Operable Cervical Cancer Research Articles

Prediction of lymph node metastasis in operable cervical cancer using clinical parameters and deep learning with MRI data: a multicentre study.

To develop and validate a magnetic resonance imaging-based (MRI) deep multiple instance learning (D-MIL) model and combine it with clinical parameters for preoperative prediction of lymph node metastasis (LNM) in operable cervical cancer. A total of 392 patients with cervical cancer were retrospectively enrolled. Clinical parameters were analysed by logistical regression to construct a clinical model (M1). A ResNet50 structure is applied to extract features at the instance level without using manual annotations about the tumour region and then construct a D-MIL model (M2). A hybrid model (M3) was constructed by M1 and M2 scores. The diagnostic performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC) and compared using the Delong method. Disease-free survival (DFS) was evaluated by the Kaplan‒Meier method. SCC-Ag, maximum lymph node short diameter (LNmax), and tumour volume were found to be independent predictors of M1 model. For the diagnosis of LNM, the AUC of the training/internal/external cohort of M1 was 0.736/0.690/0.732, the AUC of the training/internal/external cohort of M2 was 0.757/0.714/0.765, and the AUC of the training/internal/external cohort of M3 was 0.838/0.764/0.835. M3 showed better performance than M1 and M2. Through the survival analysis, patients with higher hybrid model scores had a shorter time to reach DFS. The proposed hybrid model could be used as a personalised non-invasive tool, which is helpful for predicting LNM in operable cervical cancer. The score of the hybrid model could also reflect the DFS of operable cervical cancer. Lymph node metastasis is an important factor affecting the prognosis of cervical cancer. Preoperative prediction of lymph node status is helpful to make treatment decisions, improve prognosis, and prolong survival time. • The MRI-based deep-learning model can predict the LNM in operable cervical cancer. • The hybrid model has the highest diagnostic efficiency for the LNM prediction. • The score of the hybrid model can reflect the DFS of operable cervical cancer.

Effectiveness of sentinel lymph node biopsy and bilateral pelvic nodal dissection using methylene blue dye in early-stage operable cervical cancer—A prospective study

ObjectiveTo evaluate the effectiveness of methylene blue dye in detecting sentinel lymph nodes (SLNs) in women with early-stage operable (defined as FIGO I-IIA) cervical cancer. It also aims to evaluate procedural challenges and accuracy. MethodThis prospective study, which focused on 20 women with early-stage cervical cancer, was carried out between June 2016 and December 2017. These patients had SLN mapping with methylene blue dye injections and thorough examinations, including imaging. All patients underwent radical hysterectomy and complete bilateral pelvic lymphadenectomy. No additional investigation was done on the lymph node in cases where a metastasis was found in the first H&E-stained segment of the sentinel node. Result20 patients were included in the analysis. The median age of the subjects was 53, and 95 % of them had squamous cell carcinoma. 90 % of the time, the identification of SLNs was effective, and 55 SLNs were found, of which 52.7 % were on the right side of the pelvis and 47.3 % on the left. The obturator group had the most nodes, followed by the external and internal iliac groups in descending order of occurrence. Metastasis was detected in 3 patients, resulting in a sensitivity of 100 % and a specificity of 93.75 % for SLN biopsy. Notably, no false-negative SLNs were found. Complications related to methylene blue usage included urine discoloration in 30 % of patients. ConclusionThis trial highlights the promising efficacy and safety of methylene blue dye alone for SLN identification in early-stage operable cervical cancer, with a notably higher success rate. Despite limitations like a small sample size, healthcare professionals and researchers can build upon the insights from this study to enhance cervical cancer management.

