Preoperative tumor size at MRI predicts deep myometrial invasion, lymph node metastases, and patient outcome in endometrial carcinomas

Tumor Volume Index as a Predictor of Pelvic Lymph Node Metastasis in Low-Risk Endometrial Cancer

This study aimed to identify predictors of pelvic lymph node metastasis in low-risk endometrial cancer, defined as cases with no more than half myometrial invasion, preoperative endometrial biopsy results indicating endometrioid carcinoma Grade 1 (G1) or Grade 2 (G2), and no extrauterine spread. Among the factors examined, we focused on the tumor volume index derived from MRI, calculated by multiplying the maximum longitudinal diameter along the uterine axis, the maximum anteroposterior diameter on the sagittal plane, and the maximum transverse diameter on the horizontal plane. A retrospective analysis was conducted on 117 patients who underwent the standard treatment protocol (total hysterectomy, bilateral salpingo-oophorectomy, and pelvic lymph node dissection) at our institution from July 1, 2014, to December 31, 2023. Pelvic lymph node metastasis was observed in seven cases (5.9%). Univariate analysis showed a significant association with serum cancer antigen-125 (CA-125) level (p=0.035) and tumor volume index (p=0.003). A receiver operating characteristic (ROC) analysis revealed that a tumor volume index cutoff of 38 cm³ yielded an area under the curve (AUC) of 0.83, with a true positive fraction (TPF) of 0.86 and a false positive fraction (FPF) of 0.15. Multivariate analysis also identified a tumor volume index (≥38 cm³) as an independent predictive factor (odds ratio 26.3, 95% confidence interval 2.6-272, p=0.006). Cases with a tumor volume index ≥38 cm³ accounted for 23 cases (20% of all) of the cohort; among these, six cases (25%) had pelvic lymph node metastases. In contrast, the metastasis rate was only one case (1%) in 94 cases (80% of all) with a tumor volume index <38 cm³. These findings suggest that the tumor volume index is useful for evaluating the risk of pelvic lymph node metastasis in low-risk endometrial cancer, contributing to decision-making on whether to perform pelvic lymph node dissection and risk stratification for sentinel lymph node navigation surgery.

Development of a nomogram based on whole-tumor multiparametric MRI histogram analysis to predict deep myometrial invasion in stage I endometrioid endometrial carcinoma preoperatively

BACKGROUND

The depth of myometrial invasion determines whether International Federation of Gynecology and Obstetrics stage I endometrioid endometrial carcinoma (EEC) patients undergo lymph node dissection. However, subjective evaluation results relying on magnetic resonance imaging (MRI) are not always satisfactory.

PURPOSE

To develop a nomogram based on whole-volume tumor MRI histogram parameters to preoperatively predict deep myometrial invasion (DMI) in patients with stage I EEC.

MATERIAL AND METHODS

This retrospective analysis included 131 EEC patients and a training/validation cohort of 92/39 patients at a 7:3 ratio. The histogram parameters were obtained from multiple sequences (ADC mapping and T2-weighted imaging) within volumes of interest. Univariate analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate logistic regression were used for feature selection. The performance of clinical model, histogram model, and histogram nomogram was evaluated by calculating the area under the receiver operating characteristic curve (AUC).

RESULTS

Age and two morphological features (maximum anteroposterior tumor diameter on sagittal T2-weighted images [APsag] and the tumor area ratio [TAR]) were selected to construct the clinical model. Five histogram parameters were selected for the creation of the histogram model. The nomogram, which combines the histogram parameters, age, APsag, and TAR, achieved the highest AUCs in both the training and validation cohorts (nomogram vs. histogram vs. clinical model: 0.973 vs. 0.871 vs. 0.934 [training] and 0.972 vs. 0.870 vs. 0.928 [validation]).

CONCLUSION

The MR histogram nomogram can help predict the DMI of patients with stage I EEC preoperatively, assisting physicians in the development of personalized treatment strategies.

The Added Value of ADC-based Nomogram in Assessing the Depth of Myometrial Invasion of Endometrial Endometrioid Adenocarcinoma

RATIONALE AND OBJECTIVES

To explore the potential value of the apparent diffusion coefficient (ADC)-based nomogram models in preoperatively assessing the depth of myometrial invasion of endometrial endometrioid adenocarcinoma (EEA).

MATERIALS AND METHODS

Preoperative magnetic resonance imaging (MRI) of 210 EEA patients were retrospectively analyzed. ADC histogram metrics derive from the whole-tumor regions of interest. Univariate and multivariate analyses were used to screen the ADC histogram metrics and clinical characteristics for nomogram model building. The diagnostic sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of two radiologists without and with the assistance of models were calculated and compared.

RESULTS

Two nomogram models were developed for predicting no myometrial invasion (NMI) and deep myometrial invasion (DMI) with area under the curves of 0.85 and 0.82, respectively. With the assistance of models, the overall accuracies were significantly improved [radiologist_1, 73.3% vs 86.2% (p = 0.001); radiologist_2, 80.0% vs 91.0% (p = 0.002)]. In determining NMI, the sensitivity and PPV were greatly improved but not significant for radiologist_1 (51.9% vs 77.8% and 46.7% vs 75.0%, p = 0.229 and 0.511), and under/near the significance level for radiologist_2 (59.3% vs 88.9% and 57.1% vs 82.8%, p = 0.041 and 0.065), while the specificity, accuracy, and NPV were significantly improved (all p < 0.001). In determining DMI, all sensitivity, specificity, accuracy, PPV, and NPV were significantly improved (all p < 0.001).

CONCLUSION

The ADC-based nomogram models can improve the diagnostic performance of radiologist in preoperatively assessing the depth of myometrial invasion and facilitate optimizing clinical individualized treatment decisions.

Tumor Stiffness Measurement at Multifrequency MR Elastography to Predict Lymphovascular Space Invasion in Endometrial Cancer

Background Pathologic lymphovascular space invasion (LVSI) is associated with poor outcome in endometrial cancer. Its relationship with tumor stiffness, which can be measured with use of MR elastography, has not been extensively explored. Purpose To assess whether MR elastography-based mechanical characteristics can aid in the noninvasive prediction of LVSI in patients with endometrial cancer. Materials and Methods This prospective study included consecutive adult patients with a suspected uterine tumor who underwent MRI and MR elastography between October 2022 and July 2023. A region of interest delineated on T2-weighted magnitude images was duplicated on MR elastography images and used to calculate c (stiffness in meters per second) and φ (viscosity in radians) values. Pathologic assessment of hysterectomy specimens for LVSI served as the reference standard. Data were compared between LVSI-positive and -negative groups with use of the Mann-Whitney U test. Multivariable logistic regression was used to determine variables associated with LVSI positivity and develop diagnostic models for predicting LVSI. Model performance was assessed with use of area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. Results A total of 101 participants were included, 72 who were LVSI-negative (median age, 53 years [IQR, 48-62 years]) and 29 who were LVSI-positive (median age, 54 years [IQR, 49-60 years]). The tumor stiffness in the LVSI-positive group was higher than in the LVSI-negative group (median, 4.1 m/sec [IQR, 3.2-4.6 m/sec] vs 2.2 m/sec [IQR, 2.0-2.8 m/sec]; P < .001). Tumor volume, cancer antigen 125 level, and tumor stiffness were associated with LVSI positivity (adjusted odds ratio range, 1.01-9.06; P range, <.001-.04). The combined model (AUC, 0.93) showed better performance for predicting LVSI compared with clinical-radiologic model (AUC, 0.77; P = .003) and similar performance to the MR elastography-based model (AUC, 0.89; P = .06). Conclusion The addition of tumor stiffness as measured at MR elastography into a clinical-radiologic model improved prediction of LVSI in patients with endometrial cancer. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Ehman in this issue.

Staging by imaging in gynecologic cancer and the role of ultrasound: an update of European joint consensus statements

In recent years the role of diagnostic imaging by pelvic ultrasound in the diagnosis and staging of gynecological cancers has been growing exponentially. Evidence from recent prospective multicenter studies has demonstrated high accuracy for pre-operative locoregional ultrasound staging in gynecological cancers. Therefore, in many leading gynecologic oncology units, ultrasound is implemented next to pelvic MRI as the first-line imaging modality for gynecological cancer. The work herein is a consensus statement on the role of pre-operative imaging by ultrasound and other imaging modalities in gynecological cancer, following European Society guidelines.

Whole-tumor apparent diffusion coefficient histogram analysis for preoperative risk stratification in endometrial endometrioid adenocarcinoma

OBJECTIVE

To investigate the application of whole-tumor apparent diffusion coefficient (ADC) histogram metrics for preoperative risk stratification in endometrial endometrioid adenocarcinoma (EEA).

METHODS

Preoperative MRI of 502 EEA patients were retrospectively analyzed. Whole tumor ADC histogram analysis was performed with regions of interest drawn on all tumor slices of diffusion-weighted imaging scans. Risk stratification was based on ESMO-ESTRO-ESP guidelines: low-, intermediate-, high-intermediate-, and high-risk. Univariable analysis was used to compare ADC histogram metrics (tumor volume, minADC, maxADC, and meanADC; 10th, 25th, 50th, 75th, and 90th percentiles of ADC [recorded as P10, P25, P50, P75, and P90 ADC, respectively]; skewness; and kurtosis) between different risk EEAs, and multivariable logistic regression analysis to determine the optimal metric or combined model for risk stratifications. Receiver operating characteristic curve analysis with the area under the curve (AUC) was used for diagnostic performance evaluation.

RESULTS

A decreasing tendency in multiple ADC values was observed from the low- to high-intermediate-risk EEAs. The (low + intermediate)-risk EEAs and low-risk EEAs had significantly smaller tumor volumes and higher minADCs, meanADCs, P10, P25, P50, P75, and P90 ADCs than the (high-intermediate + high)-risk EEAs and non-low-risk EEAs (all P < 0.05), respectively. The combined models of the (meanADC + volume) and the (P75 ADC + volume) yielded the largest AUCs of 0.775 and 0.780 in identifying the (low + intermediate)- and the low-risk EEAs from the other EEAs, respectively.

