Evaluation of Pre-Therapeutic Assessment in Endometrial Cancer Staging

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

RATIONALE AND OBJECTIVES

To evaluate the interest of a systematic second opinion in quality assessment and FIGO staging in the pretherapeutic imaging work-up.

MATERIALS AND METHODS

A retrospective observational study was conducted on 156 patients who underwent surgery for endometrioid cancer in our institution. 42 % had their initial MRI scans performed in expert centers (University Hospital and Cancer center) and 58 % in non-expert centers. Quality assessment, concordances between initial reports, and second opinions by a junior and a senior ICL radiologist versus histopathological data were analyzed.

RESULTS

MRI scans performed in expert centers were more complete and more likely to be rated as higher quality. The overall accuracy of T staging from initial reports vs gold standard was 0.59 (95 % CI, 0.46-0.71) in expert centers and 0.49 (95 % CI, 0.38-0.60) in non-expert centers. The overall accuracy and Kappa of a second opinion for FIGO 2009 staging from expert center and non-expert center examinations were 0.61 (95 % CI, 0.48-0.72) vs 0.50 (95 % CI, 0.39-0.60) and 0.37 vs 0.27 for junior reader and 0.62 (95 % CI, 0.49-0.74) vs 0.48 (95 % CI, 0.37-0.58) and 0.39 vs 0.24 for senior reader, respectively. There was also a significant lower confidence level of the junior radiologist in MRI FIGO staging for non-expert center examinations (p 0.003).

CONCLUSION

Accuracy in the FIGO 2009 staging and quality assessment are higher for MR examinations performed from expert centers than in non-expert centers. A systematic second opinion by radiologists in expert centers should be proposed before pre-treatment multidisciplinary consultation.

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.

Endometrial Cancer Staging: Is There Value in ADC?

PURPOSE

To assess the ability of apparent diffusion coefficient (ADC) measurements in predicting the histological grade of endometrial cancer. A secondary goal was to assess the agreement between MRI and surgical staging as an accurate measurement.

METHODS

Patients with endometrial cancers diagnosed between 2018-2020 and having received both MRI and surgical staging were retrospectively enrolled. Patients were characterized according to histology, tumor size, FIGO stage (MRI and surgical stage), and functional MRI parameters (DCE and DWI/ADC). Statistical analysis was performed to determine if an association could be identified between ADC variables and histology grade. Secondarily, we assessed the degree of agreement between the MRI and surgical stages according to the FIGO classification.

RESULTS

The cohort included 45 women with endometrial cancer. Quantitative analysis of ADC variables did not find a statistically significant association with histological tumor grades. DCE showed higher sensitivity than DWI/ADC in the assessment of myometrial invasion (85.00% versus 65.00%) with the same specificity (80.00%). A good agreement between MRI and histopathology for the FIGO stage was found (kappa of 0.72, p < 0.01). Differences in staging between MRI and surgery were detected in eight cases, which could not be justified by the interval between MRI and surgery.

CONCLUSIONS

ADC values were not useful for predicting endometrial cancer grade, despite the good agreement between MRI interpretation and histopathology of endometrial cancer staging at our center.

Prediction of Deep Myometrial Infiltration, Clinical Risk Category, Histological Type, and Lymphovascular Space Invasion in Women with Endometrial Cancer Based on Clinical and T2-Weighted MRI Radiomic Features

PURPOSE

To predict deep myometrial infiltration (DMI), clinical risk category, histological type, and lymphovascular space invasion (LVSI) in women with endometrial cancer using machine learning classification methods based on clinical and image signatures from T2-weighted MR images.

METHODS

A training dataset containing 413 patients and an independent testing dataset consisting of 82 cases were employed in this retrospective study. Manual segmentation of the whole tumor volume on sagittal T2-weighted MRI was performed. Clinical and radiomic features were extracted to predict: (i) DMI of endometrial cancer patients, (ii) endometrial cancer clinical high-risk level, (iii) histological subtype of tumor, and (iv) presence of LVSI. A classification model with different automatically selected hyperparameter values was created. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, F1 score, average recall, and average precision were calculated to evaluate different models.

RESULTS

Based on the independent external testing dataset, the AUCs for DMI, high-risk endometrial cancer, endometrial histological type, and LVSI classification were 0.79, 0.82, 0.91, and 0.85, respectively. The corresponding 95% confidence intervals (CI) of the AUCs were [0.69, 0.89], [0.75, 0.91], [0.83, 0.97], and [0.77, 0.93], respectively.

CONCLUSION

It is possible to classify endometrial cancer DMI, risk, histology type, and LVSI using different machine learning methods.