Survival differences by race and surgical approach in early-stage operable cervical Cancer

ObjectiveTo evaluate if the higher rate of open radical hysterectomy in Black patients, prior to the widespread return to open surgical techniques, mitigated survival disparities and to identify other actionable factors to target for systemic change. MethodsThis is a retrospective cohort study including patients from the National Cancer Database with cervical cancer who underwent radical hysterectomy from 2010 to 2018. Patient demographics, clinical characteristics and survival were compared by race and surgical route. Kaplan-Meier plots were constructed. Cox proportional hazards modeling was used to adjust for covariates. Results7201 patients were eligible for inclusion, 687 (9.5%) Black and 4870 (68%) White. We found that 51% of Black patients and 39% of White patients underwent open surgery. Black patients were 10% less likely to receive Guideline Concordant Care (GCC). Those with publicly-funded insurance had a 40% higher hazard of death compared to private insurance (CI 1.19–1.73 p < 0.001). Black patients who had open surgery had similar 5-year survival compared to White patients who had MIS surgery (0.90 vs 0.91, NS). After adjusting for potential confounders including age, insurance, nodal status, and lymphovascular space invasion, Black patients who had surgery had a 40% higher hazard for death (HR 1.40 95% CI 1.10–1.79, p = 0.007) compared to White patients. ConclusionsA lower 5 and 10-year survival was seen in Black patients, regardless of surgical approach. Adjustment for significant covariates did not resolve this disparity, confirming that these factors do not fully account racial disparities.

Ameliyat edilebilir Rahim Ağzı Kanserinde Hematolojik İnflamatuar Belirteçler ile Genel Özellikler Arasındaki İlişki; HALP indeksinin yeri.

Background: Cervical cancer is the most common gynecologic malignancy and the leading cause of cancer-related mortality in women worldwide. Systemic inflammatory reactions in cancer patients impact nutrition, function, and prognosis. Poor nutritional health is linked to a worse prognosis in several malignancies. A new score called the hemoglobin-albumin-lymphocyte-platelet (HALP) index was developed using dietary and inflammatory deficiencies.&#x0D; Method: This retrospective study and CC patients diagnosed from January 2012 to December 2020 who were operated for cervical cancer. Pre-treatment hemoglobin (Hb), albumin (Alb), lymphocyte count (Lc), and platelet (Plt) measurements were made for laboratory research.&#x0D; Results: This retrospective study revealed 74 non-metastatic cervical cancer (median age was 55, ranging from 30 to 86 years). According to the FIGO stage, the percentage of stages 1 and 2 were 78% (58 patients), and 22% (16 patients). Neutrophil lymphocyte ratio (NLR) and platelet lymphocyte ratio (PLR), and HALP index were analyzed with LVSI, parametrial invasion, tumor size, histologic type, and duration of hospital stay; we could not find any significant correlation between the analysis. &#x0D; Conclusion: NLR, PLR, and HALP index have no prognostic value in early operable cervical cancer patients regarding the pathologic features. Prospective multicenter and more patient studies will clarify how the hematologic parameters affect oncologic outcomes.

Identification of lymph node metastasis in pre-operation cervical cancer patients by weakly supervised deep learning from histopathological whole-slide biopsy images.

Lymph node metastasis (LNM) significantly impacts the prognosis of individuals diagnosed with cervical cancer, as it is closely linked to disease recurrence and mortality, thereby impacting therapeutic schedule choices for patients. However, accurately predicting LNM prior to treatment remains challenging. Consequently, this study seeks to utilize digital pathological features extracted from histopathological slides of primary cervical cancer patients to preoperatively predict the presence of LNM. A deep learning (DL) model was trained using the Vision transformer (ViT) and recurrent neural network (RNN) frameworks to predict LNM. This prediction was based on the analysis of 554 histopathological whole-slide images (WSIs) obtained from Qilu Hospital of Shandong University. To validate the model's performance, an external test was conducted using 336 WSIs from four other hospitals. Additionally, the efficiency of the DL model was evaluated using 190 cervical biopsies WSIs in a prospective set. In the internal test set, our DL model achieved an area under the curve (AUC) of 0.919, with sensitivity and specificity values of 0.923 and 0.905, respectively, and an accuracy (ACC) of 0.909. The performance of the DL model remained strong in the external test set. In the prospective cohort, the AUC was 0.91, and the ACC was 0.895. Additionally, the DL model exhibited higher accuracy compared to imaging examination in the evaluation of LNM. By utilizing the transformer visualization method, we generated a heatmap that illustrates the local pathological features in primary lesions relevant to LNM. DL-based image analysis has demonstrated efficiency in predicting LNM in early operable cervical cancer through the utilization of biopsies WSI. This approach has the potential to enhance therapeutic decision-making for patients diagnosed with cervical cancer.