CONCLUSION

Whole-tumor ADC histogram metrics might be helpful for preoperatively identifying low- and (low + intermediate)-risk EEAs, facilitating personalized therapeutic planning.

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.

Role of tumor volume in endometrial cancer: An imaging analysis and prognosis significance

OBJECTIVE

To evaluate the prognostic value of tumor volume on preoperative MRI in endometrial cancer (EC) patients and its association with adverse prognostic factors and survival.

METHODS

A retrospective observational study with 127 consecutive patients with endometrioid EC was carried out between 2016 and 2021 at Juan Ramón Jiménez University Hospital, Huelva (Spain). All patients underwent preoperative magnetic resonance imaging (MRI) for local staging. The tumor volume was analyzed on MRI by two different methods: by measuring the three maximum diameters of the tumor according to an ellipse formula and by manual region of interest in different sections; the ratio between tumor volume and uterus volume was also calculated as a third tool. The relationships between volume, prognostic factors, and survival were analyzed.

RESULTS

A total of 127 patients with endometroid EC underwent preoperative MRI and were included in the study. Tumor volume was significantly higher for deep myometrial invasion, cervical stromal involvement, infiltrated serosa, lymph node metastases, high-grade EC, and lymphovascular space involvement, advanced FIGO stage, and High Recurrence Risk Group (P < 0.001). ROC curves showed that tumor volume greater than 25 cm3 predicts lymph node metastases. Volume index greater than 17 cm3 was associated with reduced disease-free survival (P < 0.001) and overall survival (P < 0.003). Multivariate analysis showed that the greatest tumor volume had an independent impact on recurrence (odds ratio [OR]1.019, 95% confidence interval [CI] 1.005-1.032) and survival (OR 1.027, 95% CI 1.009-1.046).

CONCLUSIONS

This study shows an important correlation between tumor volume on MRI and poor prognostic factors. Preoperative tumor volume on MRI is a valuable biomarker to be considered for management of EC.

An MR-based radiomics nomogram including information from the peritumoral region to predict deep myometrial invasion in stage I endometrioid adenocarcinoma: a preliminary study

OBJECTIVE

To develop and validate an MR-based radiomics nomogram combining different imaging sequences (ADC mapping and T2 weighted imaging (T2WI)), different tumor regions (combined intra- and peritumoral regions), and different parameters (clinical features, tumor morphological features, and radiomics features) while considering different MR field strengths in predicting deep myometrial invasion (MI) in Stage I endometrioid adenocarcinoma (EEA).

METHODS

A total of 202 patients were retrospectively analyzed and divided into two cohorts (training cohort, 1.5 T MR, n = 131; validation cohort, 3.0 T MR, n = 71). Axial ADC mapping and T2WI were conducted. Radiomics features were extracted from intra- and peritumoral regions. Least absolute shrinkage and selection operator regression, univariate analysis, and multivariate logistic regression were used to select radiomics features and tumor morphological and clinical parameters. The area under the receiver operator characteristic curve (AUC) was calculated to evaluate the performance of the prediction model and radiomics nomogram.

RESULTS

Ten radiomics features, 4 morphological parameters and 1 clinical characteristic were selected. The radiomics nomogram achieved good discrimination between the superficial and deep MI cohorts. The AUC was 0.927 (95% confidence interval [CI]: 0.865, 0.967) in the training cohort and 0.921 (95% CI: 0.872, 0.948) in the validation cohort. The specificity and sensitivity were 92.0 and 78.9% in the training cohort and 83.0 and 77.8% in the validation cohort, respectively.

CONCLUSION

The radiomics nomogram showed good performance in predicting the depth of MI in Stage I EEA before surgery and might be useful for surgical patient management.

ADVANCES IN KNOWLEDGE

An MR-based radiomics nomogram was useful for predicting deep MI in Stage I EEA patients (AUCtrain = 0.927, AUCvalidation = 0.921). The intra- and peritumoral radiomics features complemented each other. The nomogram was developed and validated with different MR field strengths, suggesting that the model demonstrates good generalizability.

Analysis of correlation of pre-therapeutic assessment and the final diagnosis in endometrial cancer: role of tumor volume in the magnetic resonance imaging

Objective

To evaluate whether the introduction of tumor volume as new parameter in the MRI assessment could improve both concordance between preoperative and postoperative staging, and the identification of histological findings.

Methods

A retrospective observational study with 127 patients with endometrial cancer (EC) identified between 2016 and 2021 at the Juan Ramon Jimenez University Hospital, Huelva (Spain) was carried out. Tumor volume was measured in three ways. Analyses of Receiver Operating Characteristic (ROC) curve and the area under the curve (AUC) were performed.

Results

Although preoperative MRI had an 89.6% and 66.7% sensitivity for the detection of deep mucosal invasion and cervical stroma infiltration, preoperative assessment had an intraclass correlation coefficient of 0.517, underestimating tumor final stage in 12.6% of cases, with a poor agreement between preoperative MRI and postoperative staging (κ=0.082) and low sensitivity (14.3%) for serosa infiltration. The cut-off values for all three volume parameters had good/excellent AUC (0.73-0.85), with high sensitivity (70-83%) and specificity (64-84%) values for all histopathological variables. Excellent/good agreement was found all volume parameters for the identification of deep myometrial invasion (0.71), cervical stroma infiltration (0.80), serosa infiltration (0.81), and lymph node metastases (0.81).

Conclusion

Tumor volume measurements have good predictive capacity to detect histopathological findings that affect final tumor staging and might play a crucial role in the preoperative assessment of patients with endometrial cancer in the future.

Lymphovascular Space Invasion in Early-Stage Endometrial Cancer (LySEC): Patterns of Recurrence and Predictors. A Multicentre Retrospective Cohort Study of the Spain Gynecologic Oncology Group

The main aim is to compare oncological outcomes and patterns of recurrence of patients with early-stage endometrioid endometrial cancer according to lymphovascular space invasion (LVSI) status. The secondary objective is to determine preoperative predictors of LVSI. We performed a multicenter retrospective cohort study. A total of 3546 women diagnosed with postoperative early-stage (FIGO I-II, 2009) endometrioid endometrial cancer were included. Co-primary endpoints were disease-free survival (DFS), overall survival (OS), and pattern of recurrence. Cox proportional hazard models were used for time-to-event analysis. Univariate and multivariate logistical regression models were employed. Positive LVSI was identified in 528 patients (14.6%) and was an independent prognostic factor for DFS (HR 1.8), OS (HR 2.1) and distant recurrences (HR 2.37). Distant recurrences were more frequent in patients with positive LVSI (78.2% vs. 61.3%, p < 0.01). Deep myometrial invasion (OR 3.04), high-grade tumors (OR 2.54), cervical stroma invasion (OR 2.01), and tumor diameter ≥ 2 cm (OR 2.03) were independent predictors of LVSI. In conclusion, in these patients, LVSI is an independent risk factor for shorter DFS and OS, and distant recurrence, but not for local recurrence. Deep myometrial invasion, cervical stroma invasion, high-grade tumors, and a tumor diameter ≥ 2 cm are independent predictors of LVSI.

A nomogram for preoperative risk stratification based on MR morphological parameters in patients with endometrioid adenocarcinoma

PURPOSE

To develop and validate a nomogram based on MRI morphological parameters to preoperatively discriminate between low-risk and non-low-risk patients with endometrioid endometrial carcinoma (EEC).

METHODS

Two hundred eighty-one women with histologically confirmed EEC were divided into training (1.5-T MRI, n = 182) and validation cohorts (3.0-T MRI, n = 99). According to the European Society of Medical Oncology guidelines, the patients were divided into four risk groups: low, intermediate, high-intermediate, and high. Binary classification models were developed (low-risk vs. non-low-risk). Univariate logistic regression (LR) analyses were used to determine which variables to select to build the predictive models. Five classification models were constructed, and the best model was selected. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the performance of the prediction model and nomogram. P < 0.05 indicated a statistically significant difference.

RESULTS

Age and four morphological parameters (tumor size, tumor volume, maximum anteroposterior tumor diameter on sagittal T2-weighted images (APsag), and tumor area ratio (TAR)) were selected, and the LR model was used to construct an MRI morphological nomogram. The AUCs for the nomogram in predicting a non-low-risk of EEC among patients in the training and validation cohorts were 0.856 (sensitivity = 75.0%, specificity = 83.1%) and 0.849 (sensitivity = 74.6%, specificity = 85.0%), respectively.

CONCLUSION

An MRI morphological nomogram was developed and achieved high diagnostic performance for classifying low-risk and non-low-risk EEC preoperatively, which could provide support for therapeutic decision-making. Furthermore, our findings indicate that this nomogram is robust in the clinical application of various field strength data.

LncRNA-LA16c-313D11.11,A Signature to Predict Endometrial Carcinoma Patients with a Better Survival

The aim of this study was to develop and internally validate a nomogram of the probability EC patients surviving longer than 5 years. Quantitative real-time PCR (qRT-PCR) was implemented to analyze the expression of lncRNA-LA16c-313D11.11 in 60 EC tissues. The clinicopathological characteristics and follow-up data were retrospectively gathered and analyzed. To establish the prediction model, multivariate logistic regression analysis was applied, and the discrimination, calibration, and clinical practicability of the prediction model were assessed with a concordance index (C-index), calibration chart, and decision curve analysis. Bootstrap validation was performed for internal validation. The prediction factors included the age of patients, myometrial invasion, lymphovascular space invasion, histological subtype, and the expression of lncRNA-LA16C-313D11.11. The model demonstrated good calibration and modest discrimination (C-index = 0.860, 95% confidence interval: 0.724-0.946). Moreover, the interval validation achieved a high C-index value of 0.778. This study revealed the predictive value of lncRNA-LA16C-313D11.11 and successfully developed a nomogram for predicting EC patients survival longer than 5 years, which may facilitate the institution of personalized treatment algorithms, surveillance strategies, and lifestyle interventions.

