Comparison of outcomes between early-stage cervical cancer patients without high-risk factors undergoing adjuvant concurrent chemoradiotherapy and radiotherapy alone after radical surgery.

PURPOSE: For patients with early-stage cervical cancer without high-risk factors, there is no consensus regarding the optimal postoperative treatment regimen and whether postoperative concurrent radiochemotherapy (CCRT) is superior to radiotherapy (RT) alone. PATIENTS AND METHODS: The medical records of patients with stage I-IIA cervical cancer, who underwent radical surgery and postoperative RT or CCRT between June 2012 and December 2017, were retrospectively reviewed. Patients with any high-risk factors, including positive pelvic lymph node(s), positive resection margin(s), and parametrial invasion, were excluded. Patients with large tumors (≥ 4 cm), deep stromal invasion (≥ 1/2), and lymphovascular space involvement were categorized as the intermediate-risk group. Patients without intermediate-risk factors were categorized as the low-risk group. RESULTS: A total of 403 patients were enrolled and divided into 2 groups according to postoperative treatment: RT alone (n = 105); and CCRT (n = 298). For risk stratification, patients were also divided into 2 groups: intermediate-risk (n = 350); and low-risk (n = 53). The median follow-up was 51.7 months. Patients in the intermediate-risk group and those with multiple intermediate-risk factors were more likely to undergo CCRT. For patients who underwent RT alone or CCRT in the intermediate-risk group, 5-year overall survival (OS) rates were 93.4% and 93.8% (p = 0.741), and 5-year disease-free survival (DFS) rates were 90.6% and 91.4%, respectively (p = 0.733). Similarly, for patients who underwent RT alone or CCRT in the low-risk group, the 5-year OS rates were 100.0% and 93.5% (p = 0.241), and 5-year DFS rates were 94.4% and 93.5%, respectively (p = 0.736). Adjuvant CCRT or RT were not independent risk factors for either OS or DFS. Patients who underwent CCRT appeared to develop a higher proportion of grade ≥ 3 acute hematological toxicities than those in the RT group (44.0% versus 11.4%, respectively; p < 0.001). There was no significant difference in grade ≥ 3 chronic toxicities of the urogenital and gastrointestinal systems between the CCRT and RT groups. CONCLUSION: There was no significant difference in 5-year OS and DFS rates between patients with early-stage cervical cancer without high-risk factors undergoing postoperative CCRT versus RT alone. Patients who underwent CCRT appeared to develop a higher proportion of grade ≥ 3 acute hematological toxicities than those who underwent RT alone.

Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics Model for Preoperative Predicting the Deep Stromal Invasion in Patients with Early Cervical Cancer.

Deep stromal invasion is an important pathological factor associated with the treatments and prognosis of cervical cancer patients. Accurate determination of deep stromal invasion before radical hysterectomy (RH) is of great value for early clinical treatment decision-making and improving the prognosis of these patients. Machine learning is gradually applied in the construction of clinical models to improve the accuracy of clinical diagnosis or prediction, but whether machine learning can improve the preoperative diagnosis accuracy of deep stromal invasion in patients with cervical cancer was still unclear. This cross-sectional study was to construct three preoperative diagnostic models for deep stromal invasion in patients with early cervical cancer based on clinical, radiomics, and clinical combined radiomics data using the machine learning method. We enrolled 229 patients with early cervical cancer receiving RH combined with pelvic lymph node dissection (PLND). The least absolute shrinkage and selection operator (LASSO) and the fivefold cross-validation were applied to screen out radiomics features. Univariate and multivariate logistic regression analyses were applied to identify clinical predictors. All subjects were divided into the training set (n = 160) and testing set (n = 69) at a ratio of 7:3. Three light gradient boosting machine (LightGBM) models were constructed in the training set and verified in the testing set. The radiomics features were statistically different between deep stromal invasion < 1/3 group and deep stromal invasion ≥ 1/3 group. In the training set, the area under the curve (AUC) of the prediction model based on radiomics features was 0.951 (95% confidence interval (CI) 0.922-0.980), the AUC of the prediction model based on clinical predictors was 0.769 (95% CI 0.703-0.835), and the AUC of the prediction model based on radiomics features and clinical predictors was 0.969 (95% CI 0.947-0.990). The AUC of the prediction model based on radiomics features and clinical predictors was 0.914 (95% CI 0.848-0.980) in the testing set. The prediction model for deep stromal invasion in patients with early cervical cancer based on clinical and radiomics data exhibited good predictive performance with an AUC of 0.969, which might help the clinicians early identify patients with high risk of deep stromal invasion and provide timely interventions.

Preoperative magnetic resonance imaging-radiomics in cervical cancer: a systematic review and meta-analysis.

Background: The purpose of this systematic review and meta-analysis is to evaluate the potential significance of radiomics, derived from preoperative magnetic resonance imaging (MRI), in detecting deep stromal invasion (DOI), lymphatic vascular space invasion (LVSI) and lymph node metastasis (LNM) in cervical cancer (CC). Methods: A rigorous and systematic evaluation was conducted on radiomics studies pertaining to CC, published in the PubMed database prior to March 2024. The area under the curve (AUC), sensitivity, and specificity of each study were separately extracted to evaluate the performance of preoperative MRI radiomics in predicting DOI, LVSI, and LNM of CC. Results: A total of 4, 7, and 12 studies were included in the meta-analysis of DOI, LVSI, and LNM, respectively. The overall AUC, sensitivity, and specificity of preoperative MRI models in predicting DOI, LVSI, and LNM were 0.90, 0.83 (95% confidence interval [CI], 0.75-0.89) and 0.83 (95% CI, 0.74-0.90); 0.85, 0.80 (95% CI, 0.73-0.86) and 0.75 (95% CI, 0.66-0.82); 0.86, 0.79 (95% CI, 0.74-0.83) and 0.80 (95% CI, 0.77-0.83), respectively. Conclusion: MRI radiomics has demonstrated considerable potential in predicting DOI, LVSI, and LNM in CC, positioning it as a valuable tool for preoperative precision evaluation in CC patients.