Advancing Tailored Treatments: A Predictive Nomogram, Based on Ultrasound and Laboratory Data, for Assessing Nodal Involvement in Endometrial Cancer Patients.
Ida PinoElisa GozziniDavide RadiceSara BoveriAnna Daniela IacoboneAilyn Mariela Vidal UrbinatiFrancesco MultinuGiuseppe GulloGaspare CucinellaDorella FranchiPublished in: Journal of clinical medicine (2024)
Assessing lymph node metastasis is crucial in determining the optimal therapeutic approach for endometrial cancer (EC). Considering the impact of lymphadenectomy, there is an urgent need for a cost-effective and easily applicable method to evaluate the risk of lymph node metastasis in cases of sentinel lymph node (SLN) biopsy failure. This retrospective monocentric study enrolled EC patients, who underwent surgical staging with nodal assessment. Data concerning demographic, clinicopathological, ultrasound, and surgical characteristics were collected from medical records. Ultrasound examinations were conducted in accordance with the IETA statement. We identified 425 patients, and, after applying exclusion criteria, the analysis included 313 women. Parameters incorporated into the nomogram were selected via univariate and multivariable analyses, including platelet count, myometrial infiltration, minimal tumor-free margin, and CA 125. The nomogram exhibited good accuracy in predicting lymph node involvement, with an AUC of 0.88. Using a cutoff of 10% likelihood of nodal involvement, the nomogram displayed a low false-negative rate of 0.04 (95% CI 0.00-0.19) in the training set. The adaptability of this straightforward model renders it suitable for implementation across diverse clinical settings, aiding gynecological oncologists in preoperative patient evaluations and facilitating the design of personalized treatments. However, external validation is mandatory for confirming diagnostic accuracy.
Keyphrases
- lymph node metastasis
- lymph node
- endometrial cancer
- sentinel lymph node
- end stage renal disease
- squamous cell carcinoma
- neoadjuvant chemotherapy
- magnetic resonance imaging
- ejection fraction
- chronic kidney disease
- healthcare
- primary care
- papillary thyroid
- patient reported outcomes
- metabolic syndrome
- computed tomography
- early stage
- palliative care
- ultrasound guided
- smoking cessation
- polycystic ovary syndrome
- insulin resistance
- deep learning
- fine needle aspiration