Multi-institutional validation of the ESMO-ESGO-ESTRO consensus conference risk grouping in Turkish endometrial cancer patients treated with comprehensive surgical staging

Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer

BACKGROUND

Preoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC.

AIM

To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI.

METHODS

The study comprised 112 EC patients. The participants were randomly separated into training and validation groups with a 7:3 ratio. Logistic regression analysis was applied to uncover independent clinical predictors. These predictors were then used to create a clinical nomogram. Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images, the Mann-Whitney U test, Pearson test, and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features, which were subsequently utilized to generate a radiomic signature. Seven machine learning strategies were used to construct radiomic models that relied on the screening features. The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators.

RESULTS

Having an accuracy of 0.82 along with an area under the curve (AUC) of 0.915 [95% confidence interval (CI): 0.806-0.986], the random forest method trained on radiomics characteristics performed better than expected. The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram (AUC: 0.75, 95%CI: 0.611-0.899) and the combined nomogram (AUC: 0.869, 95%CI: 0.702-0.986) that integrated clinical parameters and radiomic signature.

CONCLUSION

The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.

Radiomics and Artificial Intelligence in Uterine Sarcomas: A Systematic Review

Background: Recently, artificial intelligence (AI) with computerized imaging analysis is attracting the attention of clinicians, in particular for its potential applications in improving cancer diagnosis. This review aims to investigate the contribution of radiomics and AI on the radiological preoperative assessment of patients with uterine sarcomas (USs). Methods: Our literature review involved a systematic search conducted in the last ten years about diagnosis, staging and treatments with radiomics and AI in USs. The protocol was drafted according to the systematic review and meta-analysis preferred reporting project (PRISMA-P) and was registered in the PROSPERO database (CRD42021253535). Results: The initial search identified 754 articles; of these, six papers responded to the characteristics required for the revision and were included in the final analysis. The predominant technique tested was magnetic resonance imaging. The analyzed studies revealed that even though sometimes complex models included AI-related algorithms, they are still too complex for translation into clinical practice. Furthermore, since these results are extracted by retrospective series and do not include external validations, currently it is hard to predict the chances of their application in different study groups. Conclusion: To date, insufficient evidence supports the benefit of radiomics in USs. Nevertheless, this field is promising but the quality of studies should be a priority in these new technologies.

Oncologic safety of laparoscopic surgery for women with apparent early-stage uterine serous carcinoma: A multi-institutional retrospective cohort study

OBJECTIVE

To compare the long-term survival outcomes of patients with apparent early-stage uterine serous carcinoma (USC) who underwent laparoscopic staging surgery with those who underwent open surgical staging.

METHODS

A total of 295 patients from four Chinese teaching hospitals were included. Overall survival (OS) and disease-free survival (DFS) were estimated and compared using the Kaplan-Meier method and the log-rank test among patients after laparoscopic surgery or open surgery. The Cox proportional hazards regression model was applied to adjust for potential confounding factors.

RESULTS

For patients with apparent early-stage USC, laparoscopic surgery was associated with deteriorated DFS (hazard ratio [HR] 1.83, 95% confidence interval [CI] 1.15-2.93, P = 0.012), and there was no significant difference in OS between the two groups (HR 1.74, 95% CI 0.99-3.08, P = 0.056). However, after adjusting for confounding factors, the surgical approach was not an independent prognostic factor for DFS (adjusted HR 1.16, 95% CI 0.63-2.12, P = 0.636) and OS (adjusted HR 1.11, 95% CI 0.52-2.38, P = 0.794) in apparent early-stage USC.

CONCLUSION

For apparent early-stage USC, laparoscopic surgery is safe. This needs to be confirmed by future prospective clinical trials.

Predictive value of the new ESGO-ESTRO-ESP endometrial cancer risk classification on survival and recurrence in the Danish population

OBJECTIVE

To compare the performance of the new ESGO-ESTRO-ESP (European Society of Gynecological Oncology-European Society for Radiotherapy & Oncology-European Society for Pathology) 2020 risk classification system with the previous 2016 risk classification in predicting survival and patterns of recurrence in the Danish endometrial cancer population.

METHODS

This Danish national cohort study included 4516 patients with endometrial cancer treated between 2005 and 2012. Five-year Kaplan-Meier adjusted and unadjusted survival estimates and actuarial recurrence rates were calculated for the previous and the new classification systems.

RESULTS

In the 2020 risk classification system, 81.0% of patients were allocated to low, intermediate, or high-intermediate risk compared with 69.1% in the 2016 risk classification system, mainly due to reclassification of 44.5% of patients previously classified as high risk to either intermediate or especially high-intermediate risk. The survival of the 2020 high-risk group was significantly lower, and the recurrence rate, especially the non-local recurrence rate, was significantly higher than in the 2016 high risk group (2020/2016, overall survival 59%/66%; disease specific 69%/76%; recurrence 40.5%/32.3%, non-local 34.5%/25.8%). Survival and recurrence rates in the other risk groups and the decline in overall and disease-specific survival rates from the low risk to the higher risk groups were similar in patients classified according to the 2016 and 2020 systems.

CONCLUSION

The new ESGO-ESTRO-ESP 2020 risk classification system allocated fewer patients to the high risk group than the previous risk classification system. The main differences were lower overall and disease-specific survival and a higher recurrence rate in the 2020 high risk group. The introduction of the new 2020 risk classification will potentially result in fewer patients at high risk and allocation to the new high risk group will predict lower survival, potentially allowing more specific selection for postoperative adjuvant therapy.