Imaging of Endometrial Cancer

Updates on Imaging of Common Urogenital Neoplasms

Urogenital neoplasms represent some of the most common malignancies [...].

Artificial intelligence-assisted magnetic resonance imaging technology in the differential diagnosis and prognosis prediction of endometrial cancer

It aimed to analyze the value of deep learning algorithm combined with magnetic resonance imaging (MRI) in the risk diagnosis and prognosis of endometrial cancer (EC). Based on the deep learning convolutional neural network (CNN) architecture residual network with 101 layers (ResNet-101), spatial attention and channel attention modules were introduced to optimize the model. A retrospective collection of MRI image data from 210 EC patients was used for model segmentation and reconstruction, with 140 cases as the test set and 70 cases as the validation set. The performance was compared with traditional ResNet-101 model, ResNet-101 model based on spatial attention mechanism (SA-ResNet-101), and ResNet-101 model based on channel attention mechanism (CA-ResNet-101), using accuracy (AC), precision (PR), recall (RE), and F1 score as evaluation metrics. Among the 70 cases in the validation set, there were 45 cases of low-risk EC and 25 cases of high-risk EC. Using ROC curve analysis, it was found that the area under the curve (AUC) for the diagnosis of high-risk EC of the proposed model in this article (0.918) was visibly larger as against traditional ResNet-101 (0.613), SA-ResNet-101 (0.760), and CA-ResNet-101 models (0.758). The AC, PR, RE, and F1 values of the proposed model for the diagnosis of EC risk were visibly higher (P  < 0.05). In the validation set, postoperative recurrence occurred in 13 cases and did not occur in 57 cases. Using ROC curve analysis, it was found that the AUC for postoperative recurrence prediction of the patients by the proposed model (0.926) was visibly larger as against traditional ResNet-101 (0.620), SA-ResNet-101 (0.729), and CA-ResNet-101 models (0.767). The AC, PR, RE, and F1 values of the proposed model for postoperative recurrence prediction were visibly higher (P  < 0.05). The proposed model in this article, assisted by MRI, presented superior performance in diagnosing high-risk EC patients, with higher sensitivity (Sen) and specificity (Spe), and also demonstrated excellent predictive AC in postoperative recurrence prediction.

Construction and validation of a prediction model for preoperative prediction of Ki-67 expression in endometrial cancer patients by apparent diffusion coefficient

AIM

Ki-67 is a marker of cell proliferation and is increasingly being used as a primary outcome measure in preoperative window studies of endometrial cancer (EC). This study explored the feasibility of using apparent diffusion coefficient (ADC) values in noninvasive prediction of Ki-67 expression levels in EC patients before surgery, and constructs a nomogram by combining clinical data.

MATERIAL AND METHODS

This study retrospectively analyzed 280 EC patients who underwent preoperative magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) in our hospital from January 2017 to February 2023. Evaluate the potential nonlinear relationship between ADC values and Ki-67 expression using the nomogram. The included patients were randomized into a training set (n = 186) and a validation set (n = 84). Using a combination of logistic regression and LASSO regression results, from which the four best predictors were identified for the construction of the nomogram. The accuracy and clinical applicability of the nomogram were assessed using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

RESULTS

The results of this study showed a nonlinear correlation between ADCmin and Ki-67 expression (nonlinear P = 0.019), and the nonlinear correlation between ADCmean and Ki-67 expression (nonlinear P = 0.019). In addition, this study constructed the nomogram by incorporating ADCmax, International Federation of Gynecology and Obstetrics (FIGO), and chemotherapy. The area under the curve (AUC) values of the ROC for nomogram, ADCmax, FIGO, chemotherapy and grade in the training set were 0.783, 0.718, 0.579, 0.636, and 0.654, respectively. In the validation set, the AUC values for nomogram, ADCmax, FIGO, chemotherapy, and grade were 0.820, 0.746, 0.558, 0.542, and 0.738, respectively. In addition, the calibration curves and the DCA curves suggested a better predictive efficacy of the model.

CONCLUSION

A nomogram prediction model constructed on the basis of ADCmax values combined with clinical data can be used as an effective method to noninvasively assess Ki-67 expression in EC patients before surgery.

Quantitative Assessment of Polarization and Elastic Properties of Endometrial Tissue for Precancer/Cancer Diagnostics Using Multimodal Optical Coherence Tomography

