Tian Tian

A novel function of STARD7: its tumor-promoting role involves the activation of NF-κB signaling pathway in ovarian cancer

Clinical characteristics and prognosis of rhabdomyosarcoma in the female reproductive system: a population-based analysis using SEER data

Rhabdomyosarcoma of the female reproductive system is rare, and as a result, management strategies are unclear. In this study, we categorized rhabdomyosarcoma based on location (cervical, uterine, vulvovaginal) and analyzed prognostic factors to guide individualized treatment. Data from the Surveillance, Epidemiology, and End Results database (2000-2021) were used to identify patients with cervical, uterine, and vulvovaginal rhabdomyosarcoma. Multivariate Cox regression identified prognostic factors, and Kaplan-Meier analysis assessed overall survival and disease-specific survival. For cervical rhabdomyosarcoma (n = 76), radical surgery improved disease-specific survival (91.9% vs 62.8%, p = .005), but conservative surgery was sufficient for patients aged <49 years, with a tumor diameter <4 cm, or with the embryonal subtype. In uterine rhabdomyosarcoma (n = 253), the embryonal subtype showed better overall survival (62.3% vs 23%, p < .0001) and disease-specific survival (65.9% vs 31%, p < .0001), especially for tumors ≤14 cm. For vulvovaginal rhabdomyosarcoma (n = 60), no survival differences were observed between local and radical surgery in patients aged <16 years or with tumors <8 cm. Adjuvant chemotherapy in cervical rhabdomyosarcoma has been shown to enhance the overall survival rate of patients undergoing radical surgery; however, radiotherapy appears to diminish their overall survival outcomes. In the case of uterine rhabdomyosarcoma of the embryonic subtype, chemotherapy concurrently improves both overall and disease-specific survival rates, while radiotherapy does not demonstrate a significant impact. For vulvovaginal rhabdomyosarcoma, chemotherapy is effective in improving the overall survival rate among non-surgical patients, whereas radiotherapy shows no effect on the survival outcomes across all subgroups. Conservative treatment is suitable for young patients with cervical rhabdomyosarcoma who have small tumors or the embryonal subtype, while radical surgery benefits the overall population. Embryonal uterine rhabdomyosarcoma has better outcomes, and young patients with vulvovaginal rhabdomyosarcoma of the embryonal subtype can undergo local surgery. For these 3 sites, chemotherapy is indispensable, while radiotherapy should be administered with caution.

Comparison of the survival outcome of neoadjuvant therapy followed by radical surgery with that of concomitant chemoradiotherapy in patients with stage IB2–IIIB cervical adenocarcinoma

To compare the survival outcome of neoadjuvant therapy (NAT) (chemotherapy or chemotherapy and intracavitary brachytherapy (ICBT) followed by radical surgery and of concomitant chemotherapy and radiotherapy (CCRT) in patients with locally advanced cervical adenocarcinoma and identify predictors of cervical adenocarcinoma. We retrospectively reviewed our medical records of cervical adenocarcinoma patients treated with either NAT + surgery or CCRT in our institution from January 2013 to December 2017. The patients were treated with two-dimensional radiotherapy or three-dimensional-conformal or intensity-modulated radiotherapy combined with intracavitary brachytherapy. The regimen of concomitant chemotherapy was weekly cisplatin. The neoadjuvant chemotherapy (NACT) was paclitaxel plus cisplatin. The primary end points were overall survival (OS) and progression-free survival (PFS). We enrolled 121 patients. There were 42 (34.7%) patients in the NAT + surgery group and 79 (65.3%) in the CCRT group. After univariate multivariate analysis, NAT was an independent predictor of OS (p = 0.008) and PFS (p = 0.006). After propensity score matching, the 5-year OS rates in the NAT + surgery and CCRT groups were 25% and 4%, respectively (p = 0.00014), and the 5-year PFS rates were 25% and 4%, respectively (p = 0.00015). Subgroup analysis showed that the 5-year OS and PFS rates in the NACT + surgery and CCRT groups were both 20% and 8%, respectively (p = 0.015). Compared with CCRT, NAT followed by radical surgery had better OS and PFS in locally advanced cervical adenocarcinoma. In subgroup analysis, OS and PFS were longer for NACT + surgery than for CCRT.

Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study

Abstract Background Cervical cancer remains a major global health issue. Personalized, data-driven cervical cancer prevention (CCP) strategies tailored to phenotypic profiles may improve prevention and reduce disease burden. Objective This study aimed to identify subgroups with differential cervical precancer or cancer risks using machine learning, validate subgroup predictions across datasets, and propose a computational phenomapping strategy to enhance global CCP efforts. Methods We explored the data-driven CCP subgroups by applying unsupervised machine learning to a deeply phenotyped, population-based discovery cohort. We extracted CCP-specific risks of cervical intraepithelial neoplasia (CIN) and cervical cancer through weighted logistic regression analyses providing odds ratio (OR) estimates and 95% CIs. We trained a supervised machine learning model and developed pathways to classify individuals before evaluating its diagnostic validity and usability on an external cohort. Results This study included 551,934 women (median age, 49 years) in the discovery cohort and 47,130 women (median age, 37 years) in the external cohort. Phenotyping identified 5 CCP subgroups, with CCP4 showing the highest carcinoma prevalence. CCP2–4 had significantly higher risks of CIN2+ (CCP2: OR 2.07 [95% CI: 2.03‐2.12], CCP3: 3.88 [3.78‐3.97], and CCP4: 4.47 [4.33‐4.63]) and CIN3+ (CCP2: 2.10 [2.05‐2.14], CCP3: 3.92 [3.82‐4.02], and CCP4: 4.45 [4.31‐4.61]) compared to CCP1 (P&lt;.001), consistent with the direction of results observed in the external cohort. The proposed triple strategy was validated as clinically relevant, prioritizing high-risk subgroups (CCP3-4) for colposcopies and scaling human papillomavirus screening for CCP1-2. Conclusions This study underscores the potential of leveraging machine learning algorithms and large-scale routine electronic health records to enhance CCP strategies. By identifying key determinants of CIN2+/CIN3+ risk and classifying 5 distinct subgroups, our study provides a robust, data-driven foundation for the proposed triple strategy. This approach prioritizes tailored prevention efforts for subgroups with varying risks, offering a novel and scalable tool to complement existing cervical cancer screening guidelines. Future work should focus on independent external and prospective validation to maximize the global impact of this strategy.