Development of a nomogram to predict prognosis in ovarian cancer: a SEER-based study

Prediction model of adnexal masses with complex ultrasound morphology

Background

Based on the ovarian-adnexal reporting and data system (O-RADS), we constructed a nomogram model to predict the malignancy potential of adnexal masses with sophisticated ultrasound morphology.

Methods

In a multicenter retrospective study, a total of 430 subjects with masses were collected in the adnexal region through an electronic medical record system at the Fourth Hospital of Harbin Medical University during the period of January 2019-April 2023. A total of 157 subjects were included in the exception validation cohort from Harbin Medical University Tumor Hospital. The pathological tumor findings were invoked as the gold standard to classify the subjects into benign and malignant groups. All patients were randomly allocated to the validation set and training set in a ratio of 7:3. A stepwise regression analysis was utilized for filtering variables. Logistic regression was conducted to construct a nomogram prediction model, which was further validated in the training set. The forest plot, C-index, calibration curve, and clinical decision curve were utilized to verify the model and assess its accuracy and validity, which were further compared with existing adnexal lesion models (O-RADS US) and assessments of different types of neoplasia in the adnexa (ADNEX).

Results

Four predictors as independent risk factors for malignancy were followed in the preparation of the diagnostic model: O-RADS classification, HE4 level, acoustic shadow, and protrusion blood flow score (all p < 0.05). The model showed moderate predictive power in the training set with a C-index of 0.959 (95%CI: 0.940-0.977), 0.929 (95%CI: 0.884-0.974) in the validation set, and 0.892 (95%CI: 0.843-0.940) in the external validation set. It showed that the predicted consequences of the nomogram agreed well with the actual results of the calibration curve, and the novel nomogram was clinically beneficial in decision curve analysis.

Conclusion

The risk of the nomogram of adnexal masses with complex ultrasound morphology contained four characteristics that showed a suitable predictive ability and provided better risk stratification. Its diagnostic performance significantly exceeded that of the ADNEX model and O-RADS US, and its screening performance was essentially equivalent to that of the ADNEX model and O-RADS US classification.

Nomogram to predict risk and prognosis of synchronous lung metastasis in renal cell carcinoma: A large cohort analysis

We aimed to construct and validate nomogram models that predict the incidence of lung metastasis (LM) in patients with renal cell carcinoma (RCC) and evaluate overall survival (OS) and cancer-specific survival (CSS) among RCC patients with LM. The Surveillance, Epidemiology, and End Results database was analyzed for RCC patients diagnosed between 2010 and 2015. The X-tile program was used to determine the best cutoff values for age at initial diagnosis and tumor size. Logistic regression analysis was performed to explore independent risk factors for LM, and COX regression analysis was used to identify prognostic indicators for OS and CSS in lung metastatic RCC patients. Subsequently, 3 nomograms were established, and receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were utilized to validate their accuracy. We randomly assigned 10,929 patients with RCC to 2 groups with 1:1 allocation. Multivariate logistic analyses revealed that pathology, tumor (T) stage, nodes (N) stage, race, grade, surgery, metastatic sites, and tumor size were independent risk factors for LM. Multivariate Cox analyses showed that pathology, T stage, N stage, age, surgery, metastatic sites, and residence were independent prognostic factors for OS and CSS in patients with LM. Then, nomograms were developed based on the multivariate logistic and Cox regression analyses results. The ROC and DCA curves confirmed that these nomograms achieved satisfactory discriminative power. Three effective nomograms were constructed and validated that can be used to assist clinicians in predicting the incidence of LM and evaluating the prognosis of lung metastatic RCC.