Developing a validated nomogram for predicting ovarian metastasis in endometrial cancer patients: a retrospective research

Efficacy and Safety of Niraparib as First-Line Maintenance Treatment for Patients with Advanced Ovarian Cancer: Real-World Data from a Multicenter Study in China

BACKGROUND

Poly (ADP-ribose) polymerase (PARP) inhibitors are a new maintenance therapy option for patients with ovarian cancer (OC).

OBJECTIVE

To evaluate the efficacy and influencing factors of the novel PARP inhibitor niraparib for maintenance treatment of Chinese patients with advanced OC.

PATIENTS AND METHODS

In this retrospective multicenter real-world study patients with advanced OC from 15 hospitals throughout China were enrolled. The primary endpoint was progression-free survival (PFS) and the secondary endpoints included the time to treatment discontinuation and safety. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to identify possible risk factors for PFS, after which a prediction model was established to evaluate the likelihood of achieving an 18-month PFS. The relationship between the dose of niraparib and PFS was also evaluated.

RESULTS

The PFS rates of 199 patients at 6, 12, 18, 24, and 30 months were 87.4%, 75.9%, 63.6%, 56.1%, and 51.8%, respectively. LASSO regression model revealed that only age < 65 years (P = 0.011), BRCA mutations (P < 0.001), and R0 status after cytoreductive surgery (P = 0.01) were significant factors associated with prolonged PFS times. Based on the LASSO logistic regression analysis, a clinical prediction formula was developed: - 2.412 + 1.396Age≥65yr + 2.374BRCAwt + 1.387R1 + 0.793Interval≥12w + 0.178BMI>24kg/m2 which yielded a cut-off value of 0.091, an area under the curve (AUC) of 0.839 (0.763-0.916), a sensitivity of 94.3%, and an accuracy of 78.5%. A nomogram was then built to visualize the results. The major treatment-emergent adverse events of ≥ grade 3 included a platelet count decrease (19.1%), white blood cell count decrease (15.1%), neutrophil count decrease (13.1%), and anemia (18.6%). The 18-month PFS rates in patients treated with 200 mg niraparib were somewhat higher than in patients treated with 100 mg after 3-months of therapy.

CONCLUSIONS

For Chinese OC patients, niraparib, particularly at a 200 mg individual starting dose, was an effective therapy with easily manageable safety.

Development and validation of prediction model for early warning of ovarian metastasis risk of endometrial carcinoma

Ovarian metastasis of endometrial carcinoma (EC) patients not only affects the decision of the surgeon, but also has a fatal impact on the fertility and prognosis of patients. This study aimed build a prediction model of ovarian metastasis of EC based on machine learning algorithm for clinical diagnosis and treatment management guidance. We retrospectively collected 536 EC patients treated in Hubei Cancer Hospital from January 2017 to October 2022 and 487 EC patients from Tongji Hospital (January 2017 to December 2020) as an external validation queue. The random forest model, gradient elevator model, support vector machine model, artificial neural network model (ANNM), and decision tree model were used to build ovarian metastasis prediction model for EC patients. The predictive efficacy of 5 machine learning models was evaluated by receiver operating characteristic curve and decision curve analysis. For screening of candidate predictors of ovarian metastasis of EC, the degree of tumor differentiation, lymph node metastasis, CA125, HE4, Alb, LH can be used as a potential predictor of ovarian metastasis prediction model in EC patients. The effectiveness of the prediction model constructed by the 5 machine learning algorithms was between (area under curve [AUC]: 0.729, 95% confidence interval [CI]: 0.674-0.784) and (AUC: 0.899, 95% CI: 0.844-0.954) in the training set and internal verification set, respectively. Among them, the ANNM was equipped with the best prediction effectiveness (training set: AUC: 0.899, 95% CI: 0.844-0.954) and (internal verification set: AUC: 0.892, 95% CI: 0.837-0.947). The prediction model of ovarian metastasis of EC patients based on machine learning algorithm can achieve satisfactory prediction efficiency, among which ANNM is the best, which can be used to guide clinicians in diagnosis and treatment and improve the prognosis of EC patients.

Ovarian preservation in gynecologic oncology: current indications and techniques

PURPOSE OF REVIEW

Early menopause represents a relevant clinical issue for women. Nevertheless, this issue should be balanced with the risks of ovarian metastasis, ovarian recurrence, and the risk of recurrence in hormone-sensitive gynecological cancers. The purpose of this review was to provide an overview on current indications and techniques of ovarian preservation in patients with gynecological cancers.

