Understanding the Metabolic and Genetic Regulation of Breast Cancer Recurrence Using Magnetic Resonance-Based Integrative Metabolomics

Breast cancer is the most commonly diagnosed malignancy in women and is the leading cause of cancer-related death in the female population worldwide. In these women, breast cancer recurrence--local, regional, or distant--represents the principal cause of death from this disease. The mechanisms underlying tumor recurrence remain largely unknown. To dissect those mechanisms, our laboratory has developed inducible transgenic mouse models that accurately recapitulate key features of the natural history of human breast cancer progression: primary tumor development, tumor dormancy and recurrence. Dysregulated metabolism has long been known to be a key feature in tumorigenesis. Yet, very little is known about the connection, if any, between cellular metabolic changes and breast cancer recurrence. In this work, I design and implement a systems engineering-based approach, magnetic resonance-based integrative metabolomics, to better understand the metabolic and genetic regulation of breast cancer recurrence. Through a combination of 1H and 13C magnetic resonance spectroscopy (MRS), mass spectrometry (MS) as well as gene expression profiling and functional metabolic and genetic studies, I aim to identify the metabolic profile of mammary tumors during breast cancer progression, identify the molecular basis and role of differential glutamine uptake and metabolism in breast cancer recurrence and finally, investigate the molecular basis and role of differential lactate production in breast cancer recurrence. The findings suggest an evolving metabolic phenotype of tumors during breast cancer progression as well as metabolic dysregulation in some of the key regulatory nodes that control that evolution. Identifying the metabolic changes associated with tumor recurrence can pave the way for identifying novel diagnostic strategies and therapeutic targets that can contribute to improved clinical management and outcome for breast cancer patients

Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions

Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising. There have also been several advancements in AI technologies to help predict metastases in CRC and in Computer-Aided Detection (CAD) Systems to improve miss rates for colorectal neoplasia. This article provides a comprehensive review of the role of AI in predicting risk, prognosis, and response to therapies among patients with CRC

Ablation in pancreatic cancer: Past, present and future

The insidious onset and aggressive nature of pancreatic cancer contributes to the poor treatment response and high mortality of this devastating disease. While surgery, chemotherapy and radiation have contributed to improvements in overall survival, roughly 90% of those afflicted by this disease will die within 5 years of diagnosis. The developed ablative locoregional treatment modalities have demonstrated promise in terms of overall survival and quality of life. In this review, we discuss some of the recent studies demonstrating the safety and efficacy of ablative treatments in patients with locally advanced pancreatic cancer

Quantitative peritumoral magnetic resonance imaging fingerprinting improves machine learning-based prediction of overall survival in colorectal cancer

Aim: To investigate magnetic resonance imaging (MRI)-based peritumoral texture features as prognostic indicators of survival in patients with colorectal liver metastasis (CRLM). Methods: From 2007–2015, forty-eight patients who underwent MRI within 3 months prior to initiating treatment for CRLM were identified. Clinicobiological prognostic variables were obtained from electronic medical records. Ninety-four metastatic hepatic lesions were identified on T1-weighted post-contrast images and volumetrically segmented. A total of 112 radiomic features (shape, first-order, texture) were derived from a 10 mm region surrounding each segmented tumor. A random forest model was applied, and performance was tested by receiver operating characteristic (ROC). Kaplan-Meier analysis was utilized to generate the survival curves. Results: Forty-eight patients (male:female = 23:25, age 55.3 years ± 18 years) were included in the study. The median lesion size was 25.73 mm (range 8.5–103.8 mm). Microsatellite instability was low in 40.4% (38/94) of tumors, with Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation detected in 68 out of 94 (72%) tumors. The mean survival was 35 months ± 21 months, and local disease progression was observed in 35.5% of patients. Univariate regression analysis identified 42 texture features [8 first order, 5 gray level dependence matrix (GLDM), 5 gray level run time length matrix (GLRLM), 5 gray level size zone matrix (GLSZM), 2 neighboring gray tone difference matrix (NGTDM), and 17 gray level co-occurrence matrix (GLCM)] independently associated with metastatic disease progression (P < 0.03). The random forest model achieved an area under the curve (AUC) of 0.88. Conclusions: MRI-based peritumoral heterogeneity features may serve as predictive biomarkers for metastatic disease progression and patient survival in CRLM

Social Media Tools for Department and Practice Communication and Branding in the Digital Age.

With nearly 70% of adults in the United States using at least one social media platform, a social media presence is increasingly important for departments and practices. Patients, prospective faculty and trainees, and referring physicians look to social media to find information about our organizations. The authors present a stepwise process for planning, executing, and evaluating an organizational social media strategy. This process begins with alignment with a strategic plan to set goals, identification of the target audience(s), selection of appropriate social media channels, tracking effectiveness, and resource allocation. The article concludes with a discussion of advantages and disadvantages of social media through a review of current literature. ©RSNA, 2018