A real-time prediction model for post-irradiation malignant cervical lymph nodes

Area of Science:

• Oncology
• Radiology
• Medical Imaging

Background:

• Cervical lymph node (LN) metastasis is common in cancer patients.
• Neck irradiation can alter LN sonographic characteristics, complicating malignancy assessment.
• Accurate identification of malignant LNs is crucial for guiding further treatment and biopsies.

Purpose of the Study:

• To develop and validate a real-time predictive scoring model for malignant cervical LNs in cancer patients after neck irradiation.
• To utilize sonographic features for improved diagnostic accuracy.
• To provide a tool for prompt and reliable guidance for ultrasound-guided fine-needle aspiration (USgFNA) biopsies.

Main Methods:

• A cohort of 144 patients treated with neck irradiation underwent ultrasonography and USgFNAs.
• Data were analyzed using stepwise multivariate logistic regression to construct a predictive scoring model.
• The newly developed model was compared with a previously established prediction model.

Main Results:

• A scoring model was generated: 1.35 × (L axis) + 2.03 × (S axis) + 2.27 × (margin) + 1.48 × (echogenic hilum) + 3.7.
• A score ≥ 7 indicated malignancy, achieving 85.5% sensitivity, 79.4% specificity, and 82.6% overall accuracy.
• The new model demonstrated superior performance (c-statistic = 0.89) compared to the previous model (c-statistic = 0.81).

Conclusions:

• A real-time sonographic predictive scoring model was successfully developed.
• This model offers prompt and reliable guidance for USgFNA biopsies in managing cervical LNs post-irradiation.
• The model enhances diagnostic capabilities for identifying malignant cervical lymph nodes in irradiated cancer patients.