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

Abstract

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.

Radiomics in Gynaecological Imaging: A State-of-the-Art Review

“…Nevertheless, the authors did not observe a statistically significant difference between diagnostic values obtained by the model based on both T2WI and DWI and those obtained by two radiologists' subjective assessments [43]. More recently, three other studies evaluated the performance of MRI radiomics-based machine learning models to predict DMI, obtaining comparable AUC values, including between 0.79 and 0.83 [44][45][46]. However, in the research performed by Otani and colleagues, the diagnostic performance for DMI of four radiologists did not show statistically significant improvement with the support of radiomic classifiers [46], contrary to a previous investigation [39].…”

“…In a recently published multicentric research, Zheng et al developed a model based on clinical and radiomic features for pathological grade prediction, which outperformed clinical and radiomics-only models, yielding AUCs of 0.920, 0.882, and 0.881 for the training, internal validation, and external validation sets, respectively [53]. Moreover, the classification model based on clinical and T2WI signatures generated by Li et al obtained very promising results for histological type prediction (AUC: 0.91) on the independent external testing dataset [45]. Lefebvre et al explored the possible role of harmonic signatures for predicting EC high tumour grade, describing a good performance with an AUC, a sensitivity, and a specificity of 0.81, 93%, and 63%, respectively [54].…”

“…Conversely, the model proposed by Celli et al for predicting low-risk EC, which was based on two features from ADC maps and T2WI, had a less promising performance (AUC: 0.74) [51]. Moreover, the abovementioned model generated by Li et al was also tested for predicting high-risk categories, reporting an AUC of 0.82 [45].…”

“…They evaluated an LVSI predictive model based on a single feature from ADC as a predictor, which achieved an AUC of 0.59, a sensitivity of 50%, and a specificity of 61% [51]. More recently, four investigations registered more promising performances of radiomics analysis for LVSI assessment, with reported AUC values between 0.79 and 0.85 [44][45][46]52].…”

Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features

“…This retrospective study protocol was approved by the institutional review board (IRB), and the research ethics committee of Imperial College Research Ethics Committee (ICREC) study reference number is 17/LO/0173 [ 6 , 14 ]. The requirement for written informed consent was waived by the ethics committee (ICREC) because of the retrospective nature of the study.…”