Sentinel Lymph Node Biopsy in Uterine Cancer: Time for a Modern Approach.

Since the validation of the sentinel node technique (SLN) for vulvar cancer 20 years ago, this technique has been introduced in the management of operable cervical cancer and endometrial cancer. For cervical cancer a "one fits all" attitude has mainly been presented. However, this approach, consisting of a frozen section during the operation, can be discussed in some stages. We present and discuss the main option for each stage, as well as some secondary possibilities. For endometrial cancer, SLN is now the technique of choice for the nodal staging of low- and intermediate-risk groups. Some discussion exists for the high-risk group. We also discuss the impacts of using preoperatively the molecular classification of endometrial cancer. Patients with POLE or TP53 mutations could have different nodal staging. The story of SLN in uterine cancers is not finished. We propose a comprehensive algorithm of SLN in early cervical and endometrial cancers. However, several ongoing trials will give us important data in the coming years. They could substantially change these propositions.

Development of a Novel Deep Learning-Based Prediction Model for the Prognosis of Operable Cervical Cancer.

Cervical cancer ranks as the 4th most common female cancer worldwide. Early stage cervical cancer patients can be treated with operation, but clinical staging system is not a good predictor of patients' survival. We aimed to develop a novel prognostic model to predict the prognosis for operable cervical cancer patients with better accuracy than clinical staging system. A total of 13,952 operable cervical cancer patients were retrospectively enrolled in this study. The whole dataset was randomly split into a training set (n = 9,068, 65%), validation set (n = 2,442, 17.5%), and testing set (n = 2,442, 17.5%). Cox proportional hazard (CPH) model and random survival forest (RSF) model were used as baseline models for the prediction of overall survival (OS). Then, a deep survival learning model (DSLM) was developed for OS prediction. Finally, a novel prognostic model was explored based on this DSLM. The C-indexes for the CPH and RSF model were 0.731 and 0.753, respectively. DSLM, which had four layers that had 50 neurons in each layer, achieved a C-index of 0.782 in the validation set and a C-index of 0.758 in the testing set. The novel prognostic model based on DSLM showed better performances than the conventional clinical staging system (area under receiver operating curves were 0.826 and 0.689, respectively). Personalized survival curves for individual patient using this novel model also showed notably different survival slopes. Our study developed a novel, practical, personalized prognostic model for operable cervical cancer patients. This novel prognostic model may have the potential to provide a more prognostic information to oncologists.

Predicting the Recurrence of Operable Cervical Cancer Patients Based on Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) Score and Classical Clinicopathological Parameters.

ObjectiveThe purpose of this study was to evaluate the prognostic value of hemoglobin, albumin, lymphocyte, and platelet (HALP) score in patients with operable cervical cancer, and on this basis, combined with classical clinicopathological parameters to predict the recurrence of patients.MethodsA total of 1580 patients with stage IA-IIA cervical cancer were randomly divided into training cohort (n=1054) and validation cohort (n=526) according to the predefined ratio of 2:1. In the training cohort, the receiver operating characteristic (ROC) curve and Youden index were used to determine the optimal threshold of HALP score for predicting cervical cancer recurrence. On this basis, the independent related factors with cervical cancer recurrence were screened through univariate and multivariate Cox regression analysis, and then a nomogram model was further established. The internal and external validation of the model was carried out in the training cohort and the validation cohort respectively through the consistency index (C-index) and calibration curve.ResultsROC curve and Youden index showed that the optimal threshold of HALP score for predicting cervical cancer recurrence was 39.50. Multivariate analysis confirmed that HALP score and some other classic clinicopathological parameters were independently associated with cervical cancer recurrence. Based on the results of multivariate analysis, a nomogram model for predicting cervical cancer recurrence was successfully constructed. The internal and external calibration curves showed that the fitting degree of the model was good, and the C-index (the C-index of the training cohort and the validation cohort were 0.862 and 0.847, respectively) showed that the prediction accuracy of the model proposed in this study was better than other similar models.ConclusionHALP score may be a novel predictor for predicting the cervical cancer recurrence. Nomogram model based on HALP score and classical clinicopathological parameters can better predict the recurrence of cervical cancer.

