High risk of hypogonadism in young male cancer survivors

2018 ◽  
Vol 88 (3) ◽  
pp. 432-441 ◽  
Author(s):  
S. Isaksson ◽  
K. Bogefors ◽  
O. Ståhl ◽  
J. Eberhard ◽  
Y.L. Giwercman ◽  
...  
Keyword(s):  
High Risk ◽  
Cancer Survivors ◽  
Young Male

Increased fat mass may be protective of bone mass in young male cancer survivors

Bone ◽  
2010 ◽  
Vol 47 ◽  
pp. S189-S190
Author(s):  
J.S. Walsh ◽  
R. Eastell ◽  
R.E. Coleman ◽  
J.A. Snowden ◽  
S.M. Shalet ◽  
...  
Keyword(s):  
Bone Mass ◽  
Cancer Survivors ◽  
Fat Mass ◽  
Young Male

scholarly journals Cognitive and emotional factors predicting decisional conflict among high-risk breast cancer survivors who receive uninformative BRCA1/2 results.

2009 ◽  
Vol 28 (5) ◽  
pp. 569-578 ◽  
Author(s):  
Christine Rini ◽  
Suzanne C. O'Neill ◽  
Heiddis Valdimarsdottir ◽  
Rachel E. Goldsmith ◽  
Lina Jandorf ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Cancer Survivors ◽  
Breast Cancer Survivors ◽  
Decisional Conflict ◽  
Emotional Factors ◽  
High Risk Breast Cancer ◽  
High Risk Breast ◽  
Risk Breast Cancer

Genetic testing referral patterns and clinical outcomes among high-risk breast cancer survivors.

2014 ◽  
Vol 32 (15_suppl) ◽  
pp. 9589-9589
Author(s):  
Rashmi Krishna Murthy ◽  
Huiqin Chen ◽  
Caimiao Wei ◽  
Michelle Jackson ◽  
Ashley Henriksen Woodson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Genetic Testing ◽  
High Risk ◽  
Cancer Survivors ◽  
Clinical Outcomes ◽  
Breast Cancer Survivors ◽  
Referral Patterns ◽  
High Risk Breast Cancer ◽  
High Risk Breast ◽  
Risk Breast Cancer

The feasibility and utility of a protein sparing modified fast (PSMF) in obese endometrial cancer survivors: A pilot study.

2016 ◽  
Vol 34 (3_suppl) ◽  
pp. 170-170
Author(s):  
Thomas Patrick Lawler ◽  
Mary Beth Kavanagh ◽  
Christa Irene Nagel ◽  
Kristen Taylor Ruckstuhl ◽  
Sareena Singh ◽  
...  
Keyword(s):  
Weight Loss ◽  
Body Weight ◽  
Endometrial Cancer ◽  
Pilot Study ◽  
High Risk ◽  
Cancer Survivors ◽  
High Risk Population ◽  
Rapid Weight Loss ◽  
Protein Sparing ◽  
Lipid Panel

170 Background: Endometrial cancer affects 50,000 women a year. Obesity plays a significant role in the pathogenesis of endometrial cancer. Obese endometrial cancer survivors (ECS) are at significant lifetime risk of diabetes, cardiovascular disease (CVD), recurrence and death. There are no prospective studies examining the role of diet only in attempting to achieve weight loss in obese ECS. Protein sparing modified fast (PSMF), an approach to rapid weight loss, has been used to treat obesity in a safe manner. A pilot study is underway to investigate the feasibility of a PSMF for weight loss in this high risk population. Methods: To date, seven obese (BMI > 30kg/m^2) ECS have been placed on a PSMF under the supervision of a physician and dietitian. Patients provided demographic information and Obesity Quality of Life (OQOL) questionnaire. Comprehensive metabolic panel with lipid panel and biomarkers of inflammation were drawn. Patients were instructed to eliminate carbohydrate containing foods and to augment with 1.2g/kg of protein per obesity-adjusted ideal body weight. Primary objectives are: total weight loss, subject retention, compliance, side effects, QOL and alterations in markers of obesity and inflammation. Results: The median age of the patient group was 56 years. Median baseline weight was 292.4 pounds (185.9-369). Median BMI was 44.5 kg/m2 (37.5-61.4). Mean baseline leptin level was 54.3 ng/ml (normal: 2.5-21.8). Mean baseline C-reactive protein level, a strong marker for CVD, was 4.574 (high risk for CVD > 3). At 4 weeks median percent body weight lost was -6.48% (5.19%-7.00%). At 3 months the median loss nearly doubled to -13% (8.31%-14.11%). Significant reduction in CRP and leptin occurred in 2 patients: mean decrease was 3.9 and 28 points respectively. Conclusions: Our early data demonstrate that significant weight loss in obese endometrial cancer survivors is achievable in a standard outpatient gynecologic oncology practice. While long term follow up data and elucidation of the true significance of improvement in serum inflammatory markers are needed, we do know that even a 5-10% loss of body weight can lead to substantial improvement in CVD and diabetes risk.

