROC analysis was used to evaluate the performances. We achieved AUC values of 0.80 ± 0.05, 0.78 ± 0.05 and 0.62 ± 0.05 for grade 1 versus grade 3, grade 1 versus grade 2, and grade 2 versus grade 3, respectively. This study shows the potential for (1) applying three-class BANN feature selection and classification to CADx and (2) expanding the role of DCE-MRI CADx from diagnostic to prognostic classification in distinguishing tumor grades.

Use of the CADx system improved the radiologists' performance in differentiating between malignant and benign MR imaging-depicted breast lesions.