Radiomic Features Based on Multi-sequence MRI Predict Immunohistochemical Biomarkers of Endometrial Cancer

Radiomic Features Based on Multi-sequence MRI Predict Immunohistochemical Biomarkers of Endometrial Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Radiomic Features Based on Multi-sequence MRI Predict Immunohistochemical Biomarkers of Endometrial Cancer Liting Shen, Xiaojun Chen, Lan Li, Yan Zeng, Zhihan Yan, Lu Han, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4179540/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Different molecular or genetic information influences the clinical decisions for patients diagnosed with endometrial cancer (EC). A non-invasive, precise, and efficient preoperative evaluation method is crucial for the prognosis of patients with EC. Purpose: The aim of this study was to construct MRI-based radiomics models to predict immunohistochemical biomarkers and assess the relationship between radiomic features and the Ki-67 proliferation rate in EC. Material and Methods: We retrospectively analyzed 100 estrogen receptor (ER), 94 progesterone receptor (PR), 97 P53, and 98 Ki-67 immunohistochemistry cases with EC who underwent magnetic resonance imaging (MRI) between May 2012 and June 2023 prior to surgery. Radiomic features were individually extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and the apparent diffusion coefficient (ADC). Least absolute shrinkage and selection operator (LASSO) regression was employed for feature selection. And logistic regression was employed to construct radiomics models with 5-fold cross-validation. The receiver operating characteristic (ROC) curves were analyzed to evaluate the performance of the radiomics models. Finally, Pearson's correlations were utilized to explore the association between the values of selected features and the Ki-67 proliferation rate. Results: A total of 2264 features were extracted from each patient’s MRI sequences. The selected features from the multi-sequence models were shared with or without the single sequence models. Both single sequence and multi-sequence models demonstrated good diagnostic performance, although the diagnostic performance of multi-sequence models outperformed the single sequence models. Correlation analysis showed that adc_wavelet_glszm_wavelet-HHH-SmallAreaLowGrayLevelEmphasis and t2_log_firstorder_log-sigma-2-0-mm-3D-Skewness were negatively correlated with the Ki-67 proliferation rate. Conclusions: MRI-based radiomic features are promising predictors of immunohistochemistry and prognosis in EC. endometrial cancer radiomics immunohistochemical biomarkers magnetic resonance imaging correlation analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Endometrial cancer (EC) is a common gynecologic malignancy around the world. In recent years, the incidence rate of women in our country has increased [ 1 , 2 ]. The significant heterogeneity of EC underscores the importance of accurate grading of EC for clinics [ 3 , 4 ]. Although most early-stage EC patients have a good prognosis, a small number of patients have a poor prognosis even when diagnosed early [ 5 ]. It is because ECs with the same or similar histological characteristics may harbor different molecular or genetic information, which in turn influences the prognosis of patients [ 6 ]. In recent years, great progress has been made in understanding the molecular pathology of EC. A series of molecular markers have emerged, aiding in the clinical differential diagnosis and prognosis prediction of EC, among which estrogen receptor (ER), progesterone receptor (PR), P53, and Ki-67 are four vital biological behavior markers [ 7 ]. Notably, endometrioid adenocarcinoma (EEC) is a hormonally regulated disease, and a positive PR status is associated with a more favorable prognosis [ 8 ]. In addition, progestin therapy can be an option for selected patients, particularly young women who wish to preserve fertility. Conversely, double-negative hormone receptor (ER and PR) or negative PR status has been associated with shorter survival [ 9 , 10 ]. The presence of P53 mutations is indicative of invasiveness and a poor prognosis, while the prognosis of patients with P53 wild type is better [ 11 ]. As for Ki-67, its expression was reported to be positively correlated with tumor grade in patients with EEC, and an increased risk of recurrence was found in Stage I-II EC patients with a higher Ki-67 index [ 12 , 13 ]. Therefore, accurately identification of the molecular and genetic information in EC and precise treatment are important to clinical. At present, the immunohistochemical types of EC are primarily obtained from the pathological histology after surgical resection or biopsy. However, it is an invasive procedure that highly dependent on the experience of the pathologists and sometimes prone to sampling biases [ 14 , 15 ]. Magnetic resonance imaging (MRI) offers a non-invasive technique with excellent soft tissue contrast resolution. Conventional MRI, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps are of great value in the diagnosis and staging of EC [ 16 – 18 ]. In recent years, the rapid advancements in artificial intelligence have given rise to the burgeoning field of radiomics. Many studies have used radiomics methods based on single or multi-sequence MRI to predict tumor grade, deep myometrial invasion (DMI), and lymph vascular space invasion (LVSI) in EC [ 19 – 21 ]. With the in-depth study of radiomics, it transcends the confines of tumor diagnosis and staging, delving deeper into the realm of histopathological features. Recently, Zijing Lin et al. developed a radiomics nomogram for the prediction of microsatellite instability (MSI) status in EC, demonstrating favorable calibration and clinical utility [ 22 ]. In addition, a single-center study used small data based on T2-weighted fluid-attenuated inversion recovery (T2 FLAIR) to predict immunohistochemical features of glioma, and satisfactory diagnosis accuracy was reported, indicating possible [ 23 ]. This study aimed to collect the immunohistochemical results of EC. By utilizing T2-weighted imaging (T2WI), DWI, and ADC images, the radiomics method was used to predict the immunohistochemical characteristics of EC through the extraction of radiomic features and construction of the radiomics model, which could contribute to a deeper understanding of the molecular characteristics of EC and provide new insights for guiding the precision treatment before clinical operation, improving the prognosis of patients. Materials and Methods Study participants The institutional review boards of the Second Affiliated Hospital of Wenzhou Medical University approved this retrospective study, and individual consent for the retrospective analysis was waived. The study cohorts enrolled in this research followed the criteria as follows: (1) patients had clinical symptoms and was histologically diagnosed with EC; (2) availability of immunohistochemistry results; (3) no history of chemotherapy, radiotherapy, or surgery prior to the MRI examination; and (4) MRI examinations consisting of T2WI, DWI, and ADC maps of acceptable MR image quality. A total of 105 confirmed female patients (mean age, 55 years; age range, 27–74 years), who were pathologically diagnosed with EC and underwent MRI examinations at this center between May 2012 and June 2023, were reviewed in this study. Feature extraction or immunohistochemistry were not available for all; there were 100 ER, 94 PR, 97 P53, and 98 Ki-67 immunohistochemistry cases, respectively. The patients were randomly divided into a training set and a validation set according to a ratio of 8:2. MRI acquisition The MRI scans was performed using 3.0-T scanners with phased-array abdominal coils. The patients lay in a supine position and breathed freely during the acquisition. The following sequences were obtained: Sagittal T2WI with and without fat saturation; DWI with b = 800 or 1000 s/mm 2 . The ADC map was automatically calculated based on DWI by the embedded software of the MRI equipment. More detailed information about these sequences is listed in Table 1 . Table 1 MRI examination’s parameters. Device 1 Device 2 MRI 3.0-T Discovery 750 GE 3.0-T HDXT GE T2WI TR/TE = 3475/69 TR/TE = 2900/73 Matrix = 320×320 Matrix = 320×320 thickness = 4mm thickness = 4mm FOV = 280×380mm FOV = 280×380mm DWI TR/TE = 2400/73 TR/TE = 5000/66 Matrix = 192×160 Matrix = 192×160 thickness = 5mm thickness = 6mm FOV = 288×360mm FOV = 380×380mm b = 1000 b = 1000 MRI, magnetic resonance imaging; T2WI, T2-weighted imaging; DWI, diffusion-weighted imaging; TE, echo time; TR, repetition time; FOV, field of view. Immunohistochemical status assessment All the patients involved in the current study underwent total hysterectomy, bilateral salpingo-oophorectomy, and/or pelvic/para-aortic lymphadenectomy. Immunohistochemical factors were evaluated in these patients through the acquisition of surgical samples. The specific procedures were carried out following the standard guidelines of the institution [ 24 ]. All immunohistochemical results were collected in the PACS system of the center. In the ER and PR evaluations, tumor cells were taken into account for densities, categorized as (-, ±, 1+, 2+, and 3+). In cases where the densities of tumor cells are ±, 1+,2+, and 3+, they were classified as positive (label = 1), whereas negative (label = 0). The expression level of the P53 and Ki-67 index was represented by the proportional staining (%). The proportional staining of P53 with 0% and 75–100% was considered mutant type (complete absence and overexpression, label = 1), and 1–75% was considered wild type (label = 0) [ 25 ]. And proportional staining of Ki-67 that equaled or was more than 50% was considered hyperproliferation (label = 0), and less than 50% was considered hypoproliferation (label = 1) [ 23 ]. Segmentation The regions of interest (ROIs) that covered the whole lesion were manually segmented by two radiologists, each with 6 and 10 years of experience, using an open-source program called ITK-SNAP (version 3.8.0) on MRI images while blinded to the histopathological results. For each case, ROI was first drawn by one radiologist and then reviewed by the other radiologist to ensure high-quality final segmentation results. The manual drawing of each ROI was performed slice-by-slice on T2WI, DWI, and ADC and cross-referenced with each other, taking care to avoid including nearby normal myometrium or endometrium. Feature