ECPC-IDS: A benchmark endometrial cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions.

BACKGROUND: Endometrial cancer is one of the most common tumors in the female reproductive system and is the third most common gynecological malignancy that causes death after ovarian and cervical cancer. Early diagnosis can significantly improve the 5-year survival rate of patients. With the development of artificial intelligence, computer-assisted diagnosis plays an increasingly important role in improving the accuracy and objectivity of diagnosis and reducing the workload of doctors. However, the absence of publicly available image datasets restricts the application of computer-assisted diagnostic techniques. METHODS: In this paper, a publicly available Endometrial Cancer PET/CT Image Dataset for Evaluation of Semantic Segmentation and Detection of Hypermetabolic Regions (ECPC-IDS) are published. Specifically, the segmentation section includes PET and CT images, with 7159 images in multiple formats totally. In order to prove the effectiveness of segmentation on ECPC-IDS, six deep learning semantic segmentation methods are selected to test the image segmentation task. The object detection section also includes PET and CT images, with 3579 images and XML files with annotation information totally. Eight deep learning methods are selected for experiments on the detection task. RESULTS: This study is conduct using deep learning-based semantic segmentation and object detection methods to demonstrate the distinguishability on ECPC-IDS. From a separate perspective, the minimum and maximum values of Dice on PET images are 0.546 and 0.743, respectively. The minimum and maximum values of Dice on CT images are 0.012 and 0.510, respectively. The target detection section's maximum mAP values on PET and CT images are 0.993 and 0.986, respectively. CONCLUSION: As far as we know, this is the first publicly available dataset of endometrial cancer with a large number of multi-modality images. ECPC-IDS can assist researchers in exploring new algorithms to enhance computer-assisted diagnosis, benefiting both clinical doctors and patients. ECPC-IDS is also freely published for non-commercial at: https://figshare.com/articles/dataset/ECPC-IDS/23808258.

A comparative study of mono-exponential and advanced diffusion-weighted imaging in differentiating stage IA endometrial carcinoma from benign endometrial lesions.

PURPOSE: The purpose of the current investigation is to compare the efficacy of different diffusion models and diffusion kurtosis imaging (DKI) in differentiating stage IA endometrial carcinoma (IAEC) from benign endometrial lesions (BELs). METHODS: Patients with IAEC, endometrial hyperplasia (EH), or a thickened endometrium confirmed between May 2016 and August 2022 were retrospectively enrolled. All of the patients underwent a preoperative pelvic magnetic resonance imaging (MRI) examination. The apparent diffusion coefficient (ADC) from the mono-exponential model, pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f) from the bi-exponential model, distributed diffusion coefficient (DDC), water molecular diffusion heterogeneity index from the stretched-exponential model, diffusion coefficient (Dk) and diffusion kurtosis (K) from the DKI model were calculated. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic efficiency. RESULTS: A total of 90 patients with IAEC and 91 patients with BELs were enrolled. The values of ADC, D, DDC and Dk were significantly lower and D* and K were significantly higher in cases of IAEC (p < 0.05). Multivariate analysis showed that K was the only predictor. The area under the ROC curve of K was 0.864, significantly higher compared with the ADC (0.601), D (0.811), D* (0.638), DDC (0.743) and Dk (0.675). The sensitivity, specificity and accuracy of K were 78.89%, 85.71% and 80.66%, respectively. CONCLUSION: Advanced diffusion-weighted imaging models have good performance for differentiating IAEC from EH and endometrial thickening. Among all of the diffusion parameters, K showed the best performance and was the only independent predictor. Diffusion kurtosis imaging was defined as the most valuable model in the current context.

Preoperative evaluation and a nomogram prediction model for pelvic lymph node metastasis in endometrial cancer.