QOL assessment integrated into the clinical care of cancer survivors to identify needs and direct care.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 6611-6611
Author(s):  
Christian S. Adonizio ◽  
Jamie Weeder ◽  
Erin Benner ◽  
Jesse Manikowski ◽  
Julie Hergenrather ◽  
...  
Keyword(s):  
High Risk ◽  
Cancer Therapy ◽  
Cancer Survivors ◽  
Clinical Care ◽  
Well Being ◽  
Direct Care ◽  
Care Plan ◽  
Survivorship Care Plan ◽  
Curative Intent ◽  
Curative Therapy

6611 Background: Validated survey tools have been used to measure the quality of life (QOL) of patients treated for cancer, however, there are newer studies that have shown both an improvement in QOL, and improvement in overall survival using these tools. We integrated the Functional Assessment of Cancer Therapy – General Population (FACT-GP v.4) to direct the deployment of resources and interventions to improve the care of patients who have completed potentially curative therapy for cancer. Methods: This is an observational study of patients who received cancer therapy with curative intent in the last 18 months. The FACT-GP was administered by an RN via telephone. Patients contacted received and reviewed a Survivorship Care Plan (SCP) as defined by the American College of Surgeons Committee on Cancer. Patients who had a total score less than 60 on FACT-GP and/or had a score less than 12 on the Emotional Well-Being subscale (EWB) were considered high-risk and were referred to the Survivorship MDC for in-person evaluation. Results: From 10/1/2018 to 12/31/2018, 114 patients were referred to the cancer survivorship program. Of these, 64 (56%) patients had FACT-GP administered and were evaluated. 45 of these (70%) only completed the FACT-GP and received an SCP. 21 patients had a total score less than 60 and/or an EWB sub-score less than 12 and were identified as high-risk. 15 (72%) patients were seen in MDC, 4 (19%) patients were seen in conjunction with a scheduled appointment by the MDC team, 2 (9%) patients refused further evaluation. 66.7% of patients in the survivorship program were referred to Oncology Behavioral Health compared to 18.2% of all oncology patients. Survivorship patients in the cohort had a baseline utilization of the emergency department (ED) of 4.1% (10 of 241) from 1/1/2018 to 9/30/2018 and 0 (0 of 64) after the initiation of the intervention from 10/1/2018 to 12/31/2018. Conclusions: Integrating a validated QOL tool (FACT-GP) as a therapeutic intervention is feasible and may both identify needs and direct services for cancer survivors while possibly decreasing ED utilization. Clinical trial information: NCT03835052.

Risk for osteoporosis in gastric cancer survivorship.

2018 ◽  
Vol 36 (7_suppl) ◽  
pp. 121-121
Author(s):  
Sumin Jeong ◽  
Dong Wook Shin ◽  
Ji Eun Lee
Keyword(s):  
Gastric Cancer ◽  
Relative Risk ◽  
High Risk ◽  
Bone Mass ◽  
Cancer Survivors ◽  
Cancer Survivorship ◽  
Serum Vitamin ◽  
Adjusted Relative Risk ◽  
T Score ◽  
History Of