Preoperative imaging with positron emission tomography and computed tomography (18F-FDG PET/CT) or contrast-enhanced computed tomography (CECT) scan in operable cervical cancer: a prospective study

We evaluated the accuracy of preoperative PET/CT and CECT for lymph node staging in cervical cancer, which is relevant in the context of recently revised staging. Patients with International Federation of Gynaecology and Obstetrics (FIGO) 2009 stage IB1–IB2 cervical cancer underwent preoperative 18F-FDG PET/CT and CECT abdomen and pelvis. Lymph nodes in various anatomical stations (paraaortic, various right and left pelvic regions) were scored separately as involved or not involved on both imaging modalities and compared with histopathology. The sensitivity and specificity of PET/CT and CECT were estimated for each lymph node station, and all lymph node stations combined, per patient. The study population comprised 57 patients (stage IB1 37, IB2 19) with median age of 45 (32–64) years, 39 (68.7%) with squamous carcinoma, median tumor size of 3.1 (0.5–9.0) cm, mean (range) lymph node harvest of 23.5 (8–46), and 13 (22.8%) patients with histopathologically positive lymph node(s). The sensitivity and specificity of PET/CT for various pelvic lymph node stations were 22.2–100% and 96.3–100%, respectively, and of CECT were 22.2–100% and 96.2–100%, respectively. At patient level, for all lymph node stations combined, the sensitivity and specificity of PET/CT and CECT were 53.9% (95% confidence interval (CI), 26.8%–80.9%) and 90.5% (95% CI, 81.6%–99.4%), and 69.2% (95% CI, 44.1%–94.3%) and 95.0% (95% CI, 88.3%–100%), respectively. Primary tumor maximum standardized uptake value (SUVmax) was the only independent prognostic factor for overall survival. The sensitivity of PET/CT and CECT for lymph node staging in operable cervical cancer is modest, and these modalities can miss lymph node involvement, but they have high specificity and, when indicative of lymph node involvement, may be clinically useful in triaging patients for primary chemoradiation therapy.

Diagnostic Accuracy of FEC-PET/CT, FDG-PET/CT, and Diffusion-Weighted MRI in Detection of Nodal Metastases in Surgically Treated Endometrial and Cervical Carcinoma.

