Lymph Nodes In Cervical Cancer Research Articles

Feasibility and Diagnostic Accuracy of Ultrastaging in the Detection of Micrometastases in Sentinel and Non-sentinel Lymph Nodes in Cervical Cancer: A Single-Center Retrospective Study With a Five-Year Follow-Up Period.

Cervical cancer is the fourth most common cause of malignant tumor-related deaths among women in developing nations. Cervical cancer has been estimated to cause 527.600 new cases and 265.700 deaths globally per year. This study aimed to evaluate patients with cervical cancer by ultrastaging all the lymph nodes (LN), sentinel LN (SLN) and non-SLN, to increase the sensitivity of the detection of LN metastases and the diagnostic accuracy in cervical cancer with a five-year follow-up. Thisis a retrospective study of 14 cervical cancer cases from 2017 to 2019 at the Municipal Emergency Clinical Hospital of Timisoara, Romania. The cases were selected based on their high risk of LN involvement but negative intraoperative pathologic LN. After re-evaluating all paraffin block biopsy samples from 29 cases, 14 cases were included in the study, which met all criteria for ultrastaging on surgical biopsy samples. Patients' ages included in the study ranged from 43 to 70 years (median: 57.14 years). According to the International Federation of Gynecology and Obstetrics (FIGO) staging, the majority of the patients were in stage IB: seven cases (50%). The study revealed a positive correlation between patient age and FIGO staging, with Pearson's correlation coefficient of 0.707 and a p-value of less than 0.05, indicating that older patients were more likely to be diagnosed with a higher FIGO stage. The mean follow-up was 34.5 months, and the median follow-up was 36 months (range: 6-60 months). We obtained 167 nodes, with a mean of 11.92 nodes/case. Twenty-one LN were found to be positive with the ultrastaging method. We detected 11 LN with macrometastases (MAC) (52.38%), seven with micrometastasis (MIC) (33.3%), and three with tumor cell islets (14.4%). That would be 13% of newly diagnosed ultrastaging cases as positive nodes.This ultrastaging method detected nodal MIC in eight (57.1%) out of the 14 patients, who initially tested negative for LN involvement using the routine Hematoxylin and Eosin (HE) method. The detection of micrometastases in these patients underscored the superior sensitivity of ultrastaging, which was further highlighted by the subsequent relapse of four (28.57%) out of these eight patients. The study also found no correlation between the FIGO standardization and the number of MIC found in these patients. Predicting cervical LN metastasis (LNM) is crucial for improving survival rates and reducing recurrence. Very few small cohort studies used an ultrastaging method to assess non-SLNs; most of them only assessed SLNs. We showed in our study that the ultrastaging method, both in the case of SLN and non-SLN, is superior compared with H&E analysis, with a 13% rate of new positive nodule diagnosis. Metastatic involvement of non-SLN was found in over 50% of all cases (8/14) according to the ultrastaging method. Additionally, our study confirms that the sensitivity of SLN ultrastaging is high for the presence of both MIC and MAC in SLN pelvic LN. As a result, we feel that ultrastaging is the most effective method for SLN analysis in patients with early-stage cervical cancer, and bilateral detection is preferable, significantly reducing false-negative results. The routine use of SLN along with ultrastaging would lead to more accurate surgical staging and better oncological follow-up of cases.

Tumor-to-stroma cd8+ t cells ratio combined with cancer-associated fibroblasts: an innovative approach to predicting lymph node metastases of cervical cancer