Predicting histopathology markers of endometrial carcinoma with a quantitative image analysis approach based on spherical harmonics in multiparametric MRI

PURPOSE

Identifying optimal machine learning pipelines for computer-aided diagnosis is key for the development of robust, reproducible, and clinically relevant imaging biomarkers for endometrial carcinoma. The purpose of this study was to introduce the mathematical development of image descriptors computed from spherical harmonics (SPHARM) decompositions as well as the associated machine learning pipeline, and to evaluate their performance in predicting deep myometrial invasion (MI) and histopathological high-grade in preoperative multiparametric magnetic resonance imaging (MRI).

PATIENTS AND METHODS

This retrospective study included 128 women with histopathology-confirmed endometrial carcinomas who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. SPHARM descriptors of each tumor were computed on multiparametric MRI images (T2-weighted, diffusion-weighted, dynamic contrast-enhanced-MRI and apparent diffusion coefficient maps). Tensor-based logistic regression was used to classify two-dimensional SPHARM rotationally-invariant descriptors. Head-to-head comparisons with radiomics analyses were performed with DeLong tests with Bonferroni-Holm correction to compare diagnostic performances.

RESULTS

With all MRI contrasts, SPHARM analysis resulted in area under the curve, sensitivity, specificity, and balanced accuracy values of 0.94 (95% confidence interval [CI]: 0.85, 1.00), 100% (95% CI: 100, 100), 74% (95% CI: 51, 92), 87% (95% CI: 78, 98), respectively, for predicting deep MI. For predicting high-grade tumor histology, the corresponding values for the same diagnostic metrics were 0.81 (95% CI: 0.64, 0.90), 93% (95% CI: 67, 100), 63% (95% CI: 45, 79) and 78% (95% CI: 64, 86). The corresponding values achieved via radiomics were 0.92 (95% CI: 0.82, 0.95), 82% (95% CI: 65, 93), 80% (95% CI: 51, 94), 81% (95% CI: 70, 91) for deep MI and 0.72 (95% CI: 0.58, 0.83), 93% (95% CI: 65, 100), 55% (95% CI: 41, 69), 74% (95% CI: 52, 88) for high-grade histology. The diagnostic performance of the SPHARM analysis was not significantly different (P = 0.62) from that of radiomics for predicting deep MI but was significantly higher (P = 0.044) for predicting high-grade histology.

CONCLUSION

The proposed SPHARM analysis yields similar or higher diagnostic performance than radiomics in identifying deep MI and high-grade status in histology-proven endometrial carcinoma.

Data Set for the Reporting of Endometrial Cancer: Recommendations From the International Collaboration on Cancer Reporting (ICCR)

Endometrial cancer is one of the most common cancers among women. The International Collaboration on Cancer Reporting (ICCR) developed a standardized endometrial cancer data set in 2011, which provided detailed recommendations for the reporting of resection specimens of these neoplasms. A new data set has been developed, which incorporates the updated 2020 World Health Organization Classification of Female Genital Tumors, the Cancer Genome Atlas (TCGA) molecular classification of endometrial cancers, and other major advances in endometrial cancer reporting, all of which necessitated a major revision of the data set. This updated data set has been produced by a panel of expert pathologists and an expert clinician and has been subject to international open consultation. The data set includes core elements which are unanimously agreed upon as essential for cancer diagnosis, clinical management, staging, or prognosis and noncore elements which are clinically important, but not essential. Explanatory notes are provided for each element. Adoption of this updated data set will result in improvements in endometrial cancer patient care.

Is preoperative ultrasound tumor size a prognostic factor in endometrial carcinoma patients?

Objective

We aimed to assess the prognostic value of preoperative ultrasound tumor size in EC through a single center, observational, retrospective, cohort study.

Methods

Medical records and electronic clinical databases were searched for all consecutive patients with EC, preoperative ultrasound scans available to ad hoc estimate tumor size, and a follow-up of at least 2-year, at our Institution from January 2010 to June 2018. Patients were divided into two groups based on different dimensional cut-offs for the maximum tumor diameter: 2, 3 and 4 cm. Differences in overall survival (OS), disease specific survival (DSS) and progression-free survival (PFS) were assessed among the groups by using the Kaplan-Meier estimator and the log-rank test.

Results

108 patients were included in the study. OS, DSS and PFS did not significantly differ between the groups based on the different tumor diameter cut-offs. No significant differences were found among the groups sub-stratified by age, BMI, FIGO stage, FIGO grade, lymphovascular space invasion status, myometrial invasion, lymph nodal involvement, histotype, and adjuvant treatment.

Conclusions

Preoperative ultrasound tumor size does not appear as a prognostic factor in EC women.

The value of magnetic resonance imaging-based tumor shape features for assessing microsatellite instability status in endometrial cancer

BACKGROUND

Microsatellite instability (MSI) status can be used for the classification and risk stratification of endometrial cancer (EC). This study aimed to investigate whether magnetic resonance imaging (MRI)-based tumor shape features can help assess MSI status in EC before surgery.

METHODS

The medical records of 88 EC patients with MSI status were retrospectively reviewed. Quantitative and subjective shape features based on MRI were used to assess MSI status. Variables were compared using the Student's t-test, χ² test, or Wilcoxon rank-sum test where appropriate. Univariate and multivariate analyses were performed by the logistic regression model. The area under the curve (AUC) was used to estimate the discrimination performance of variables.

RESULTS

There were 23 patients with MSI, and 65 patients with microsatellite stability (MSS) in this study. Eccentricity and shape type showed significant differences between MSI and MSS (P=0.039 and P=0.033, respectively). The AUC values of eccentricity, shape type, and the combination of 2 features for assessing MSI were 0.662 [95% confidence interval (CI): 0.554-0.770], 0.627 (95% CI: 0.512-0.743), and 0.727 (95% CI: 0.613-0.842), respectively. Considering the International Federation of Gynecology and Obstetrics (FIGO) staging, eccentricity maintained a significant difference in stages I-II (P=0.039), while there was no statistical difference in stages III-IV (P=0.601).

CONCLUSIONS

It is possible that MRI-based tumor shape features, including eccentricity and shape type, could be promising markers for assessing MSI status. The features may aid in the preliminary screening of EC patients with MSI.

Improving Risk Assessment for Metastatic Disease in Endometrioid Endometrial Cancer Patients Using Molecular and Clinical Features: An NRG Oncology/Gynecologic Oncology Group Study

Objectives: A risk assessment model for metastasis in endometrioid endometrial cancer (EEC) was developed using molecular and clinical features, and prognostic association was examined. Methods: Patients had stage I, IIIC, or IV EEC with tumor-derived RNA-sequencing or microarray-based data. Metastasis-associated transcripts and platform-centric diagnostic algorithms were selected and evaluated using regression modeling and receiver operating characteristic curves. Results: Seven metastasis-associated transcripts were selected from analysis in the training cohorts using 10-fold cross validation and incorporated into an MS7 classifier using platform-specific coefficients. The predictive accuracy of the MS7 classifier in Training-1 was superior to that of other clinical and molecular features, with an area under the curve (95% confidence interval) of 0.89 (0.80-0.98) for MS7 compared with 0.69 (0.59-0.80) and 0.71 (0.58-0.83) for the top evaluated clinical and molecular features, respectively. The performance of MS7 was independently validated in 245 patients using RNA sequencing and in 81 patients using microarray-based data. MS7 + MI (myometrial invasion) was preferrable to individual features and exhibited 100% sensitivity and negative predictive value. The MS7 classifier was associated with lower progression-free and overall survival (p ≤ 0.003). Conclusion: A risk assessment classifier for metastasis and prognosis in EEC patients with primary tumor derived MS7 + MI is available for further development and optimization as a companion clinical support tool.

The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization

Simple Summary

Modern, personalized therapy approaches are increasingly changing advanced cancer into a chronic disease. Compared to imaging, novel omics methodologies in molecular biology have already achieved an individual characterization of cancerous lesions. With quantitative imaging biomarkers, analyzed by radiomics or deep learning, an imaging-based assessment of tumoral biology can be brought into clinical practice. Combining these with other non-invasive methods, e.g., liquid profiling, could allow for more individual decision making regarding therapies and applications.

Abstract

Similar to the transformation towards personalized oncology treatment, emerging techniques for evaluating oncologic imaging are fostering a transition from traditional response assessment towards more comprehensive cancer characterization via imaging. This development can be seen as key to the achievement of truly personalized and optimized cancer diagnosis and treatment. This review gives a methodological introduction for clinicians interested in the potential of quantitative imaging biomarkers, treating of radiomics models, texture visualization, convolutional neural networks and automated segmentation, in particular. Based on an introduction to these methods, clinical evidence for the corresponding imaging biomarkers—(i) dignity and etiology assessment; (ii) tumoral heterogeneity; (iii) aggressiveness and response; and (iv) targeting for biopsy and therapy—is summarized. Further requirements for the clinical implementation of these imaging biomarkers and the synergistic potential of personalized molecular cancer diagnostics and liquid profiling are discussed.

Preoperative pelvic MRI and 2-[18F]FDG PET/CT for lymph node staging and prognostication in endometrial cancer-time to revisit current imaging guidelines?

OBJECTIVE

This study presents the diagnostic performance of four different preoperative imaging workups (IWs) for prediction of lymph node metastases (LNMs) in endometrial cancer (EC): pelvic MRI alone (IW1), MRI and [¹⁸F]FDG-PET/CT in all patients (IW2), MRI with selective [¹⁸F]FDG-PET/CT if high-risk preoperative histology (IW3), and MRI with selective [¹⁸F]FDG-PET/CT if MRI indicates FIGO stage ≥ 1B (IW4).

METHODS

In 361 EC patients, preoperative staging parameters from both pelvic MRI and [¹⁸F]FDG-PET/CT were recorded. Area under receiver operating characteristic curves (ROC AUC) compared the diagnostic performance for the different imaging parameters and workups for predicting surgicopathological FIGO stage. Survival data were assessed using Kaplan-Meier estimator with log-rank test.