121 Background: Gastrectomy is a risk factor for low bone mass. We aimed to investigate the relative risk for osteoporosis in gastric cancer survivorship compared to general population. Methods: Using Korea National Health and Nutrition Examination Survey (KNHANES III-IV), 2008-2011, we identified the 8,156 individuals over 50 years old who have been tested the dual-energy X-ray absorptiometry (DXA). Gastric cancer survivors (n = 103) who had a history of gastric cancer in questionnaire were defined as case. Control subjects were matched to case subjects by age (plus or minus 1 year) and sex with 1:5 ratio. Osteopenia ( -2.5 < T-score < -1.0 ) and osteoporosis (T-score ≤ -2.5) were used to define the status of bone mass. We performed multinominal logistic regression to compare the risk for osteopenia and osteoporosis between case and control. Results: After adjusting for sex, age, body mass index, smoking status, alcohol consumption, physical activity and bone health related history (history of fracture or family history of osteoporosis), there was a significant high risk for osteopenia (adjusted relative risk (RR) = 2.90; 95% confidence interval (CI) 1.16–7.25) and osteoporosis (adjusted RR = 4.63; 95% CI 1.12–13.3) in gastric cancer survivor. The risk for osteoporosis was most prominent for femur total in gastric cancer survivors (adjusted RR = 16.3; 95% CI 3.35–82.6). In addition, the serum Vitamin D level was lower in gastric cancer survivors (20.3 ± 0.5 IU vs 17.5 ± 1.2 IU, p-value = 0.011). Conclusions: Gastric cancer survivors showed significantly high risk for osteoporosis. Our finding clinically implies the importance of managing osteoporosis in gastric cancer survivors.[Table: see text]

Deep learning for improved prediction of late-onset cardiomyopathy among childhood cancer survivors: A report from the St. Jude Lifetime Cohort (SJLIFE).

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 10545-10545
Author(s):  
Fatma Gunturkun ◽  
Robert L Davis ◽  
Gregory T. Armstrong ◽  
John L. Jefferies ◽  
Kirsten K. Ness ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Risk ◽  
Cancer Survivors ◽  
Childhood Cancer ◽  
Childhood Cancer Survivors ◽  
Learning Approaches ◽  
Baseline Evaluation ◽  
Sensitivity Specificity ◽  
Ecg Data

10545 Background: Early identification of survivors at high risk for treatment-induced cardiomyopathy may allow for prevention and/or early intervention. We utilized deep learning methods using COG guideline-recommended baseline electrocardiography (ECG) to improve prediction of future cardiomyopathy. Methods: SJLIFE is a cohort of 5-year clinically assessed childhood cancer survivors including baseline ECG measurements. Development of cardiomyopathy was identified from clinical and echocardiographic measurement using CTCAE criteria (grade 3-4). We applied deep learning approaches to ECG, treatment exposure and demographic data obtained at baseline SJLIFE assessment. We trained a cascaded model combining a 12-layer 1D convolutional neural network to extract features from waveform ECG signals with a 2-layer dense neural network to embed features from other phenotypic data in tabular format to determine if use of deep learning with ECG data could improve prediction of cardiomyopathy. Results: Among 1,218 subjects (median age 31.7 years, range 18.4-66.4) without cardiomyopathy at baseline evaluation, 616 (51%) were male, 1,041 (85%) white, 157 (13%) African American and 792 (65%) were survivors of lymphoma/leukemia. Follow-up averaged 5 (0.5 to 9) years from baseline examination. Mean chest radiation dose was 1350 cGy (range 0 to 6,200 cGy) and mean cumulative anthracycline dose was 191 mg/m2 (range o to 734 mg/m2). A total of 114 (9.4%) survivors developed cardiomyopathy after baseline. A cascaded deep learning model built on a training set (N = 974 participants) classified cardiomyopathy in the test set (N = 244 participants) using both clinical and ECG data with a sensitivity of 70%, specificity of 73%, and AUC of 0.74 (95% CI 0.63-0.85), compared to a model using clinical data alone (sensitivity 61%, specificity 62%, and AUC 0.67, 95% CI 0.56-0.79). In subgroup analyses, models predicting cardiomyopathy within 0-4 years following baseline had a sensitivity, specificity, and AUC of 77%, 78%, and 0.78 (0.65-0.91), respectively. When predicting cardiomyopathy 5-9 years following baseline, model performance dropped to a sensitivity, specificity, and AUC of 70%, 70%, and 0.68 (0.50-0.87), respectively. Conclusions: Deep learning using ECG at baseline evaluation significantly improved prediction of cardiomyopathy in childhood cancer survivors at high risk for cardiomyopathy. Future directions will incorporate deep learning approaches to echocardiography to further improve prediction.

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