Preoperative nodal staging is important for planning treatment in cervical cancer and endometrial cancer, but remains challenging. We compare nodal staging accuracy of 18F-ethyl-choline-(FEC)-PET/CT, 18F-fluoro-deoxy-glucose-(FDG)-PET/CT, and diffusion-weighted-MRI (DW-MRI) with conventional morphologic MRI. A prospective, multicenter observational study of diagnostic accuracy for nodal metastases was undertaken in 5 gyne-oncology centers. FEC-PET/CT, FDG-PET/CT, and DW-MRI were compared with nodal size and morphology on MRI. Reference standard was strictly correlated nodal histology. Eligibility included operable cervical cancer stage ≥ 1B1 or endometrial cancer (grade 3 any stage with myometrial invasion or grade 1-2 stage ≥ II). Among 162 consenting participants, 136 underwent study DW-MRI and FDG-PET/CT and 60 underwent FEC-PET/CT. In 118 patients, 267 nodal regions were strictly correlated at histology (nodal positivity rate, 25%). Sensitivity per patient (n = 118) for nodal size, morphology, DW-MRI, FDG- and FEC-PET/CT was 40%*, 53%, 53%, 63%*, and 67% for all cases (*, P = 0.016); 10%, 10%, 20%, 30%, and 25% in cervical cancer (n = 40); 65%, 75%, 70%, 80% and 88% in endometrial cancer (n = 78). FDG-PET/CT outperformed nodal size (P = 0.006) and size ratio (P = 0.04) for per-region sensitivity. False positive rates were all <10%. All imaging techniques had low sensitivity for detection of nodal metastases and cannot replace surgical nodal staging. The performance of FEC-PET/CT was not statistically different from other techniques that are more widely available. FDG-PET/CT had higher sensitivity than size in detecting nodal metastases. False positive rates were low across all methods. The low false positive rate demonstrated by FDG-PET/CT may be helpful in arbitration of challenging surgical planning decisions.

Treatment and Outcomes of Early and Operable Recurrent Cervical Cancer: A Prospective Study.

Objectives:The aim of this study was to evaluate outcomes, survival, patterns of recurrence, and morbidity in both early and recurrent operable cervical cancer patients following radical hysterectomy, pelvic lymph node dissection, and adjuvant radiotherapy.Materials and Methods:This was a prospective analysis of 55 patients of stage IA–IIA and recurrent operable cervical cancer treated with radical hysterectomy, pelvic lymphadenectomy, and adjuvant radiotherapy from 2014 to 2017. Overall survival (OS), disease-free survival (DFS), morbidity, and mortality rates were the end points of this study. Survival analysis was performed using the Kaplan–Meir method.Results:The median age of the study group was 45 years (range 18–68 years). The most common presentation was stage IB2 disease in 34.5% of patients. Fifty (90%) patients had squamous histology, whereas 5 (9.1%) had adenocarcinoma. Upfront radical hysterectomy was performed in 90.9% of patients, whereas 9.1% underwent surgery for recurrent cervical cancer. The most common indication for adjuvant radiotherapy was lymph node involvement, followed by parametrium involvement in 20% and 13% patients, respectively. Median follow-up period was 48 months (range 6–60 months). The OS and DFS rates were 85.0% and 81.8%, respectively. The most frequent complication encountered was paralytic ileus in 4 (7.2%) patients.Conclusion:Radical hysterectomy with pelvic lymphadenectomy for early cervical cancer has a favorable survival outcome with acceptable long-term morbidity.

Treatment Outcomes of Early Carcinoma Cervix Before and After Sub-specialization.

This study aimed to compare the treatment outcomes in carcinoma cervix before and after gynecologic oncology sub-specialization at a tertiary care hospital, in India. This was a retrospective cohort study comparing women with operable cervical cancer who underwent radical hysterectomy before and after gynecologic oncology sub-specialization. Electronic medical records of women operated for early carcinoma cervix between 2001 and 2010 and 2011-2015 were reviewed and compared for treatment and oncological outcomes. Seventy-four patients were operated over 5years after sub-specialization as against 59 over 10years before sub-specialization, with similar clinical characteristics. After surgical-pathological examination, both cohorts were comparable with regard to mean tumor size, lymph nodes retrieved, deep stromal invasion, and involvement of lymph nodes, parametrium, and vaginal margins. After sub-specialization, the rate of intraoperative (3% versus 14%, p = 0.018) and postoperative complications (15% versus 46%, p < 0.001) was lower. Adjuvant radiation was used more after sub-specialization (50% versus 24%, p < 0.001). The follow-up rates were similar in both groups with comparable 5-year recurrence-free survival and overall survival rates. The hazard ratio for death after sub-specialization was 0.39 (95% CI 0.12 to 1.22) after adjusting for histology, stage, grade, and presence of intermediate or high risk factors. Gynecological oncologic sub-specialization decreased intraoperative and postoperative complications, improved pathological reporting, and enabled appropriate tailoring of adjuvant therapy.