RESULTS

MRI and [¹⁸F]FDG-PET/CT staging parameters yielded similar AUCs for predicting corresponding FIGO staging parameters in low-risk versus high-risk histology groups (p ≥ 0.16). The sensitivities, specificities, and AUCs for LNM prediction were as follows: IW1-33% [9/27], 95% [185/193], and 0.64; IW2-56% [15/27], 90% [174/193], and 0.73 (p = 0.04 vs. IW1); IW3-44% [12/27], 94% [181/193], and 0.69 (p = 0.13 vs. IW1); and IW4-52% [14/27], 91% [176/193], and 0.72 (p = 0.06 vs. IW1). IW3 and IW4 selected 34% [121/361] and 54% [194/361] to [¹⁸F]FDG-PET/CT, respectively. Employing IW4 identified three distinct patient risk groups that exhibited increasing FIGO stage (p < 0.001) and stepwise reductions in survival (p ≤ 0.002).

CONCLUSION

Selective [¹⁸F]FDG-PET/CT in patients with high-risk MRI findings yields better detection of LNM than MRI alone, and similar diagnostic performance to that of MRI and [¹⁸F]FDG-PET/CT in all.

KEY POINTS

• Imaging by MRI and [¹⁸F]FDG PET/CT yields similar diagnostic performance in low- and high-risk histology groups for predicting central FIGO staging parameters. • Utilizing a stepwise imaging workup with MRI in all patients and [¹⁸F]FDG-PET/CT in selected patients based on MRI findings identifies preoperative risk groups exhibiting significantly different survival. • The proposed imaging workup selecting ~54% of the patients to [¹⁸F]FDG-PET/CT yield better detection of LNMs than MRI alone, and similar LNM detection to that of MRI and [¹⁸F]FDG-PET/CT in all.

Association of Tumor Size With Prognosis in Patients With Resectable Endometrial Cancer: A SEER Database Analysis

This study aimed to explore the relationship between tumor size (Ts) and prognosis in endometrial cancer (EC). A total of 52,208 patients with EC who underwent total hysterectomy were selected from the Surveillance, Epidemiology, and End Results Program database. Overall survival (OS) and endometrial cancer-specific survival (ESS) were chosen as survival outcomes. The Cox proportional hazards model was used to explore the effect of Ts on prognosis. The restricted cubic splines based on the Cox regression model were used to determine the nonlinear relationship between Ts and survival. When Ts was analyzed as a categorical variable, the risk of death increased with Ts, with the highest risk in patients with Ts > 9 cm with regard to all-cause death (ACD) (hazard ratio [HR] 1.317; 95% confidence interval [CI], 1.196-1.450; P < 0.001) and endometrial cancer-specific death (ESD) (HR, 1.378; 95% CI, 1.226-1.549; P < 0.001). As a continuous variable, Ts showed a nonlinear relationship with ACD (HR, 1.061; 95% CI, 1.053-1.069; P < 0.001) and ESD (HR, 1.062; 95% CI, 1.052-1.073; P < 0.001). The risk of mortality increased quickly with Ts when Ts was less than 7.5 cm and then leveled off when Ts was larger than 7.5 cm in all patients. Among patients with lymph node metastasis, the risk of poor prognosis decreased rapidly with Ts when Ts was less than 3.5 cm, and subsequently increased sharply with Ts when Ts ranged from 3.5 cm to 7.5 cm, and then increased slowly when Ts was larger than 7.5 cm (P < 0.001 for nonlinearity). There was a nonlinear relationship between Ts and prognosis in patients with EC. Clinicians should not ignore the impact of small tumors on prognosis in EC patients with lymph node metastasis.

What MRI-based tumor size measurement is best for predicting long-term survival in uterine cervical cancer?

Background

Tumor size assessment by MRI is central for staging uterine cervical cancer. However, the optimal role of MRI-derived tumor measurements for prognostication is still unclear.

Material and methods

This retrospective cohort study included 416 women (median age: 43 years) diagnosed with cervical cancer during 2002–2017 who underwent pretreatment pelvic MRI. The MRIs were independently read by three radiologists, measuring maximum tumor diameters in three orthogonal planes and maximum diameter irrespective of plane (MAX_imaging). Inter-reader agreement for tumor size measurements was assessed by intraclass correlation coefficients (ICCs). Size was analyzed in relation to age, International Federation of Gynecology and Obstetrics (FIGO) (2018) stage, histopathological markers, and disease-specific survival using Kaplan–Meier-, Cox regression-, and time-dependent receiver operating characteristics (tdROC) analyses.

Results

All MRI tumor size variables (cm) yielded high areas under the tdROC curves (AUCs) for predicting survival (AUC 0.81–0.84) at 5 years after diagnosis and predicted outcome (hazard ratios [HRs] of 1.42–1.76, p < 0.001 for all). Only MAX_imaging independently predicted survival (HR = 1.51, p = 0.03) in the model including all size variables. The optimal cutoff for maximum tumor diameter (≥ 4.0 cm) yielded sensitivity (specificity) of 83% (73%) for predicting disease-specific death after 5 years. Inter-reader agreement for MRI-based primary tumor size measurements was excellent, with ICCs of 0.83–0.85.

Conclusion

Among all MRI-derived tumor size measurements, MAX_imaging was the only independent predictor of survival. MAX_imaging ≥ 4.0 cm represents the optimal cutoff for predicting long-term disease-specific survival in cervical cancer. Inter-reader agreement for MRI-based tumor size measurements was excellent.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-022-01239-y.

MRI-assessed tumor-free distance to serosa predicts deep myometrial invasion and poor outcome in endometrial cancer

OBJECTIVES

To explore the diagnostic accuracy of preoperative magnetic resonance imaging (MRI)-derived tumor measurements for the prediction of histopathological deep (≥ 50%) myometrial invasion (pDMI) and prognostication in endometrial cancer (EC).

METHODS

Preoperative pelvic MRI of 357 included patients with histologically confirmed EC were read independently by three radiologists blinded to clinical information. The radiologists recorded imaging findings (T1 post-contrast sequence) suggesting deep (≥ 50%) myometrial invasion (iDMI) and measured anteroposterior tumor diameter (APD), depth of myometrial tumor invasion (DOI) and tumor-free distance to serosa (iTFD). Receiver operating characteristic (ROC) curves for the prediction of pDMI were plotted for the different MRI measurements. The predictive and prognostic value of the MRI measurements was analyzed using logistic regression and Cox proportional hazard model.

RESULTS

iTFD yielded highest area under the ROC curve (AUC) for the prediction of pDMI with an AUC of 0.82, whereas DOI, APD and iDMI yielded AUCs of 0.74, 0.81 and 0.74, respectively. Multivariate analysis for predicting pDMI yielded highest predictive value of iTFD <  6 mm with OR of 5.8 (p < 0.001) and lower figures for DOI ≥ 5 mm (OR = 2.8, p = 0.01), APD ≥ 17 mm (OR = 2.8, p < 0.001) and iDMI (OR = 1.1, p = 0.82). Patients with iTFD < 6 mm also had significantly reduced progression-free survival with hazard ratio of 2.4 (p < 0.001).

CONCLUSION

For predicting pDMI, iTFD yielded best diagnostic performance and iTFD < 6 mm outperformed other cutoff-based imaging markers and conventional subjective assessment of deep myometrial invasion (iDMI) for diagnosing pDMI. Thus, iTFD at MRI represents a promising preoperative imaging biomarker that may aid in predicting pDMI and high-risk disease in EC.

A radiogenomics application for prognostic profiling of endometrial cancer

Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). We employed radiogenomics to integrate preoperative magnetic resonance imaging (MRI, n = 487 patients) with histologic-, transcriptomic- and molecular biomarkers (n = 550 patients) aiming to identify aggressive tumor features in a study including 866 EC patients. Whole-volume tumor radiomic profiling from manually (radiologists) segmented tumors (n = 138 patients) yielded clusters identifying patients with high-risk histological features and poor survival. Radiomic profiling by a fully automated machine learning (ML)-based tumor segmentation algorithm (n = 336 patients) reproduced the same radiomic prognostic groups. From these radiomic risk-groups, an 11-gene high-risk signature was defined, and its prognostic role was reproduced in orthologous validation cohorts (n = 554 patients) and aligned with The Cancer Genome Atlas (TCGA) molecular class with poor survival (copy-number-high/p53-altered). We conclude that MRI-based integrated radiogenomics profiling provides refined tumor characterization that may aid in prognostication and guide future treatment strategies in EC.

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

PURPOSE

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.

EXPERIMENTAL DESIGN

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).

RESULTS

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%.

CONCLUSIONS

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.

Simplifying Tumor Volume Estimation from Linear Dimensions for Intra-Cranial Lesions Treated with Stereotactic Radiosurgery

Aims:

This study aims to derive simple yet robust formula(s) for the calculation of cranial tumor volume using linear tumor dimensions in anterioposterior (AP), mediolateral (ML) and craniocaudal (CC) directions and also propose a reproducible methodology for tumor dimension measurements.

Materials and Methods:

Magnetic resonance images (MRI) of 337 patients planned for Gammaknife Stereotactic Radiosurgery for different types of brain tumors were analyzed using Leksell Gamma Plan (LGP) software. Tumor volume in three dimensional was outlined and maximum tumor diameters were measured in three orthogonal directions AP, ML, and CC on the MRI. Formulas were derived to calculate tumor volume from AP, ML, and CC diameters using linear regression technique. An agreement between the calculated volume and standard volume observed from LGP software was determined using Bland Altman (B-A) plot. A comparison was made between the volume calculated using traditionally used formula of ellipsoid, standard volume obtained from LGP software and volume calculated from formulas derived in the present study.