Objectives: The most important phase in the endometrial pathologies diagnostics is the histological examination of tissue biopsies obtained under visual hysteroscopic control. However, the unclear visual diagnostics characteristics of subtle focal endometrial pathologies often lead to selection errors regarding suspicious endometrial lesions and to a subsequent false pathological diagnosis/underestimation of precancer or early-stage cancer. Methods: In this study, we investigate the potential of Multimodal Optical Coherence Tomography (MM OCT) to verify suspicious endometrial lesion regions before biopsy collection. We study the polarization (by cross-polarization OCT, CP OCT) and elastic (by compression OCT-elastography, C-OCE) properties of ex vivo endometrial tissue samples in normal conditions (proliferative and secretory phases to the menstrual cycle, atrophic endometrium) with endometrial hyperplasia (non-atypical and endometrial intraepithelial neoplasia) and endometrial cancer subtypes (low-grade, high-grade, clear cell and serous). Results: To the best of our knowledge, this is the first quantitative assessment of relevant OCT parameters (depth-resolved attenuation coefficient in co-[Att(co) values] and cross-[(Att(cross) values] polarizations and Young's elastic modulus [stiffness values]) for the selection of the most objective criteria to identify the clinically significant endometrial pathologies: endometrial intraepithelial neoplasia and endometrial cancer. The study demonstrates the possibility of detecting endometrial pathologies and establishing optimal threshold values of MM OCT criteria for the identification of endometrial cancer using CP OCT (by Att(co) values = 3.69 mm-1, Sensitivity (Se) = 86.1%, Specificity (Sp) = 92.6%; by Att(cross) values = 2.27 mm-1, Se = 86.8%, Sp = 87.0%) and C-OCE (by stiffness values = 122 kPa, Se = 93.2%, Sp = 91.1%). The study also differentiates endometrial intraepithelial neoplasia from non-atypical endometrial hyperplasia and normal endometrium using C-OCE (by stiffness values = 95 kPa, Se = 87.2%, Sp = 90.1%). Conclusions: The results are indicative of the efficacy and potential of clinical implementation of in vivo hysteroscopic-like MM OCT in the diagnosis of endometrial pathologies.

Endometrial carcinoma with cervical stromal invasion: Three case reports

BACKGROUND

Endometrial cancer is a kind of well-known tumors of female genitourinary system. Cervical stromal invasion is an adverse factor for poor prognosis of endometrial cancer. There is still controversy regarding the use of magnetic resonance imaging (MRI) in the diagnosis of cervical stromal invasion of endometrial cancer. The diagnosis of cervical stromal invasion varies significantly between different observers and institutions. We present a limited case series of the particular pattern of endometrial cancer, which infiltrates the cervical stroma and is often overlooked.

CASE SUMMARY

We present three cases of endometrial carcinoma with cervical stromal invasion with cancer-free uterine cavity. One patient, a reproductive-aged woman, exhibited irregular menstruation and was diagnosed with endometrial polyps by hysteroscopy and segmental curettage. A MRI scan revealed polypoid nodules within the internal cervical orifice. The other two cases were postmenopausal women who presented with abnormal vaginal bleeding. Hysteroscopy and segmental curettage suggested atypical hyperplasia of the endometrium. MRI scans did not detect any malignant signs in the endometrium. In one case, a non-thickened endometrium was observed, while in another, hyperplasia of the endometrium was seen. Notably, none of these patients had malignant tumors identified in the uterine cavity via MRI scans. However, postoperative pathological results following hysterectomy consistently indicated cervical stromal invasion.

CONCLUSION

Cervical stromal invasion is easily missed if no cancer is found in the uterine body on MRI. Immunohistochemistry of endoscopic curettage specimens should be conducted to avoid underestimation of the disease.

Role of Multiparametric MRI in the Preoperative Evaluation of Endometrial Carcinoma: A Cross-Sectional Study

Background Endometrial carcinoma (EC) is a major global concern in females throughout the world with increasing incidence in India. Hence, early detection and prompt intervention will reduce morbidity and mortality associated with it. Multiple studies showed a promising role of multiparametric magnetic resonance imaging (mpMRI) in the evaluation and early detection of the disease. In view of the paucity of such studies in the Indian population, we assessed the role of mpMRI in the evaluation of EC by utilizing a 3T MR scanner. Objectives To assess the efficacy of mpMRI in detecting myometrial invasion and locoregional staging in suspected or diagnosed cases of EC. Materials and methods Nineteen cases of EC with mpMRI were included in the study, and 15 of these underwent surgicopathological staging. The preoperative staging was done using the International Federation of Gynecology and Obstetrics (FIGO) 2009 staging system based on mpMRI findings and compared with postoperative FIGO staging. All the data were compiled in a Microsoft Excel (Microsoft® Corp., Redmond, WA) file and analyzed in Statistical Product and Service Solutions (SPSS, version 21.0; IBM SPSS Statistics for Windows, Armonk, NY) using appropriate tools. Results In our study, EC was commonly seen in more than 50-year females with a predominant complaint being postmenopausal bleeding. EC most commonly appeared heterogeneously hyperintense on T2-weighted sequence (T2W) and areas of diffusion restriction on diffusion-weighted imaging (DWI) in all cases. Dynamic contrast-enhanced (DCE) MRI (DCE-MRI) showed mild heterogeneous enhancement in all phases with better delineation of adjacent myometrial infiltration in the equilibrium phase. Diffusion tensor imaging (DTI) parameters had significantly lower values in involved myometrium vis-a-vis uninvolved myometrium. A statistically significant correlation was seen between preoperative mpMRI FIGO staging utilizing T2W, DWI, DCE-MRI, and DTI with surgicopathological FIGO staging. Conclusion mpMRI, particularly T2W, DWI, DCE-MRI, and DTI, yields a significant correlation between MR imaging and histopathological findings in assessing myometrial infiltration and thereby could be helpful in preoperative staging and extent of lymph-nodal dissection.

