Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement

Purpose: To investigate methods developed for the characterisation of the morphology and enhancement kinetic features of both mass and non-mass lesions, and to determine their diagnostic performance to differentiate between malignant and benign lesions that present as mass versus non-mass types. Methods: Quantitative analysis of morphological features and enhancement kinetic parameters of breast lesions were used to differentiate among four groups of lesions: 88 malignant (43 mass, 45 non-mass) and 28 benign (19 mass, 9 non-mass). The enhancement kinetics was measured and analysed to obtain transfer constant (Ktrans) and rate constant (kep). For each mass eight shape/margin parameters and 10 enhancement texture features were obtained. For the lesions presenting as nonmass-like enhancement, only the texture parameters were obtained. An artificial neural network (ANN) was used to build the diagnostic model. Results: For lesions presenting as mass, the four selected morphological features could reach an area under the ROC curve (AUC) of 0.87 in differentiating between malignant and benign lesions. The kinetic parameter (kep) analysed from the hot spot of the tumour reached a comparable AUC of 0.88. The combined morphological and kinetic features improved the AUC to 0.93, with a sensitivity of 0.97 and a specificity of 0.80. For lesions presenting as non-mass-like enhancement, four texture features were selected by the ANN and achieved an AUC of 0.76. The kinetic parameter kepfrom the hot spot only achieved an AUC of 0.59, with a low added diagnostic value. Conclusion: The results suggest that the quantitative diagnostic features can be used for developing automated breast CAD (computer-aided diagnosis) for mass lesions to achieve a high diagnostic performance, but more advanced algorithms are needed for diagnosis of lesions presenting as non-mass-like enhancement. © The Author(s) 2009

Distinguishing between the metabolome and xenobiotic exposome in environmental field samples analysed by direct-infusion mass spectrometry based metabolomics and lipidomics

Environmental metabolomics is increasingly used to investigate organismal responses to complex chemical mixtures, including waste water effluent (WWE). In parallel, increasingly sensitive analytical methods are being used in metabolomics studies, particularly mass spectrometry. This introduces a considerable, yet overlooked, challenge that high analytical sensitivity will not only improve the detection of endogenous metabolites in biological specimens but also exogenous chemicals. If these often unknown xenobiotic features are not removed from the “biological” dataset, they will bias the interpretation and could lead to incorrect conclusions about the biotic response. Here we illustrate and validate a novel workflow classifying the origin of peaks detected in biological samples as: endogenous, xenobiotics, or metabolised xenobiotics. The workflow is demonstrated using direct infusion mass spectrometry-based metabolomic analysis of testes from roach exposed to different concentrations of a complex WWE. We show that xenobiotics and their metabolic products can be detected in roach testes (including triclosan, chloroxylenol and chlorophene), and that these compounds have a disproportionately high level of statistical significance within the total (bio)chemical changes induced by the WWE. Overall we have demonstrated that this workflow extracts more information from an environmental metabolomics study of complex mixture exposures than was possible previously. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-014-0693-3) contains supplementary material, which is available to authorized users