Results:

The tumors were divided into two categories based on their size for better volume prediction. The tumors having product of their diameters in the range 0–2.5cc were called “small tumors” and the formula proposed for their volume estimation (V = 1.513) × (AP × ML × CC) + 0.047 ) was found to predict the tumor volume with an average bias of 0.0005cc. For “large tumors,” having product of diameters in the range 2.5–36cc, the proposed formula (V = 0.444 × (AP × ML × CC) + 0.339 ) predicted the tumor volume with an average bias of 0.007cc.

Conclusions:

The two formulas proposed in the study are more accurate as compared to the commonly used formula that considers the tumors as ellipsoids. The methodology proposed in the study for measurement of linear tumor dimensions is simple and reproducible.

Lymphovascular space invasion in endometrial carcinoma: Tumor size and location matter

OBJECTIVE

To analyze histological factors possibly associated with lymphovascular space invasion (LVSI) and to determine which of those can act as independent surrogate markers.

METHODS

Retrospective cohort study performed between January 2001 and December 2014. LVSI was defined as the presence of tumor cells inside a space completely surrounded by endothelial cells. Risk factors evaluated included myometrial invasion, tumor grade, size, location, and cervical invasion. Univariate logistical regression models were applied to study any possible association of LVSI with these factors. Values were adjusted by multivariate logistic regression analysis.

RESULTS

A total of 327 patients with endometrial carcinoma treated in our Centre were included. LVSI was observed in 120 patients (36.7%). Lower uterine segment involvement (OR 5.21, 95% CI:2.6-10.4, p < 0.001) and size ≥2 cm (OR 2.62, 95% CI: 1.14-6.1, p < 0.001) were independent factors for LSVI in multivariate analysis. In univariate analysis, LVSI was a surrogate marker in type 1 tumors with deep myometrial invasion (IB, 51.9% vs. IA, 16.0%; p < 0.001), grade 3 (G3 55.8% vs. G1 16.2%; p < 0.001), size ≥2 cm (37.9% vs. 16.1%, p = 0.005), those with involving the lower segment of the uterus (58.9% vs. 22.5%, p < 0.001) and/or with cervical stromal invasion (65.4% vs. 26.1%, p < 0.001), and in type 2 tumors (61.5% vs. 30.5%, p < 0.001). The use of uterine manipulator did not increase the rate of LVSI (35.5% vs. 40.5%, p = 0.612) as compared to no manipulator use.

CONCLUSIONS

Size ≥2 cm and involvement of the lower uterine segment are independent factors for LSVI, in type 1 tumors, which can be used for surgical planning. LVSI is also more common in type 1 tumors with deep myometrial invasion, grade 3 and/or cervical stromal invasion, and also in type 2 tumors. The use of a uterine manipulator does not increase LVSI.

Effect of Tumor Size on Long-Term Survival After Resection for Solitary Intrahepatic Cholangiocarcinoma

Background

The relationship between tumor size and survival in intrahepatic cholangiocarcinoma (ICC) is still controversial. This study aimed to evaluate the prognostic ability of tumor size for solitary ICC after resection and explore optimal cut-off values in different subgroups.

Methods

Patients with solitary ICC who underwent liver resection from the Surveillance, Epidemiology, and End Results Program and Shandong Provincial Hospital were retrospectively analyzed. Kaplan-Meier and Cox regression analysis were used to assess the prognostic ability of tumor size. The log-rank test was used to determine the optimal cut-off values, and a minimum P was regarded as the optimal one in different subgroups.

Results

Large tumor size groups had worse overall survival (OS) than small tumor size groups. Cox regression analysis suggested that tumor size was an independent prognostic factor for OS for solitary ICC after resection. Subgroup analysis showed tumor size was associated with OS for both solitary ICC with and without vascular invasion (VI). Furthermore, the optimal cut-off values for solitary ICC with and without VI were found to be 8 and 3 cm, respectively, which could divide the patients into two groups with significant differences in OS.

Conclusion

Tumor size was an independent prognostic factor for solitary ICC after resection. The existing American Joint Committee on Cancer (AJCC) staging system could be improved if the cut-off value of the T1 stage was changed to 8 cm and if the T2 stage incorporated a tumor size with a cut-off value of 3 cm. Further studies with more cases are needed to validate these findings.

Automated segmentation of endometrial cancer on MR images using deep learning

Preoperative MR imaging in endometrial cancer patients provides valuable information on local tumor extent, which routinely guides choice of surgical procedure and adjuvant therapy. Furthermore, whole-volume tumor analyses of MR images may provide radiomic tumor signatures potentially relevant for better individualization and optimization of treatment. We apply a convolutional neural network for automatic tumor segmentation in endometrial cancer patients, enabling automated extraction of tumor texture parameters and tumor volume. The network was trained, validated and tested on a cohort of 139 endometrial cancer patients based on preoperative pelvic imaging. The algorithm was able to retrieve tumor volumes comparable to human expert level (likelihood-ratio test, [Formula: see text]). The network was also able to provide a set of segmentation masks with human agreement not different from inter-rater agreement of human experts (Wilcoxon signed rank test, [Formula: see text], [Formula: see text], and [Formula: see text]). An automatic tool for tumor segmentation in endometrial cancer patients enables automated extraction of tumor volume and whole-volume tumor texture features. This approach represents a promising method for automatic radiomic tumor profiling with potential relevance for better prognostication and individualization of therapeutic strategy in endometrial cancer.

Whole-Volume Tumor MRI Radiomics for Prognostic Modeling in Endometrial Cancer

Background

In endometrial cancer (EC), preoperative pelvic MRI is recommended for local staging, while final tumor stage and grade are established by surgery and pathology. MRI‐based radiomic tumor profiling may aid in preoperative risk‐stratification and support clinical treatment decisions in EC.

Purpose

To develop MRI‐based whole‐volume tumor radiomic signatures for prediction of aggressive EC disease.

Study Type

Retrospective.

Population

A total of 138 women with histologically confirmed EC, divided into training (n_T = 108) and validation cohorts (n_V = 30).

Field Strength/Sequence

Axial oblique T₁‐weighted gradient echo volumetric interpolated breath‐hold examination (VIBE) at 1.5T (71/138 patients) and DIXON VIBE at 3T (67/138 patients) at 2 minutes postcontrast injection.

Assessment

Primary tumors were manually segmented by two radiologists with 4 and 8 years' of experience. Radiomic tumor features were computed and used for prediction of surgicopathologically‐verified deep (≥50%) myometrial invasion (DMI), lymph node metastases (LNM), advanced stage (FIGO III + IV), nonendometrioid (NE) histology, and high‐grade endometrioid tumors (E3). Corresponding analyses were also conducted using radiomics extracted from the axial oblique image slice depicting the largest tumor area.

Statistical Tests

Logistic least absolute shrinkage and selection operator (LASSO) was applied for radiomic modeling in the training cohort. The diagnostic performances of the radiomic signatures were evaluated by area under the receiver operating characteristic curve in the training (AUC_T) and validation (AUC_V) cohorts. Progression‐free survival was assessed using the Kaplan–Meier and Cox proportional hazard model.

Results

The whole‐tumor radiomic signatures yielded AUC_T/AUC_V of 0.84/0.76 for predicting DMI, 0.73/0.72 for LNM, 0.71/0.68 for FIGO III + IV, 0.68/0.74 for NE histology, and 0.79/0.63 for high‐grade (E3) tumor. Single‐slice radiomics yielded comparable AUC_T but significantly lower AUC_V for LNM and FIGO III + IV (both P < 0.05). Tumor volume yielded comparable AUC_T to the whole‐tumor radiomic signatures for prediction of DMI, LNM, FIGO III + IV, and NE, but significantly lower AUC_T for E3 tumors (P < 0.05). All of the whole‐tumor radiomic signatures significantly predicted poor progression‐free survival with hazard ratios of 4.6–9.8 (P < 0.05 for all).

Data Conclusion

MRI‐based whole‐tumor radiomic signatures yield medium‐to‐high diagnostic performance for predicting aggressive EC disease. The signatures may aid in preoperative risk assessment and hence guide personalized treatment strategies in EC.

Level of Evidence

4

Technical Efficacy Stage

2

Tumour texture features from preoperative CT predict high-risk disease in endometrial cancer

BACKGROUND

To enable more individualised treatment of endometrial cancer, improved methods for preoperative tumour characterization are warranted. Texture analysis is a method for quantification of heterogeneity in images, increasingly reported as a promising diagnostic tool in oncological imaging, but largely unexplored in endometrial cancer AIM: To explore whether tumour texture features from preoperative computed tomography (CT) are related to known prognostic histopathological features and to outcome in endometrial cancer patients.

MATERIALS AND METHODS

Preoperative pelvic contrast-enhanced CT was performed in 155 patients with histologically confirmed endometrial cancer. Tumour ROIs were manually drawn on the section displaying the largest cross-sectional tumour area, using dedicated texture analysis software. Using the filtration-histogram technique, the following texture features were calculated: mean, standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis. These imaging markers were evaluated as predictors of histopathological high-risk features and recurrence- and progression-free survival using multivariable logistic regression and Cox regression analysis, including models adjusting for high-risk status based on preoperative biopsy, magnetic resonance imaging (MRI) findings, and age.

RESULTS

High tumour entropy independently predicted deep myometrial invasion (odds ratio [OR] 3.7, p=0.008) and cervical stroma invasion (OR 3.9, p=0.02). High value of MPP (MPP5 >24.2) independently predicted high-risk histological subtype (OR 3.7, p=0.01). Furthermore, high tumour kurtosis tended to independently predict reduced recurrence- and progression-free survival (HR 1.1, p=0.06).

CONCLUSION

CT texture analysis yields promising imaging markers in endometrial cancer and may supplement other imaging techniques in providing a more refined preoperative risk assessment that may ultimately enable better tailored treatment strategies.

