OVS+Tumor: a tool for enhanced lung tumor annotation in VR for machine learning training and analysis

OVS+Tumor creates a seamless VR environment designed for intuitive interaction aiding in the complex task of parsing through 3D CT-scans and annotating candidate tumors. Through interactive subsetting and on-the-fly iso-cloud generation, a wider range of users beyond just domain experts (radiologists/surgeons) can generate a viable machine-learning training dataset

OVS+Tumor: a tool for enhanced lung tumor annotation in VR for machine learning training and analysis

OVS+Tumor creates a seamless VR environment designed for intuitive interaction aiding in the complex task of parsing through 3D CT-scans and annotating candidate tumors. Through interactive subsetting and on-the-fly iso-cloud generation, a wider range of users beyond just domain experts (radiologists/surgeons) can generate a viable machine-learning training dataset

Association Between Benign Breast Disease in African American and White American Women and Subsequent Triple-Negative Breast Cancer

Importance: Compared with white American (WA) women, African American (AA) women have a 2-fold higher incidence of breast cancers that are negative for estrogen receptor, progesterone receptor, and ERBB2 (triple-negative breast cancer [TNBC]). Triple-negative breast cancer, compared with non-TNBC, likely arises from different pathogenetic pathways, and benign breast disease (BBD) predicts future non-TNBC. Objective: To determine whether AA identity remains associated with TNBC for women with a prior diagnosis of BBD. Design, Setting, and Participants: This study is a retrospective analysis of data of a cohort of 2588 AA and 3566 WA women aged between 40 and 70 years with a biopsy-proven BBD diagnosis. The data-obtained from the Pathology Information System of Henry Ford Health System (HFHS), an integrated multihospital and multispecialty health care system headquartered in Detroit, Michigan-include specimens of biopsies performed between January 1, 1994, and December 31, 2005. Data analysis was performed from November 1, 2015, to June 15, 2016. Main Outcomes and Measures: Subsequent breast cancer was stratified on the basis of combinations of hormone receptor and ERBB2 expression. Results: Case management, follow-up, and outcomes received or obtained by our cohort of 2588 AA and 3566 WA patients were similar, demonstrating that HFHS delivered care equitably. Subsequent breast cancers developed in 103 (4.1%) of AA patients (mean follow-up interval of 6.8 years) and 143 (4.0%) of WA patients (mean follow-up interval of 6.1 years). More than three-quarters of subsequent breast cancers in each subset were ductal carcinoma in situ or stage I. The 10-year probability estimate for developing TNBC was 0.56% (95% CI, 0.32%-1.0%) for AA patients and 0.25% (95% CI, 0.12%-0.53%) for WA patients. Among the 66 AA patients who developed subsequent invasive breast cancer, 16 (24.2%) developed TNBC compared with 7 (7.4%) of the 94 WA patients who developed subsequent invasive breast cancers and had complete biomarker data (P = .01). Conclusions and Relevance: This study is the largest analysis to date of TNBC in the context of racial/ethnic identity and BBD as risk factors. The study found that AA identity persisted as a significant risk factor for TNBC. This finding suggests that AA identity is associated with inherent susceptibility for TNBC pathogenetic pathways

A Framework for Evaluating Biomarkers for Early Detection: Validation of Biomarker Panels for Ovarian Cancer

A panel of biomarkers may improve predictive performance over individual markers. Although many biomarker panels have been described for ovarian cancer, few studies used pre-diagnostic samples to assess the potential of the panels for early detection. We conducted a multi-site systematic evaluation of biomarker panels using pre-diagnostic serum samples from the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) screening trial

A novel approach for increasing sensitivity and correcting saturation artifacts of radioactively labeled cDNA arrays

The radioactivity labeled DNA array platform is a robust and accurate way for a high-throughput measurement of gene expression levels in biological samples. Despite its high degree of sensitivity and reproducibility, this platform has several sources of variation. These are related to the presence of saturation effects in the array images and impede the degree of accuracy at which gene expression levels are determined. Here we describe a simple, but effective, approach for combining expression data from a series of autoradiographic exposures of variable length. This technique increases the sensitivity of this array platform by detecting low-expressed genes at longer exposures. It also improves the measurement accuracy of highly abundant genes by considering only values from the linear portion of dependency between the exposure times and gene intensities. As a result, the described approach improves the outcome of the subsequent steps of array data normalization and mining

Normalization of single-channel DNA array data by principal component analysis

Motivation: Detailed comparison and analysis of the output of DNA gene expression arrays from multiple samples require global normalization of the measured individual gene intensities from the different hybridizations. This is needed for accounting for variations in array preparation and sample hybridization conditions. Results: Here, we present a simple, robust and accurate procedure for the global normalization of datasets generated with single-channel DNA arrays based on principal component analysis. The procedure makes minimal assumptions about the data and performs well in cases where other standard procedures produced biased estimates. It is also insensitive to data transformation, filtering (thresholding) and pre-screening

Synthetic biomarkers: a twenty-first century path to early cancer detection

Detection of cancer at an early stage when it is still localized improves patient response to medical interventions for most cancer types. The success of screening tools such as cervical cytology to reduce mortality has spurred significant interest in new methods for early detection (for example, using non-invasive blood-based or biofluid-based biomarkers). Yet biomarkers shed from early lesions are limited by fundamental biological and mass transport barriers - such as short circulation times and blood dilution - that limit early detection. To address this issue, synthetic biomarkers are being developed. These represent an emerging class of diagnostics that deploy bioengineered sensors inside the body to query early-stage tumours and amplify disease signals to levels that could potentially exceed those of shed biomarkers. These strategies leverage design principles and advances from chemistry, synthetic biology and cell engineering. In this Review, we discuss the rationale for development of biofluid-based synthetic biomarkers. We examine how these strategies harness dysregulated features of tumours to amplify detection signals, use tumour-selective activation to increase specificity and leverage natural processing of bodily fluids (for example, blood, urine and proximal fluids) for easy detection. Finally, we highlight the challenges that exist for preclinical development and clinical translation of synthetic biomarker diagnostics