PurposePrecise identification of lymph node metastases is vital for the management of cervical cancer. However, the existing diagnostic methods for lymph node metastases have certain drawbacks. In this study, we aim to explore the expression of cancer-associated fibroblasts (CAFs) and tumor-to-stroma CD8+ T cells ratio (CD8+ T cells T:S ratio) and its association with lymph node metastases of cervical cancer.MethodsHundred and ten cervical cancer tissues and 39 biopsy tissues from patients were investigated immunocytochemically for the expression of CAFs and CD8+ T cells. The statistical correlation analysis was carried out using the SPSS system.ResultsA strong and statistically significant negative correlation (r= − 0.690; P < 0.001) was observed between CAF density and CD8+ T cells T:S ratio. Not only were CAFs density and CD8+ T cells T:S ratio correlated with lymph node metastases respectively (P < 0.001), but the combination of them also significantly correlated with lymph node metastases (P < 0.001). Then, we constructed the combined diagnosis model (Logit (P) = − 4.446 + 0.300 × CAFs + 0.752 × CD8+ T cells T:S Ratio) of cervical cancer lymph node metastases. ROC curves analysis showed that the ROC curves areas for CAFs, CD8+ T cells T:S ratio, and a combination of both are 0.879, 0.747, and 0.951. Then, the prediction model was verified by biopsy specimens and consistent results were obtained.ConclusionsThe combination of CAF density and CD8+ T cells T:S ratio has a significant predictive value for lymph node metastases in patients with cervical cancer.

Concordance of standard Hematoxylin and Eosin staining and One-step nucleic acid amplification (OSNA) in detection of metastases in sentinel lymph nodes in cervical cancer

Concordance of standard Hematoxylin and Eosin staining and One-step nucleic acid amplification (OSNA) in detection of metastases in sentinel lymph nodes in cervical cancer

Negative Instance Guided Self-Distillation Framework for Whole Slide Image Analysis.

Histopathology image classification is an important clinical task, and current deep learning-based whole-slide image (WSI) classification methods typically cut WSIs into small patches and cast the problem as multi-instance learning. The mainstream approach is to train a bag-level classifier, but their performance on both slide classification and positive patch localization is limited because the instance-level information is not fully explored. In this article, we propose a negative instance-guided, self-distillation framework to directly train an instance-level classifier end-to-end. Instead of depending only on the self-supervised training of the teacher and the student classifiers in a typical self-distillation framework, we input the true negative instances into the student classifier to guide the classifier to better distinguish positive and negative instances. In addition, we propose a prediction bank to constrain the distribution of pseudo instance labels generated by the teacher classifier to prevent the self-distillation from falling into the degeneration of classifying all instances as negative. We conduct extensive experiments and analysis on three publicly available pathological datasets: CAMELYON16, PANDA, and TCGA, as well as an in-house pathological dataset for cervical cancer lymph node metastasis prediction. The results show that our method outperforms existing methods by a large margin. Code will be publicly available.

Predicting Lymph Node Metastasis From Primary Cervical Squamous Cell Carcinoma Based on Deep Learning in Histopathologic Images

We developed a deep learning framework to accurately predict the lymph node status of patients with cervical cancer based on hematoxylin and eosin–stained pathological sections of the primary tumor. In total, 1524 hematoxylin and eosin–stained whole slide images (WSIs) of primary cervical tumors from 564 patients were used in this retrospective, proof-of-concept study. Primary tumor sections (1161 WSIs) were obtained from 405 patients who underwent radical cervical cancer surgery at the Fudan University Shanghai Cancer Center (FUSCC) between 2008 and 2014; 165 and 240 patients were negative and positive for lymph node metastasis, respectively (including 166 with positive pelvic lymph nodes alone and 74 with positive pelvic and para-aortic lymph nodes). We constructed and trained a multi-instance deep convolutional neural network based on a multiscale attention mechanism, in which an internal independent test set (100 patients, 228 WSIs) from the FUSCC cohort and an external independent test set (159 patients, 363 WSIs) from the Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma cohort of the Cancer Genome Atlas program database were used to evaluate the predictive performance of the network. In predicting the occurrence of lymph node metastasis, our network achieved areas under the receiver operating characteristic curve of 0.87 in the cross-validation set, 0.84 in the internal independent test set of the FUSCC cohort, and 0.75 in the external test set of the Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma cohort of the Cancer Genome Atlas program. For patients with positive pelvic lymph node metastases, we retrained the network to predict whether they also had para-aortic lymph node metastases. Our network achieved areas under the receiver operating characteristic curve of 0.91 in the cross-validation set and 0.88 in the test set of the FUSCC cohort. Deep learning analysis based on pathological images of primary foci is very likely to provide new ideas for preoperatively assessing cervical cancer lymph node status; its true value must be validated with cervical biopsy specimens and large multicenter datasets.