OBJECTIVE: The primary objective of this study is to explore the preoperative risk factors of pelvic lymph node metastasis (PLNM) in endometrial cancer patients, and construct a nomogram prediction model. MATERIALS AND METHODS: We retrospectively collected various preoperative clinical characteristics of patients and analyzed their relationship with PLNM. Logistic regression analysis was used to screen for independent risk factors for PLNM of endometrial cancer. A nomogram prediction model was constructed, the receiver operating characteristic (ROC), calibration curve and decision curve analysis (DCA) were constructed and used to assess discrimination, calibration, and net benefit. RESULTS: Out of the 276 patients, 74 (26.81%) with postoperative pathological confirmation of PLNM. Multivariate logistic regressive analysis demonstrated that preoperative depth of myometrial invasion (DIM) ≥50% determined by Magnetic Resonance Imaging (MRI) (p = 0.003), carbohydrate antigen 125 (CA125) (p = 0.030), carbohydrate antigen 19-9 (CA 19-9) (p = 0.044), and platelet/lymphocyte ratio (PLR) (p = 0.025) could serve as independent risk factors for PLNM. A risk factors-based nomogram prediction model was constructed, which showed good discrimination (AUC = 0.841, p < 0.001) and good efficacy (C-index = 0.842) and good calibration (mean absolute error = 0.046). DCA showed that the model can provide clinical benefits. CONCLUSIONS: Preoperative DIM ≥50% determined by MRI, serum CA 19-9, CA125 and PLR could be utilized to predict PLNM in endometrial cancer patients. This nomogram prediction model can provide preoperative help for evaluation and identification of patients with endometrial cancer, and provide a theoretical basis for clinical intervention.

Application of magnetic resonance imaging radiomics in endometrial cancer: a systematic review and meta-analysis.

PURPOSE: We aimed to systematically assess the methodological quality and clinical potential application of published magnetic resonance imaging (MRI)-based radiomics studies about endometrial cancer (EC). METHODS: Studies of EC radiomics analyses published between 1 January 2000 and 19 March 2023 were extracted, and their methodological quality was evaluated using the radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pairwise correlation analyses and separate meta-analyses of studies exploring differential diagnoses and risk prediction were also performed. RESULTS: Forty-five studies involving 3 aims were included. The mean RQS was 13.77 (range: 9-22.5); publication bias was observed in the areas of 'index test' and 'flow and timing'. A high RQS was significantly associated with therapy selection-aimed studies, low QUADAS-2 risk, recent publication year, and high-performance metrics. Raw data from 6 differential diagnosis and 34 risk prediction models were subjected to meta-analysis, revealing diagnostic odds ratios of 23.81 (95% confidence interval [CI] 8.48-66.83) and 18.23 (95% CI 13.68-24.29), respectively. CONCLUSION: The methodological quality of radiomics studies involving patients with EC is unsatisfactory. However, MRI-based radiomics analyses showed promising utility in terms of differential diagnosis and risk prediction.

Identifying safe diagnostic algorithms for sentinel lymph node mapping in high-risk endometrial cancer: The SENTIREC-endo study.

INTRODUCTION: It is unclear if sentinel node (SLN) mapping can replace pelvic- (PLD) and paraaortic lymphadenectomy (PALD) for high-risk endometrial cancer (EC). A diagnostically safe surgical algorithm, taking failed mapping cases into account, is not defined. We aimed to investigate the diagnostic accuracy of SLN mapping algorithms in women with exclusively high-risk EC. METHODS: We undertook a prospective national diagnostic cohort study of SLN mapping in women with high-risk EC from March 2017 to January 2023. The power calculation was based on the negative predictive value (NPV). Women underwent SLN mapping, PLD and PALD besides removal of suspicious and any FDG/PET-positive lymph nodes. Accuracy analyses were performed for five algorithms. RESULTS: 170/216 included women underwent SLN mapping, PLD and PALD and were included in accuracy analyses. 42/170 (24.7%) had nodal metastasis. The algorithm SLN and PLD in case of failed mapping, demonstrated a sensitivity of 86% (95% CI 74-100) and an NPV of 96% (95% CI 91-100). The sensitivity increased to 93% (95% CI 83-100) and the NPV to 98% (95% CI 94-100) if PLD was combined with removal of any PET-positive lymph nodes. Equivalent results were obtained if PLD and PALD were performed in non-mapping cases; sensitivity 93% (95% CI 83-100) and NPV 98% (95% CI 95-100). CONCLUSION: SLN-mapping is a safe staging procedure in women with high-risk EC if strictly adhering to a surgical algorithm including removal of any PET-positive lymph nodes independent of location and PLD or PLD and PALD in case of failed mapping.