Metabolomic Biomarkers for Detection, Prognosis and Identifying Recurrence in Endometrial Cancer

Metabolic reprogramming is increasingly recognised as one of the defining hallmarks of tumorigenesis. There is compelling evidence to suggest that endometrial cancer develops and progresses in the context of profound metabolic dysfunction. Whilst the incidence of endometrial cancer continues to rise in parallel with the global epidemic of obesity, there are, as yet, no validated biomarkers that can aid risk prediction, early detection, prognostic evaluation or surveillance. Advances in high-throughput technologies have, in recent times, shown promise for biomarker discovery based on genomic, transcriptomic, proteomic and metabolomic platforms. Metabolomics, the large-scale study of metabolites, deals with the downstream products of the other omics technologies and thus best reflects the human phenotype. This review aims to provide a summary and critical synthesis of the existing literature with the ultimate goal of identifying the most promising metabolite biomarkers that can augment current endometrial cancer diagnostic, prognostic and recurrence surveillance strategies. Identified metabolites and their biochemical pathways are discussed in the context of what we know about endometrial carcinogenesis and their potential clinical utility is evaluated. Finally, we underscore the challenges inherent in metabolomic biomarker discovery and validation and provide fresh perspectives and directions for future endometrial cancer biomarker research.

MR volumetry in predicting the aggressiveness of endometrioid adenocarcinoma: correlation with final pathological results

Background

Magnetic resonance (MR) has been widely used in predicting the aggressiveness of endometrioid adenocarcinoma. However, the diagnostic value of the MR volume of the lesion has been controversial.

Purpose

To determine whether the whole-lesion MR volume measurement could be used as a better predictor for evaluating the aggressiveness of endometrioid adenocarcinoma.

Material and Methods

In this retrospective study, we include 357 patients with pathologically demonstrated endometrioid adenocarcinoma at our institution between 1 January 2013 and 31 December 2018. Whole-lesion MR volume was calculated on sagittal T2-weighted images with ITK-SNAP software on a personal computer.

Results

According to the receiver operating characteristics curve analysis, whole-lesion MR volume has the competitive advantage in evaluating deep myometrial invasion compared with the frozen results, generating area under the curve (AUC) values of 0.751 vs. 0.834 ( P = 0.0629, Z = 1.860). The AUC of tumor maximum diameter, simple tumor volume, and whole-lesion MR volume in predicting deep myometrial invasion was 63.8%, 67.6%, and 75.1%, respectively.

Conclusion

Whole-lesion MR volume is a good diagnostic tool for prediction of deep myometrial invasion, lymph node metastasis, and lymphovascular invasion. MR volumetry could reflect the aggressiveness of endometrioid adenocarcinoma more accurately than traditional lesion measurements.

Preoperative 18F-FDG PET/CT tumor markers outperform MRI-based markers for the prediction of lymph node metastases in primary endometrial cancer

OBJECTIVES

To compare the diagnostic accuracy of preoperative 18F-FDG PET/CT and MRI tumor markers for prediction of lymph node metastases (LNM) and aggressive disease in endometrial cancer (EC).

METHODS

Preoperative whole-body 18F-FDG PET/CT and pelvic MRI were performed in 215 consecutive patients with histologically confirmed EC. PET/CT-based tumor standardized uptake value (SUVmax and SUVmean), metabolic tumor volume (MTV), and PET-positive lymph nodes (LNs) (SUVmax > 2.5) were analyzed together with the MRI-based tumor volume (VMRI), mean apparent diffusion coefficient (ADCmean), and MRI-positive LN (maximum short-axis diameter ≥ 10 mm). Imaging parameters were explored in relation to surgicopathological stage and tumor grade. Receiver operating characteristic (ROC) curves were generated yielding optimal cutoff values for imaging parameters, and regression analyses were used to assess their diagnostic performance for prediction of LNM and progression-free survival.

RESULTS

For prediction of LNM, MTV yielded the largest area under the ROC curve (AUC) (AUC = 0.80), whereas VMRI had lower AUC (AUC = 0.72) (p = 0.03). Furthermore, MTV > 27 ml yielded significantly higher specificity (74%, p < 0.001) and accuracy (75%, p < 0.001) and also higher odds ratio (12.2) for predicting LNM, compared with VMRI > 10 ml (58%, 62%, and 9.7, respectively). MTV > 27 ml also tended to yield higher sensitivity than PET-positive LN (81% vs 50%, p = 0.13). Both VMRI > 10 ml and MTV > 27 ml were significantly associated with reduced progression-free survival.

CONCLUSIONS

Tumor markers from 18F-FDG PET/CT outperform MRI markers for the prediction of LNM. MTV > 27 ml yields a high diagnostic performance for predicting aggressive disease and represents a promising supplement to conventional PET/CT reading in EC.

KEY POINTS

• Metabolic tumor volume (MTV) outperforms other 18F-FDG PET/CT and MRI markers for preoperative prediction of lymph node metastases (LNM) in endometrial cancer patients. • Using cutoff values for tumor volume for prediction of LNM, MTV > 27 ml yielded higher specificity and accuracy than VMRI> 10 ml. • MTV represents a promising supplement to conventional PET/CT reading for predicting aggressive disease in EC.

Can Magnetic Resonance Imaging Predict Pathologic Findings for Endometrioid Endometrial Cancer?

This pilot study aimed to assess the feasibility of precisely measuring tumor diameter and myometrial invasion in patients with endometrioid endometrial cancer (EEC) using preoperative contrast-enhanced magnetic resonance imaging (MRI). Adult patients with confirmed diagnosis of complex hyperplasia with atypia or EEC were included. Three radiologists separately measured tumor diameter and myometrial invasion. Basic descriptive statistics were used to describe patient characteristics and to compare radiology- and pathology-measured tumor diameter and myometrial invasion. Using the pathology results for tumor diameter as the gold standard for comparison, at least 1 radiologist was able to predict largest tumor diameter within 5 mm for 41.7% of patients. Similarly, based on pathology results for myometrial invasion, at least 1 radiologist was able to predict myometrial invasion within 5% for 50% of patients. All radiologists were able to predict superficial (<50%) or deep (≥50%) myometrial invasion for 75% of patients, with greater sensitivity, specificity, and accuracy for deep myometrial invasion. Given variation among radiologic measurements, it is difficult to recommend preoperative MRI as a basis for measuring tumor diameter and myometrial invasion. Even so, the ability to predict superficial versus deep myometrial invasion may benefit patients with EEC for whom surgery is not a viable option or for those seeking fertility-sparing treatment options.

Can conventional DWI accurately assess the size of endometrial cancer?

PURPOSE

To compare T2-weighted image (T2WI) and conventional Diffusion-weighted image (cDWI) of magnetic resonance imaging (MRI) for sensitivity of qualitative diagnosis and accuracy of tumor size (TS) measurement in endometrial cancer (EC). Meanwhile, the effect of the lesion size itself and tumor grade on the ability of T2WI and cDWI of TS assessment was explored. Ultimately, the reason of deviation on size evaluation was studied.

MATERIALS AND METHODS

34 patients with EC were enrolled. They were all treated with radical hysterectomy and performed MR examinations before operation. Firstly, the sensitivity of T2WI alone and T2WI-DWI in qualitative diagnosis of EC were compared according to pathology. Secondly, TS on T2WI and cDWI described with longitudinal (LD) and horizontal diameter (HD) were compared to macroscopic surgical specimen (MSS) quantitatively in the entire lesions and the subgroup lesions which grouped by postoperative tumor size itself and tumor grade. Thirdly, the discrepancy of mean ADC values (ADC mean) and range ADC values (ADC range) between different zones of EC were explored.

RESULTS

For qualitative diagnosis, the sensitivity of T2WI-DWI (97%) was higher than T2WI alone (85%) (p = 0.046).For TS estimation, no significant difference (PLD = 0.579; PHD = 0.261) was observed between T2WI (LDT2WI = 3.90 cm; HDT2WI = 2.88 cm) and MSS (LD = 4.00 cm; HD = 3.06 cm), whereas TS of cDWI (LDDWI = 3.01 cm; HDDWI = 2.54 cm) were smaller than MSS (PLD = 0.002; PHD = 0.002) in all lesions. In subgroup of tumor with G1 (grade 1) and small lesion (defined as maximum diameter < 3 cm), both T2WI and cDWI were not significantly different from MSS; In subgroup of tumor with G2 + 3 (grade 2 and grade 3) and big lesion (maximum diameter ≥ 3 cm), T2WI matched well with MSS still, but DWI lost accuracy significantly. The result of ADC values between different zones of tumor showed ADC mean of EC rose from central zone to peripheral zone of tumor gradually and ADC range widened gradually.

CONCLUSION

cDWI can detect EC very sensitively. The TS on cDWI was smaller than the fact for the ECs with G2/3 and big size. The TS of T2WI was in accordance with the actual size for all ECs. The heterogeneity may be responsible for the inaccuracy of cDWI.

Quantitative assessment of diffusion kurtosis imaging depicting deep myometrial invasion: a comparative analysis with diffusion-weighted imaging

PURPOSE

We aimed to investigate histogram analysis of diffusion kurtosis imaging (DKI) and conventional diffusion-weighted imaging (DWI) to distinguish between deep myometrial invasion and superficial myometrial invasion in endometrial carcinoma (EC).

METHODS

A total of 118 pathologically confirmed EC patients with preoperative DWI were included. The data were postprocessed with a DKI (b value of 0, 700, 1400, and 2000 s/mm2) model for quantitation of apparent diffusion values (D) and apparent kurtosis coefficient values (K) for non-Gaussian distribution. The apparent diffusion coefficient (ADC) was postprocessed with a conventional DWI model (b values of 0 and 800 s/mm2). A whole-tumor analysis approach was used. Comparisons of the histogram parameters of D, K, and ADC were carried out for the deep myometrial invasion and superficial myometrial invasion subgroups. Diagnostic performance of the imaging parameters was assessed.