Digital diagnostics: A computer application for lymph node metastases in cervical cancer

BACKGROUND: Since 2020, staging accuracy improvements in cervical cancer (CC) revealed that major errors occur due to missed metastases to regional lymph nodes. The presence or absence of metastases seriously affects the staging of cancer and, consequently, its treatment methods, since metastases make cancer inoperable.&#x0D; AIM: To develop an application to help the doctor in predicting metastases to regional lymph nodes in CC, with the ability of installing on any personal computer regardless of the operating system (Windows, Linux, or Mac OS) and saving data about the patients.&#x0D; METHODS: The development was performed on the .NET platform (framework v4.7.2), by means of C# language, based on a proprietary prognostic model using seven blood parameters such as erythrocyte sedimentation rate, erythrocytes, hemoglobin, fibrinogen, D-dimer, and platelet aggregation with adenosine diphosphate and soluble fibrin monomer complexes.&#x0D; RESULTS: A window application was developed that automatically calculates the probability of metastases to regional lymph nodes in СС and provides the ability to save the data from the input form and the predicted value into TXT and/or CSV files. TXT and CSV formats are supported in various operating systems. The .txt file allows each individual patients prognosis results to be saved in a separate file for easy printing and attachment to the patients electronic or paper medical record. The CSV file format enables the aggregation of data of all patients by adding line-by-line data from the input form to the end of the file.&#x0D; CONCLUSIONS: The developed application facilitates the process of using the patented formula to diagnose metastases to regional lymph nodes in СС at the early stages of examination, which allows choosing the optimal tactics and, consequently, improving the prognosis of the disease.

Imaging evaluation of para-aortic lymph nodes in cervical cancer.

In recent years, much literature has reported the diagnostic value of computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET)-CT in para-aortic lymph node metastasis of cervical cancer. To compare and analyze the para-aortic lymph node presentations found in cervical cancer on different images in order to determine the best precise imaging method for identifying metastatic lymph nodes. PubMed, Web of Science, MEDLINE, and other databases were searched for the non-invasive detection of metastatic lymph nodes for a comprehensive comparison. Positive lymph nodes on CT are significantly related to the following factors: short axis ≥10 mm; and round or central necrosis. Positive lymph nodes on MRI are significantly related to the following factors: short axis ≥8 mm; inhomogeneous signal intensity; morphology: round, irregular edge, extracapsular invasion, central necrosis, loss of lymph node structure, burrs, or lobes; and ADC value decreases, combined with local actuality. On PET-CT examination, when the short axis of the lymph node is >5 mm, the SUV is >2.5, or the FDG uptake is greater than that of the surrounding tissue, it is a metastatic lymph node. In conclusion, different imaging techniques show metastatic lymph nodes in different ways. Combining the patient's medical history with the symptoms of the aforementioned lymph nodes, together with one or more imaging techniques, is important to diagnose para-aortic lymph nodes in cervical cancer.

Developing a nomogram for preoperative prediction of cervical cancer lymph node metastasis by multiplex immunofluorescence

BackgroundMost traditional procedures can destroy tissue natural structure, and the information on spatial distribution and temporal distribution of immune milieu in situ would be lost. We aimed to explore the potential mechanism of pelvic lymph node (pLN) metastasis of cervical cancer (CC) by multiplex immunofluorescence (mIF) and construct a nomogram for preoperative prediction of pLN metastasis in patients with CC.MethodsPatients (180 IB1-IIA2 CC patients of 2009 FIGO (International Federation of Gynecology and Obstetrics)) were divided into two groups based on pLN status. Tissue microarray (TMA) was prepared and tumor-infiltrating immune markers were assessed by mIF. Multivariable logistic regression analysis and nomogram were used to develop the predicting model.ResultsMultivariable logistic regression analysis constructs a predictive model and the area under the curve (AUC) can reach 0.843. By internal validation with the remaining 40% of cases, a new ROC curve has emerged and the AUC reached 0.888.ConclusionsThis study presents an immune nomogram, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CC.