A Novel Prognostic Marker Systemic Inflammation Response Index (SIRI) for Operable Cervical Cancer Patients.

It has been confirmed that the systemic inflammation response index (SIRI) based on peripheral blood neutrophil, monocyte and lymphocyte counts can be used for the prognostication of patients with various malignant tumors. However, the prognostic value of SIRI in cervical cancer patients has not yet been reported. This study found that a higher SIRI was related to lymphovascular invasion and was also significantly associated with FIGO stage, radiotherapy, neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and monocyte/lymphocyte ratio (MLR) but not related to other clinical and pathological parameters. According to the Kaplan-Meier survival analysis, a high SIRI was associated with the poor prognosis of cervical cancer patients in the primary and validation groups. SIRI, NLR, PLR, and MLR can all be used to determine the prognosis of patients with operable cervical cancer. Moreover, it was confirmed that only SIRI was an independent prognostic factor for patients with operable cervical cancer. The same result was obtained in the propensity score matching (PSM) analysis. In the ROC curve analysis, SIRI was more accurate in predicting the prognosis of cervical cancer patients. Then, a nomogram was established based on SIRI, FIGO stage and lymphovascular invasion, which could determine the prognosis of cervical cancer patients more accurately than FIGO stage. The validation cohort showed the same results. In addition, the changes in SIRI relative to the baseline value at 4–8 weeks after surgery were closely related to the survival of cervical cancer patients. Compared with those with unchanged SIRI (absolute value of variation <25%), cervical cancer patients with an increase in SIRI > 75% had worse OS (P < 0.001), while patients with a decrease in SIRI > 75% had a better prognosis (P < 0.001). SIRI can serve as a new independent prognostic index and a potential marker for therapeutic response monitoring in patients with curable cervical cancer. Compared with the traditional FIGO staging system, the nomogram integrating SIRI can predict the survival of cervical cancer patients more objectively and reliably after radical surgery.

Clinically Significant Discrepancy between Clinical and Pathologic Stage of Early Operable Cervical Cancer

Objectives: The cornerstone of the management of cervical cancer (CC) traditionally relies on clinical examination (CE) of tumor size (TS) and local extension of disease. The goal of this study is to determine the accuracy of CE in comparison to final pathology (FP) in early operable CC. Methods: This is a multi-center retrospective review of patients with early CC (FIGO 2009 Stage IB1, IIA1). CE of TS, parametrial invasion (PI), and vaginal involvement (VI) were compared to FP. Results: The final analysis included 135 patients. Overall, there was a significant difference between CE of TS compared to FP; mean error of 1.22 cm (p&lt;0.0001). In tumors  2cm the mean error was 1.28 cm (p&lt;0.0001). No significant discrepancy was observed in tumors &lt;2 cm (mean error: 1.10cm; p=0.5). CE of TS of endophytic tumors was poor (mean error 1.68cm; p=0.004) compared to exophytic tumors (mean error: 1.12 cm; p=0.693). There was no significant difference in the identification of VI between CE and FP (3.7% vs. 8.89%; p=0.067). 14.07% of patients were found to have PI on FP (p&lt;0.0001). There was no difference in the accuracy CE of TS between non-obese (&lt;30 kg/m2 ) and obese patients (30 kg/m2 ) (p=0.061). As a result of FP, 55 patients (40.7%) received adjuvant RT and 38 patients (28.14%) were upstaged from IB1 to IB2. Conclusion: CE of TS and PI is inaccurate, especially in tumors  2cm and

Development and Validation of a Deep Learning Radiomics Model Predicting Lymph Node Status in Operable Cervical Cancer.