extraction and selection All ROIs from T2WI, DWI, and ADC maps, as well as the original MRI, were processed in batches by the uAI Data Assistant (United Imaging Intelligence, China). Subsequently, the radiomics module within the within the uAI Research Portal software (uRP) (United Imaging Intelligence, China) [ 26 ] was applied for feature extraction from each sequence, which complies with the standards set by the IBSI recommendation. And all extracted features were normalized using a Z-value normalization algorithm. Then, least absolute shrinkage and selection operator (LASSO) regression was used to reduce the dimension of features so as to obtain the optimized subset of features for constructing the final model. The LASSO analysis included choosing the regular parameter λ and determining the number of features. Once the number of features was determined, the most predictive feature subset was chosen, and the corresponding coefficients were evaluated. In this study, we aimed to compare the difference in predictive performance between the single-sequence model and the multi-sequence model, so we mixed the top 20 features from the three single-sequence models for each immunohistochemical parameter and conducted LASSO analysis again for feature dimensionality reduction to facilitate the subsequent establishment of the multi-sequence model. The process of radiomics analysis is shown in Fig. 1 . Model building and evaluation Logistic regression was employed to construct radiomic models. In model training, 5-fold cross-validation was used for algorithm hyperparameter tuning based on the training set. Finally, receiver operating characteristic (ROC) curves were plotted. The area under the curve (AUC) and additional indices, including accuracy, sensitivity, and specificity, were calculated to evaluate the performance of the radiomics models. Correlation analysis Pearson's correlations were used to explore the correlation between the values of radiomic features selected from the multi-sequence model (Fig. 2 D) and the Ki-67 proliferation rate. This work was performed on SPSS (version 26.0) and GraphPad Prism (version 9), with statistical significance set at p < 0.05. Due to condition, we did not calculate the correlation between the screened radiomic features and the expression of ER, PR, and P53. Result Study participants Among the 105 patients reviewed, 100 ER, 94 PR, 97 P53, and 98 Ki-67 immunohistochemistry cases were included, respectively. The EC was staged based on FIGO 2018. The histological subtype and histological characteristics of EC were determined from a surgical specimen or biopsy. Detailed patient demographics are presented in Table 2 . Table 2 The detailed demographic information of EC patients. Parameters ER PR P53 Ki-67 Age (mean ± SD, year) 55 ± 8 56 ± 7 55 ± 8 55 ± 8 Total number 100 94 97 98 Label Label = 0 80 (80.00%) 67 (71.28%) 59 (60.82%) 35 (35.71%) Label = 1 20 (20.00%) 27 (28.72%) 38 (39.18%) 63 (64.29%) Histological subtype EEC 85 (85.00%) 82 (87.23%) 83 (85.57%) 84 (85.71%) Non-EEC 10 (1.00%) 9 (9.57%) 9 (9.28%) 9 (9.18%) unknown 5 (5.00%) 3 (3.20%)) 5 (5.15%) 5 (5.11%) Histological grade G1 29 (29.00%) 28 (29.79%) 28 (28.87%) 28 (28.57%) G2 33 (33.00%) 32 (34.04%) 33 (34.02%) 33 (33.67%) G3 16 (16.00%) 15 (15.96%) 16 (6.19%) 16 (16.33%) unknown 22 (22.00%) 19 (20.21) 20 (20.62%) 21 (21.43%) FIGO staging I 89 (89.00%) 83 (88.30%) 86 (88.66%) 88 (89.80%) II 2 (2.00%) 2 (2.13%) 2 (2.06%) 1 (1.02%) III 7 (7.00%) 7 (7.44%) 7 (7.22%) 7 (7.14%) IV 2 (2.00%) 2 (2.13%) 2 (2.06%) 2 (2.04%) EC, Endometrial cancer; ER, estrogen receptor; PR, progesterone receptor; the positive, mutant type, and hypoproliferation groups were labeled 0; the negative, wide type, and hyperproliferation groups were labeled 1; EEC, endometrioid adenocarcinoma. Feature extraction and selection A total of 2264 features were extracted from each sequence (T2WI, DWI, and ADC maps) for each patient. Following feature selection, the most effective features that were retained by each model were illustrated in Fig. 2 . The results showed that the selected features were different between single-sequence and multi-sequence, as follows: In ER models, there were two features from the multi-sequence model shared with the ADC model (wavelet_firstorder_wavelet-LLL-Maximum and discretegaussian_glcm_ClusterShade); two features from the multi-sequence model shared with the DWI model (wavelet_glacm wavelet-LLH-ClusterShade and boxmean_firstorder_Skewness); and two features from the multi-sequence model shared with the T2WI model (wavelet_glszm_wavelet-HHL-SizeZoneNonUnifemityNormalized and wavelet_firstorder_wavelet-LHH-Skewness). Only one feature from the multi-sequence model shared without the ADC model (log_firstorder_log-sigma-1-0-mm-3D-Kurtosis). In PR models, there were three features from the multi-sequence model shared with the ADC model (boxsigmaimage_glcm_DifferenceVariance, mean_glcm_CluterShade and wavelet_firstorder_wavelry-HLL-Skewness); one feature from the multi-sequence model shared with the DWI model (wavelet_ngtdm_wavelet-HHH-Contrast); and two features from the multi-sequence model shared with the T2WI model (log_firstorder_log-sigma-0-5-mm-3D-Kurtosis and wavelet_firstorder_wavelet-LHH-Skewness). And two features from the multi-sequence model shared without the ADC and DWI models (wavelet_glrlm_wavelet-HHL-ShortRunLowGraylevelEmphasis, selected from ADC maps; boxmean_glcm_ldm, selected from DWI). In P53 models, there were three features from the multi-sequence model shared with the ADC model (wavelet_gldm_wavelet-HLH-LargeDependenceLowGrayLevelEmphasis, log_gldm_log-sigma-0-5-mm-3D-DependenceVariance and boxsigmaimage_glrlm_LowGrayLevelRunEmphasis); three features from the multi-sequence model shared with the DWI model (wavelet_glcm_wavelet-LLH-Contrast, wavelet_glszm_wavelet-HHL-SizeZnoeNonUniformityNomalilized and normalize_firstorder_Maximum); and two features from the multi-sequence model shared with the T2WI model (wavelet_firstorder_wavelet-HLL-Kurtosis and normalize_glrlm_ShortRunLowGrayLevelEmphasis). And no feature from multi-sequence model shared without the ADC, DWI, and T2WI models. In Ki-67 models, there were three features from the multi-sequence model shared with the ADC model (boxmean_glcm_correlation, wavelet_glszm_wavelet-hhh-smallarealowgraylevelemphasis and gldm_log-sigma-4-0-mm-3d-largedependencehighgraylevelemphasis); no features from the multi-sequence model shared with the DWI model; and no features from the multi-sequence model shared with the T2WI model. And two features from the multi-sequence model not shared with ADC, DWI, and T2WI models (log_firstorder_log-sigma-2-0-mm-ed-maximum and, selected from DWIs; log_firstorder_log sigma-2-0-mm-3d-skewness and wavelet_dirstorder_wavelet-hhl-median, selected from T2WI). And no feature from the multi-sequence model shared without the ADC, DWI, and T2WI models. Model performance We proposed four multiple sequence models for revealing the immunohistochemical typing of ER, PR, P53, and Ki-67. The ROC curves of four models on both the training and validation sets are shown in Fig. 3 . The performance of four multi-sequence models in predicting the immunohistochemical subtypes was shown in Fig. 4 . And the accuracy, sensitivity, and specificity to quantify the predictive performance are listed in Table 3 . The results showed that both single-sequence model and multi-sequence model performed well in predicting the four immunohistochemical parameters of EC with high accuracy. However, the AUC, accuracy, sensitivity, and specificity of the multi-sequence model were generally higher than those of the single-sequence model, which showed higher predictive performance. Table 3 The predictive performance of sixteen models on different data sets. Sequence Data Set AUC (95% CI) Sensitivity Specificity Accuracy ER T2WI Training Set 0.838 (0.728–0.949) 0.625 0.881 0.830 Validation Set 0.829 (0.568-1.000) 0.600 0.888 0.830 DWI Training Set 0.818 (0.718–0.919) 0.512 0.881 0.807 Validation Set 0.804 (0.589–0.986) 0.500 0.850 0.780 ADC Training Set 0.887 (0.810–0.966) 0.812 0.809 0.810 Validation Set 0.863 (0.678-1.000) 0.800 0.812 0.810 Multi Training Set 0.930 (0.874–0.990) 0.900 0.816 0.832 Validation Set 0.894 (0.758–0.997) 0.850 0.812 0.820 PR T2WI Training Set 0.828 (0.730–0.926) 0.611 0.858 0.787 Validation Set 0.820 (0.620–0.997) 0.660 0.849 0.799 DWI Training Set 0.863 (0.779–0.946) 0.647 0.840 0.785 Validation Set 0.818 (0.619–0.992) 0.560 0.793 0.723 ADC Training Set 0.862 (0.759–0.967) 0.776 0.836 0.819 Validation Set 0.821 (0.600-1.000) 0.707 0.795 0.767 Multi Training Set 0.918 (0.851–0.989) 0.778 0.884 0.854 Validation Set 0.874 (0.733–0.993) 0.787 0.838 0.820 P53 T2WI Training Set 0.825 (0.736–0.917) 0.717 0.725 0.722 Validation Set 0.730 (0.490–0.952) 0.611 0.661 0.641 DWI Training Set 0.851 (0.769–0.937) 0.731 0.784 0.763 Validation Set 0.771 (0.548–0.973) 0.739 0.744 0.742 ADC Training Set 0.848 (0.767–0.932) 0.757 0.754 0.755 Validation Set 0.770 (0.569–0.954) 0.714 0.748 0.733 Multi Training Set 0.873 (0.800-0.951) 0.803 0.737 0.763 Validation Set 0.833 (0.663–0.977) 0.768 0.747 0.755 Ki-67 T2WI Training Set 0.709 (0.593–0.827) 0.687 0.579 0.648 Validation Set 0.683 (0.429–0.929) 0.653 0.600 0.633 DWI Training Set 0.645 (0.515–0.776) 0.559 0.614 0.579 Validation Set 0.602 (0.327–0.879) 0.554 0.571 0.560 ADC Training Set 0.795 (0.693-0.900) 0.627 0.750 0.671 Validation Set 0.788 (0.572–0.976) 0.588 0.714 0.632 Multi Training Set 0.848 (0.756–0.943) 0.861 0.707 0.806 Validation Set 0.802 (0.596–0.982) 0.840 0.600 0.755 AUC, area under the curve; CI, confidence interval; Multi, multiple sequences. Correlation analysis assessments The correlation analysis found that the value of adc_wavelet_glszm_wavelet-HHH-SmallAreaLowGrayLevelEmphasis (the prefix indicated that it was selected from ADC maps) was negatively correlated with Ki-67 proliferation rate (r = -0.221, p = 0.029, Fig. 5 ). And correlation analysis found that the value of t2_log_firstorder_log-sigma-2-0-mm-3D-Skewness (the prefix indicated that it was selected from T2WI) was negatively correlated with Ki-67 proliferation rate (r = -0.209, p = 0.040, Fig. 5 ). Discussion In this study, we constructed twelve single-sequence and four multi-sequence models based on T2WI, DWI, and ADC maps for predicting immunohistochemical biomarkers in EC patients, with labels for ER, PR, P53, and Ki-67 as labels 0 or 1. Our