Prognostic Significance of the Criteria for Metastatic Involvement of Regional Lymph Nodes in Cervical Cancer According to Magnetic Resonance Imaging Findings

The objective of this study is to assess the criteria for metastatic involvement of the LN (MLN) according to magnetic resonance imaging (MRI) and to identify the most prognostically significant. The MRI data of 159 removed LN were compared with the results of a morphological study of the resected preparations. ROC- analysis (Receiver Operating Characteristic curve) was used to determine the diagnostic performance of threshold values. The test performance was evaluated depending on the area under the ROC curve — the AUC parameter (Area Under Curve). In order to evaluate the prognostic significance of MRI predictors for metastatic involvement of the LN, uni-and multivariate regression logistic analyses were performed. According to the results of multivariate analysis, the configuration index is less than or equal to 1.65, the absence of a signal from the fatty tissue of the hilum of LN, and perinodular edema are the most prognostically significant criteria for metastatic involvement of the LN (p &lt; 0.05).

Feasibility of Sentinel Lymph Node Mapping With Carbon Nanoparticles in Cervical Cancer: A Retrospective Study.

This retrospective study aims to investigate the feasibility of using carbon nanoparticles to detect sentinel lymph nodes (SLNs) in cervical cancer. This study involved 174 patients with cervical cancer. Cervix tissues adjacent to the cancer were injected with 1mL of carbon nanoparticles (CNPs) at the 3 and 9 o'clock positions according to the instructions. The pelvic lymph nodes were then dissected, and the black-stained sentinel lymph nodes were sectioned for pathological examination. Of 174 cases, 88.5% of patients (154/174) had at least 1 sentinel lymph node, and 131 patients (75.29%) had bilateral pelvic sentinel lymph nodes. The left pelvic lymph node was the most common sentinel lymph node (34.16%). At least 1 sentinel lymph node was observed in 285 out of 348 hemipelvises, with a detection rate of a side-specific sentinel lymph node of 81.89%. In total, 47 hemipelvises had metastasis of the lymph node, and 33 involved the sentinel lymph node, with a sensitivity of 70.21% and a false-negative rate of 29.79%. There were 238 hemipelvises with no metastasis of the lymph node, as well as negative sentinel lymph nodes, with a specificity of 100% and a negative predictive value of 94.44%. The univariate analysis demonstrated that risk factors included tumor size (OR .598, 95% CI: .369-.970) and deep stromal invasion (OR .381, 95% CI: .187-.779). The deep stromal invasion was the only variable for the false-negative detection of a sentinel lymph node. Sentinel lymph node mapping with carbon nanoparticles might be applied to predict the metastasis of pelvic lymph nodes in cervical cancer. However, tumor size and deep stromal invasion might negative influence the detection rate of SLN.

Simultaneous or Sequential Boost to Clinically Involved Lymph Nodes in Cervical Cancer