RECENT FINDINGS

The potential discussion about ovarian conservation could be proposed to patients with FIGO-stage IA grade 1-2 endometrioid endometrial cancer aged 40 years or less, FIGO-stage IB1-IB2 node-negative cervical cancer with squamous cell carcinoma and HPV-associated adenocarcinoma, FIGO-stage IA-IC grade 1-2 serous, endometrioid, mucinous expansile pattern ovarian cancer, any stage germ cell ovarian tumors, and FIGO-stage IA sex cord-stromal tumors. Technique to perform ovarian transposition in cervix cancer is also reported.

SUMMARY

Ovarian conservation is a surgical approach that involves preserving one or both ovaries during the treatment of gynecologic cancers. This approach has gained popularity in recent years, as it offers several benefits to the patient, including the preservation of hormonal function and fertility. The decision to perform ovarian conservation depends on several factors, such as the stage and type of cancer, the patient's age, fertility desire, and should be carefully discussed with patients.

Comprehensive analysis of novel prognosis-related proteomic signature effectively improve risk stratification and precision treatment for patients with cervical cancer

OBJECTIVE

Cervical cancer (CC) is one of the most common types of malignant female cancer, and its incidence and mortality are not optimistic. Protein panels can be a powerful prognostic factor for many types of cancer. The purpose of our study was to investigate a proteomic panel to predict the survival of patients with common CC.

METHODS AND RESULTS

The protein expression and clinicopathological data of CC were downloaded from The Cancer Proteome Atlas and The Cancer Genome Atlas database, respectively. We selected the prognosis-related proteins (PRPs) by univariate Cox regression analysis and found that the results of functional enrichment analysis were mainly related to apoptosis. We used Kaplan-Meier analysis and multivariable Cox regression analysis further to screen PRPs to establish a prognostic model, including BCL2, SMAD3, and 4EBP1-pT70. The signature was verified to be independent predictors of OS by Cox regression analysis and the area under curves. Nomogram and subgroup classification were established based on the signature to verify its clinical application. Furthermore, we looked for the co-expressed proteins of three-protein panel as potential prognostic proteins.

CONCLUSION

A proteomic signature independently predicted OS of CC patients, and the predictive ability was better than the clinicopathological characteristics. This signature can help improve prediction for clinical outcome and provides new targets for CC treatment.

Development and Validation of a Nomogram Based on Metabolic Risk Score for Assessing Lymphovascular Space Invasion in Patients with Endometrial Cancer

OBJECTIVE

This study assessed the predictive value of the metabolic risk score (MRS) for lymphovascular space invasion (LVSI) in endometrial cancer (EC) patients.

METHODS

We included 1076 patients who were diagnosed with EC between January 2006 and December 2020 in Peking University People's Hospital. All patients were randomly divided into the training and validation cohorts in a ratio of 2:1. Data on clinicopathological indicators were collected. Univariable and multivariable logistic regression analysis was used to define candidate factors for LVSI. A backward stepwise selection was then used to select variables for inclusion in a nomogram. The performance of the nomogram was evaluated by discrimination, calibration, and clinical usefulness.

RESULTS

Independent predictors of LVSI included differentiation grades (G2: OR = 1.800, 95% CI: 1.050-3.070, p = 0.032) (G3: OR = 3.49, 95% CI: 1.870-6.520, p < 0.001), histology (OR = 2.723, 95% CI: 1.370-5.415, p = 0.004), MI (OR = 4.286, 95% CI: 2.663-6.896, p < 0.001), and MRS (OR = 1.124, 95% CI: 1.067-1.185, p < 0.001) in the training cohort. A nomogram was established to predict a patient's probability of developing LVSI based on these factors. The ROC curve analysis showed that an MRS-based nomogram significantly improved the efficiency of diagnosing LVSI compared with the nomogram based on clinicopathological factors (p = 0.0376 and p = 0.0386 in the training and validation cohort, respectively). Subsequently, the calibration plot showed a favorable consistency in both groups. Moreover, we conducted a decision curve analysis, showing the great clinical benefit obtained from the application of our nomogram. However, our study faced several limitations. Further external validation and a larger sample size are needed in future studies.

CONCLUSION

MRS-based nomograms are useful for predicting LVSI in patients with EC and may facilitate better clinical decision-making.