results showed that the high order radiomic features are important potential predictors. Both single-sequence and multi-sequence models showed good predictive performance. Therefore, the radiomics models can provide a noninvasive and personalized management method for EC patients. Our feature selection results showed that a significant proportion of features selected in the multi-sequence model were shared with those selected in the single-sequence model, although a few did not share features selected in the single-sequence model, which may be related to the weight of the radiomic features, and the repeatedly selected features may be important radiomic markers for predicting immunohistochemical features. Subsequent correlation analysis validated this hypothesis. The results of our correlation study showed that there were two values of radiomic features that are negatively correlated with Ki-67 proliferation rate: adc_wavelet_glszm_wavelet-HHH-SmallAreaLowGrayLevelEmphasis and t2_log_firstorder_log-sigma-2-0-mm-3D-Skewness, respectively. Similar to previous studies exploring the relationship between the gene expression of caners and the values of radiomic features values, including breast cancer [ 27 ], lung cancer [ 28 ] and retinoblastoma[ 29 ], our findings showed that specific features were correlated with gene expression. For future research, we intend to further expand the sample size and even gene sequencing to explore the relationship between radiomic features and gene expression to further validate our hypothesis. It is worth noting that our results showed that both single-sequence and multi-sequence models performed well in predicting immunohistochemical biomarkers with high accuracy. However, the prediction performance of the multi-sequence model was better than that of the single-sequence model. In previous radiomics studies on EC, we found that in two studies from the same team, the predictive power of the model for deep myometrial invasion (DMI), lympho-vascular space invasion (LVSI), and histologic high-grade EC improved after they added sequences [ 20 , 30 ], which is consistent with our findings. This is because increasing the sequence can obtain more tumor information, and the radiomic features that rank highly in a single sequence are not necessarily effective compared to the radiomic features of other sequences. Adding sequences aims to capture the most effective radiomic features. Our study had several limitations. Firstly, it difficult to obtain both MRI and postoperative immunohistochemical information for patients, resulting our current sample size was relatively small. Future cooperation with other institutions could help collect more retrospective and prospective samples to retain a more stable model. Secondly, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was not part of this study. In the scanning process, the image quality is different due to different MR equipment and technicians, and in clinical work, there are not many patients who undergo DCE-MRI scanning, so we plan to carry out prospective studies in the future to ensure the integrity of the data. Finally, we did not include clinical measures such as estrogen levels, BMI, and tumor indicators. We tried to collect this information to further improve the performance of our radiomic models, but in the medical record system, the indicators contained in the preoperative examination of patients were not exactly the same or some information was missing. Future inclusion of more samples is anticipated to enrich our experiment. Conclusion This study developed single-sequence and multi-sequence predicting models for four kinds of immunohistochemical biomarkers (ER, PR, P53, and Ki-67) in EC patients using T2WI, DWI, and ADC maps with good performance. Further correlation analysis showed an association between specific radiomic features and immunohistochemical biomarkers, providing a new way for clinicians to predict the molecular and gene information of patients preoperatively, contributing to advancing personalized precision medicine and improving the prognosis of patients. Abbreviations EC endometrial cancer ER estrogen receptor PR progesterone receptor EEC endometrioid adenocarcinoma MRI Magnetic resonance imaging DWI diffusion-weighted imaging ADC apparent diffusion coefficient DMI deep myometrial invasion LVSI lymph vascular space invasion T2 FLAIR T2-weighted fluid-attenuated inversion recovery T2WI T2-weighted imaging uRP uAI Research Portal software LASSO least absolute shrinkage and selection operator DCE-MRI dynamic contrast-enhanced magnetic resonance imaging. Declarations Ethics approval and consent to participate The institutional review boards of the Second Affiliated Hospital of Wenzhou Medical University approved this retrospective study, and individual consent for the retrospective analysis was waived. We confirmed that the study was carried out in accordance with relevant guidelines and regulations of Helsinki Declaration. Consent for publication Not applicable. Availability of data and materials Not applicable. Competing Interests The authors declare that there is no conflict of interest. Funding This work was supported by grants from Wenzhou Science and Technology Bureau in China (No. Y2020816). Authors' contributions LS, XC: Drafting of the manuscript. LH, XW, ZY and JP: Critical revision of the manuscript. LS and YZ: Data analysis or interpretation. LS, LL and XC: Conception, Data acquisition. All authors: All authors reviewed and approved the manuscript. A cknowledgements Not applicable. References Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA Cancer J Clin. 2021;71(1):7–33. Crosbie EJ, Kitson SJ, McAlpine JN, Mukhopadhyay A, Powell ME, Singh N. Endometrial cancer. Lancet. 2022;399(10333):1412–28. Jamieson A, Bosse T, McAlpine JN. The emerging role of molecular pathology in directing the systemic treatment of endometrial cancer. Ther Adv Med Oncol. 2021;13:17588359211035959. Stelloo E, Nout RA, Osse EM, Jürgenliemk-Schulz IJ, Jobsen JJ, Lutgens LC, van der Steen-Banasik EM, Nijman HW, Putter H, Bosse T, et al. Improved Risk Assessment by Integrating Molecular and Clinicopathological Factors in Early-stage Endometrial Cancer-Combined Analysis of the PORTEC Cohorts. Clin Cancer Res. 2016;22(16):4215–24. Tortorella L, Restaino S, Zannoni GF, Vizzielli G, Chiantera V, Cappuccio S, Gioè A, La Fera E, Dinoi G, Angelico G, et al. Substantial lymph-vascular space invasion (LVSI) as predictor of distant relapse and poor prognosis in low-risk early-stage endometrial cancer. J Gynecol Oncol. 2021;32(2):e11. Pasanen A, Loukovaara M, Ahvenainen T, Vahteristo P, Bützow R. Differential impact of clinicopathological risk factors within the 2 largest ProMisE molecular subgroups of endometrial carcinoma. PLoS ONE. 2021;16(9):e0253472. Jia M, Jiang P, Huang Z, Hu J, Deng Y, Hu Z. The combined ratio of estrogen, progesterone, Ki-67, and P53 to predict the recurrence of endometrial cancer. J Surg Oncol. 2020;122(8):1808–14. Zhang Y, Zhao D, Gong C, Zhang F, He J, Zhang W, Zhao Y, Sun J. Prognostic role of hormone receptors in endometrial cancer: a systematic review and meta-analysis. World J Surg Oncol. 2015;13(1):208. Huvila J, Talve L, Carpén O, Edqvist PH, Pontén F, Grénman S, Auranen A. Progesterone receptor negativity is an independent risk factor for relapse in patients with early stage endometrioid endometrial adenocarcinoma. Gynecol Oncol. 2013;130(3):463–9. Guan J, Xie L, Luo X, Yang B, Zhang H, Zhu Q, Chen X. The prognostic significance of estrogen and progesterone receptors in grade I and II endometrioid endometrial adenocarcinoma: hormone receptors in risk stratification. J Gynecol Oncol. 2019;30(1):e13. Vermij L, Léon-Castillo A, Singh N, Powell ME, Edmondson RJ, Genestie C, Khaw P, Pyman J, McLachlin CM, Ghatage P, et al. p53 immunohistochemistry in endometrial cancer: clinical and molecular correlates in the PORTEC-3 trial. Mod Pathol. 2022;35(10):1475–83. Stefansson IM, Salvesen HB, Immervoll H, Akslen LA. Prognostic impact of histological grade and vascular invasion compared with tumour cell proliferation in endometrial carcinoma of endometrioid type. Histopathology. 2004;44(5):472–9. Jiang P, Jia M, Hu J, Huang Z, Deng Y, Lai L, Ding S, Hu Z. Prognostic Value of Ki67 in Patients with Stage 1–2 Endometrial Cancer: Validation of the Cut-off Value of Ki67 as a Predictive Factor. Onco Targets Ther. 2020;13:10841–50. Dueholm M, Hjorth IM. Structured imaging technique in the gynecologic office for the diagnosis of abnormal uterine bleeding. Best Pract Res Clin Obstet Gynaecol. 2017;40:23–43. Di Spiezio Sardo A, De Angelis MC, Della Corte L, Carugno J, Zizolfi B, Guadagno E, Gencarelli A, Cecchi E, Simoncini T, Bifulco G, et al. Should endometrial biopsy under direct hysteroscopic visualization using the grasp technique become the new gold standard for the preoperative evaluation of the patient with endometrial cancer? Gynecol Oncol. 2020;158(2):347–53. Lee Y, Kim KA, Song MJ, Park YS, Lee J, Choi JW, Lee CH. Multiparametric magnetic resonance imaging of endometrial polypoid lesions. Abdom Radiol (NY). 2020;45(11):3869–81. Chen J, Fan W, Gu H, Wang Y, Liu Y, Chen X, Ren S, Wang Z. The value of the apparent diffusion coefficient in differentiating type II from type I endometrial carcinoma. Acta Radiol. 2021;62(7):959–65. Bakir B, Sanli S, Bakir VL, Ayas S, Yildiz SO, Iyibozkurt AC, Kartal MG, Yavuz E. Role of diffusion weighted MRI in the differential diagnosis of endometrial cancer, polyp, hyperplasia, and physiological thickening. Clin Imaging. 2017;41:86–94. Yan BC, Li Y, Ma FH, Zhang GF, Feng F, Sun MH, Lin GW, Qiang JW. Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study. Eur Radiol. 2021;31(1):411–22. Ueno Y, Forghani B, Forghani R, Dohan A, Zeng XZ, Chamming's F, Arseneau J, Fu L, Gilbert L, Gallix B, et al. Endometrial Carcinoma: MR Imaging-based Texture Model for Preoperative Risk Stratification-A Preliminary Analysis. Radiology. 2017;284(3):748–57. Ytre-Hauge S, Dybvik JA, Lundervold A, Salvesen ØO, Krakstad C, Fasmer KE, Werner HM, Ganeshan B, Høivik E, Bjørge L, et al. Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer. J Magn Reson Imaging. 