<h3>Purpose/Objective(s)</h3> The optimal approach to metastatic lymph nodes (LNs) in locally advanced cervical cancer (LACC) is controversial. This study aimed to evaluate the efficacy and toxicity profile of dose escalation with simultaneous integrated (SIB) or sequential boost (SB) for clinically positive LNs in LACC. <h3>Materials/Methods</h3> The data of 43 patients who underwent chemoradiotherapy (CRT) with a SIB or SB dose to positive LNs between 2015 and 2021 for LACC were retrospectively analyzed. All patients were applied 50.4 Gy intensity-modulated radiotherapy (IMRT) or modulated volumetric arc therapy (VMAT) to the lymphatic and primary regions in 28 fractions with concurrent platinum-based chemotherapy followed by 28 Gy HDR intracavitary brachytherapy (BRT) in four fractions. Response evaluations were performed via magnetic resonance imaging (MRI) and positron emission tomography (PET)-CT on the third month. <h3>Results</h3> Median age was 53 years (range: 34-80 years). The FIGO stage was IIIC1 in 30 (70%), IIIC2 in 10 (23%), and IVA in three patients (7%), respectively. Pelvic CRT was applied to 25 patients (58%), and pelvic-paraaortic CRT was applied to 18 patients (41%). A total of 94 LNs were boosted. The median longest diameter of the LNs was 1.7 cm (range: 1–7 cm) and median volume was 2.5 cc (range: 0.6-21.5 cc). A SIB was applied in 24 (56%), SB in 13 (30%), and both in 6 patients (14%), respectively. Patients in the SIB group received a total dose of 56-64 Gy in 28 fractions. The patients with SB on the other hand received 56.4-60.4 Gy in 31-33 fractions and patients with SIB+SB received 60-66 Gy in 30-32 fractions. The dose from the BRT was also calculated to the LNs and the minimum dose to LNs from BRT was between 0-7.6 Gy (median 3.6 Gy). The maximum dose from BRT was as 0-9.7 Gy (median 4.8 Gy). Median follow-up (FU) was 21 months (range: 3-77 months). Following CRT, 41 (98%) patients had a complete response (CR) and one (2%) had a partial response (PR) in boosted LNs on MRI whereas 39 (98%) patients had a CR and one (2%) had a PR on PET/CT. Recurrence occurred in 12 patients during FU: one local recurrence (LR), two regional recurrence out of the RT field, seven distant metastases (DM), and two LR+DM. There was no recurrence in any of the 94 boosted LNs and the local control (LC) rate was 100%. The 1- and 2-year rate of overall survival was 89.5% and 82.3%, disease-free survival was 80% and 65.8%, locoregional recurrence-free survival was 84.6% and 70.5%, and DM-free survival rate was 80.4% and 69.9%, respectively. Treatment was well tolerated without any serious acute toxicity. Grade 3 late toxicity was observed in two patients (1-rectovaginal fistula, 1-ureteral stenosis). <h3>Conclusion</h3> Dose escalation with IMRT/VMAT yields an excellent LC rate for the involved LNs, even for very large ones, in LACC with a low toxicity profile. Routine LN dissection may not be necessary for patients with a LACC, but prospective randomized trials are needed to clarify this.

An exploratory study of the dose correlation between point B and metastatic lymph nodes in three‐dimensional high‐dose‐rate brachytherapy for cervical cancer

AbstractObjectiveTo determine the dose correlation between point B and metastatic lymph nodes in cervical cancer treated with three‐dimensional (3D) high‐dose‐rate (HDR) brachytherapy.MethodsA retrospective study was conducted involving 30 cervical cancer patients with lymph node metastases who completed 3D HDR brachytherapy at Liaoning Cancer Hospital and Institute from May 2019 to November 2020. Brachytherapy was performed with a Fletcher applicator at a prescribed dose of 28 Gy/4 f. Computed tomography images of the external irradiation target were fused with delineated images of the brachytherapy target on the MIM system. The minimum dose received by 90% of the volume (D90) was read on the MIM system for all metastatic lymph nodes and the point B dose was read for the brachytherapy target area. In total, 78 metastatic lymph nodes from 30 patients with cervical cancer were included in this study, including para‐aortic, common iliac, external iliac, internal iliac, obturator, and inguinal lymph nodes. Paired t‐tests and Pearson correlation coefficient analyses were performed to examine the difference in the equivalent dose between each group of metastatic lymph nodes and point B.ResultsThe mean equivalent point B dose was 6.69 ± 1.08 Gy. There were significant differences in dose between point B and the para‐aortic, common iliac, external iliac, internal iliac, and inguinal lymph nodes (p &lt; 0.05), with a weak positive correlation (r &lt; 0.4). There was no statistically significant difference in dose between obturator lymph nodes and point B (p = 0.3999), with a positive correlation (r = 0.65).Conclusion3D HDR brachytherapy contributed significantly to pelvic lymph node doses in cervical cancer patients with lymph node metastasis, but less to para‐aortic and inguinal lymph node doses. The dose at point B grossly overestimated the mean dose to pelvic lymph nodes; only the dose to obturator lymph nodes was similar to that of point B, with a slightly positive correlation.

Diagnostic Value of Multimodal Magnetic Resonance Imaging in Discriminating Between Metastatic and Non-Metastatic Pelvic Lymph Nodes in Cervical Cancer.

