Xiance Jin

Radiomics Harmonization in Ultrasound Images for Cervical Cancer Lymph Node Metastasis Prediction Using Cycle-GAN

Background: Ultrasound (US) based radiomics is susceptible to variations in scanners, sonographers. Objective: To retrospectively investigate the feasibility of an adapted cycle generative adversarial networks (CycleGAN) in the style transfer to improve US based radiomics in the prediction of lymph node metastasis (LNM) with images from multiple scanners for patients with early cervical cancer (ECC). Methods: The CycleGAN was firstly trained to transfer paired US phantom images from one US device to another one; the model was then further trained and tested with clinical US images of ECC by transferring images from four US devices to one specific device; finally, the adapted model was tested with its effects on the radiomics feature harmonization and accuracy of LNM prediction in US based radiomics for ECC patients. Results: Phantom study demonstrated an increased radiomics harmonization using CycleGAN with an average Pearson correlation coefficient of 0.60 and 0.81 for radiomics features extracted from original and generated images in correlation with the target phantom images, respectively. Additionally, the image quality metric Peak Signal-to-Noise Ratio (PSNR) was increased from 11.18 for the original images to 15.45 for the generated image. Clinical US images of 169 ECC patients were enrolled for style transfer model training and validation. The area under curve (AUC) of LNM prediction radiomics models with features extracted from generated images of different style transfer models ranged from 0.73 to 0.85. The AUC was improved from 0.78 with features extracted from original images to 0.85 with style transferred images. Conclusions: The adapted CycleGAN network is able to increase the radiomics feature harmonization for images from different ultrasound equipment based on image domain and improve the LNM prediction accuracy for ECC.

Automatic radiotherapy planning for deliverable plans using deep learning dose prediction and dose rings optimization in cervical cancer

Abstract Background Automatic radiotherapy (RT) planning based on deep learning (DL) has been extensively researched. However, it is challenging to import the predicted dose distribution into mainstream treatment planning systems (TPSs) and generate clinically deliverable plans. Purpose To investigate the feasibility and accuracy of an automatic volumetric modulated arc therapy (VMAT) and intensity‐modulated radiation therapy (IMRT) planning method for generation of universally deliverable plans based on DL dose prediction and dose rings optimization. Methods First, dose distributions were predicted using a three‐dimensional (3D) Fusion Residual Unet (F‐ResUnet) DL network with data from two hospitals, which included 230 and 210 gynecological cancer (GC) patients underwent VMAT and IMRT, respectively. Then, the predicted dose distributions were discretized into dose rings to optimize the plans automatically in two mainstream TPSs based on the dose rings. Finally, the deliverability of generated plans was verified with patient‐specific quality assurance (PSQA). Results The predicted dose distributions were clinically acceptable with a target coverage over 95%. Compared with the clinical plans, the automatic plans optimized with dose rings achieved a similar dose coverage on planning target volumes (PTV) with an average target coverage over 96.5%. For organs at risk (OARs) sparing, automatic VMAT plans markedly decreased the V 30Gy of left femoral head ( p  = 0.05), right femoral head ( p  = 0.004), and small intestine ( p  = 0.04). The V 45Gy of bladder and rectum in the automatic IMRT plans were reduced by approximately 7% and 9%, respectively. Deliverability verification with PSQA achieved a mean gamma passing rate of 99.1%, 97.1% and 98.3%, 95.0% under the criteria of 3%/3 mm and 3%/2 mm for VMAT and IMRT plans, respectively. Conclusions The proposed automatic planning method combining DL dose prediction and dose rings optimization was feasible to generate universally deliverable VMAT and IMRT plans for gynecological cancer (GC) patients.

MRI radiomics nomogram integrating postoperative adjuvant treatments in recurrence risk prediction for patients with early-stage cervical cancer

Adjuvant treatments are valuable to decrease the recurrence rate and improve survival for early-stage cervical cancer patients (ESCC), Therefore, recurrence risk evaluation is critical for the choice of postoperative treatment. A magnetic resonance imaging (MRI) based radiomics nomogram integrating postoperative adjuvant treatments was constructed and validated externally to improve the recurrence risk prediction for ESCC. 212 ESCC patients underwent surgery and adjuvant treatments from three centers were enrolled and divided into the training, internal validation, and external validation cohorts. Their clinical data, pretreatment T2-weighted images (T2WI) were retrieved and analyzed. Radiomics models were constructed using machine learning methods with features extracted and screen from sagittal and axial T2WI. A nomogram for recurrence prediction was build and evaluated using multivariable logistic regression analysis integrating radiomic signature and adjuvant treatments. A total of 8 radiomic features were screened out of 1020 extracted features. The extreme gradient boosting (XGboost) model based on MRI radiomic features performed best in recurrence prediction with an area under curve (AUC) of 0.833, 0.822 in the internal and external validation cohorts, respectively. The nomogram integrating radiomic signature and clinical factors achieved an AUC of 0.806, 0.718 in the internal and external validation cohorts, respectively, for recurrence risk prediction for ESCC. In this study, the nomogram integrating T2WI radiomic signature and clinical factors is valuable to predict the recurrence risk, thereby allowing timely planning for effective treatments for ESCC with high risk of recurrence.