18F-FDG PET/CT Findings of Tibia Metastasis From Endometrial Adenocarcinoma.

ABSTRACT: Bone metastases from endometrial carcinoma are rare, especially when the bone is the sole metastatic site. A 55-year-old woman with a history of endometrial carcinoma was referred for FGD PET/CT scan due to pain in the left knee. The images showed that multiple lesions with intense activity were detected in the left tibia. Histopathological examination and immunohistochemistry of the left tibial lesion confirmed metastases from the endometrial adenocarcinoma.

Ultrasound finding of endometrial polyp and factors increasing risk of malignancy.

This article presents a comprehensive review of factors that increase the risk of malignancy in ultrasound findings of an endometrial polyp. We collected original studies, reviews, and meta-analyses that dealt with the topic of endometrial polyps and the risk of developing endometrial cancer. Each presumed risk factor was analysed individually. According to searched studies, abnormal uterine bleeding, old age, and body mass index are valid risk factors for developing endometrial cancer in endometrial polyps. Lynch syndrome patients are also in a high-risk group for endometrial cancer. On the other hand, the number of polyps, their size, diabetes mellitus, hypertension, and positive family history are factors with inconclusive results. There are either not enough data or different results among several studies.

DSFF-GAN: A novel stain transfer network for generating immunohistochemical image of endometrial cancer.

Immunohistochemistry (IHC) is a commonly used histological examination technique. Compared to Hematoxylin and Eosin (H&E) staining, it enables the examination of protein expression and localization in tissues, which is valuable for cancer treatment and prognosis assessment, such as the detection and diagnosis of endometrial cancer. However, IHC involves multiple staining steps, is time-consuming and expensive. One potential solution is to utilize deep learning networks to generate corresponding virtual IHC images from H&E images. However, the similarity of the IHC image generated by the existing methods needs to be further improved. In this work, we propose a novel dual-scale feature fusion (DSFF) generative adversarial network named DSFF-GAN, which comprises a cycle structure-color similarity loss, and DSFF block to constrain the model's training process and enhance its stain transfer capability. In addition, our method incorporates labeling information of positive cell regions as prior knowledge into the network to further improve the evaluation metrics. We train and test our model using endometrial cancer and publicly available breast cancer IHC datasets, and compare it with state-of-the-art methods. Compared to previous methods, our model demonstrates significant improvements in most evaluation metrics on both datasets. The research results show that our method further improves the quality of image generation and has potential value for the future clinical application of virtual IHC images.

The role of shear wave elastography in predicting endometrial cancer in patients presenting with abnormal uterine bleeding.

OBJECTIVE: Shear Wave Elastography (SWE) is an objective quantitative ultrasound elastography technique that can demonstrate the stiffness of anatomical structures to aid in their detection and characterization. We aimed to evaluate the role of shear wave elastography in differentiating endometrial carcinoma from benign uterine pathologies in women with abnormal uterine bleeding. PATIENTS AND METHODS: This prospective study was conducted at our institution from January 2020 to April 2020. A hundred patients with endometrial sampling planned and SWE due to abnormal uterine bleeding were included in the study. According to the histopathological results of the patients, those with normal and atrophic endometrium results were defined as group I (control group), those with benign results such as polyps and endometrial hyperplasia were defined as group II, and those with endometrial cancers were defined as group III. RESULTS: After adjustment for age, a statistically significant difference was found in Emean (mean and adjusted mean) value between the study groups (F2.96=86.37, p<.001, η2=0.64). The post-hoc analysis was performed with a Bonferroni adjustment. The mean Emean value was found to be statistically significantly higher in group III (17.14±0.40) compared to group I (10.39±0.26) and group II (11.49±0.32) (p<0.001). In addition, a statistically significant difference was found between the benign and normal groups. CONCLUSIONS: As a new diagnostic technique in gynecology, elastography appears to be a valuable tool in differentiating malign endometrial pathologies from normal or benign endometrial pathologies in females with abnormal uterine bleeding.