BackgroundThe status of pelvic lymph node (PLN) metastasis affects treatment and prognosis plans in patients with cervical cancer. However, it is hard to be diagnosed in clinical practice.PurposeThe present study aimed to evaluate the diagnostic value of multimodal magnetic resonance imaging (MRI) in discriminating between metastatic and non-metastatic pelvic lymph nodes (PLNs) in cervical cancer.MethodsThis retrospective study analyzed MRIs of 209 PLNs in 25 women with pathologically proven cervical cancer. All PLNs had been assessed by pre-treatment multimodal MRIs, and their status was finally confirmed by histopathology. In conventional MRI, lymph node characteristics were compared between metastatic and non-metastatic PLNs. Signal intensity, time–intensity curve (TIC) patterns minimal and mean apparent diffusion coefficients (ADC) were compared between them in DWI. In DCE-MRI, quantitative (Ktrans, Kep and Ve) analyses were performed on DCE-MRI sequences, and their predictive values were analyzed by ROC curves.ResultsOf 209 PLNs, 22 (10.53%) were metastases and 187 (89.47%) were non-metastases at histopathologic examination. Considering a comparison of lymph node characteristics, the short axis size, the long axis size, and the boundary differed significantly between the two groups (P<0.05).The differences in ADCmin, TIC types, Ktrans and Ve between metastatic and non-metastatic PLNs were significant as well (P<0.05). The good diagnostic performance of multimodal MRI was shown in discriminating between metastatic and non-metastatic PLNs, with the sensitivity of 85.0% (17/20), specificity of 97.3% (184/189), and accuracy of 96.2% (201/209). ROC analyses showed that the diagnostic accuracy of ADCmin, Ktrans and Ve for discriminating between metastatic and non-metastatic PLNs in cervical cancer was 83.7%, 91.4%, and 92.4% with the cut-off values of 0.72 × 10−3mm2/s, 0.52 min−1, and 0.53 min−1, respectively.ConclusionMultimodal MRI showed good diagnostic performance in determining PLN status in cervical cancer.

Receptor for activated C kinase 1 promotes cervical cancer lymph node metastasis via the glycolysis‑dependent AKT/mTOR signaling.

Cervical cancer (CC), an aggressive form of squamous cell carcinoma, is characterized by early-stage lymph node metastasis and an extremely poor prognosis. The authors have previously demonstrated that patients with CC have aberrant glycolysis. The upregulation of receptor for activated C kinase 1 (RACK1) is associated with CC lymph node metastasis (LNM). However, its role in mediating aerobic glycolysis in CC LNM remains unclear. In the present study, 1H nuclear magnetic resonance analysis revealed a significant association between RACK1 expression and the glycolysis/gluconeogenesis pathway. Additionally, RACK1 knockdown inhibited aerobic glycolysis and lymphangiogenesis in vitro and suppressed CC LNM in vivo. Furthermore, protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling was identified as a critical RACK1-regulated pathway that increased lymphangiogenesis in CC. Co-immunoprecipitation, immunofluorescence and western blot analysis revealed that RACK1 activated AKT/mTOR signaling by interacting with insulin-like growth factor 1 receptor (IGF1R). POU class 2 homeobox 2 (POU2F2) bound to the RACK1 promoter and regulated its transcription, thereby functionally contributing to glycolysis and lymphangiogenesis in CC. Of note, the admin-istration of 2-deoxy-D-glucose, which attenuates glycolysis, inhibited RACK1-induced lymphangiogenesis in CC. The correlations between RACK1, IGF1R, POU2F2 and hexokinase 2 were further confirmed in CC tissues. Thus, RACK1 plays a crucial role in CC tumor LNM by regulating glycolysis via IGF1R/AKT/mTOR signaling. Thus, the targeting of the POU2F2/RACK1/IGF1R/AKT/mTOR signaling pathway may provide a novel treatment strategy for CC.

PET-CT versus MRI in the diagnosis of lymph node metastasis of cervical cancer: A meta-analysis.

