Approach to intensely enhancing neck nodes

Imaging Approach for Cervical Lymph Node Metastases from Unknown Primary Tumor

Neck swelling due to lymph node (LN) metastasis is one of the initial symptoms of head and neck cancer, and in some cases, the primary tumor is not clinically evident. The purpose of imaging for LN metastasis from an unknown primary site is to identify the primary tumor or detect its absence, which leads to the correct diagnosis and optimal treatment. The authors discuss diagnostic imaging approaches for identifying the primary tumor in cases of unknown primary cervical LN metastases. The distribution and characteristics of LN metastases may help locate the primary site. Unknown primary LN metastasis often occurs at nodal levels II and III, and in recent reports, these were mostly related to human papillomavirus (HPV)-positive squamous cell carcinoma of the oropharynx. Another characteristic imaging finding suggestive of metastasis from HPV-associated oropharyngeal cancer is a cystic change in LN metastases. Other characteristic imaging findings such as calcification may help predict the histologic type and locate the primary site. In cases of LN metastases at nodal levels IV and VB, a primary lesion located outside the head and neck region must also be considered. One clue for detecting the primary lesion at imaging is the disruption of anatomic structures, which can help in identifying small mucosal lesions or submucosal tumors at each subsite. Additionally, fluorine 18 fluorodeoxyglucose PET/CT may help identify a primary tumor. These imaging approaches for identifying primary tumors enable prompt identification of the primary site and assist clinicians in making the correct diagnosis. ^© RSNA, 2023 Quiz questions for this article are available through the Online Learning Center.

Cystic cervical lymph nodes of papillary thyroid carcinoma, tuberculosis and human papillomavirus positive oropharyngeal squamous cell carcinoma: utility of deep learning in their differentiation on CT

OBJECTIVES

Cervical lymph nodes with internal cystic changes are seen with several pathologies, including papillary thyroid carcinoma (PTC), tuberculosis (TB), and HPV-positive oropharyngeal squamous cell carcinoma (HPV+OPSCC). Differentiating these lymph nodes is difficult in the absence of a known primary tumor or reliable medical history. In this study, we assessed the utility of deep learning in differentiating the pathologic lymph nodes of PTC, TB, and HPV+OPSCC on CT.

METHODS

A total of 173 lymph nodes (55 PTC, 58 TB, and 60 HPV+OPSCC) were selected based on pathology records and suspicious morphological features. These lymph nodes were divided into the training set (n = 131) and the test set (n = 42). In deep learning analysis, JPEG lymph node images were extracted from the CT slice that included the largest area of each node and fed into a deep learning training session to create a diagnostic model. Transfer learning was used with the deep learning model architecture of ResNet-101. Using the test set, the diagnostic performance of the deep learning model was compared against the histopathological diagnosis and to the diagnostic performances of two board-certified neuroradiologists.

RESULTS

Diagnostic accuracy of the deep learning model was 0.76 (=32/42), whereas those of Radiologist 1 and Radiologist 2 were 0.48 (=20/42) and 0.41 (=17/42), respectively. Deep learning derived diagnostic accuracy was significantly higher than both of the two neuroradiologists (P < 0.01, respectively).

CONCLUSION

Deep learning algorithm holds promise to become a useful diagnostic support tool in interpreting cervical lymphadenopathy.

Cystic cervical lymph nodes of papillary thyroid carcinoma, tuberculosis and human papillomavirus positive oropharyngeal squamous cell carcinoma: Comparative CT analysis for their differentiation

PURPOSE

Cervical lymph nodes with cystic changes are an important finding seen with several pathologies including papillary thyroid carcinoma (PTC), tuberculosis (TB) and HPV-positive oropharyngeal squamous cell carcinoma (HPV + OPSCC). In the absence of known primary tumor or conclusive medical history, differentiating among these nodes is difficult. We compared the pathologic nodes of PTC, TB and HPV + OPSCC to identify imaging features useful for their differentiation.

MATERIALS AND METHODS

Fifty-five PTC, 58 TB and 51 HPV + OPSCC nodes were selected based on surgical pathology records and suspicious morphological features. These nodes were compared for morphological features: long axis length, nodal shape, nodal location, presence of cystic change, area of cystic change:area of entire node ratio, Hounsfield unit of the cystic component, degree of enhancement, enhancement pattern, presence of calcification, presence of perinodal infiltration, and presence of surrounding inflammatory changes.

RESULTS

PTC nodes formed calcifications more frequently and demonstrated greater enhancement (P < 0.01). TB nodes were characterized by their irregular shape (P < 0.05), irregular enhancement surrounding the cystic change (P < 0.01), greater frequencies of perinodal infiltration (P < 0.01) and surrounding inflammatory changes (P < 0.01). While no unique features were seen with HPV+OPSCC, they were characterized by the absence of those features that distinguished the other groups: these nodes tended to have smooth, circumscribed margins with no hyperenhancement, calcifications or inflammatory changes. PTC and TB nodes were more frequently identified in the lower neck, while HPV+OPSCC nodes were localized to the upper neck (P < 0.01).

CONCLUSIONS

PTC, TB and HPV + OPSCC lymph nodes can be differentiated based on their morphologies and locations.