RESULTS

The Dmean, D10th, and D90th in deep myometrial invasion group were significantly lower than those in superficial invasion group (P < 0.001, P < 0.001, and P = 0.023, respectively), as well as the ADCmean, ADC10th, and ADC90th (P = 0.001, P = 0.001, and P = 0.042, respectively). The Kmean and K90th were significantly higher in deep invasion group than those in superficial myometrial invasion group (P = 0.002 and P = 0.026, respectively). The D10th, Kmean, and ADC10th had a relatively higher area under the curve (AUC) (0.72, 0.66, and 0.71, respectively) than other parameters for distinguishing deep myometrial invasion of EC. D10th showed a relatively higher AUC than ADC10th for the differentiation of lesions with deep myometrial invasion from those with superficial myometrial invasion (0.72 vs. 0.71), but the variation was not statistically significant (P = 0.35).

CONCLUSION

Distribution of DKI and conventional DWI parameters characterized by histogram analysis may represent an indicator for deep myometrial invasion in EC. Both DKI and DWI models showed relatively equivalent effectiveness.

Development of prediction models for lymph node metastasis in endometrioid endometrial carcinoma

BACKGROUND

In endometrioid endometrial cancer (EEC), current clinical algorithms do not accurately predict patients with lymph node metastasis (LNM), leading to both under- and over-treatment. We aimed to develop models that integrate protein data with clinical information to identify patients requiring more aggressive surgery, including lymphadenectomy.

METHODS

Protein expression profiles were generated for 399 patients using reverse-phase protein array. Three generalised linear models were built on proteins and clinical information (model 1), also with magnetic resonance imaging included (model 2), and on proteins only (model 3), using a training set, and tested in independent sets. Gene expression data from the tumours were used for confirmatory testing.

RESULTS

LNM was predicted with area under the curve 0.72-0.89 and cyclin D1; fibronectin and grade were identified as important markers. High levels of fibronectin and cyclin D1 were associated with poor survival (p = 0.018), and with markers of tumour aggressiveness. Upregulation of both FN1 and CCND1 messenger RNA was related to cancer invasion and mesenchymal phenotype.

CONCLUSIONS

We demonstrate that data-driven prediction models, adding protein markers to clinical information, have potential to significantly improve preoperative identification of patients with LNM in EEC.

Diagnostic Accuracy of Clinical Biomarkers for Preoperative Prediction of Lymph Node Metastasis in Endometrial Carcinoma: A Systematic Review and Meta-Analysis

BACKGROUND

In endometrial carcinoma (EC), preoperative classification is based on histopathological criteria, with only moderate diagnostic performance for the risk of lymph node metastasis (LNM). So far, existing molecular classification systems have not been evaluated for prediction of LNM. Optimized use of clinical biomarkers as recommended by international guidelines might be a first step to improve tailored treatment, awaiting future molecular biomarkers.

AIM

To determine the diagnostic accuracy of preoperative clinical biomarkers for the prediction of LNM in endometrial cancer.

METHODS

A systematic review was performed according to the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. Studies identified in MEDLINE and EMBASE were selected by two independent reviewers. Included biomarkers were based on recommended guidelines (cancer antigen 125 [Ca-125], lymphadenopathy on magnetic resonance imaging, computed tomography, and ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography [¹⁸FDG PET-CT]) or obtained by physical examination (body mass index, cervical cytology, blood cell counts). Pooled sensitivity, specificity, area under the curve (AUC), and likelihood ratios were calculated with bivariate random-effects meta-analysis. Likelihood ratios were classified into small (0.5-1.0 or 1-2.0), moderate (0.2-0.5 or 2.0-5.0) or large (0.1-0.2 or ≥ 5.0) impact.

RESULTS

Eighty-three studies, comprising 18,205 patients, were included. Elevated Ca-125 and thrombocytosis were associated with a moderate increase in risk of LNM; lymphadenopathy on imaging with a large increase. Normal Ca-125, cytology, and no lymphadenopathy on ¹⁸FDG PET-CT were associated with a moderate decrease. AUCs were above 0.75 for these biomarkers. Other biomarkers had an AUC <0.75 and incurred only small impact.

CONCLUSION

Ca-125, thrombocytosis, and imaging had a large and moderate impact on risk of LNM and could improve preoperative risk stratification.

IMPLICATIONS FOR PRACTICE

Routine lymphadenectomy in clinical early-stage endometrial carcinoma does not improve outcome and is associated with 15%-20% surgery-related morbidity, underlining the need for improved preoperative risk stratification. New molecular classification systems are emerging but have not yet been evaluated for the prediction of lymph node metastasis. This article provides a robust overview of diagnostic performance of all clinical biomarkers recommended by international guidelines. Based on these, at least measurement of cancer antigen 125 serum level, assessment of thrombocytosis, and imaging focused on lymphadenopathy should complement current preoperative risk stratification in order to better stratify these patients by risk.

Asparaginase-like protein 1 expression in curettage independently predicts lymph node metastasis in endometrial carcinoma: a multicentre study

OBJECTIVE

Correct preoperative identification of high-risk patients is important to optimise surgical treatment and improve survival. We wanted to explore if asparaginase-like protein 1 (ASRGL1) expression in curettage could predict lymph node metastases and poor outcome, potentially improving preoperative risk stratification.

DESIGN

Multicentre study.

SETTING

Ten hospitals in Norway, Sweden and Belgium.

POPULATION

Women diagnosed with endometrial carcinoma.

METHODS

ASRGL1 expression in curettage specimens from 1144 women was determined by immunohistochemistry.

MAIN OUTCOME MEASURES

ASRGL1 status related to disease-specific survival, lymph node status, preoperative imaging parameters and clinicopathological data.

RESULTS

ASRGL1 expression had independent prognostic value in multivariate survival analyses, both in the whole patient population (hazard ratio (HR) 1.63, 95% CI 1.11-2.37, P = 0.012) and in the low-risk curettage histology subgroup (HR 2.54, 95% CI 1.44-4.47, P = 0.001). Lymph node metastases were more frequent in women with low expression of ASRGL1 compared with women with high ASRGL1 levels (23% versus 10%, P < 0.001), and low ASRGL1 level was found to independently predict lymph node metastases (odds ratio 2.07, 95% CI 1.27-3.38, P = 0.003).

CONCLUSIONS

Low expression of ASRGL1 in curettage independently predicts lymph node metastases and poor disease-specific survival.

TWEETABLE ABSTRACT

Low ASRGL1 expression in curettage predicts lymph node metastasis and poor survival in endometrial carcinoma.

Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer

BACKGROUND

Improved methods for preoperative risk stratification in endometrial cancer are highly requested by gynecologists. Texture analysis is a method for quantification of heterogeneity in images, increasingly reported as a promising diagnostic tool in various cancer types, but largely unexplored in endometrial cancer.

PURPOSE

To explore whether tumor texture parameters from preoperative MRI are related to known prognostic features (deep myometrial invasion, cervical stroma invasion, lymph node metastases, and high-risk histological subtype) and to outcome in endometrial cancer patients.

STUDY TYPE

Prospective cohort study.

POPULATION/SUBJECTS

In all, 180 patients with endometrial carcinoma were included from April 2009 to November 2013 and studied until January 2017.

FIELD STRENGTH/SEQUENCES

Preoperative pelvic MRI including contrast-enhanced T₁ -weighted (T₁ c), T₂ -weighted, and diffusion-weighted imaging at 1.5T.

ASSESSMENT

Tumor regions of interest (ROIs) were manually drawn on the slice displaying the largest cross-sectional tumor area, using the proprietary research software TexRAD for analysis. With a filtration-histogram technique, the texture parameters standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis were calculated.

STATISTICAL TESTS

Associations between texture parameters and histological features were assessed by uni- and multivariable logistic regression, including models adjusting for preoperative biopsy status and conventional MRI findings. Multivariable Cox regression analysis was used for survival analysis.

RESULTS

High tumor entropy in apparent diffusion coefficient (ADC) maps independently predicted deep myometrial invasion (odds ratio [OR] 3.2, P lt  0.001), and high MPP in T₁ c images independently predicted high-risk histological subtype (OR 1.01, P = 0.004). High kurtosis in T₁ c images predicted reduced recurrence- and progression-free survival (hazard ratio [HR] 1.5, P lt  0.001) after adjusting for MRI-measured tumor volume and histological risk at biopsy.

DATA CONCLUSION

MRI-derived tumor texture parameters independently predicted deep myometrial invasion, high-risk histological subtype, and reduced survival in endometrial carcinomas, and thus, represent promising imaging biomarkers providing a more refined preoperative risk assessment that may ultimately enable better tailored treatment strategies in endometrial cancer.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1637-1647.

Preoperative quantitative dynamic contrast-enhanced MRI and diffusion-weighted imaging predict aggressive disease in endometrial cancer

Background Quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) may yield preoperative tumor biomarkers relevant for prognosis and therapy in cancer. Purpose To explore the value of preoperative DCE-MRI and DWI for the prediction of aggressive disease in endometrial cancer patients. Material and Methods Preoperative MRI (1.5-T) from 177 patients were analyzed and imaging parameters reflecting tumor microvasculature (from DCE-MRI) and tumor microstructure (from DWI) were estimated. The derived imaging parameters were explored in relation to clinico-pathological stage, histological subtype and grade, molecular markers, and patient outcome. Results Low tumor blood flow (F_b) and low rate constant for contrast agent intravasation (k_ep) were associated with high-risk histological subtype ( P ≤ 0.04 for both) and tended to be associated with poor prognosis ( P ≤ 0.09). Low tumor apparent diffusion coefficient (ADC) value and large tumor volume were both significantly associated with deep myometrial invasion ( P < 0.001 for both) and were also unfavorable prognostic factors ( P = 0.05 and P < 0.001, respectively). Conclusion DCE-MRI and DWI represent valuable supplements to conventional MRI by providing preoperative imaging biomarkers that predict aggressive disease in endometrial cancer patients.

