No abstract available.
Asian Continental Ancestry Group ; Humans

OBJECTIVE: To determine factors affecting voiding recovery on the day of Foley catheter removal (postoperative day 7, POD7) after nerve-sparing radical hysterectomy (NSRH) for early-stage cervical cancer. METHODS: Early-stage cervical cancer patients, who underwent type C1 radical hysterectomy between January 2006 and June 2016 were included. Clinical and pathological data were reviewed. Association between inability to attain adequate voiding function on POD7 and potential predicting factors were evaluated in univariate and multivariate analysis. RESULTS: Of 755 patients, 383 (50.7%) resumed adequate voiding function on POD7 while 372 (49.3%) did not. Tumor size was larger in patients whose voiding function was inadequate (2.5 vs. 2.0 cm, p=0.001). Lengths of resected parametria and adjacent vagina were more extensive in patients with inadequate voiding function (p < 0.001). In univariate analysis, factors significantly associated with inability to attain adequate voiding function included tumor size >4 cm (p < 0.001), primary surgeon (p < 0.001), postoperative urinary tract infection (p < 0.01), grossly visible tumor (p < 0.01), and not having prior conization (p < 0.01). In multivariate analysis, tumor size >4 cm, postoperative urinary tract infection, and primary surgeon were significantly associated with inability to attain adequate voiding function on POD7. CONCLUSION: Extent of disease represented by tumor size, urinary tract infection as well as individual surgeon's technique independently predict resumption of adequate voiding function on POD7 following NSRH.
Catheters ; Conization ; Humans ; Hysterectomy* ; Multivariate Analysis ; Urinary Tract Infections ; Uterine Cervical Neoplasms ; Vagina

No abstract available.
Asian Continental Ancestry Group ; Humans

Objective: To compare survival outcomes, posttreatment complications, and quality of life (QoL) of early-stage cervical cancer patients with intermediate-risk factors between those who received adjuvant pelvic radiation and those without adjuvant treatment. Methods: Stages IB–IIA cervical cancer patients classified as having intermediate-risk following primary radical surgery were included. After propensity score weighted adjustment, all baseline demographic and pathological characteristics of 108 women who received adjuvant radiation and 111 women who had no adjuvant treatment were compared. The primary outcomes were progression-free survival (PFS) and overall survival (OS). The secondary outcomes included treatment-related complications and QoL. Results: Median follow-up time was 76.1 months in the adjuvant radiation group and 95.4 months in the observation group. The 5-year PFS (91.6% in the adjuvant radiation group and 88.4% in the observation group, p=0.42) and OS (90.1% in the adjuvant radiation group and 93.5% in the observation group, p=0.36) were not significantly different between the groups. There was no significant association between adjuvant treatment and overall recurrence/death in the Cox proportional hazard model. However, a substantial reduction in pelvic recurrence was observed in participants with adjuvant radiation (hazard ratio=0.15; 95% confidence interval=0.03–0.71). Grade 3/4 treatment-related morbidities and QoL scores were not significantly different between the groups. Conclusion Adjuvant radiation was associated with a lower risk of pelvic recurrence. However, its significant benefit in reducing overall recurrence and improving survival in early-stage cervical cancer patients with intermediate-risk factors could not be demonstrated.

Objective: To develop a novel machine learning-based preoperative prediction model for pelvic lymph node metastasis (PLNM) in early-stage cervical cancer by combining the clinical findings and preoperative computerized tomography (CT) of the whole abdomen and pelvis. Methods: Patients diagnosed with International Federation of Gynecology and Obstetrics stage IA2-IIA1 squamous cell carcinoma, adenocarcinoma, and adenosquamous carcinoma of the cervix who had primary radical surgery with bilateral pelvic lymphadenectomy from January 1, 2003 to December 31, 2020, were included. Seven supervised machine learning algorithms, including logistic regression, random forest, support vector machine, adaptive boosting, gradient boosting, extreme gradient boosting, and category boosting, were used to evaluate the risk of PLNM. Results: PLNM was found in 199 (23.9%) of 832 patients included. Younger age, larger tumor size, higher stage, no prior conization, tumor appearance, adenosquamous histology, and vaginal metastasis as well as the CT findings of larger tumor size, parametrial metastasis, pelvic lymph node enlargement, and vaginal metastasis, were significantly associated with PLNM. The models’ predictive performance, including accuracy (89.1%–90.6%), area under the receiver operating characteristics curve (86.9%–91.0%), sensitivity (77.4%–82.4%), specificity (92.1%–94.3%), positive predictive value (77.0%–81.7%), and negative predictive value (93.0%–94.4%), appeared satisfactory and comparable among all the algorithms.After optimizing the model’s decision threshold to enhance the sensitivity to at least 95%, the ‘highly sensitive’ model was obtained with a 2.5%–4.4% false-negative rate of PLNM prediction. Conclusion We developed prediction models for PLNM in early-stage cervical cancer with promising prediction performance in our setting. Further external validation in other populations is needed with potential clinical applications.

