Ning Xie

Xu J, et al. J Gynecol Oncol. 2027 ;38:e4. https://doi.org/10.3802/jgo.2027.38.e4

Purpose Radiation proctitis (RP) is a frequent and clinically relevant complication of radiotherapy (RT) in cervical cancer (CC). However, effective tools for individualized RP risk prediction remain limited. This study aimed to develop and validate an MRI-based radiomic risk score for early RP prediction in CC patients. Methods This retrospective study included 375 CC patients who underwent radical RT. Patients were randomly assigned to training (80%) and validation (20%) cohorts. Radiomics features were extracted from pre-treatment MRI and screened using Mann–Whitney U test, intraclass correlation coefficient, and least absolute shrinkage and selection operator regression. Logistic regression analyses identified independent predictors of RP, and a clinical-radiomics nomogram was constructed. Model performance was assessed by the area under the curve (AUC), calibration, and decision curve analysis (DCA). Results Of 1,906 extracted features, 9 were selected to construct the radiomics risk score. Independent predictors of RP included lymphocyte counts ≥ 2.6 × 10⁹/L, platelet–lymphocyte ratio ≥ 196, rectal mean dose ≥ 47.22 Gy, and radiomics risk score≥-0.4. The nomogram incorporating these factors achieved AUCs of 0.810 (training) and 0.730 (validation), with good calibration and clinical utility demonstrated by DCA. Conclusion The proposed MRI-based radiomic risk score enables accurate, noninvasive prediction of RP in CC patients and provides a practical tool to optimize individualized RT planning and toxicity management.

Aims:This study aimed to identify the prognostic value of tumor invasion distance (TID) based on MRI findings in cervical-cancer (CC) patients treated with radiotherapy (RT). Methods: A total of 218 CC patients diagnosed at Fujian Cancer Hospital from December 2018 to December 2019 were included in the study. Cox regression analyses were conducted to identify independent prognostic factors for overall survival (OS), including low 1/3 vaginal involvement, a longer TID, and RT without chemotherapy. These factors were subsequently used to construct a nomogram for individualized risk prediction. Kaplan–Meier survival analysis was employed to evaluate survival outcomes and establish a risk stratification system. The performance of the new stratification was assessed using the linear trend χ² test, Akaike information criterion, and Harrell’s concordance index. Results: A longer TID was associated with worse 3-year OS (p < 0.001, HR: 3.42, 95% CI: 1.67–7.00). A longer TID, lower 1/3 vaginal involvement, and concurrent chemotherapy were independent prognostic survival factors for CC patients. Compared with the 2018 FIGO staging system, the new risk stratification system provided better monotonicity with a higher linear trend χ² value (28.03 vs. 9.35), better discriminatory ability with smaller Akaike information criterion (312 vs. 331), and a greater Harrell C statistic (0.74 vs. 0.65) for predicting 3-year OS. Conclusions: This was the first study to demonstrate the prognostic value of TID in CC patients who received RT. The new risk stratification system based on TID could complement the 2018 FIGO staging system in identifying high-risk patients for more intense treatment and care. Further prospective research with larger samples is warranted to confirm the significance of TID for CC patients treated with RT.

Backgrounds: Platelet‐to‐albumin ratio (PAR) is a new systemic inflammatory prognostic indicator associated with many inflammatory diseases. However, its role in radiation cystitis (RC) is obscure. This study aimed to explore whether PAR could be used as an effective parameter for predicting the RC risk in local advanced cervical cancer (CC) treated with radiotherapy.Methods: A total of 319 local advanced CC patients who received radical radiotherapy at Fujian Cancer Hospital were enrolled between December 2018 and January 2021. Demographics and clinical parameters were retrospectively analyzed. Univariate and multivariate analyses were used to identify the risk factors for RC. Backward and stepwise regression was applied to construct two monograms‐one with primary significant factors and the other with extra inflammatory biomarkers. A DeLong test was applied to compare the prediction abilities of two nomograms. Calibration curves and decision curve analysis (DCA) evaluated its prediction consistency, discrimination ability, and clinical net benefit.Results: Univariate analysis showed that age, tumor size, stage, total radiation dose, pelvic radiation dose, Systemic Immune‐Inflammation Index (SII), platelet‐to‐lymphocyte ratio (PLR), and PAR were significantly associated with RC occurrence (all p < 0.05). Multivariate analyses indicated that age, tumor size, stage, total radiation dose, and PAR were independent factors (all p < 0.05). Then, the area under curve (AUC) value of the nomogram_SII+PAR was higher (AUC = 0.774) compared to that of the baseline nomogram (AUC = 0.726) (p_Delong = 0.02). Also, the five‐cross validation confirmed the stability of the nomogram_SII+PAR. Moreover, the calibration curve and DCA exhibited the nomograms' good prediction consistency and clinical practicability.Conclusions: PAR and SII could be valued for CC patients who are treated with radiation therapy. The nomogram based on PAR and SII could stratify patients who need extra intervention and nursing care to prevent bladder radiation damage and improve patients' quality of life.

Introduction: With the advancements in cancer prevention and diagnosis, the proportion of newly diagnosed early-stage cervical cancers has increased. Adjuvant therapies based on high-risk postoperative histopathological factors significantly increase the morbidity of treatment complications and seriously affect patients’ quality of life. Objectives: Our study aimed to establish a diagnostic nomogram for vaginal invasion (VI) among early-stage cervical cancer (CC) that can be used to reduce the occurrence of positive or close vaginal surgical margins. Methods: We assembled the medical data of early-stage CC patients between January 2013 and December 2021 from the Fujian Cancer Hospital. Data on demographics, laboratory tests, MRI features, physical examination (PE), and pathological outcomes were collected. Univariate and multivariate logistic regression analyses were employed to estimate the diagnostic variables for VI in the training set. Finally, the statistically significant factors were used to construct an integrated nomogram. Results: In this retrospective study, 540 CC patients were randomly divided into training and validation cohorts according to a 7:3 ratio. Multivariate logistic analyses showed that age [odds ratio (OR) = 2.41, 95% confidence interval (CI), 1.29-4.50, P = 0.006], prognostic nutritional index (OR = 0.18, 95% CI, 0.04-0.77, P = 0.021), histological type (OR = 0.28, 95% CI, 0.08-0.94, P = 0.039), and VI based on PE (OR = 3.12, 95% CI, 1.52-6.45, P = 0.002) were independent diagnostic factors of VI. The diagnostic nomogram had a robust ability to predict VI in the training [area under the receiver operating characteristic curve (AUC) = 0.76, 95% CI: 0.70-0.82] and validation (AUC = 0.70, 95% CI: 0.58-0.83) cohorts, and the calibration curves, decision curve analysis, and confusion matrix showed good prediction power. Conclusion: Our diagnostic nomograms could help gynaecologists quantify individual preoperative VI risk, thereby optimizing treatment options, and minimizing the incidence of multimodality treatment-related complications and the economic burden.