Aim: To develop and validate a deep learning radiomics model, which could predict the lymph node metastases preoperatively in cervical cancer patients.Patients and methods: We included a cohort of 226 pathological proven operable cervical cancer patients in two academic medical institutions from December 2014 to November 2017. Then this dataset was split into training set (n = 176) and independent testing set (n = 50) randomly. Five radiomic features were selected and a radiomic signature was established. We then combined these five radiomic features with the preoperative tumor histology and grade of these patients together. Baseline logistic regression model (LRM) and support vector machine model (SVM) were established for the comparison. We then explored the performance of a deep neural network (DNN), which is a popular deep learning model nowadays. Finally, performance of this DNN was validated in another independent test set including 50 cases of operable cervical cancer patients.Results: One thousand forty-five radiomic features were extracted for each patient. Twenty-eight features were found to be significantly correlated with the lymph node status in these patients (P < 0.05). Five radiomic features were further selected for further study due to their higher predictive powers. Baseline LRM incorporating these five radiomic and two clinicopathological features was established, which had an area under receiver operating characteristic curve (ROC) of 0.7372 and an accuracy of 89.20%. The established DNN model had four neural layers, in which layer there were 10 neurons. Adagrad optimizer and 1,500 iterations were used in training. The trained DNN had an area under curve (AUC) of 0.99 and an accuracy of 97.16% in the internal validation. To exclude the overfitting, independent external validation was also performed. AUC and accuracy in test set could still retain 0.90 and 92.00% respectively.Conclusion: This study used deep learning method to provide a comprehensive predictive model using preoperative CT images, tumor histology, and grade in cervical cancer patients. This model showed an acceptable accuracy in the prediction of lymph node status in cervical cancer. Our model may help identifying those patients who could benefit a lot from radiation therapy rather than primary hysterectomy surgery if this model could resist strict testing of future randomized controlled trials (RCTs).

Prognostic factors for and pattern of lymph-node involvement in patients with operable cervical cancer.

Lymph node metastases significantly worsen the prognosis in cervical carcinoma. Risk factors-pathological and patient related-could select patients at high risk for lymph node involvement. This retrospective analysis was performed by analyzing data from patients with cervical carcinoma treated between 2000 and 2017 at the Department of Obstetrics and Gynecology of the University Hospital Ulm. In total, 261 patients with cervical carcinoma (International Federation of Gynecology and Obstetrics (FIGO) stage IA-IIB) and lymphadenectomy with at least 10 removed lymph nodes were available for analysis. Overall, 86 (33.0%) patients had lymph node metastases; 73 patients had pelvic lymph node metastases only and 13 patients had both pelvic and paraaortic lymph node metastases. Lymph node metastases were found most often in the region of the external iliac artery and obturator fossa, with 57.0% and 54.7% of all 86 node-positive patients, respectively. Univariable analyses showed that presence of lymph node metastases was significantly associated with both preoperative FIGO stage (p = 0.001) and final pathological tumor stage (p < 0.001), status of resection margin (p = 0.002), lymphovascular space invasion (LVSI), (p < 0.001) and vascular space invasion, (p < 0.001). In a multivariable logistic regression model with presence of lymph node metastases (yes/no) as binary response variable, only LVSI (p < 0.001) and body mass index (BMI), (p = 0.035) remained as significant independent predictors of lymph node involvement. Subgroup analyses showed that LVSI was a significant predictive factor for lymph node involvement in patients with a preoperatively assessed FIGO stage < IIB (p < 0.001), but not for patients with a preoperatively assessed FIGO stage ≥ IIB (p = 0.122). The risk factor LVSI should play an important role in deciding whether an individualized therapy concept is based on escalating or deescalating treatment. In future, the sentinel concept could reduce morbidity and at the same time provide an important prognostic assessment for a subset of cervical cancer patients.

Prediction of 10-year Overall Survival in Patients with Operable Cervical Cancer using a Probabilistic Neural Network.