Multiparametric MRI radiomics improves preoperative diagnostic performance for local staging in patients with endometrial cancer.

PURPOSE: To determine whether multiparametric magnetic resonance imaging (MRI) radiomics-based machine learning methods can improve preoperative local staging in patients with endometrial cancer (EC). METHODS: Data of patients with histologically confirmed EC who underwent preoperative MRI were retrospectively analyzed and divided into a training or test set. Radiomic features extracted from multiparametric MR images were used to train and test the prediction of deep myometrial invasion (DMI) and cervical stromal invasion (CSI). Two radiologists assessed the presence of DMI and CSI on conventional MR images. A combined model incorporating a radiomic signature and conventional MR images was constructed and presented as a nomogram. Performance of the predictive models was assessed using the area under curve (AUC) in the receiver operating curve analysis and pairwise comparison using DeLong's test with Bonferroni correction. RESULTS: This study included 198 women (training set = 138, test set = 60). Conventional MRI achieved AUCs of 0.837 and 0.799 for detecting DMI and 0.825 and 0.858 for detecting CSI in the training and test sets, respectively. The nomogram achieved AUCs of 0.928 and 0.869 for detecting DMI and 0.913 and 0.937 for detecting CSI in the training and test sets, respectively. The ability of the nomogram to detect DMI and CSI in the two sets was superior to that of conventional MRI (adjusted p < 0.05), except for the ability to detect CSI in the test set (adjusted p > 0.05). CONCLUSION: A nomogram incorporating radiomics signature into conventional MRI improved the efficacy of preoperative local staging of EC.

Clinical outcomes of sentinel node navigation surgery in patients with preoperatively estimated stage IA endometrial cancer and evaluation of validity for continuing sentinel node navigation surgery based on dispersion of recurrence probability.

BACKGROUND: To evaluate the feasibility of the use and continuation of sentinel lymph node navigation surgery (SNNS) as an alternative to pelvic lymph node dissection (PLND) for patients with preoperatively estimated stage IA endometrial cancer. METHODS: This retrospective study selected the electronic medical records of all patients who had received CT scans and MRI imaging before surgery from April 1, 2009 to March 31, 2021. Sentinel lymph nodes (SLNs) were detected by administrating 99mTc-phytate and/or indocyanine green into the cervix, and the clinical outcomes of the patients who underwent SNNS or PLND were evaluated. Furthermore, in case of nodal recurrence, a new procedure to determine whether the facility should continue with SNNS or not was developed that compares the maximum likelihood hypothesis and an alternative one based on recurrence rates. RESULTS: Among 137 patients, SLN biopsies with ultrastaging were performed on 91 patients. The SLN detection rate was 95.6%. Over a 59-month median observation period, no statistically significant differences were shown in overall survival, disease-specific survival and disease-free survival between the SNNS and PLND groups when introducing the propensity score method (p-values: 0.06, 0.153, and 0.625, respectively). Our procedure demonstrated that, in our department without recurrence up to the 65th attempt, it was possible to continue SNNS if a recurrence occurs at the 66th attempt. CONCLUSION: This study suggests the validity of SNNS as an alternative to PLND. Even in the absence of evidence from randomized controlled trials, we can confirm the validity of continuing SNNS using our procedure.

Diagnostic accuracy of MRI for assessing lymphovascular space invasion in endometrial carcinoma: a meta-analysis.

