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

A combination of ultrasound and contrast-enhanced ultrasound improves diagnostic accuracy for the differentiation of cervical tuberculous lymphadenitis from primary lymphoma

OBJECTIVES

To present a method combining ultrasound (US) and contrast-enhanced ultrasound (CEUS) features for differential diagnosis of cervical tuberculous lymphadenitis (CTL) and primary lymphoma.

METHODS

A total of 155 patients with CTL (n = 49) and lymphoma (n = 106) who underwent US and CEUS were retrospectively included. The features extracted from US and CEUS and the significant clinical data were created three models using the least absolute shrinkage and selection operator and logistic regression analysis. The diagnostic performance of the models was assessed using the area under the curve (AUC).

RESULTS

The combined model outperformed US model and CEUS model in distinguish CTL from lymphoma achieved favorable performances in training set and validation set with AUCs of 0.958 and 0.946 as well as high accuracies (91.7% and 87.2%), sensitivities (95.9% and 84.4%) and specificities (82.4% and 93.3%). Delong's test showed that among the three models, combined model was significantly different from the other two models in training set (p = 0.011 and 0.029, respectively) and validation set (p = 0.018 and 0.001, respectively).

CONCLUSIONS

A combination of US and CEUS achieved good diagnostic performance in differentiating lymphoma and CTL, which might aid in clinical decision-making.

Cell free HPV DNA provides an accurate and rapid diagnosis of HPV-associated head and neck cancer

PURPOSE

HPV-associated Head and Neck Squamous Cell Carcinoma(HPV+HNSCC) is the most common HPV-associated malignancy in the United States and continues to increase in incidence. Current diagnostic approaches for HPV+HNSCC rely on tissue biopsy followed by histomorphologic assessment and detection of HPV indirectly by p16 immunohistochemistry. Such approaches are invasive and have variable sensitivity.

EXPERIMENTAL DESIGN

We conducted a prospective observational study in 140 subjects (70 cases and 70 controls) to test the hypothesis that a non-invasive diagnostic approach for HPV+HNSCC would have improved diagnostic accuracy, lower cost, and shorter Diagnostic Interval compared to standard approaches. Blood was collected, processed for circulating tumor HPV DNA(ctHPVDNA) and analyzed with custom ddPCR assays for HPV genotypes 16,18, 33, 35 and 45. Diagnostic performance, cost and Diagnostic Interval were calculated for standard clinical work up and compared to a non-invasive approach using ctHPVDNA combined with cross-sectional imaging and physical exam findings.

RESULTS

Sensitivity and specificity of ctHPVDNA for detecting HPV+HNSCC was 98.4% and 98.6%. Sensitivity and specificity of a composite non-invasive diagnostic using ctHPVDNA and imaging/physical exam were 95.1% and 98.6%. Diagnostic accuracy of this non-invasive approach was significantly higher than standard of care (Youden index 0.937 vs 0.707, p=0.0006). Costs of non-invasive diagnostic were 36-38% less than standard clinical work up and the median Diagnostic Interval was 26 days less.

CONCLUSIONS

A non-invasive diagnostic approach for HPV+HNSCC demonstrated improved accuracy, reduced cost and a shorter time to diagnosis compared to standard clinical workup and could be a viable alternative in the future.

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.