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Updated: Mar 20, 2026

Quantification of Tumor Cell Adhesion in Lymph Node Cryosections
Published on: February 9, 2020
Clinical predictors of malignancy in lymphadenopathy: A multivariable analysis from a quick diagnosis unit
Eloi Garcia-Vives1, Jaime Rodriguez-Morera1, Ariadna Brase Arnau1
1Internal Medicine Department, Hospital del Mar, Passeig Marítim de la Barceloneta n°25-29, Barcelona, Spain.
Background:
Peripheral lymphadenopathy (LA) has diverse causes and may indicate malignancy, particularly in referred patients.
Aim:
To characterise patients referred for unexplained LA to a quick diagnosis unit, and identify independent predictors of malignancy.
Design And Methods:
We conducted a retrospective study of 485 consecutive adults evaluated for unexplained LA between 2017 and 2023. The primary outcome was malignancy. Secondary outcomes included diagnostic delay and time to oncology referral. Demographic, clinical and laboratory variables were compared across aetiological groups. A parsimonious multivariable logistic regression model included five clinically relevant predictors identified in univariable analyses and supported by biological plausibility.
Results:
Median age was 46 years, and time to first visit was 11 days. Cervical nodes were most frequent (51.9%), followed by supraclavicular (18.6%). Malignancy was diagnosed in 20.8% of patients, with diagnostic delay of 26.5 days (15.5-42). Other specific diagnoses were established in 35.5% of cases, while 43.7% were reactive. Malignant cases were older (60.8 vs 42 years), predominantly male (68.3% vs 44.5%), had higher drug exposure (50.0% vs 29.8%), and shorter symptom duration (45 vs 90 days). In multivariable analysis, independent predictors of malignancy were: age (odds ratio (OR) 1.71 per 10-year increase), male sex (OR 3.25), lymph node size (OR 1.36 per 5 mm increase), indurated consistency (OR 3.42), and supraclavicular location (OR 4.96). Median time to oncology evaluation was 47 days.
Conclusion:
The QDU model enables timely diagnosis and detects malignancy in over 20% of cases. Recognising clinical predictors may help prioritise high-risk patients and streamline diagnostic pathways.
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