BACKGROUND: The lymphovascular space invasion (LVSI) status of endometrial cancer (EC) has guiding significance in lymph node dissection. However, LVSI can only be obtained after surgery. Researchers have tried to extract the information of LVSI using magnetic resonance imaging (MRI). PURPOSE: To evaluate the ability of preoperative MRI to predict the LVSI status of EC. MATERIAL AND METHODS: A search was conducted by using the PubMed/MEDLINE, EMBASE, Web of Science, and the Cochrane Library databases. Articles were included according to the criteria. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2. A bivariate random effects model was used to obtain pooled summary estimates, heterogeneity, and the area under the summary receiver operating characteristic curve (AUC). A subgroup analysis was performed to identify sources of heterogeneity. RESULTS: A total of nine articles (814 patients) were included. The risk of bias was low or unclear for most studies, and the applicability concerns were low or unclear for all studies. The summary AUC values as well as pooled sensitivity and specificity of LVSI status in EC were 0.82, 73%, and 77%, respectively. According to the subgroup analysis, radiomics/non-radiomics features, country/region, sample size, age, MR manufacturer, magnetic field, scores of risk bias, and scores of applicability concern may have caused heterogeneity. CONCLUSION: Our meta-analysis showed that MRI has moderate diagnostic efficacy for LVSI status in EC. Large-sample, uniformly designed studies are needed to verify the true value of MRI in assessing LVSI.

The fluorescence imaging for laparoscopic and laparotomic endometrial sentinel lymph node biopsy (FILLES) trial: Siriraj gynecologic sentinel node of endometrial cancer (SiGN-En) study.

BACKGROUND AND OBJECTIVES: The objective of this study is to establish the detection rate of sentinel lymph node (SLN) biopsies and to determine the sensitivity and false-negative rate of SLN biopsies compared with those of systematic pelvic and para-aortic lymphadenectomies in endometrial cancer. METHODS: This prospective cohort study enrolled patients with endometrial cancer who were scheduled for surgical staging. Patients with a history of chemotherapy or radiotherapy, an abnormal liver function test, or an allergy to indocyanine green (ICG) were excluded. All patients underwent surgical staging with an ICG injection at the cervix. SLNs were identified by a near-infrared fluorescent camera. All SLNs were sent to a pathologist for ultrastaging. RESULTS: From November 2019 to June 2023, 142 patients underwent SLN mapping and surgical staging. SLNs were not detected bilaterally in 8 patients. The detection rate of the SLN biopsies in this study was 91.2%. Thus, the accuracy of the SLN biopsies was 97.6%. The sensitivity for finding metastatic SLNs was 84.2%, with a negative predictive value of 97.22%. CONCLUSIONS: A SLN biopsy in endometrial cancer has a high detection rate and high accuracy. However, surgical expertise and a learning curve are required.

Synchronous Endometrial Cancer Detected on 68 Ga-DOTATATE PET/CT.

ABSTRACT: A 50-year-old woman with history of metastatic neuroendocrine tumor underwent a follow-up PET/CT with 68 Ga-DOTATATE. An enlarged uterus with diffuse increased uptake warranted further investigation with an MRI that showed mass-like thickening of the endometrium, T2 hyperintensity, lobulated contour, and invasion of more than 50% of the myometrium. Biopsy revealed endometrial carcinoma with mucinous features. Mild to moderate uptake of the 68 Ga-DOTATATE by the uterus can be a normal physiological finding; however, it should prompt further investigation, particularly in a postmenopausal woman with concerning imaging or clinical findings.

Evaluation of pre-therapeutic imaging work-up in the staging of endometrial cancer: Interest in a systematic second opinion in a cancer center.

RATIONALE AND OBJECTIVES: To evaluate the interest of a systematic second opinion in quality assessment and FIGO staging in the pretherapeutic imaging work-up. MATERIALS AND METHODS: A retrospective observational study was conducted on 156 patients who underwent surgery for endometrioid cancer in our institution. 42 % had their initial MRI scans performed in expert centers (University Hospital and Cancer center) and 58 % in non-expert centers. Quality assessment, concordances between initial reports, and second opinions by a junior and a senior ICL radiologist versus histopathological data were analyzed. RESULTS: MRI scans performed in expert centers were more complete and more likely to be rated as higher quality. The overall accuracy of T staging from initial reports vs gold standard was 0.59 (95 % CI, 0.46-0.71) in expert centers and 0.49 (95 % CI, 0.38-0.60) in non-expert centers. The overall accuracy and Kappa of a second opinion for FIGO 2009 staging from expert center and non-expert center examinations were 0.61 (95 % CI, 0.48-0.72) vs 0.50 (95 % CI, 0.39-0.60) and 0.37 vs 0.27 for junior reader and 0.62 (95 % CI, 0.49-0.74) vs 0.48 (95 % CI, 0.37-0.58) and 0.39 vs 0.24 for senior reader, respectively. There was also a significant lower confidence level of the junior radiologist in MRI FIGO staging for non-expert center examinations (p 0.003). CONCLUSION: Accuracy in the FIGO 2009 staging and quality assessment are higher for MR examinations performed from expert centers than in non-expert centers. A systematic second opinion by radiologists in expert centers should be proposed before pre-treatment multidisciplinary consultation.