Radiomics in cervical cancer: Current applications and future potential

Cervical cancer is the most commonly diagnosed cancer among women. Early diagnosis and prediction will greatly improve the treatment outcome. Many clinical parameters have been used as diagnostic and prognostic factors for cervical cancer patients, including tumor stage, histological type, lymph node status, but with limitations in prediction accuracy. The development of noninvasive biomarker with the potential to provide more specific tumor characterization before treatment begins or during therapy is urgent needed, which may permit clinicians to administer a more individualized anti-cancer treatment. Radiomics is a mathematical-statistical procedure extracting information from medial images, which has the potential for prediction of staging, histological type, node status, relapse and survival in patients with cervical cancer. In this manuscript, we reviewed recent clinical studies and future potential for the application of radiomics in the treatment of patients with cervical cancer, and discussed the current challenges and limitations of radiomics for oncology.

The Influence of Different Ultrasonic Machines on Radiomics Models in Prediction Lymph Node Metastasis for Patients with Cervical Cancer

Objective To investigate the effects of different ultrasonic machines on the performance of radiomics models using ultrasound (US) images in the prediction of lymph node metastasis (LNM) for patients with cervical cancer (CC) preoperatively. Methods A total of 536 CC patients with confirmed histological characteristics and lymph node status after radical hysterectomy and pelvic lymphadenectomy were enrolled. Radiomics features were extracted and selected with US images acquired with ATL HDI5000, Voluson E8, MyLab classC, ACUSON S2000, and HI VISION Preirus to build radiomics models for LNM prediction using support vector machine (SVM) and logistic regression, respectively. Results There were 148 patients (training vs validation: 102:46) scanned in machine HDI5000, 75 patients (53:22) in machine Voluson E8, 100 patients (69:31) in machine MyLab classC, 110 patients (76:34) in machine ACUSON S2000, and 103 patients (73:30) in machine HI VISION Preirus, respectively. Few radiomics features were reproducible among different machines. The area under the curves (AUCs) ranged from 0.75 to 0.86, 0.73 to 0.86 in the training cohorts, and from 0.71 to 0.82, 0.70 to 0.80 in the validation cohorts for SVM and logistic regression models, respectively. The highest difference in AUCs for different machines reaches 17.8% and 15.5% in the training and validation cohorts, respectively. Conclusions The performance of radiomics model is dependent on the type of scanner. The problem of scanner dependency on radiomics features should be considered, and their effects should be minimized in future studies for US images.

The Effects of Automatic Segmentations on Preoperative Lymph Node Status Prediction Models With Ultrasound Radiomics for Patients With Early Stage Cervical Cancer

Introduction: The purpose of this study is to investigate the effects of automatic segmentation algorithms on the performance of ultrasound (US) radiomics models in predicting the status of lymph node metastasis (LNM) for patients with early stage cervical cancer preoperatively. Methods: US images of 148 cervical cancer patients were collected and manually contoured by two senior radiologists. The four deep learning-based automatic segmentation models, namely U-net, context encoder network (CE-net), Resnet, and attention U-net were constructed to segment the tumor volumes automatically. Radiomics features were extracted and selected from manual and automatically segmented regions of interest (ROIs) to predict the LNM of these cervical cancer patients preoperatively. The reliability and reproducibility of radiomics features and the performances of prediction models were evaluated. Results: A total of 449 radiomics features were extracted from manual and automatic segmented ROIs with Pyradiomics. Features with an intraclass coefficient (ICC) > 0.9 were all 257 (57.2%) from manual and automatic segmented contours. The area under the curve (AUCs) of validation models with radiomics features extracted from manual, attention U-net, CE-net, Resnet, and U-net were 0.692, 0.755, 0.696, 0.689, and 0.710, respectively. Attention U-net showed best performance in the LNM prediction model with a lowest discrepancy between training and validation. The AUCs of models with automatic segmentation features from attention U-net, CE-net, Resnet, and U-net were 9.11%, 0.58%, –0.44%, and 2.61% higher than AUC of model with manual contoured features, respectively. Conclusion: The reliability and reproducibility of radiomics features, as well as the performance of radiomics models, were affected by manual segmentation and automatic segmentations.

The Accuracy and Radiomics Feature Effects of Multiple U-net-Based Automatic Segmentation Models for Transvaginal Ultrasound Images of Cervical Cancer

Ultrasound (US) imaging has been recognized and widely used as a screening and diagnostic imaging modality for cervical cancer all over the world. However, few studies have investigated the U-net-based automatic segmentation models for cervical cancer on US images and investigated the effects of automatic segmentations on radiomics features. A total of 1102 transvaginal US images from 796 cervical cancer patients were collected and randomly divided into training (800), validation (100) and test sets (202), respectively, in this study. Four U-net models (U-net, U-net with ResNet, context encoder network (CE-net), and Attention U-net) were adapted to segment the target of cervical cancer automatically on these US images. Radiomics features were extracted and evaluated from both manually and automatically segmented area. The mean Dice similarity coefficient (DSC) of U-net, Attention U-net, CE-net, and U-net with ResNet were 0.88, 0.89, 0.88, and 0.90, respectively. The average Pearson coefficients for the evaluation of the reliability of US image-based radiomics were 0.94, 0.96, 0.94, and 0.95 for U-net, U-net with ResNet, Attention U-net, and CE-net, respectively, in their comparison with manual segmentation. The reproducibility of the radiomics parameters evaluated by intraclass correlation coefficients (ICC) showed robustness of automatic segmentation with an average ICC coefficient of 0.99. In conclusion, high accuracy of U-net-based automatic segmentations was achieved in delineating the target area of cervical cancer US images. It is feasible and reliable for further radiomics studies with features extracted from automatic segmented target areas.