2018;48(6):1637–47. Lin Z, Wang T, Li H, Xiao M, Ma X, Gu Y, Qiang J. Magnetic resonance-based radiomics nomogram for predicting microsatellite instability status in endometrial cancer. Quant Imaging Med Surg. 2023;13(1):108–20. Li J, Liu S, Qin Y, Zhang Y, Wang N, Liu H. High-order radiomics features based on T2 FLAIR MRI predict multiple glioma immunohistochemical features: A more precise and personalized gliomas management. PLoS ONE. 2020;15(1):e0227703. Yu X, Guo S, Song W, Xiang T, Yang C, Tao K, Zhou L, Cao Y, Liu S. Estrogen receptor α (ERα) status evaluation using RNAscope in situ hybridization: a reliable and complementary method for IHC in breast cancer tissues. Hum Pathol. 2017;61:121–9. Singh N, Piskorz AM, Bosse T, Jimenez-Linan M, Rous B, Brenton JD, Gilks CB, Köbel M. p53 immunohistochemistry is an accurate surrogate for TP53 mutational analysis in endometrial carcinoma biopsies. J Pathol. 2020;250(3):336–45. Wu J, Xia Y, Wang X, Wei Y, Liu A, Innanje A, Zheng M, Chen L, Shi J, Wang L, et al. uRP: An integrated research platform for one-stop analysis of medical images. Front Radiol. 2023;3:1153784. Kim GR, Ku YJ, Kim JH, Kim EK. Correlation between MR Image-Based Radiomics Features and Risk Scores Associated with Gene Expression Profiles in Breast Cancer. Taehan Yongsang Uihakhoe Chi. 2020;81(3):632–43. Wang T, Gong J, Duan HH, Wang LJ, Ye XD, Nie SD. Correlation between CT based radiomics features and gene expression data in non-small cell lung cancer. J Xray Sci Technol. 2019;27(5):773–803. Jansen RW, Roohollahi K, Uner OE, de Jong Y, de Bloeme CM, Göricke S, Sirin S, Maeder P, Galluzzi P, Brisse HJ et al. Correlation of gene expression with magnetic resonance imaging features of retinoblastoma: a multi-center radiogenomics validation study. Eur Radiol 2023. Lefebvre TL, Ueno Y, Dohan A, Chatterjee A, Vallières M, Winter-Reinhold E, Saif S, Levesque IR, Zeng XZ, Forghani R, et al. Development and Validation of Multiparametric MRI-based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer. Radiology. 2022;305(2):375–86. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4179540","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290374885,"identity":"34373e67-6344-4ee4-9f6c-518ccd1aece7","order_by":0,"name":"Liting Shen","email":"","orcid":"","institution":"Second Affiliated Hospital \u0026 Yuying Children's Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liting","middleName":"","lastName":"Shen","suffix":""},{"id":290374887,"identity":"77784a5c-c35b-4ab4-a438-34aff53ffd41","order_by":1,"name":"Xiaojun Chen","email":"","orcid":"","institution":"Affiliated Jinhua Hospital, Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaojun","middleName":"","lastName":"Chen","suffix":""},{"id":290374889,"identity":"1d6dc746-dc2b-4653-80e6-40185fcdaf0c","order_by":2,"name":"Lan Li","email":"","orcid":"","institution":"Second Affiliated Hospital \u0026 Yuying Children's Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Li","suffix":""},{"id":290374890,"identity":"431f764c-1445-45c8-b2f9-08c6b06c0036","order_by":3,"name":"Yan Zeng","email":"","orcid":"","institution":"United Imaging Healthcare (China)","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zeng","suffix":""},{"id":290374892,"identity":"55863e4d-2589-4b69-8770-1137a73d3b33","order_by":4,"name":"Zhihan Yan","email":"","orcid":"","institution":"Second Affiliated Hospital \u0026 Yuying Children's Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhihan","middleName":"","lastName":"Yan","suffix":""},{"id":290374894,"identity":"d6cb9e9e-d443-41e3-9de2-cadbd3ffc676","order_by":5,"name":"Lu Han","email":"","orcid":"","institution":"Philips (China)","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Han","suffix":""},{"id":290374896,"identity":"fab6f537-4412-4389-a3bb-a304cd3e96d4","order_by":6,"name":"Jiangfeng Pan","email":"","orcid":"","institution":"Affiliated Jinhua Hospital, Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiangfeng","middleName":"","lastName":"Pan","suffix":""},{"id":290374897,"identity":"ca992e0c-7870-47fd-9230-ab18b6f6920b","order_by":7,"name":"Xue Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIie3PMQrCMBSA4ZepS6BrimKv0CKIQ27i8oIQl+rStWBEcOoBFE/RGxQCnXqAugW8gG6dxIKTU9JNMP/8Pt57AD7fDxbGZtMj5fs4UI4kUmiYmUpMy9qRJLUwkeEaoUPnLRoTzOSOXO5VBwVfWUlITojY8jyYyHwJjdwq65YjxVqUkhyu2YIRpe0kacKnEi9N1K11JS2sAakWqqOOJDqDHIicp+XwC7r8EjKQpKd8Fge66h4Ft5OvIxmOGf+QscLn8/n+ozczk0LGKvXgqAAAAABJRU5ErkJggg==","orcid":"","institution":"Second Affiliated Hospital \u0026 Yuying Children's Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xue","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-03-28 04:46:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4179540/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4179540/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54783502,"identity":"1ce31ef9-4253-4ccd-b687-b2c86a3d9495","added_by":"auto","created_at":"2024-04-16 17:27:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":377633,"visible":true,"origin":"","legend":"\u003cp\u003eThe workflow of this study.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4179540/v1/660c6a5bac114448663ba1f4.png"},{"id":54783503,"identity":"308c5014-fbc2-4db7-b812-8796d2daba45","added_by":"auto","created_at":"2024-04-16 17:27:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":342806,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe names and coefficients of selected radiomic features in sixteen models. \u003c/strong\u003eVertical ADC, DWI, T2WI, and Multi, respectively, represent features filtered from different sequences, and Multi represents multi-sequence models. The feature prefix of the multi-sequence model represents the sequence name. A. The selected radiomic features in the ER models. B. The selected radiomic features in the PR models. C. The selected radiomic features in the P53 models. D. The selected radiomic features in the Ki-67 models.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4179540/v1/0e616c4255f0663b6a112010.png"},{"id":54783504,"identity":"268c2989-6fa9-4060-b207-246fd2896026","added_by":"auto","created_at":"2024-04-16 17:27:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":171826,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves of sixteen models for predicting the immunohistochemical subtypes. \u003c/strong\u003eThe first row showed the ROC curves of the radiomic models in the training set, and the second row showed the ROC curves of the radiomic models in the validation set.\u003cstrong\u003e \u003c/strong\u003eA and B. The ROC curves of ER; C and D. The ROC curves of PR; E and F. The ROC curves of P53; G and H. The ROC curves of Ki-67.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4179540/v1/514d8d1f84db5106e9c3d377.png"},{"id":54783505,"identity":"d4ac62c6-aa0b-4586-8160-83ad8dd0ec13","added_by":"auto","created_at":"2024-04-16 17:27:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":121694,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe performance of four multi-sequence models for predicting the immunohistochemical subtypes. \u003c/strong\u003eThe first row showed the identification effect of the radiomic models in the training set, and the second row showed the identification effect of the radiomic models in the validation set.\u003cstrong\u003e \u003c/strong\u003eA and B. The ROC curves of ER; C and D. The ROC curves of PR; E and F. The ROC curves of P53; G and H. The ROC curves of Ki-67.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4179540/v1/5b156fc80347edf491e6f198.png"},{"id":54783506,"identity":"ed6f4311-5b9a-44ee-b57f-cddd3e19adae","added_by":"auto","created_at":"2024-04-16 17:27:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":109544,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. Association of adc_wavelet_glszm_wavelet-HHH-SmallAreaLowGrayLevelEmphasis values with Ki-67 proliferation rate. \u003c/strong\u003eThe adc_wavelet_glszm_wavelet-HHH-SmallAreaLowGrayLevelEmphasis values were negatively correlated with the Ki-67 proliferation rate (r = -0.221, p = 0.029). \u003cstrong\u003eB. Association of t2_log_firstorder_log-sigma-2-0-mm-3D-Skewness values with Ki-67 proliferation rate. \u003c/strong\u003eThe t2_log_firstorder_log-sigma-2-0-mm-3D-Skewness values were negatively correlated with the Ki-67 proliferation rate (r = -0.209, p = 0.040).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4179540/v1/f8f13af0439134c576b3d227.png"},{"id":60499102,"identity":"18f0afb8-ebdd-4034-ba9e-f1183b5c572d","added_by":"auto","created_at":"2024-07-17 12:14:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1901420,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4179540/v1/d191e2a4-8afd-4e48-82e8-ccff9dd597b8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Radiomic Features Based on Multi-sequence MRI Predict Immunohistochemical Biomarkers of Endometrial Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometrial cancer (EC) is a common gynecologic malignancy around the world. In recent years, the incidence rate of women in our country has increased [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The significant heterogeneity of EC underscores the importance of accurate grading of EC for clinics [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although most early-stage EC patients have a good prognosis, a small number of patients have a poor prognosis even when diagnosed early [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is because ECs with the same or similar histological characteristics may harbor different molecular or genetic information, which in turn influences the prognosis of patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, great progress has been made in understanding the molecular pathology of EC. A series of molecular markers have emerged, aiding in the clinical differential diagnosis and prognosis prediction of EC, among which estrogen receptor (ER), progesterone receptor (PR), P53, and Ki-67 are four vital biological behavior markers [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Notably, endometrioid adenocarcinoma (EEC) is a hormonally regulated disease, and a positive PR status is associated with a more favorable prognosis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, progestin therapy can be an option for selected patients, particularly young women who wish to preserve fertility. Conversely, double-negative hormone receptor (ER and PR) or negative PR status has been associated with shorter survival [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The presence of P53 mutations is indicative of invasiveness and a poor prognosis, while the prognosis of patients with P53 wild type is better [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. As for Ki-67, its expression was reported to be positively correlated with tumor grade in patients with EEC, and an increased risk of recurrence was found in Stage I-II EC patients with a higher Ki-67 index [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, accurately identification of the molecular and genetic information in EC and precise treatment are important to clinical.