The prognostic significance of various systemic inflammatory and nutritional markers in hypopharyngeal squamous cell carcinoma (HPSCC) remains unclear. This study aimed to develop a nomogram to predict survival probabilities in patients undergoing HPSCC resection surgery based on these markers, which could help in the treatment of HPSCC. The study included data from 236 HPSCC patients. The most predictive systemic inflammatory and nutritional markers were identified through the area under the prognostic curve (AUC). Using COX regression analysis, independent risk factors were pinpointed and used to create and validate a predictive nomogram. The cut-off values of systemic immune-inflammatory index (SII) and advanced lung cancer inflammatory index (ALI) were 27.80 and 791.35, respectively. The constructed nomogram incorporated tumor stage, age, ALI, and SII. The AUC values for 1-year, 3-year, and 5-year survival prediction were 0.820, 0.721, and 0.723, respectively. Calibration and decision curves demonstrated the substantial clinical utility of the model. SII and ALI possess significant prognostic importance in HPSCC. The developed nomogram, which includes these markers, offers a practical tool for estimating patient survival probabilities, aiding physicians in clinical decision-making for high-risk patients.
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http://dx.doi.org/10.1016/j.jcms.2024.11.001 | DOI Listing |
Neurogastroenterol Motil
January 2025
Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA.
Background: The carbon-13 spirulina gastric emptying breath test (GEBT) is approved to identify delayed, but not accelerated, gastric emptying (GE). We compared the utility of the GEBT to scintigraphy for diagnosing abnormal GE in patients with diabetes mellitus.
Methods: Twenty-eight patients with diabetes ate a 230-kcal test meal labeled with technetium 99 m and C-spirulina, after which 10 scintigraphic images and breath samples (baseline, 15, 30, 45, 60, 90, 120, 150, 180, 210, and 240 min) were collected on 2 occasions 1 week apart.
Ann Surg Oncol
January 2025
Department of Gastrointestinal Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Background: This study aimed to develop a dynamic survival prediction model utilizing conditional survival (CS) analysis and machine learning techniques for gastric neuroendocrine carcinomas (GNECs).
Patients And Methods: Data from the Surveillance, Epidemiology, and End Results (SEER) database (2004-2015) were analyzed and split into training and validation groups (7:3 ratio). CS profiles for patients with GNEC were examined in the full cohort.
Langenbecks Arch Surg
January 2025
Department of Urology, Qilu Hospital, Shandong University, No 107, Wenhuaxi Road, Jinan, 250012, PR China.
Background: Primary aldosteronism (PA) is the leading surgically treatable cause of hypertension, with adrenalectomy as the definitive treatment for unilateral PA (UPA). However, some patients have persistent hypertension after surgery. This study aims to identify preoperative factors affecting surgical outcomes and develop a predictive model for postoperative hypertension resolution.
View Article and Find Full Text PDFLangenbecks Arch Surg
January 2025
Department of General Surgery, Gansu Provincial Hospital, Lanzhou, 730000, China.
Background: In the last two decades, robotic-assisted gastrectomy has become a widely adopted surgical option for gastric cancer (GC) treatment. Despite its popularity, postoperative complications can significantly deteriorate patient quality of life and prognosis. Therefore, identifying risk factors for these complications is crucial for early detection and intervention.
View Article and Find Full Text PDFAnn Med
December 2025
Central Laboratory, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Background: Despite surgical and intravesical chemotherapy interventions, non-muscle invasive bladder cancer (NMIBC) poses a high risk of recurrence, which significantly impacts patient survival. Traditional clinical characteristics alone are inadequate for accurately assessing the risk of NMIBC recurrence, necessitating the development of novel predictive tools.
Methods: We analyzed microarray data of NMIBC samples obtained from the ArrayExpress and GEO databases.
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