To compare the clinical application value of positron emission tomography-computed tomography (PET-CT) and magnetic resonance imaging (MRI) in the diagnosis of cervical cancer lymph node metastasis. We searched PubMed and other databases for the studies comparing the use of PET-CT and MRI for the diagnosis of cervical cancer lymph node metastasis up to January 20, 2021. We strictly followed the inclusion and exclusion criteria to screen the literature and extract the data. Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool was used for quality evaluation of included studies, and Revman 5.3 and Stata 15.0 software were used for evaluating heterogeneity, synthesize sensitivity (SEN), specificity (SPE), positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR), and the area under the curve (AUC) and comparing the pretest and posttest probabilities. Finally, 11 studies were included for meta-analysis. The synthesized results indicated that the SEN value of PET-CT was 0.65 (0.60 ~ 0.69) and SPE was 0.93 (0.91 ~ 0.94), and the SEN value of MRI was 0.58 (0.54 ~ 0.63) and SPE was 0.91 (0.90 ~ 0.92). AUC of PET-CT was 0.824, which was significantly higher than that of MRI (AUC=0.702; p < .05). The subgroup analysis showed that the AUC value of the study based on study design and use of blinding methods was not statistically significant (all p > .05). There was no obvious publication bias in the synthesized analysis of the diagnostic value of PET-CT and MRI (all p > .05). HIGHLIGHTS: To compare positron emission tomography-computed tomography (PET-CT) and magnetic resonance imaging (MRI) in diagnosis of cervical cancer lymph node metastasis. Synthesize sensitivity value of PET-CT was comparable with that of MRI. Area under the curve of PET-CT was significantly higher than that of MRI. There was no obvious publication bias in synthesized analysis.

Deep learning nomogram for predicting lymph node metastasis using computed tomography image in cervical cancer.

Deep learning (DL) has been used on medical images to grade, differentiate, and predict prognosis in many tumors. To explore the effect of computed tomography (CT)-based deep learning nomogram (DLN) for predicting cervical cancer lymph node metastasis (LNM) before surgery. In total, 418 patients with stage IB-IIB cervical cancer were retrospectively enrolled for model exploration (n = 296) and internal validation (n = 122); 62 patients from another independent institution were enrolled for external validation. A convolutional neural network (CNN) was used for DL features extracting from all lesions. The least absolute shrinkage and selection operator (Lasso) logistic regression was used to develop a deep learning signature (DLS). A DLN incorporating the DLS and clinical risk factors was proposed to predict LNM individually. The performance of the DLN was evaluated on internal and external validation cohorts. Stage, CT-reported pelvic lymph node status, and DLS were found to be independent predictors and could be used to construct the DLN. The combination showed a better performance than the clinical model and DLS. The proposed DLN had an area under the curve (AUC) of 0.925 in the training cohort, 0.771 in the internal validation cohort, and 0.790 in the external validation cohort. Decision curve analysis and stratification analysis suggested that the DLN has potential ability to generate a personalized probability of LNM in cervical cancer. The proposed CT-based DLN could be used as a personalized non-invasive tool for preoperative prediction of LNM in cervical cancer, which could facilitate the choice of clinical treatment methods.

Clinical value of MRI, serum SCCA, and CA125 levels in the diagnosis of lymph node metastasis and para-uterine infiltration in cervical cancer

BackgroundCervical cancer shows great differences in depth of invasion, metastasis, and other biological behaviors. The location of the lesion is special, so it is usually difficult to determine the clinical stage. This study aimed to explore the clinical value of magnetic resonance imaging (MRI) and tumor serum markers for the preoperative diagnosis of cervical cancer lymph node metastasis and para-uterine invasion.MethodsA total of 200 patients with cervical cancer admitted to our hospital from January 2019 to January 2020 were collected as the research subjects. Comparing the diagnosis results of preoperative MRI scan, serum tumor markers, and postoperative pathological examination using single factor comparison, we determined the MRI scan results, the comprehensive matching rate between serum tumor markers (squamous cell carcinoma antigen (SCCA), carbohydrate antigen 125 (CA125)) and postoperative pathological results, and the differences of sensitivity, specificity, and accuracy in the prediction of lymph node metastasis and para-uterine infiltration of cervical cancer.ResultsThe levels of SCCA and CA125 in patients with para-uterine invasion and lymph node metastasis were higher than those of patients without invasion and metastasis. Among them, the level of SCCA was significantly different (P<0.05). The level of CA125 was not statistically significant (P>0.05), so MRI combined with serum SCCA was selected for combined diagnosis in the later period. The sensitivity, specificity, and accuracy of MRI diagnosis of cervical cancer and para-uterine infiltrating lymph node metastasis and metastasis were 55.2, 91.6, and 89.5% and 55.2, 91.6, and 89.5%, respectively. These data in MRI combined with serum SCCA were 76.3, 95.3, and 94.3% and 63.2, 96.0, and 95.1%, respectively. The accuracy of tumor markers combined with MRI in the diagnosis of cervical cancer lymph node metastasis and para-uterine invasion was higher than that of MRI.ConclusionsMRI combined with serum SCCA can more accurately identify cervical cancer lymph node metastasis and para-uterine invasion compared with MRI alone. Tumor marker combined with MRI diagnosis is an important auxiliary method for cervical cancer treatment and can provide comprehensive and reliable clinical evidence for evaluation before cervical cancer surgery.