\u003c/p\u003e \u003cp\u003eAt present, the immunohistochemical types of EC are primarily obtained from the pathological histology after surgical resection or biopsy. However, it is an invasive procedure that highly dependent on the experience of the pathologists and sometimes prone to sampling biases [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Magnetic resonance imaging (MRI) offers a non-invasive technique with excellent soft tissue contrast resolution. Conventional MRI, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps are of great value in the diagnosis and staging of EC [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In recent years, the rapid advancements in artificial intelligence have given rise to the burgeoning field of radiomics. Many studies have used radiomics methods based on single or multi-sequence MRI to predict tumor grade, deep myometrial invasion (DMI), and lymph vascular space invasion (LVSI) in EC [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. With the in-depth study of radiomics, it transcends the confines of tumor diagnosis and staging, delving deeper into the realm of histopathological features. Recently, Zijing Lin et al. developed a radiomics nomogram for the prediction of microsatellite instability (MSI) status in EC, demonstrating favorable calibration and clinical utility [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition, a single-center study used small data based on T2-weighted fluid-attenuated inversion recovery (T2 FLAIR) to predict immunohistochemical features of glioma, and satisfactory diagnosis accuracy was reported, indicating possible [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aimed to collect the immunohistochemical results of EC. By utilizing T2-weighted imaging (T2WI), DWI, and ADC images, the radiomics method was used to predict the immunohistochemical characteristics of EC through the extraction of radiomic features and construction of the radiomics model, which could contribute to a deeper understanding of the molecular characteristics of EC and provide new insights for guiding the precision treatment before clinical operation, improving the prognosis of patients.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003e The institutional review boards of the Second Affiliated Hospital of Wenzhou Medical University approved this retrospective study, and individual consent for the retrospective analysis was waived. The study cohorts enrolled in this research followed the criteria as follows: (1) patients had clinical symptoms and was histologically diagnosed with EC; (2) availability of immunohistochemistry results; (3) no history of chemotherapy, radiotherapy, or surgery prior to the MRI examination; and (4) MRI examinations consisting of T2WI, DWI, and ADC maps of acceptable MR image quality. A total of 105 confirmed female patients (mean age, 55 years; age range, 27\u0026ndash;74 years), who were pathologically diagnosed with EC and underwent MRI examinations at this center between May 2012 and June 2023, were reviewed in this study. Feature extraction or immunohistochemistry were not available for all; there were 100 ER, 94 PR, 97 P53, and 98 Ki-67 immunohistochemistry cases, respectively. The patients were randomly divided into a training set and a validation set according to a ratio of 8:2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMRI acquisition\u003c/h2\u003e \u003cp\u003eThe MRI scans was performed using 3.0-T scanners with phased-array abdominal coils. The patients lay in a supine position and breathed freely during the acquisition. The following sequences were obtained: Sagittal T2WI with and without fat saturation; DWI with b\u0026thinsp;=\u0026thinsp;800 or 1000 s/mm\u003csup\u003e2\u003c/sup\u003e. The ADC map was automatically calculated based on DWI by the embedded software of the MRI equipment. More detailed information about these sequences is listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMRI examination\u0026rsquo;s parameters.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDevice 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDevice 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0-T Discovery 750 GE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0-T HDXT GE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR/TE\u0026thinsp;=\u0026thinsp;3475/69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTR/TE\u0026thinsp;=\u0026thinsp;2900/73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatrix\u0026thinsp;=\u0026thinsp;320\u0026times;320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMatrix\u0026thinsp;=\u0026thinsp;320\u0026times;320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ethickness\u0026thinsp;=\u0026thinsp;4mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ethickness\u0026thinsp;=\u0026thinsp;4mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFOV\u0026thinsp;=\u0026thinsp;280\u0026times;380mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFOV\u0026thinsp;=\u0026thinsp;280\u0026times;380mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR/TE\u0026thinsp;=\u0026thinsp;2400/73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTR/TE\u0026thinsp;=\u0026thinsp;5000/66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatrix\u0026thinsp;=\u0026thinsp;192\u0026times;160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMatrix\u0026thinsp;=\u0026thinsp;192\u0026times;160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ethickness\u0026thinsp;=\u0026thinsp;5mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ethickness\u0026thinsp;=\u0026thinsp;6mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFOV\u0026thinsp;=\u0026thinsp;288\u0026times;360mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFOV\u0026thinsp;=\u0026thinsp;380\u0026times;380mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eb\u0026thinsp;=\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eb\u0026thinsp;=\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eMRI, magnetic resonance imaging; T2WI, T2-weighted imaging; DWI, diffusion-weighted imaging; TE, echo time; TR, repetition time; FOV, field of view.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemical status assessment\u003c/h2\u003e \u003cp\u003eAll the patients involved in the current study underwent total hysterectomy, bilateral salpingo-oophorectomy, and/or pelvic/para-aortic lymphadenectomy. Immunohistochemical factors were evaluated in these patients through the acquisition of surgical samples. The specific procedures were carried out following the standard guidelines of the institution [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. All immunohistochemical results were collected in the PACS system of the center. In the ER and PR evaluations, tumor cells were taken into account for densities, categorized as (-, \u0026plusmn;, 1+, 2+, and 3+). In cases where the densities of tumor cells are \u0026plusmn;, 1+,2+, and 3+, they were classified as positive (label\u0026thinsp;=\u0026thinsp;1), whereas negative (label\u0026thinsp;=\u0026thinsp;0). The expression level of the P53 and Ki-67 index was represented by the proportional staining (%). The proportional staining of P53 with 0% and 75\u0026ndash;100% was considered mutant type (complete absence and overexpression, label\u0026thinsp;=\u0026thinsp;1), and 1\u0026ndash;75% was considered wild type (label\u0026thinsp;=\u0026thinsp;0) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. And proportional staining of Ki-67 that equaled or was more than 50% was considered hyperproliferation (label\u0026thinsp;=\u0026thinsp;0), and less than 50% was considered hypoproliferation (label\u0026thinsp;=\u0026thinsp;1) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSegmentation\u003c/h2\u003e \u003cp\u003eThe regions of interest (ROIs) that covered the whole lesion were manually segmented by two radiologists, each with 6 and 10 years of experience, using an open-source program called ITK-SNAP (version 3.8.0) on MRI images while blinded to the histopathological results. For each case, ROI was first drawn by one radiologist and then reviewed by the other radiologist to ensure high-quality final segmentation results. The manual drawing of each ROI was performed slice-by-slice on T2WI, DWI, and ADC and cross-referenced with each other, taking care to avoid including nearby normal myometrium or endometrium.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFeature extraction and selection\u003c/h2\u003e \u003cp\u003eAll ROIs from T2WI, DWI, and ADC maps, as well as the original MRI, were processed in batches by the uAI Data Assistant (United Imaging Intelligence, China). Subsequently, the radiomics module within the within the uAI Research Portal software (uRP) (United Imaging Intelligence, China) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] was applied for feature extraction from each sequence, which complies with the standards set by the IBSI recommendation. And all extracted features were normalized using a Z-value normalization algorithm. Then, least absolute shrinkage and selection operator (LASSO) regression was used to reduce the dimension of features so as to obtain the optimized subset of features for constructing the final model. The LASSO analysis included choosing the regular parameter λ and determining the number of features. Once the number of features was determined, the most predictive feature subset was chosen, and the corresponding coefficients were evaluated. In this study, we aimed to compare the difference in predictive performance between the single-sequence model and the multi-sequence model, so we mixed the top 20 features from the three single-sequence models for each immunohistochemical parameter and conducted LASSO analysis again for feature dimensionality reduction to facilitate the subsequent establishment of the multi-sequence model. The process of radiomics analysis is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eModel building and evaluation\u003c/h2\u003e \u003cp\u003eLogistic regression was employed to construct radiomic models. In model training, 5-fold cross-validation was used for algorithm hyperparameter tuning based on the training set. Finally, receiver operating characteristic (ROC) curves were plotted. The area under the curve (AUC) and additional indices, including accuracy, sensitivity, and specificity, were calculated to evaluate the performance of the radiomics models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis\u003c/h2\u003e \u003cp\u003ePearson's correlations were used to explore the correlation between the values of radiomic features selected from the multi-sequence model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) and the Ki-67 proliferation rate. This work was performed on SPSS (version 26.0) and GraphPad Prism (version 9), with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Due to condition, we did not calculate the correlation between the screened radiomic features and the expression of ER, PR, and P53.