الفهرس 
Introduction & aim of the workOnly 14 pages are availabe for public view
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Abstract
Evaluation of cervical lymph nodes is an important procedure for patients with head and neck tumors because it assesses the prognosis of the patients and helps to select appropriate treatment.
Our aim of this study was to determine the role of US versus MDCT in differentiating benign from malignant lymphadenopathy of the neck.
Between September 2013 and January 2016, 50 consecutive patients were presented by cervical lymphadenopathies and were referred from ENT and Surgery departments to Radiodiagnosis Departments of Mansoura University Hospital and Mansoura Oncology Center for differentiating benign from malignant neck lymphadenopathies using ultrasonography (US) and multidetector CT (MDCT).
All patients were subjected to complete history taking, full clinical examination and US & MDCT examination of the neck.
Analysis of the data obtained by US and MDCT revealed that most of the benign nodes identified by US and CT were found in submental, submandibular, upper and mid cervical regions (representing 30 (60%) and 32 (64%), respectively), and most of the malignant nodes were found in lower cervical, posterior triangle, pre-laryngeal, para-tracheal and retropharyngeal regions (representing 19 (38 %) and 24 (48%) by US and CT, respectively).
Benign cervical benign nodes identified by US and CT show mean short axis diameter was 0.6 c.m (less than 1 cm) and mean long axis diameter was 1.3 cm by US versus 1.2 cm by MDCT (less than 2 cm) representing 30 (60%) and 32 (64%) Doppler examination we found that in benign cervical lymphadenopathies it was central in 31 (93.3%), peripheral in 2 (6.1%) and no detectable mixed vascularity. While in malignant cervical lymphadenopathies, it was central in 1 (5.9%), peripheral in 10 (58.8 %) and mixed in 6 (35.5%).
By doppler waveforms of nodal vascularity we found that most of benign cervical lymph nodes have low resistive index representing by median = 0.63 (less than 0.7), low peak systolic velocity representing by median =9 and low end diatolic velocity representing by median= 3 while most of malignant cervical lymph nodes have high resistive index representing by median =0.73 (more than 0.7), high peak systolic velocity representing by median=14.5 and high end diastolic velocity representing by median=4.5. We found that by MDCT examination most of benign cervical lymph nodes show homogenous enhancement representing 26 (74.3%) of total 35 patients and most of malignant cervical lymph nodes show heterogeneous enhancement representing 10 (66.7%) of total 15 patients.
By US examination we found that benign nodes were suspected in 33 (66%) out of 50 patients of which 32 are reactive (97%) and 1 case with T.B (3 %). Malignant nodes were suspected in 17 (34 %) out of 50 patients of which 13 are primary (lymphomas) (76.5 %) and 4 cases with metastatic L.Ns (23.5 %).
By MDCT examination we found that benign nodes were suspected in 35 (70%) out of 50 patients of which 34 are reactive (97.15%) and 1 case with T.B (2.85%). Malignant nodes were suspected in 15 (30 %) out of 50 patients of which 12 are primary (lymphomas) (80%) and 3 cases with metastatic L.Ns (20 %).
Final diagnoses achieved by pathological and /or clinical assessment revealed that out of the 50 examined patients; 34 (68%) had benign nodes; 33 (66%) had reactive hyperplasia and 1 (2%) had tuberculous nodes. The other 16 (32%) patients had malignant nodes; 4(8%) had metastatic nods; 3 (6%) from head and neck primaries and 1(2%) from distant primaries and 12(24%) had malignant lymphomas; 9 (18%) were Hodgkin’s and 3 (6%) were non-Hodgkin’s lymphomas.
Using of US in classifying benign and malignant neck nodes yielded a higher sensitivity (100%), specificity (97 %), positive predictive value (94.1%), negative predictive value (100%) and accuracy (98 %).While using of MDCT in classifying benign and malignant neck nodes yielded a higher specificity (100%), sensitivity (93.7%), positive predictive value (100 %), negative predictive value (97.1%) and accuracy (98 %)