Retrospective analysis of the 18F-FDG PET/CT cutoff value for metabolic parameters was performed as a prediction model to evaluate risk factors for endometrial cancer.

PURPOSE: The study retrospectively analyzed the accuracy and predictive ability of preoperative integrated whole-body 18F-FDG PET/CT for the assessment of high-risk factors in patients with endometrial carcinoma (EC). MATERIALS AND METHODS: A total of 205 patients with endometrial cancer who underwent preoperative PET/CT at Shanghai General Hospital from January 2018 to December 2021 were retrospectively evaluated and last follow-up was June 2023. Our study evaluated the ability and optimal cutoff values of three metabolic and volumetric parameters-standardized uptake value (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG)-to predict deep myometrial invasion (DMI), endocervical stroma invasion (ESI) and lymph node metastases (LNM) in endometrial cancer. The accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of PET/CT were used to assess the diagnostic performance for the prediction. RESULTS: Our study demonstrated a significant relationship between SUVmax (11.29, 17.38, 9.47), SUVmean (5.20, 6.12, 4.49), MTV (38.15, 36.28, 33.79 ml), and TLG (199.30, 225.10, 156.40 g) on PET/CT and histologically confirmed DMI, ESI and LNM in endometrial carcinoma (EC), with sensitivity, specificity, accuracy, PPV, and NPV of 100%/100%/100%, 96.53%/98.89%/87.14%, 97.56%/99.02%/91.22%, 92.42%/92.85%/78.31%, and 100%/100%/100%, respectively. Our study showed a risk model based on optimal cutoff values for MTV and TLG of 19.6 ml/126.3 g, 20.54 ml/84.80 g and 24 ml/49.83 g to preoperatively predict DMI, ESI, and LNM, respectively, in endometrial carcinoma. The 4-year OS (HR) for Stage IA, IB, II, III and IV according to 2009 FIGO was 98.00% (0.22), 95.20% (0.04), 83.90% (0.18), 90.50% (0.09) and 60% (0.51). Accordingly, estimated 4-year DFS (HR) for the stage IA-III was 98% (0.02), 95.20% (0.05), 76.90% (0.27) and 76.30% (0.35), all the patients in stage IV occurred recurrence and progression. CONCLUSION: The present study showed patients with MTV > = 19.6 ml of MI and PET- positive LN with MTV cutoff > = 24 ml tended to predict poor OS and PFS in endometrial carcinoma. The cutoff of MTV and TLG in PET/CT assessment could be an independent prognostic factors to predict aggressive forms of EC.

Radiomics model based on multi-sequence MRI for preoperative prediction of ki-67 expression levels in early endometrial cancer.

