Outcomes After Definitive Treatment for Cutaneous Angiosarcomas of the Face and Scalp: Reevaluating the Role of Surgery and Radiation Therapy

INTRODUCTION: We investigated outcomes and prognostic factors for patients treated for cutaneous angiosarcoma (CA). METHODS: We conducted a retrospective review of patients treated for CA of the face and scalp from 1962 to 2019. All received definitive treatment with surgery, radiation (RT), or a combination (S-XRT). The Kaplan-Meier method was used to estimate outcomes. Multivariable analyses were conducted using the Cox proportional hazards model. RESULTS: For the 143 patients evaluated median follow-up was 33 months. Five-year LC was 51% and worse in patients with tumors \u3e5 cm, multifocal tumors, those treated pre-2000, and with single modality therapy (SMT). These remained associated with worse LC on multivariable analysis. The 5-year disease-specific survival (DSS) for the cohort was 56%. Tumor size \u3e5 cm, non-scalp primary site, treatment pre-2000, and SMT were associated with worse DSS. CONCLUSION: Large or multifocal tumors are negative prognostic factors in patients with head and neck CA. S-XRT improved outcomes

Adult-onset multifocal kaposiform hemangioendothelioma in the bone marrow, lung, liver, and brain: a case report

Kaposiform hemangioendothelioma (KHE), a rare form of vascular neoplasm, is typically seen in children. In this paper, we report a unique case of KHE replacing bone marrow tissue mimicking myeloproliferative neoplasm with additional involvement in the lung, liver, and brain in a 60-year-old Caucasian woman. The patient was initially seen in the hematology department for the chief complaint of epigastric pain and anemia. Abdominal magnetic resonance imaging (MRI) revealed mild splenomegaly with iron deposition secondary to extramedullary hematopoiesis. Additional workup was inconclusive. Subsequent bone marrow and lung biopsies eventually revealed bone marrow with extensive grade 3 fibrosis and multiple foci of low-grade vasoformative neoplasm in the lung suggestive of KHE. Although rare, KHE can present as an aggressive disease with indolent behavior in adults and can be distinguished from other vascular malignancies based on histopathology and imaging findings

The Perils and Promises of Self-Disclosure on Social Media

In addition to their professional social media accounts, individuals are increasingly using their personal profiles and casual posts to communicate their identities to work colleagues. They do this in order to ‘stand out from the crowd’ and to signal attributes that are difficult to showcase explicitly in a work setting. Existing studies have tended to treat personal posts viewed in a professional context as a problem, since they can threaten impression management efforts. These accounts focus on the attempts of individuals to separate their life domains on social media. In contrast, we present the narratives of professional IT workers in India who intentionally disrupt the boundaries between personal and professional profiles in order to get noticed by their employers. Drawing on the dramaturgical vocabulary of Goffman (1959) we shed light on how individuals cope with increased levels of self-disclosure on social media. We argue that their self-presentations can be likened to post-modern performances in which the traditional boundaries between actor and audience are intentionally unsettled. These casual posts communicate additional personal traits that are not otherwise included in professional presentations. Since there are no strict boundaries between formal front-stage and relaxed back-stage regions in these types of performance, a liminal mental state is often used, which enables a better assessment of the type of information to present on social media

Artificial Intelligence (AI) and machine learning (ML) in risk prediction of hospital acquired pressure injuries (HAPIs) among oncology inpatients.

e18095 Background: Utilizing AI and ML is an emerging method to improve risk identification, characterization and stratification for clinical outcomes such as HAPIs. The Jvion Cognitive Clinical Success Machine (CCSM) utilizes the Eigen Sphere technique to factor in clinical, socioeconomic, and behavioral covariates at the individual patient level to maximize accuracy of risk prediction and provide insights on prevention of HAPIs. Methods: A retrospective analysis was performed utilizing claims and EHR data on 63,476 inpatient admissions between June 2016 and June 2018 at M D Anderson Cancer Center, a 660-bed oncology facility (Table). All risk assessment indicators in the data were removed prior to analysis by the CCSM to compute unbiased risk probabilities for HAPIs. The performance of the CCSM risk prediction for all new stage 2 and above HAPIs ( &gt; = Stage 2 HAPIs) was compared with the Braden scale using AUC. Results: The Jvion CCSM had an AUC of 0.84 compared to the AUC of 0.72 for the Braden scale in the prediction of &gt; = Stage 2 HAPIs. Conclusions: The AUC value indicates that the Jvion CCSM has better predictive accuracy than the Braden scale. It also has better discrimination in risk identification. Thus, Jvion CCSM can be a valuable tool in risk screening for HAPIs which can lead to early preventative interventions. [Table: see text] </jats:p

Artificial Intelligence (AI) and machine learning (ML) in risk prediction of hospital acquired pressure injuries (HAPIs) among oncology inpatients.

309 Background: Utilizing AI and ML is an emerging method to improve risk identification, characterization and stratification for clinical outcomes such as HAPIs. The Jvion Cognitive Clinical Success Machine (CCSM) utilizes the Eigen Sphere technique to factor in clinical, socioeconomic, and behavioral covariates at the individual patient level to maximize accuracy of risk prediction and provide insights on prevention of HAPIs. Methods: A retrospective analysis was performed utilizing claims and EHR data on 63,476 inpatient admissions between June 2016 and June 2018 at M D Anderson Cancer Center, a 660-bed oncology facility (Table 1) . All risk assessment indicators in the data were removed prior to analysis by the CCSM to compute unbiased risk probabilities for HAPIs. The performance of the CCSM risk prediction for all new stage 2 and above HAPIs (&gt;=Stage 2 HAPIs) was compared with the Braden scale using AUC. Results: The Jvion CCSM had an AUC of 0.84 compared to the AUC of 0.72 for the Braden scale in the prediction of &gt;=Stage 2 HAPIs. Conclusions: The AUC indicates that the Jvion CCSM has better predictive accuracy than the Braden scale. It also has better discrimination in risk identification. Thus, Jvion CCSM can be a valuable tool in risk screening for HAPIs which can lead to early preventative interventions. Characteristics of admission records used to compare accuracy of Jvion CCSM and Braden scale in predicting new &gt;=Stage 2 HAPIs (N=58,228). [Table: see text] </jats:p