Preoperative tumor size is associated with deep myometrial invasion and lymph node metastases and is a negative prognostic indicator for patients with endometrial carcinoma

We examined the usefulness of evaluating tumor size determined using preoperative magnetic resonance imaging (MRI) for prognosis in patients with endometrial carcinoma (EC). Patients (N = 184) with EC who underwent surgery at Shimane University Hospital between 1997 and 2013 were enrolled. We investigated the association between the tumor size of EC assessed prior to surgery by MRI (anteroposterior [AP], transverse [TV], and craniocaudal [CC] diameters) and various clinical parameters including deep myometrial invasion and lymph node metastases. We subsequently examined the prognostic significance of tumor size in patients with EC. Survival analysis was performed using the Kaplan-Meier method, and prognostic factors were evaluated using the Cox's proportional hazards regression model. Multivariate analysis identified increased AP diameter as an independent negative prognostic factor for overall survival (OS) (P = 0.037). A long AP diameter has prognostic value and the potential to be a predictive marker for surgical outcomes in patients with EC. Furthermore, AP diameter exhibited the greatest area under the curve (AUC) (0.727) for deep myometrial invasion, and CC diameter had the greatest AUC for lymph node metastases (0.854). Evaluation of tumor size parameters may aid in the identification of high-risk populations, which could improve treatment selection and patient outcomes.

Preoperative Magnetic Resonance Volumetry in Predicting Myometrial Invasion, Lymphovascular Space Invasion, and Tumor Grade: Is It Valuable in International Federation of Gynecology and Obstetrics Stage I Endometrial Cancer?

Objective

The aim of this retrospective single-center study was to evaluate the relationship between maximum tumor size, tumor volume, tumor volume ratio (TVR) based on preoperative magnetic resonance (MR) volumetry, and negative histological prognostic parameters (deep myometrial invasion [MI], lymphovascular space invasion, tumor histological grade, and subtype) in International Federation of Gynecology and Obstetrics stage I endometrial cancer.

Methods/Materials

Preoperative pelvic MR imaging studies of 68 women with surgical-pathologic diagnosis of International Federation of Gynecology and Obstetrics stage I endometrial cancer were reviewed for assessment of MR volumetry and qualitative assessment of MI. Volume of the tumor and uterus was measured with manual tracing of each section on sagittal T2-weighted images. Tumor volume ratio was calculated according to the following formula: TVR = (total tumor volume/total uterine volume) × 100. Receiver operating characteristics curve was performed to investigate a threshold for TVR associated with MI. The Mann-Whitney U test, Kruskal-Wallis test, and linear regression analysis were applied to evaluate possible differences between tumor size, tumor volume, TVR, and negative prognostic parameters.

Results

Receiver operating characteristics curve analysis of TVR for prediction of deep MI was statistically significant (P = 0.013). An optimal TVR threshold of 7.3% predicted deep myometrial invasion with 85.7% sensitivity, 46.8% specificity, 41.9% positive predictive value, and 88.0% negative predictive value. Receiver operating characteristics curve analyses of TVR, tumor size, and tumor volume for prediction of tumor histological grade or lymphovascular space invasion were not significant. The concordance between radiologic and pathologic assessment for MI was almost excellent (κ value, 0.799; P < 0.001). Addition of TVR to standard radiologic assessment of deep MI increased the sensitivity from 90.5% to 95.2%.

Conclusions

Tumor volume ratio, based on preoperative MR volumetry, seems to predict deep MI independently in stage I endometrial cancer with insufficient sensitivity and specificity. Its value in clinical practice for risk stratification models in endometrial cancer has to be studied in larger cohort of patients.

Magnetic Resonance Imaging of Gynecological Malignancies: Role in Personalized Management

Gynecological malignancies are a leading cause of mortality and morbidity in women and pose a significant health problem around the world. Currently used staging systems for management of gynecological malignancies have unresolved issues, the most important being recommendations on the use of imaging. Although not mandatory as per the International Federation of Gynecology and Obstetrics recommendations, preoperative cross-sectional imaging is strongly recommended for adequate and optimal management of patients with gynecological malignancies. Standardized disease-specific magnetic resonance imaging protocols help assess disease spread accurately and avoid pitfalls. Multiparametric imaging holds promise as a roadmap to personalized management in gynecological malignancies. In this review, we will highlight the role of magnetic resonance imaging in cervical, endometrial, and ovarian carcinomas.

What Is the Best Preoperative Imaging for Endometrial Cancer?

Although endometrial cancer is surgicopathologically staged, preoperative imaging is recommended for diagnostic work-up to tailor surgery and adjuvant treatment. For preoperative staging, imaging by transvaginal ultrasound (TVU) and/or magnetic resonance imaging (MRI) is valuable to assess local tumor extent, and positron emission tomography-CT (PET-CT) and/or computed tomography (CT) to assess lymph node metastases and distant spread. Preoperative imaging may identify deep myometrial invasion, cervical stromal involvement, pelvic and/or paraaortic lymph node metastases, and distant spread, however, with reported limitations in accuracies and reproducibility. Novel structural and functional imaging techniques offer visualization of microstructural and functional tumor characteristics, reportedly linked to clinical phenotype, thus with a potential for improving risk stratification. In this review, we summarize the reported staging performances of conventional and novel preoperative imaging methods and provide an overview of promising novel imaging methods relevant for endometrial cancer care.

Predictive ability of maximal tumor diameter on MRI for high-risk endometrial cancer

AIM

To investigate the predictive ability of tumor size for deep myometrial invasion (≥50%) and metastatic lymphadenopathy, on maximal tumor diameter (MRI) of endometrial cancer.

MATERIALS AND METHODS

Our study population consisted of 105 patients (mean age: 59.8 years) with histologically confirmed endometrial cancer. All patients underwent preoperative pelvic MRI. Tumor maximal diameter (size) was calculated on multiple sequences, and the largest value was recorded. Logistic regression analysis was performed to investigate the association of maximal tumor diameter (MRI) with the depth of myometrial invasion and the presence of pelvic nodal metastases (histology); optimal tumor size cut-off for the prediction of deep myometrial involvement and nodal metastases was calculated using ROC analysis. Surgicopathological specimen examination was the standard of reference.

RESULTS

Tumor size on MRI, independently predicted deep myometrial invasion. Optimal maximal tumor diameter cut-off for the prediction of deep myometrial invasion was 2 cm (SE 90%, SP 50.9%). When tumor size was used as a categorical variable in the multiple logistic regression model, tumor size >2 cm had 10.04 times greater odds of deep myometrial invasion (95% CI 3.34-30.17, p < 0.001). Optimal tumor size cut-off for prediction of nodal metastases was 4 cm (SE 60%, SP 76.9%). Multiple logistic regression analysis with nodal metastases as a dependent variable showed that tumor size >4 cm had 4.79 times greater odds for malignant dissemination to the lymph nodes (95% CI 1.00-23.09, p = 0.047).

CONCLUSION

Maximal tumor diameter on preoperative MRI may be yet another prognosticator for deep myometrial invasion and metastatic lymphadenopathy in patients with endometrial carcinoma.

J. W, Krakstad C, Haldorsen IS. Tissue and imaging biomarkers for hypoxia predict poor outcome in endometrial cancer

Hypoxia is frequent in solid tumors and linked to aggressive phenotypes and therapy resistance. We explored expression patterns of the proposed hypoxia marker HIF-1α in endometrial cancer (EC) and investigate whether preoperative functional imaging parameters are associated with tumor hypoxia. Expression of HIF-1α was explored both in the epithelial and the stromal tumor component. We found that low epithelial HIF-1α and high stromal HIF-1α expression were significantly associated with reduced disease specific survival in EC. Only stromal HIF-1α had independent prognostic value in Cox regression analysis. High stromal HIF-1α protein expression was rare in the premalignant lesions of complex atypical hyperplasia but increased significantly to invasive cancer. High stromal HIF-1α expression was correlated with overexpression of important genes downstream from HIF-1α, i.e. VEGFA and SLC2A1 (GLUT1). Detecting hypoxic tumors with preoperative functional imaging might have therapeutic benefits. We found that high stromal HIF-1α expression associated with high total lesion glycolysis (TLG) at PET/CT. High expression of a gene signature linked to hypoxia also correlated with low tumor blood flow at DCE-MRI and increased metabolism measured by FDG-PET. PI3K pathway inhibitors were identified as potential therapeutic compounds in patients with lesions overexpressing this gene signature. In conclusion, we show that high stromal HIF-1α expression predicts reduced survival in EC and is associated with increased tumor metabolism at FDG-PET/CT. Importantly; we demonstrate a correlation between tissue and imaging biomarkers reflecting hypoxia, and also possible treatment targets for selected patients.

Long-term outcomes of magnetic resonance imaging-invisible endometrial cancer

OBJECTIVE

Magnetic resonance imaging (MRI) is useful for staging endometrial cancer. The treatment and prognosis of MRI-invisible endometrial cancer remain unclear. The purpose of this study was to retrospectively evaluate the long-term outcomes of patients with MRI-invisible endometrial cancer.

METHODS

Between February 1995 and December 2011, we reviewed the medical records of 433 patients with endometrial cancer, which was staged IA on MRI. Of these patients, 89 had MRI-invisible cancer and 344 had MRI-visible cancer. Both cancers were treated with simple hysterectomy with or without lymph node dissection according to the surgeon's decision. Both cancers were compared regarding pathologic findings, recurrence rates, and survival rates.

RESULTS

The median sizes of MRI-invisible and MRI-visible cancers were 4 mm (0 to 40 mm) and 20 mm (0 to 89 mm), respectively (p<0.001). Myometrial invasion of these groups were detected in 20.2% (18/89) and 56.7% (195/344), respectively (p<0.001). Lymphadenectomy and follow-up imaging revealed no lymph node metastasis in patients with MRI-invisible cancers, while those revealed in 4.7% (16/344) of patients with MRI-visible cancers (p=0.052). The recurrence rates of MRI-invisible and MRI-visible cancers were 1.1% (1/89) and 7.8% (27/344), respectively (p=0.026). The recurrence-free survival rates of these groups were 98.9% (88/89) and 91.6% (315/344), respectively (p=0.022).

CONCLUSION

MRI-invisible endometrial cancer can be treated with less invasive surgery because of its lower tumor burden and better prognosis. This cancer may not require lymphadenectomy because of no metastasis or recurrence in lymph nodes.