Construction and validation of a prediction model for inguinal lymph node metastasis of penile malignancy.

BACKGROUND

Penile squamous cell carcinoma is a relatively rare malignancy among male malignancies, there are more than 30,000 new cases and more than 10,000 deaths of penile cancer annually. In patients with penile malignancy, inguinal lymph node metastasis (ILNM) significantly reduces patient survival. Thus, we identified the risk factors for ILNM in penile malignancies, aiming to develop a precise prediction model.

METHODS

We retrospectively analyzed 112 male patients with penile cancer. All subjects underwent penile surgery and inguinal lymphadenectomy at the same time, and postoperative pathology confirmed ILNM. Fisher's exact test, -test, and Wilcoxon rank sum test were used to assess differences in demographic information and clinical features between the two groups, followed by logical least absolute shrinkage and selection operator (LASSO) regression analysis to determine risk factors of ILNM. The prediction model was constructed using nomogram.

RESULTS

LASSO regression revealed that age [β=-0.005, odds ratio (OR) =0.995], smoking history (β=-0.006, OR =0.994) and interleukin 2 (IL-2) level (β=-0.0112, OR =0.989) were protective against ILNM. However, lymph node diameter (β=0.3117, OR =1.366), T-stage (β=0.1254, OR =1.134), fibrinogen (β=0.0377, OR =1.038), IL-4 level (β=0.004, OR =1.001), and neutrophil-to-lymphocyte ratio (β=0.0355, OR =1.034) were risk factors for developing ILNM. When assessing the risk of metastasis, it is crucial to balance these factors. The aforementioned characteristics were utilized to establish the predictive model, which demonstrated a good predictive ability with an area under the curve (AUC) value of 0.81. Moreover, internal leave-one-way cross-validation was used to construct a nomogram showing consistency, with an AUC of 0.75.

CONCLUSIONS

The diagnosis of ILNM in penile malignant tumors can be predicted through clinicopathological features, biochemical tests, and prediction models based on tumor markers.