Associations between income and survival in cholangiocarcinoma: A comprehensive subtype-based analysis.

Backgrounds/Aims: Socioeconomic determinants of health are incompletely characterized in cholangiocarcinoma (CCA). We assessed how socioeconomic status influences initial treatment decisions and survival outcomes in patients with CCA, additionally performing multiple sub-analyses based on anatomic location of the primary tumor. Methods: Observational study using the 2018 submission of the Surveillance, Epidemiology, and End Results (SEER)-18 Database. In total, 5,476 patients from 2004-2015 with a CCA were separated based on median household income (MHI) into low income (< 25th percentile of MHI) and high income (> 25th percentile of MHI) groups. Seventy-three percent of patients had complete follow up data, and were included in survival analyses. Survival and treatment outcomes were calculated using R-studio. Results: When all cases of CCA were included, the high-income group was more likely than the low-income to receive surgery, chemotherapy, and local tumor destruction modalities. Initial treatment modality based on income differed significantly between tumor locations. Patients of lower income had higher overall and cancer-specific mortality at 2 and 5 years. Non-cancer mortality was similar between the groups. Survival differences identified in the overall cohort were maintained in the intrahepatic CCA subgroup. No differences between income groups were noted in cancer-specific or overall mortality for perihilar tumors, with variable differences in the distal cohort. Conclusions: Lower income was associated with higher rates of cancer-specific mortality and lower rates of surgical resection in CCA. There were significant differences in treatment selection and outcomes between intrahepatic, perihilar, and distal tumors. Population-based strategies aimed at identifying possible etiologies for these disparities are paramount to improving patient outcomes.

Mobile sensing to advance tumor modeling in cancer patients: A conceptual framework.

As mobile and wearable devices continue to grow in popularity, there is strong yet unrealized potential to harness people's mobile sensing data to improve our understanding of their cellular and biologically-based diseases. Breakthrough technical innovations in tumor modeling, such as the three dimensional tumor microenvironment system (TMES), allow researchers to study the behavior of tumor cells in a controlled environment that closely mimics the human body. Although patients' health behaviors are known to impact their tumor growth through circulating hormones (cortisol, melatonin), capturing this process is a challenge to rendering realistic tumor models in the TMES or similar tumor modeling systems. The goal of this paper is to propose a conceptual framework that unifies researchers from digital health, data science, oncology, and cellular signaling, in a common cause to improve cancer patients' treatment outcomes through mobile sensing. In support of our framework, existing studies indicate that it is feasible to use people's mobile sensing data to approximate their underlying hormone levels. Further, it was found that when cortisol is cycled through the TMES based on actual patients' cortisol levels, there is a significant increase in pancreatic tumor cell growth compared to when cortisol levels are at normal healthy levels. Taken together, findings from these studies indicate that continuous monitoring of people's hormone levels through mobile sensing may improve experimentation in the TMES, by informing how hormones should be introduced. We hope our framework inspires digital health researchers in the psychosocial sciences to consider how their expertise can be applied to advancing outcomes across levels of inquiry, from behavioral to cellular.

Using Graph Representation Learning to Predict Salivary Cortisol Levels in Pancreatic Cancer Patients.

Cortisol is a glucocorticoid hormone that is critical to immune system functioning. Studies show that prolonged exposure to high levels of cortisol can lead to a range of physical health ailments including the progression of tumor growth. The ability to monitor cortisol levels over time can therefore be used to facilitate decision-making during cancer treatment. However, collecting serum or saliva samples to monitor cortisol in situ is inconvenient, costly, and impractical. In this paper, we propose a general predictive modeling process that uses passively sensed actigraphy data to predict underlying salivary cortisol levels using graph representation learning. We compare machine learning models with handcrafted feature engineering and with graph representation learning, which includes Graph2Vec, FeatherGraph, GeoScattering and NetLSD. Our preliminary results generated from data from 10 newly diagnosed pancreatic cancer patients demonstrate that machine learning models with graph representation learning can outperform the handcrafted feature engineering to predict salivary cortisol levels.

Comparative Effectiveness of Lymphadenectomy Strategies During Curative Resection for Gastric Adenocarcinoma.

BACKGROUND: The purpose of this study was to compare the long-term effectiveness of three lymphadenectomy strategies in patients with gastric cancer. We hypothesized that, compared with the traditional standard (D2) lymph node dissection strategy, the less aggressive modified standard (mD2) lymphadenectomy may offer superior effectiveness due to reduced operative morbidity and comparable long-term recurrence-free survival. METHODS: A Markov decision analysis model was created to simulate 5-year outcomes across three lymphadenectomy approaches for gastric cancer: limited regional (D1), traditional standard (D2), and modified standard (mD2). The primary outcome was discounted quality-adjusted life-years (dQALY). Model variable estimates were derived from outcomes data and quality of life estimates published in Europe and America within the last 15 years. One-way and probabilistic sensitivity analyses were performed for clinically relevant variables. RESULTS: The mD2 lymphadenectomy offered 3.03 dQALY over 5 years, outperforming D2 (2.62 dQALY) and D1 (2.37 dQALY). Monte Carlo simulations indicated that both mD2 and D2 lymph node dissection strategies outperformed D1 in 94.9% of simulations. Sensitivity analyses demonstrated that the mD2 approach would be less effective than D2 if the perioperative mortality rate of mD2 was greater than 6.9% (3.2% baseline). CONCLUSIONS: Across modern series, the modified standard mD2 lymphadenectomy is an effective alternative to the traditional D2 lymphadenectomy for patients with gastric cancer. A D1-limited regional lymphadenectomy is not recommended during gastric cancer resection.

Angiogenesis inhibition as a therapeutic strategy in non-small cell lung cancer (NSCLC).

In many cancers, including non-small cell lung cancer (NSCLC), tumor angiogenesis pathways have been identified as important therapeutic targets. Angiogenesis is essential in the process of primary tumor growth, proliferation and metastasis. One of the best characterized group of protein factors for angiogenesis include the members of the vascular endothelial growth factor (VEGF) family, consisting of VEGF-(A-D), and placenta growth factor (PIGF). Targeting tumor angiogenesis has been approached through two primary methods, monoclonal antibodies that block VEGF-vascular endothelial growth factor receptor (VEGFR) binding or small molecule tyrosine kinase inhibitors (TKIs) that inhibit the downstream VEGFR mediated signaling. Many TKIs inhibit multiple pro-angiogenic and pro-proliferative pathways such as the mitogen activated protein (MAP) kinase pathway. Bevacizumab and ramucirumab, monoclonal antibodies targeting VEGF and the VEGFR, respectively, have each led to improvements in overall survival (OS) for NSCLC when added to standard first and second line chemotherapy, respectively. Small incremental gains seen with both bevacizumab and ramucirumab may be further improved upon by incorporating novel agents and treatment strategies, and many additional trials are ongoing.

Symptomatic Wingspan stent stenosis and occlusion: stent-in-stent rescue.

The use of the Wingspan stent in severe intracranial stenosis is associated with a relatively high in-stent re-stenosis rate. Reported management strategies for re-stenosis have included angioplasty alone or angioplasty with placement of a second Wingspan stent. A case is presented in which thrombosis within a Wingspan stent was treated with a balloon expandable cobalt-chromium stent within the Wingspan stent. Subsequent follow-up imaging has shown persistent patency of the treated vessel, with no subsequent in-stent stenosis.