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eAmong the 105 patients reviewed, 100 ER, 94 PR, 97 P53, and 98 Ki-67 immunohistochemistry cases were included, respectively. The EC was staged based on FIGO 2018. The histological subtype and histological characteristics of EC were determined from a surgical specimen or biopsy. Detailed patient demographics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe detailed demographic information of EC patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP53\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKi-67\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabel\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (80.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (71.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (60.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (35.71%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabel\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (20.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (28.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (39.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63 (64.29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85 (85.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (87.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (85.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84 (85.71%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-EEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (1.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (9.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (9.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (9.18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eunknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (5.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.20%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (5.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (5.11%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (29.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (29.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (28.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 (28.57%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (33.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (34.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (34.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (33.67%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (16.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (15.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (6.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (16.33%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eunknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (22.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (20.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (20.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (21.43%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIGO staging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (89.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83 (88.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (88.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88 (89.80%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.02%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (7.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (7.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (7.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (7.14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2.04%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eEC, Endometrial cancer; ER, estrogen receptor; PR, progesterone receptor; the positive, mutant type, and hypoproliferation groups were labeled 0; the negative, wide type, and hyperproliferation groups were labeled 1; EEC, endometrioid adenocarcinoma.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFeature extraction and selection\u003c/h2\u003e \u003cp\u003eA total of 2264 features were extracted from each sequence (T2WI, DWI, and ADC maps) for each patient. Following feature selection, the most effective features that were retained by each model were illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The results showed that the selected features were different between single-sequence and multi-sequence, as follows:\u003c/p\u003e \u003cp\u003eIn ER models, there were two features from the multi-sequence model shared with the ADC model (wavelet_firstorder_wavelet-LLL-Maximum and discretegaussian_glcm_ClusterShade); two features from the multi-sequence model shared with the DWI model (wavelet_glacm wavelet-LLH-ClusterShade and boxmean_firstorder_Skewness); and two features from the multi-sequence model shared with the T2WI model (wavelet_glszm_wavelet-HHL-SizeZoneNonUnifemityNormalized and wavelet_firstorder_wavelet-LHH-Skewness). Only one feature from the multi-sequence model shared without the ADC model (log_firstorder_log-sigma-1-0-mm-3D-Kurtosis).\u003c/p\u003e \u003cp\u003eIn PR models, there were three features from the multi-sequence model shared with the ADC model (boxsigmaimage_glcm_DifferenceVariance, mean_glcm_CluterShade and wavelet_firstorder_wavelry-HLL-Skewness); one feature from the multi-sequence model shared with the DWI model (wavelet_ngtdm_wavelet-HHH-Contrast); and two features from the multi-sequence model shared with the T2WI model (log_firstorder_log-sigma-0-5-mm-3D-Kurtosis and wavelet_firstorder_wavelet-LHH-Skewness). And two features from the multi-sequence model shared without the ADC and DWI models (wavelet_glrlm_wavelet-HHL-ShortRunLowGraylevelEmphasis, selected from ADC maps; boxmean_glcm_ldm, selected from DWI).\u003c/p\u003e \u003cp\u003eIn P53 models, there were three features from the multi-sequence model shared with the ADC model (wavelet_gldm_wavelet-HLH-LargeDependenceLowGrayLevelEmphasis, log_gldm_log-sigma-0-5-mm-3D-DependenceVariance and boxsigmaimage_glrlm_LowGrayLevelRunEmphasis); three features from the multi-sequence model shared with the DWI model (wavelet_glcm_wavelet-LLH-Contrast, wavelet_glszm_wavelet-HHL-SizeZnoeNonUniformityNomalilized and normalize_firstorder_Maximum); and two features from the multi-sequence model shared with the T2WI model (wavelet_firstorder_wavelet-HLL-Kurtosis and normalize_glrlm_ShortRunLowGrayLevelEmphasis). And no feature from multi-sequence model shared without the ADC, DWI, and T2WI models.\u003c/p\u003e \u003cp\u003eIn Ki-67 models, there were three features from the multi-sequence model shared with the ADC model (boxmean_glcm_correlation, wavelet_glszm_wavelet-hhh-smallarealowgraylevelemphasis and gldm_log-sigma-4-0-mm-3d-largedependencehighgraylevelemphasis); no features from the multi-sequence model shared with the DWI model; and no features from the multi-sequence model shared with the T2WI model. And two features from the multi-sequence model not shared with ADC, DWI, and T2WI models (log_firstorder_log-sigma-2-0-mm-ed-maximum and, selected from DWIs; log_firstorder_log sigma-2-0-mm-3d-skewness and wavelet_dirstorder_wavelet-hhl-median, selected from T2WI). And no feature from the multi-sequence model shared without the ADC, DWI, and T2WI models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eModel performance\u003c/h2\u003e \u003cp\u003eWe proposed four multiple sequence models for revealing the immunohistochemical typing of ER, PR, P53, and Ki-67. The ROC curves of four models on both the training and validation sets are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The performance of four multi-sequence models in predicting the immunohistochemical subtypes was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. And the accuracy, sensitivity, and specificity to quantify the predictive performance are listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The results showed that both single-sequence model and multi-sequence model performed well in predicting the four immunohistochemical parameters of EC with high accuracy. However, the AUC, accuracy, sensitivity, and specificity of the multi-sequence model were generally higher than those of the single-sequence model, which showed higher predictive performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe predictive performance of sixteen models on different data sets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Set\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.838 (0.728\u0026ndash;0.949)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.829 (0.568-1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDWI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.818 (0.718\u0026ndash;0.919)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.804 (0.589\u0026ndash;0.986)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.887 (0.810\u0026ndash;0.966)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.863 (0.678-1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.930 (0.874\u0026ndash;0.990)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.894 (0.758\u0026ndash;0.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.828 (0.730\u0026ndash;0.926)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.820 (0.620\u0026ndash;0.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDWI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.863 (0.779\u0026ndash;0.946)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.818 (0.619\u0026ndash;0.