Purpose:To evaluate the incremental value of diffusion-weighted (DW) imaging and apparent diffusion coeffi cient (ADC) mapping in relation to conventional breast magnetic resonance (MR) imaging in the characterization of benign versus malignant breast lesions at 3.0 T. Materials and Methods:This retrospective HIPAA-compliant study was approved by the institutional review board, with the requirement for informed patient consent waived. Of 550 consecutive patients who underwent bilateral breast MR imaging over a 10-month period, 93 women with 101 lesions met the following study inclusion criteria: They had undergone three-dimensional (3D) high -spatial-resolution T1-weighted contrast material-enhanced MR imaging, dynamic contrastenhanced MR imaging, and DW imaging examinations at 3.0 T and either had received a pathologic analysis-proven diagnosis (96 lesions) or had lesion stability confi rmed at more than 2 years of follow-up (fi ve lesions). DW images were acquired with b values of 0 and 600 sec/mm 2 . Regions of interest were drawn on ADC maps of breast lesions and normal glandular tissue. Morphologic features (margin, enhancement pattern), dynamic contrastenhanced MR results (semiquantitative kinetic curve data), absolute ADCs, and glandular tissue-normalized ADCs were included in multivariate models to predict a diagnosis of benign versus malignant lesion. Results:Forty-one (44%) of the 93 patients were premenopausal, and 52 (56%) were postmenopausal. Thirty-three (32.7%) of the 101 lesions were benign, and 68 (67.3%) were malignant. Normalized ADCs were signifi cantly different between the benign (mean ADC, 1.1 3 10 2 3 mm 2 /sec 6 0.4 [standard deviation]) and malignant (mean ADC, 0.55 3 10 2 3 mm 2 /sec 6 0.16) lesions ( P , .001). Adding normalized ADCs to the 3D T1-weighted and dynamic contrast-enhanced MR data improved the diagnostic performance of MR imaging: The area under the receiver operating characteristic curve improved from 0 .89 to 0.98, and the false-positive rate decreased from 36% (nine of 25 lesions) to 24% (six of 25 lesions). Conclusion:DW imaging with glandular tissue-normalized ADC assessment improves the characterization of breast lesions beyond the characterization achieved with conventional 3D T1-weighted and dynamic contrast-enhanced MR imaging at 3.0 T.q RSNA, 2010

CT screening can reduce death from lung cancer. We sought to improve the diagnostic accuracy of lung cancer screening using ultrasensitive methods and a lung cancer-specific gene panel to detect DNA methylation in sputum and plasma. This is a case-control study of subjects with suspicious nodules on CT imaging. Plasma and sputum were obtained preoperatively. Cases ( = 150) had pathologic confirmation of node-negative (stages I and IIA) non-small cell lung cancer. Controls ( = 60) had non-cancer diagnoses. We detected promoter methylation using quantitative methylation-specific real-time PCR and methylation-on-beads for cancer-specific genes (, and ). DNA methylation was detected in plasma and sputum more frequently in people with cancer compared with controls ( < 0.001) for five of six genes. The sensitivity and specificity for lung cancer diagnosis using the best individual genes was 63% to 86% and 75% to 92% in sputum, respectively, and 65% to 76% and 74% to 84% in plasma, respectively. A three-gene combination of the best individual genes has sensitivity and specificity of 98% and 71% using sputum and 93% and 62% using plasma. Area under the receiver operating curve for this panel was 0.89 [95% confidence interval (CI), 0.80-0.98] in sputum and 0.77 (95% CI, 0.68-0.86) in plasma. Independent blinded random forest prediction models combining gene methylation with clinical information correctly predicted lung cancer in 91% of subjects using sputum detection and 85% of subjects using plasma detection. High diagnostic accuracy for early-stage lung cancer can be obtained using methylated promoter detection in sputum or plasma. .

To better understand the biology of hormone receptor-positive and negative breast cancer and to identify methylated gene markers of disease progression, we performed a genome-wide methylation array analysis on 103 primary invasive breast cancers and 21 normal breast samples using the Illumina Infinium HumanMethylation27 array that queried 27,578 CpG loci. Estrogen and/or progesterone receptor-positive tumors displayed more hypermethylated loci than ER-negative tumors. However, the hypermethylated loci in ER-negative tumors were clustered closer to the transcriptional start site compared to ER-positive tumors. An ER-classifier set of CpG loci was identified, which independently partitioned primary tumors into ER-subtypes. Forty (32 novel, 8 previously known) CpG loci showed differential methylation specific to either ER-positive or ER-negative tumors. Each of the 40 ER-subtype-specific loci was validated in silico using an independent, publicly available methylome dataset from The Cancer Genome Atlas (TCGA). In addition, we identified 100 methylated CpG loci that were significantly associated with disease progression; the majority of these loci were informative particularly in ER-negative breast cancer. Overall, the set was highly enriched in homeobox containing genes. This pilot study demonstrates the robustness of the breast cancer methylome and illustrates its potential to stratify and reveal biological differences between ER-subtypes of breast cancer. Further, it defines candidate ER-specific markers and identifies potential markers predictive of outcome within ER subgroups.