Background: Toward the goal of predicting individual long-term cancer survival to guide treatment decisions, this study evaluated the ability of a probabilistic neural network (PNN), an established model used for decision-making in research and clinical settings, to predict the 10-year overall survival in patients with cervical cancer who underwent primary surgical treatment.Patients and Method: The input dataset was derived from 102 patients with cervical cancer FIGO stage IA2-IIB treated by radical hysterectomy. We identified 4 demographic parameters, 13 tumor-related parameters, and 6 selected perioperative variables for each patient and performed computer simulations with DTREG software. The predictive ability of the model was determined on the basis of its error, sensitivity, and specificity, as well as area under the receiver operating characteristic curve. The results of the PNN predictive model were compared with those of logistic regression analysis and a single decision tree as reference models.Results: The PNN model had very high predictive ability, with a sensitivity of 0.949, a specificity of 0.679, and an error rate of 12.5%. The PNN's area under the receiver operating characteristic curve was high, 0.809, a value greater than those for both logistic regression analysis and the single decision tree.Conclusion: The PNN model effectively and reliably predicted 10-year overall survival in women with operable cervical cancer, and may therefore serve as a tool for decision-making process in cancer treatment.

Robotic Radical Trachelectomy for Early Stage Cervical Cancer: Case Series of Single Institution

Objective: Because women with operable cervical cancer opting for fertility preservation are scarce, not many cases have been reported, especially following robotic procedures. The objective of this study was to determine whether robotic radical. Radical trachelectomy is an alternative treatment for preserving fertility in patients with cervical cancer.

Quality of Life in Gynaecological Cancers and Tools for Its Improvement: An Indian Perspective

Background: Gynecologic cancer and treatment related morbidities adversely affect the quality of life (QOL) and overall outcome. QOL assessment and focus on QOL improvement still lag behind in the priority list of cancer management in Indian women. Aim: To assess the quality of life in India gynecologic cancer patients and possible tools for its improvement. Methods: This review is based on PubMed literature search of all the studies related to QOL in gynecologic cancer patients in India. The studies were categorized into those assessing QOL and improving QOL. Results: Unlike the abundant literature on QOL of patients with breast and oral cancers, the studies on QOL of women with gynecologic cancers are scarce. Nonetheless, twenty-five articles could be retrieved that directed efforts to identify parameters and tools assessing QOL as well as to modify treatment strategies for better QOL. The principal of nerve sparing technique and easily reproducible steps of laparoscopy and robotic hysterectomy has been recently used in Indian patients. It helped in improving QOL of operable cervical cancer patients. Ayurveda holds an important place in Indian medicine and has effective remedies for many diseases. Studies on the use of Ayurvedic drugs during chemotherapy and radiotherapy have shown reduction in treatment related toxicities. Conclusion: Improvement in 'quality of life' should be considered as important as increment in overall survival while managing women with gynecologic cancers. Better QOL would boost the patient's and family's compliance to treatment and improve disease specific outcome. Simpler QOL assessment tools and refined multimodal therapy techniques suitable for Indian population need to be integrated in national cancer treatment guidelines.

Nomogram Predicting Overall Survival in Operable Cervical Cancer Patients.

Nomograms are widely used as predictive tools to predict oncological outcomes intuitively and precisely. The aim of our study was to develop a nomogram for predicting overall survival (OS) of patients with early stage cervical cancer. In this retrospective study, the clinical, pathological, and hematological data and prognosis of 795 cervical cancer patients were investigated. We identified and incorporated independent significant prognostic factors for OS to develop a nomogram. The predictive accuracy and discriminative ability were measured by concordance index. By univariable analysis and subsequent multivariable analysis, we identified body mass index, albumin, platelet, leukocyte, tumor differentiation, and the status of the pelvic lymph node (PLN) (all P < 0.05) as independent prognostic factors. The concordance index of the nomogram integrating these 6 variables was 0.74. The calibration curves for probability of 3- and 5-year OS also demonstrated ideal agreement between nomogram prediction and actual observation. We developed a novel nomogram that can provide prediction of OS for patients with early stage cervical cancer individually. Furthermore, studies are required to validate whether it can be applied to other cohorts.