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.862 (0.759\u0026ndash;0.967)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.821 (0.600-1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.918 (0.851\u0026ndash;0.989)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.874 (0.733\u0026ndash;0.993)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.825 (0.736\u0026ndash;0.917)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.730 (0.490\u0026ndash;0.952)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDWI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.851 (0.769\u0026ndash;0.937)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.771 (0.548\u0026ndash;0.973)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.848 (0.767\u0026ndash;0.932)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.770 (0.569\u0026ndash;0.954)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.873 (0.800-0.951)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.833 (0.663\u0026ndash;0.977)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi-67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.709 (0.593\u0026ndash;0.827)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.683 (0.429\u0026ndash;0.929)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDWI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.645 (0.515\u0026ndash;0.776)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.602 (0.327\u0026ndash;0.879)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.795 (0.693-0.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.788 (0.572\u0026ndash;0.976)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.848 (0.756\u0026ndash;0.943)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation Set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.802 (0.596\u0026ndash;0.982)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAUC, area under the curve; CI, confidence interval; Multi, multiple sequences.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis assessments\u003c/h2\u003e \u003cp\u003eThe correlation analysis found that the value of adc_wavelet_glszm_wavelet-HHH-SmallAreaLowGrayLevelEmphasis (the prefix indicated that it was selected from ADC maps) was negatively correlated with Ki-67 proliferation rate (r = -0.221, p\u0026thinsp;=\u0026thinsp;0.029, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). And correlation analysis found that the value of t2_log_firstorder_log-sigma-2-0-mm-3D-Skewness (the prefix indicated that it was selected from T2WI) was negatively correlated with Ki-67 proliferation rate (r = -0.209, p\u0026thinsp;=\u0026thinsp;0.040, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we constructed twelve single-sequence and four multi-sequence models based on T2WI, DWI, and ADC maps for predicting immunohistochemical biomarkers in EC patients, with labels for ER, PR, P53, and Ki-67 as labels 0 or 1. Our results showed that the high order radiomic features are important potential predictors. Both single-sequence and multi-sequence models showed good predictive performance. Therefore, the radiomics models can provide a noninvasive and personalized management method for EC patients.\u003c/p\u003e \u003cp\u003eOur feature selection results showed that a significant proportion of features selected in the multi-sequence model were shared with those selected in the single-sequence model, although a few did not share features selected in the single-sequence model, which may be related to the weight of the radiomic features, and the repeatedly selected features may be important radiomic markers for predicting immunohistochemical features. Subsequent correlation analysis validated this hypothesis.\u003c/p\u003e \u003cp\u003eThe results of our correlation study showed that there were two values of radiomic features that are negatively correlated with Ki-67 proliferation rate: adc_wavelet_glszm_wavelet-HHH-SmallAreaLowGrayLevelEmphasis and t2_log_firstorder_log-sigma-2-0-mm-3D-Skewness, respectively. Similar to previous studies exploring the relationship between the gene expression of caners and the values of radiomic features values, including breast cancer [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], lung cancer [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and retinoblastoma[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], our findings showed that specific features were correlated with gene expression. For future research, we intend to further expand the sample size and even gene sequencing to explore the relationship between radiomic features and gene expression to further validate our hypothesis.\u003c/p\u003e \u003cp\u003eIt is worth noting that our results showed that both single-sequence and multi-sequence models performed well in predicting immunohistochemical biomarkers with high accuracy. However, the prediction performance of the multi-sequence model was better than that of the single-sequence model. In previous radiomics studies on EC, we found that in two studies from the same team, the predictive power of the model for deep myometrial invasion (DMI), lympho-vascular space invasion (LVSI), and histologic high-grade EC improved after they added sequences [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], which is consistent with our findings. This is because increasing the sequence can obtain more tumor information, and the radiomic features that rank highly in a single sequence are not necessarily effective compared to the radiomic features of other sequences. Adding sequences aims to capture the most effective radiomic features.\u003c/p\u003e \u003cp\u003eOur study had several limitations. Firstly, it difficult to obtain both MRI and postoperative immunohistochemical information for patients, resulting our current sample size was relatively small. Future cooperation with other institutions could help collect more retrospective and prospective samples to retain a more stable model. Secondly, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was not part of this study. In the scanning process, the image quality is different due to different MR equipment and technicians, and in clinical work, there are not many patients who undergo DCE-MRI scanning, so we plan to carry out prospective studies in the future to ensure the integrity of the data. Finally, we did not include clinical measures such as estrogen levels, BMI, and tumor indicators. We tried to collect this information to further improve the performance of our radiomic models, but in the medical record system, the indicators contained in the preoperative examination of patients were not exactly the same or some information was missing. Future inclusion of more samples is anticipated to enrich our experiment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study developed single-sequence and multi-sequence predicting models for four kinds of immunohistochemical biomarkers (ER, PR, P53, and Ki-67) in EC patients using T2WI, DWI, and ADC maps with good performance. Further correlation analysis showed an association between specific radiomic features and immunohistochemical biomarkers, providing a new way for clinicians to predict the molecular and gene information of patients preoperatively, contributing to advancing personalized precision medicine and improving the prognosis of patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eendometrial cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eestrogen receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprogesterone receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEEC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eendometrioid adenocarcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMagnetic resonance imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDWI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ediffusion-weighted imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eapparent diffusion coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edeep myometrial invasion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLVSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elymph vascular space invasion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT2 FLAIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eT2-weighted fluid-attenuated inversion recovery\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT2WI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eT2-weighted imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003euRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003euAI Research Portal software\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eleast absolute shrinkage and selection operator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCE-MRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edynamic contrast-enhanced magnetic resonance imaging.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe institutional review boards of the Second Affiliated Hospital of Wenzhou Medical University approved this retrospective study, and individual consent for the retrospective analysis was waived. We confirmed that the study was carried out in accordance with relevant guidelines and regulations of Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from Wenzhou Science and Technology Bureau in China (No. Y2020816).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLS, XC: Drafting of the manuscript. LH, XW, ZY and JP: Critical revision of the manuscript. LS and YZ: Data analysis or interpretation. LS, LL and XC: Conception, Data acquisition. All authors: All authors reviewed and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003ecknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA Cancer J Clin. 2021;71(1):7\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrosbie EJ, Kitson SJ, McAlpine JN, Mukhopadhyay A, Powell ME, Singh N. Endometrial cancer. Lancet. 2022;399(10333):1412\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJamieson A, Bosse T, McAlpine JN. The emerging role of molecular pathology in directing the systemic treatment of endometrial cancer. Ther Adv Med Oncol. 2021;13:17588359211035959.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStelloo E, Nout RA, Osse EM, J\u0026uuml;rgenliemk-Schulz IJ, Jobsen JJ, Lutgens LC, van der Steen-Banasik EM, Nijman HW, Putter H, Bosse T, et al. Improved Risk Assessment by Integrating Molecular and Clinicopathological Factors in Early-stage Endometrial Cancer-Combined Analysis of the PORTEC Cohorts. Clin Cancer Res. 2016;22(16):4215\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTortorella L, Restaino S, Zannoni GF, Vizzielli G, Chiantera V, Cappuccio S, Gio\u0026egrave; A, La Fera E, Dinoi G, Angelico G, et al. Substantial lymph-vascular space invasion (LVSI) as predictor of distant relapse and poor prognosis in low-risk early-stage endometrial cancer. J Gynecol Oncol. 