Sentinel Lymph Node Staging with Indocyanine Green for Patients with Cervical Cancer: The Safety and Feasibility of Open Approach Using SPY-PHI Technique.

(1) Background: Sentinel lymph node staging (SLN) with indocyanine green (ICG) in cervical cancer is the standard of care in most national and international guidelines. However, the vast majority of relevant studies about the safety and feasibility of this method are conducted on minimally invasive surgery; (2) Methods: This study is a retrospective analysis of a retrospective collected database of 76 consecutive patients with cervical cancers, who were operated laparoscopically (50%), or laparotomy (50%). Sentinel nodes were defined as the ICG-positive pelvic nodes in the first and second echelons. False negative cases were defined as positive non-sentinel lymph nodes despite successful sentinel mapping or failed mapping bilaterally by per-patient assessment or unilaterally by pelvic sidewall assessment; (3) Results: Regardless of the approach (open or laparoscopic), the SLN technique achieved a total sensitivity, specificity, and negative predictive value (NPV) of 94.7%, 98.6%, and 94.7%, respectively in the entire sample. The bilateral detection rate was as high as 93.4% with identical results in both approaches. The sensitivity and NPV for SNL in open surgery was found to be similar to minimal access surgery; (4) Conclusions: ICG and SPY-PHI technique is a reliable tool to detect sentinel lymph nodes in cervical cancer during laparotomy.

PH-0376 Dose escalation to lymph nodes in cervical cancer using a sequential boost on the MR-Linac (MRL)

PH-0376 Dose escalation to lymph nodes in cervical cancer using a sequential boost on the MR-Linac (MRL)

The role of lymph nodes in cervical cancer: incidence and identification of lymph node metastases-a literature review.

Correct identification of patients with lymph node metastasis from cervical cancer prior to treatment is of great importance, because it allows more tailored therapy. Patients may be spared unnecessary surgery or extended field radiotherapy if the nodal status can be predicted correctly. This review captures the existing knowledge on the identification of lymph node metastases in cervical cancer. The risk of nodal metastases increases per 2009 FIGO stage, with incidences in the pelvic region ranging from 2% (stage IA2) to 14-36% (IB), 38-51% (IIA) and 47% (IIB); and in the para-aortic region ranging from 2 to 5% (stage IB), 10-20% (IIA), 9% (IIB), 13-30% (III) and 50% (IV). In addition, age, tumor size, lymph vascular space invasion, parametrial invasion, depth of stromal invasion, histological type, and histological grade are reported to be independent prognostic factors for the risk of nodal metastases. Furthermore, biomarkers can contribute to predict a patient's nodal status, of which the squamous cell carcinoma antigen (SCC-Ag) is currently the most widely used in squamous cell cervical cancer. Still, pre-treatment lymph node assessment is primarily performed by imaging, of which diffusion-weighted magnetic resonance imaging has the highest sensitivity and 2-deoxy-2-[18F]fluoro-D-glucose positron emission computed tomography the highest specificity. Imaging results can be combined with clinical parameters in nomograms to increase the accuracy of predicting positives nodes. Despite all the progress regarding pre-treatment prediction of lymph node metastases in cervical cancer in recent years, prediction rates are not robust enough to safely abandon surgical staging of the pelvic or para-aortic region yet.