Background: Well-differentiated/dedifferentiated (WD/DD) liposarcoma is a rare malignancy of putative adipocyte origin. To our knowledge, there have only been isolated case reports describing second primary cancer in patients with this disease. We report on a combined case series of such patients and explore the frequency of this occurrence using a national cancer database.
Materials And Methods: Demographics and clinicopathological data were collected from patients with WD/DD liposarcoma who were found to have a concurrent or subsequent second primary cancer, at one of three sarcoma referral centers from 2014-2016. The Surveillance, Epidemiology and End Results (SEER) database was also queried to identify adult patients diagnosed with WD/DD liposarcoma between 1973-2012. Observed/expected (O/E) ratios of second primary malignancies among these cases were calculated by comparison to the age-adjusted cancer incidence in the general population using SEER*stat software.
Results: In total, 26 out of 312 consecutive patients (8.3%) with WD/DD liposarcoma at our centers had a second primary cancer identified within 2 years of liposarcoma diagnosis. In the SEER database, among 1,845 patients with WD/DD liposarcoma, 75 (4.1%) had a second cancer within 2 years after liposarcoma diagnosis (O/E ratio=1.81, 99% confidence interval(CI)=1.33-2.40). Patients less than 50 years old at the time of liposarcoma diagnosis had a higher O/E ratio for second primary malignancy compared to older patients. A total of 269 patients (14.6%) developed a second cancer (O/E=1.33, 99% CI=1.15-1.54).
Conclusion: In some patients with WD/DD liposarcoma, there appears to be an increased risk of having a second primary cancer. Further validation and investigation is needed, as this finding may have implications (e.g. closer screening) for patients with this disease.
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http://dx.doi.org/10.21873/anticanres.12625 | DOI Listing |
JAMA Neurol
January 2025
Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Importance: Trials have not demonstrated superiority of alteplase or tenecteplase vs standard care in patients with mild stroke and have raised safety concerns. Prourokinase is an alternative fibrinolytic that may have a favorable safety profile, and the benefit-risk profile of prourokinase in mild stroke is unknown.
Objective: To investigate the efficacy and safety of prourokinase in mild ischemic stroke within 4.
Int J Colorectal Dis
January 2025
Internal Medicine, Jilin Cancer Hospital, Changchun, China.
Purpose: This phase II study is designed to evaluate the combination therapy involving suvemcitug and envafolimab with FOLFIRI in microsatellite-stable or mismatch repair-proficient (MSS/pMMR) colorectal cancer (CRC) in the second-line treatment setting.
Methods: This study is a non-randomized, open-label prospective study comprising multiple cohorts (NCT05148195). Here, we only report the data from the CRC cohort.
Int Urogynecol J
January 2025
Department of Urology, Institution - Jessenius Faculty of Medicine, Comenius University Bratislava, Martin, Slovak Republic.
Introduction And Hypothesis: Measuring changes in the vaginal pressure profile (VPP) with the Femfit® by JUNOFEM during various sports will help improve understanding of the magnitude of pressures generated during such activities and the pelvic floor's response to those pressures. This could aid in selecting safe exercises for women with pelvic floor dysfunction. The primary aim of this study was to measure intravaginal pressure changes using a novel pressure sensor array (Femfit®) during high-risk sports activities in elite female athletes with and without stress urinary incontinence (SUI).
View Article and Find Full Text PDFJ Glaucoma
January 2025
Department of Ophthalmology and Visual Sciences, University of British Colombia, Vancouver, BC.
Precis: Goniotomy is effective in managing intraocular pressure in childhood glaucoma secondary to uveitis with cumulative probability of success 0.94 after 1 year and 0.77 after 5 years.
View Article and Find Full Text PDFRadiology
January 2025
Stanford University School of Medicine, Department of Radiation Oncology, Stanford, CA, US.
Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician variability. Purpose To develop and evaluate an ensemble deep learning model for automating identification and segmentation of lung tumors on CT scans. Materials and Methods A retrospective study was conducted between July 2019 and November 2024 using a large dataset of CT simulation scans and clinical lung tumor segmentations from radiotherapy plans.
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