2021;32(2):e11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePasanen A, Loukovaara M, Ahvenainen T, Vahteristo P, B\u0026uuml;tzow R. Differential impact of clinicopathological risk factors within the 2 largest ProMisE molecular subgroups of endometrial carcinoma. PLoS ONE. 2021;16(9):e0253472.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia M, Jiang P, Huang Z, Hu J, Deng Y, Hu Z. The combined ratio of estrogen, progesterone, Ki-67, and P53 to predict the recurrence of endometrial cancer. J Surg Oncol. 2020;122(8):1808\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Zhao D, Gong C, Zhang F, He J, Zhang W, Zhao Y, Sun J. Prognostic role of hormone receptors in endometrial cancer: a systematic review and meta-analysis. World J Surg Oncol. 2015;13(1):208.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuvila J, Talve L, Carp\u0026eacute;n O, Edqvist PH, Pont\u0026eacute;n F, Gr\u0026eacute;nman S, Auranen A. Progesterone receptor negativity is an independent risk factor for relapse in patients with early stage endometrioid endometrial adenocarcinoma. Gynecol Oncol. 2013;130(3):463\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuan J, Xie L, Luo X, Yang B, Zhang H, Zhu Q, Chen X. The prognostic significance of estrogen and progesterone receptors in grade I and II endometrioid endometrial adenocarcinoma: hormone receptors in risk stratification. J Gynecol Oncol. 2019;30(1):e13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVermij L, L\u0026eacute;on-Castillo A, Singh N, Powell ME, Edmondson RJ, Genestie C, Khaw P, Pyman J, McLachlin CM, Ghatage P, et al. p53 immunohistochemistry in endometrial cancer: clinical and molecular correlates in the PORTEC-3 trial. Mod Pathol. 2022;35(10):1475\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStefansson IM, Salvesen HB, Immervoll H, Akslen LA. Prognostic impact of histological grade and vascular invasion compared with tumour cell proliferation in endometrial carcinoma of endometrioid type. Histopathology. 2004;44(5):472\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang P, Jia M, Hu J, Huang Z, Deng Y, Lai L, Ding S, Hu Z. Prognostic Value of Ki67 in Patients with Stage 1\u0026ndash;2 Endometrial Cancer: Validation of the Cut-off Value of Ki67 as a Predictive Factor. Onco Targets Ther. 2020;13:10841\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDueholm M, Hjorth IM. Structured imaging technique in the gynecologic office for the diagnosis of abnormal uterine bleeding. Best Pract Res Clin Obstet Gynaecol. 2017;40:23\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Spiezio Sardo A, De Angelis MC, Della Corte L, Carugno J, Zizolfi B, Guadagno E, Gencarelli A, Cecchi E, Simoncini T, Bifulco G, et al. Should endometrial biopsy under direct hysteroscopic visualization using the grasp technique become the new gold standard for the preoperative evaluation of the patient with endometrial cancer? Gynecol Oncol. 2020;158(2):347\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee Y, Kim KA, Song MJ, Park YS, Lee J, Choi JW, Lee CH. Multiparametric magnetic resonance imaging of endometrial polypoid lesions. Abdom Radiol (NY). 2020;45(11):3869\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J, Fan W, Gu H, Wang Y, Liu Y, Chen X, Ren S, Wang Z. The value of the apparent diffusion coefficient in differentiating type II from type I endometrial carcinoma. Acta Radiol. 2021;62(7):959\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBakir B, Sanli S, Bakir VL, Ayas S, Yildiz SO, Iyibozkurt AC, Kartal MG, Yavuz E. Role of diffusion weighted MRI in the differential diagnosis of endometrial cancer, polyp, hyperplasia, and physiological thickening. Clin Imaging. 2017;41:86\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan BC, Li Y, Ma FH, Zhang GF, Feng F, Sun MH, Lin GW, Qiang JW. Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study. Eur Radiol. 2021;31(1):411\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUeno Y, Forghani B, Forghani R, Dohan A, Zeng XZ, Chamming's F, Arseneau J, Fu L, Gilbert L, Gallix B, et al. Endometrial Carcinoma: MR Imaging-based Texture Model for Preoperative Risk Stratification-A Preliminary Analysis. Radiology. 2017;284(3):748\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYtre-Hauge S, Dybvik JA, Lundervold A, Salvesen \u0026Oslash;O, Krakstad C, Fasmer KE, Werner HM, Ganeshan B, H\u0026oslash;ivik E, Bj\u0026oslash;rge L, et al. Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer. J Magn Reson Imaging. 2018;48(6):1637\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin Z, Wang T, Li H, Xiao M, Ma X, Gu Y, Qiang J. Magnetic resonance-based radiomics nomogram for predicting microsatellite instability status in endometrial cancer. Quant Imaging Med Surg. 2023;13(1):108\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Liu S, Qin Y, Zhang Y, Wang N, Liu H. High-order radiomics features based on T2 FLAIR MRI predict multiple glioma immunohistochemical features: A more precise and personalized gliomas management. PLoS ONE. 2020;15(1):e0227703.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu X, Guo S, Song W, Xiang T, Yang C, Tao K, Zhou L, Cao Y, Liu S. Estrogen receptor α (ERα) status evaluation using RNAscope in situ hybridization: a reliable and complementary method for IHC in breast cancer tissues. Hum Pathol. 2017;61:121\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh N, Piskorz AM, Bosse T, Jimenez-Linan M, Rous B, Brenton JD, Gilks CB, K\u0026ouml;bel M. p53 immunohistochemistry is an accurate surrogate for TP53 mutational analysis in endometrial carcinoma biopsies. J Pathol. 2020;250(3):336\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu J, Xia Y, Wang X, Wei Y, Liu A, Innanje A, Zheng M, Chen L, Shi J, Wang L, et al. uRP: An integrated research platform for one-stop analysis of medical images. Front Radiol. 2023;3:1153784.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim GR, Ku YJ, Kim JH, Kim EK. Correlation between MR Image-Based Radiomics Features and Risk Scores Associated with Gene Expression Profiles in Breast Cancer. Taehan Yongsang Uihakhoe Chi. 2020;81(3):632\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang T, Gong J, Duan HH, Wang LJ, Ye XD, Nie SD. Correlation between CT based radiomics features and gene expression data in non-small cell lung cancer. J Xray Sci Technol. 2019;27(5):773\u0026ndash;803.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJansen RW, Roohollahi K, Uner OE, de Jong Y, de Bloeme CM, G\u0026ouml;ricke S, Sirin S, Maeder P, Galluzzi P, Brisse HJ et al. Correlation of gene expression with magnetic resonance imaging features of retinoblastoma: a multi-center radiogenomics validation study. Eur Radiol 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLefebvre TL, Ueno Y, Dohan A, Chatterjee A, Valli\u0026egrave;res M, Winter-Reinhold E, Saif S, Levesque IR, Zeng XZ, Forghani R, et al. Development and Validation of Multiparametric MRI-based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer. Radiology. 2022;305(2):375\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"endometrial cancer, radiomics, immunohistochemical biomarkers, magnetic resonance imaging, correlation analysis","lastPublishedDoi":"10.21203/rs.3.rs-4179540/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4179540/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eDifferent molecular or genetic information influences the clinical decisions for patients diagnosed with endometrial cancer (EC). A non-invasive, precise, and efficient preoperative evaluation method is crucial for the prognosis of patients with EC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e The aim of this study was to construct MRI-based radiomics models to predict immunohistochemical biomarkers and assess the relationship between radiomic features and the Ki-67 proliferation rate in EC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterial and Methods: \u003c/strong\u003eWe retrospectively analyzed 100 estrogen receptor (ER), 94 progesterone receptor (PR), 97 P53, and 98 Ki-67 immunohistochemistry cases with EC who underwent magnetic resonance imaging (MRI) between May 2012 and June 2023 prior to surgery. Radiomic features were individually extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and the apparent diffusion coefficient (ADC). Least absolute shrinkage and selection operator (LASSO) regression was employed for feature selection. And logistic regression was employed to construct radiomics models with 5-fold cross-validation. The receiver operating characteristic (ROC) curves were analyzed to evaluate the performance of the radiomics models. Finally, Pearson's correlations were utilized to explore the association between the values of selected features and the Ki-67 proliferation rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 2264 features were extracted from each patient’s MRI sequences. The selected features from the multi-sequence models were shared with or without the single sequence models. Both single sequence and multi-sequence models demonstrated good diagnostic performance, although the diagnostic performance of multi-sequence models outperformed the single sequence models. Correlation analysis showed that adc_wavelet_glszm_wavelet-HHH-SmallAreaLowGrayLevelEmphasis and t2_log_firstorder_log-sigma-2-0-mm-3D-Skewness were negatively correlated with the Ki-67 proliferation rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eMRI-based radiomic features are promising predictors of immunohistochemistry and prognosis in EC.\u003c/p\u003e","manuscriptTitle":"Radiomic Features Based on Multi-sequence MRI Predict Immunohistochemical Biomarkers of Endometrial Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-16 17:27:11","doi":"10.21203/rs.3.rs-4179540/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"399443e2-e7dc-4c5e-a4b0-6c4da8b68332","owner":[],"postedDate":"April 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-17T11:42:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-16 17:27:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4179540","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4179540","identity":"rs-4179540","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}