Objective: To develop a novel machine learning-based preoperative prediction model for pelvic lymph node metastasis (PLNM) in early-stage cervical cancer by combining the clinical findings and preoperative computerized tomography (CT) of the whole abdomen and pelvis. Methods: Patients diagnosed with International Federation of Gynecology and Obstetrics stage IA2-IIA1 squamous cell carcinoma, adenocarcinoma, and adenosquamous carcinoma of the cervix who had primary radical surgery with bilateral pelvic lymphadenectomy from January 1, 2003 to December 31, 2020, were included. Seven supervised machine learning algorithms, including logistic regression, random forest, support vector machine, adaptive boosting, gradient boosting, extreme gradient boosting, and category boosting, were used to evaluate the risk of PLNM. Results: PLNM was found in 199 (23.9%) of 832 patients included. Younger age, larger tumor size, higher stage, no prior conization, tumor appearance, adenosquamous histology, and vaginal metastasis as well as the CT findings of larger tumor size, parametrial metastasis, pelvic lymph node enlargement, and vaginal metastasis, were significantly associated with PLNM. The models’ predictive performance, including accuracy (89.1%–90.6%), area under the receiver operating characteristics curve (86.9%–91.0%), sensitivity (77.4%–82.4%), specificity (92.1%–94.3%), positive predictive value (77.0%–81.7%), and negative predictive value (93.0%–94.4%), appeared satisfactory and comparable among all the algorithms.After optimizing the model’s decision threshold to enhance the sensitivity to at least 95%, the ‘highly sensitive’ model was obtained with a 2.5%–4.4% false-negative rate of PLNM prediction. Conclusion We developed prediction models for PLNM in early-stage cervical cancer with promising prediction performance in our setting. Further external validation in other populations is needed with potential clinical applications.

Objective: To develop a novel machine learning-based preoperative prediction model for pelvic lymph node metastasis (PLNM) in early-stage cervical cancer by combining the clinical findings and preoperative computerized tomography (CT) of the whole abdomen and pelvis. Methods: Patients diagnosed with International Federation of Gynecology and Obstetrics stage IA2-IIA1 squamous cell carcinoma, adenocarcinoma, and adenosquamous carcinoma of the cervix who had primary radical surgery with bilateral pelvic lymphadenectomy from January 1, 2003 to December 31, 2020, were included. Seven supervised machine learning algorithms, including logistic regression, random forest, support vector machine, adaptive boosting, gradient boosting, extreme gradient boosting, and category boosting, were used to evaluate the risk of PLNM. Results: PLNM was found in 199 (23.9%) of 832 patients included. Younger age, larger tumor size, higher stage, no prior conization, tumor appearance, adenosquamous histology, and vaginal metastasis as well as the CT findings of larger tumor size, parametrial metastasis, pelvic lymph node enlargement, and vaginal metastasis, were significantly associated with PLNM. The models’ predictive performance, including accuracy (89.1%–90.6%), area under the receiver operating characteristics curve (86.9%–91.0%), sensitivity (77.4%–82.4%), specificity (92.1%–94.3%), positive predictive value (77.0%–81.7%), and negative predictive value (93.0%–94.4%), appeared satisfactory and comparable among all the algorithms.After optimizing the model’s decision threshold to enhance the sensitivity to at least 95%, the ‘highly sensitive’ model was obtained with a 2.5%–4.4% false-negative rate of PLNM prediction. Conclusion We developed prediction models for PLNM in early-stage cervical cancer with promising prediction performance in our setting. Further external validation in other populations is needed with potential clinical applications.

OBJECTIVE: Retrospective evaluation of the outcome of stage IVB, recurrent or persistent cervical cancer treated with cisplatin and generic topotecan (CT) in a tertiary care hospital in Thailand. METHODS: The medical records of patients treated with CT regimen at Chiang Mai University Hospital between January 2005 and December 2007 were reviewed and analyzed. The treatment protocol consisted of IV topotecan 0.75 mg/m2 on days 1, 2, and 3; combined with cisplatin 50 mg/m2 IV on day 1 and repeated every 21 days until progression or unacceptable toxicity for a maximum of 6 cycles. The outcomes were evaluated based on the response rate, progression free survival (PFS), and overall survival (OS) by using the World Health Organization criteria. The adverse effects of the treatments were also determined. RESULTS: Twenty-one cervical cancer patients received the CT regimen. The tumor response rate was 28.6%. The median PFS and OS was 4 and 11 months, respectively. With 87 cycles of chemotherapy, the most common grade 3 & 4 hematologic toxicity was neutropenia (57.9%). CONCLUSION: Advanced and recurrent cervical cancer patients treated with cisplatin and generic topotecan had a favorable outcome with manageable toxicity.
Cisplatin ; Clinical Protocols ; Disease-Free Survival ; Humans ; Medical Records ; Neutropenia ; Recurrence ; Retrospective Studies ; Tertiary Healthcare ; Thailand ; Topotecan ; Uterine Cervical Neoplasms ; World Health Organization