To validate a radiomics model based on multi-sequence magnetic resonance imaging (MRI) in predicting the ki-67 expression levels in early-stage endometrial cancer, 131 patients with early endometrial cancer who had undergone pathological examination and preoperative MRI scan were retrospectively enrolled and divided into two groups based on the ki-67 expression levels. The radiomics features were extracted from the T2 weighted imaging (T2WI), dynamic contrast enhanced T1 weighted imaging (DCE-T1WI), and apparent diffusion coefficient (ADC) map and screened using the Pearson correlation coefficients (PCC). A multi-layer perceptual machine and fivefold cross-validation were used to construct the radiomics model. The receiver operating characteristic (ROC) curves analysis, calibration curves, and decision curve analysis (DCA) were used to assess the models. The combined multi-sequence radiomics model of T2WI, DCE-T1WI, and ADC map showed better discriminatory powers than those using only one sequence. The combined radiomics models with multi-sequence fusions achieved the highest area under the ROC curve (AUC). The AUC value of the validation set was 0.852, with an accuracy of 0.827, sensitivity of 0.844, specificity of 0.773, and precision of 0.799. In conclusion, the combined multi-sequence MRI based radiomics model enables preoperative noninvasive prediction of the ki-67 expression levels in early endometrial cancer. This provides an objective imaging basis for clinical diagnosis and treatment.

Validation of Sentinel Lymph Node Biopsy in Robotic Endometrial Cancer Staging Surgery: Results From a High-Volume Center in India.

PURPOSE: Lymph node involvement is one of the most important factors influencing recurrence and survival in patients with endometrial cancer (EC). However, the therapeutic role of lymphadenectomy in early-stage disease has been called into question. Sentinel lymph node (SLN) mapping may be an acceptable alternative to omitting lymphadenectomy or performing a complete lymphadenectomy in patients with EC.To validate SLN biopsy (SLNB) using indocyanine green (ICG) dye and near-infrared imaging in the background of comprehensive lymphadenectomy in patients with EC undergoing robotic staging surgery at Tata Medical Center. METHODS: This was a single-center, prospective observational study involving patients with EC undergoing robotic staging. Patients received a standardized cervical injection of ICG at the 3- and 9-o'clock positions, with the dye reinjected if mapping failed. Depending on preoperative histology and radiological staging, patients had SLNB or comprehensive systematic lymphadenectomy in addition to SLNB. RESULTS: The study included 105 female patients, of whom 71 underwent SLN and full lymphadenectomy and 34 underwent only SLN. There was bilateral mapping in 92 (87.61%) patients, with no mapping in one patient. In 18 patients, ICG dye was reinjected. With the exception of one, the rest had successful mapping after reinjection. The sensitivity of the SLN-ICG algorithm was 92.3%, and the negative predictive value was 98.3%. Ultrastaging necessitated upstaging in 8.57% of patients. CONCLUSION: With a very high negative predictive value, SLN mapping with ICG dye has a high degree of diagnostic accuracy in detecting lymph node metastases in EC.

Exploring the significance of tumor volume in endometrial cancer: Clinical pathological features, prognosis, and adjuvant therapies.

To assist clinicians in formulating treatment strategies for endometrial cancer (EC), this retrospective study explores the relationship between tumor volume and clinical pathological features, as well as prognosis, in patients undergoing staging surgery. Preoperative pelvic MRI examinations were conducted on 234 histologically confirmed EC patients. The ITK-SNAP software was employed to manually delineate the region of interest in the MRI images and calculate the tumor volume (MRI-TV). The analysis focused on investigating the relationship between MRI-TV and the clinical pathological features and prognosis of EC patients. Larger MRI-TV was found to be associated with various adverse prognostic factors (G3, deep myometrial invasion, cervical stromal invasion, lymphovascular space invasion, lymph node metastasis, advanced international federation of gynecology and obstetrics staging, and receipt of adjuvant therapy). The receiver operating characteristic curve indicated that MRI-TV ≥ 8 cm3 predicted deep myometrial invasion, and MRI-TV ≥ 12 cm3 predicted lymph node metastasis. Penalized spline (P-spline) regression analysis identified 14 cm3 of MRI-TV as the optimal prognostic cutoff value. MRI-TV ≥ 14 cm3 was an independent prognostic factor for overall survival and disease-free survival. For patients with MRI-TV ≥ 14 cm3, the disease-free survival rate with adjuvant therapy was superior to that of the sole staging surgery group. This study demonstrates a significant correlation between MRI-TV and clinical pathological features and prognosis in EC. For patients with MRI-TV ≥ 14 cm3, staging surgery followed by adjuvant therapy was superior to sole staging surgery.