Is it safe to operate selected low-risk endometrial cancer patients in secondary hospitals?

INTRODUCTION

The aim of this study was to assess the accuracy of a preoperative screening algorithm in identifying low-risk endometrial cancer (EC) patients to ensure optimal care.

METHODS

A total of 277 patients with primary EC confirmed through biopsy underwent magnetic resonance imaging (MRI). Patients with risk factors for advanced high-risk EC, such as non-endometrioid histology, high-grade differentiation status, deep myometrial invasion, or spread beyond the uterine corpus, were systematically excluded. The remaining preoperatively screened patients with stage IA low-grade endometrioid EC (EEC) (n = 93) underwent surgery in a tertiary hospital. The accuracy of the preoperative diagnosis was evaluated by comparing the findings with the postoperative histopathological results. Disease-free survival (DFS) and overall survival (OS) were analyzed using 8-year follow-up data.

RESULTS

Postoperative histopathological analysis revealed that all patients had grade 1-2 EEC localized to the corpus uteri. Only three patients had deep myometrial invasion (stage IB), but they remained disease-free after 6-9 years of follow-up. The median follow-up time for all patients was 8.7 years. The DFS was 7.6 years, and the OS was 8.6 years. Two patients with stage IA grade 1 EEC experienced relapse and, despite treatment, died of EC. No other EC-related deaths occurred.

CONCLUSIONS

The screening algorithm accurately identified low-risk EC patients without compromising survival. Therefore, the algorithm appears to be feasible for selecting patients for surgery in secondary hospitals.

Endometrial Cancer: 2023 Revised FIGO Staging System and the Role of Imaging

The FIGO endometrial cancer staging system recently released updated guidance based on clinical evidence gathered after the previous version was published in 2009. Different imaging modalities are beneficial across various stages of endometrial cancer (EC) management. Additionally, ongoing research studies are aimed at improving imaging in EC. Gynecological cancer is a crucial element in the practice of a body radiologist. With a new staging system in place, it is important to address the role of radiology in the EC diagnostic pathway. This article is a comprehensive review of the changes made to the FIGO endometrial cancer staging system and the impact of imaging in the staging of this disease.

LncRNAs in necroptosis: Deciphering their role in cancer pathogenesis and therapy

Necroptosis, a controlled type of cell death that is different from apoptosis, has become a key figure in the aetiology of cancer and offers a possible target for treatment. A growing number of biological activities, including necroptosis, have been linked to long noncoding RNAs (lncRNAs), a varied family of RNA molecules with limited capacity to code for proteins. The complex interactions between LncRNAs and important molecular effectors of necroptosis, including mixed lineage kinase domain-like pseudokinase (MLKL) and receptor-interacting protein kinase 3 (RIPK3), will be investigated. We will explore the many methods that LncRNAs use to affect necroptosis, including protein-protein interactions, transcriptional control, and post-transcriptional modification. Additionally, the deregulation of certain LncRNAs in different forms of cancer will be discussed, highlighting their dual function in influencing necroptotic processes as tumour suppressors and oncogenes. The goal of this study is to thoroughly examine the complex role that LncRNAs play in controlling necroptotic pathways and how that regulation affects the onset and spread of cancer. In the necroptosis for cancer treatment, this review will also provide insight into the possible therapeutic uses of targeting LncRNAs. Techniques utilising LncRNA-based medicines show promise in controlling necroptotic pathways to prevent cancer from spreading and improve the effectiveness of treatment.

Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows

Endometrial cancer (EC) is intricately linked to obesity and diabetes, which are widespread risk factors. Medical imaging, especially magnetic resonance imaging (MRI), plays a major role in EC assessment, particularly for disease staging. However, the diagnostic performance of MRI exhibits variability in the detection of clinically relevant prognostic factors (e.g., deep myometrial invasion and metastatic lymph nodes assessment). To address these challenges and enhance the value of MRI, radiomics and artificial intelligence (AI) algorithms emerge as promising tools with a potential to impact EC risk assessment, treatment planning, and prognosis prediction. These advanced post-processing techniques allow us to quantitatively analyse medical images, providing novel insights into cancer characteristics beyond conventional qualitative image evaluation. However, despite the growing interest and research efforts, the integration of radiomics and AI to EC management is still far from clinical practice and represents a possible perspective rather than an actual reality. This review focuses on the state of radiomics and AI in EC MRI, emphasizing risk stratification and prognostic factor prediction, aiming to illuminate potential advancements and address existing challenges in the field.