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2. 2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-center Study

Author

Meng, Lingwei, Dong, Di, Chen, Xin, Fang, Mengjie, Wang, Rongpin, Li, Jing, Liu, Zaiyi, and Tian, Jie

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Signal Processing, Quantitative Biology - Quantitative Methods
Abstract

Objective: Radiomics, an emerging tool for medical image analysis, is potential towards precisely characterizing gastric cancer (GC). Whether using one-slice 2D annotation or whole-volume 3D annotation remains a long-time debate, especially for heterogeneous GC. We comprehensively compared 2D and 3D radiomic features' representation and discrimination capacity regarding GC, via three tasks. Methods: Four-center 539 GC patients were retrospectively enrolled and divided into the training and validation cohorts. From 2D or 3D regions of interest (ROIs) annotated by radiologists, radiomic features were extracted respectively. Feature selection and model construction procedures were customed for each combination of two modalities (2D or 3D) and three tasks. Subsequently, six machine learning models (Model_2D^LNM, Model_3D^LNM; Model_2D^LVI, Model_3D^LVI; Model_2D^pT, Model_3D^pT) were derived and evaluated to reflect modalities' performances in characterizing GC. Furthermore, we performed an auxiliary experiment to assess modalities' performances when resampling spacing is different. Results: Regarding three tasks, the yielded areas under the curve (AUCs) were: Model_2D^LNM's 0.712 (95% confidence interval, 0.613-0.811), Model_3D^LNM's 0.680 (0.584-0.775); Model_2D^LVI's 0.677 (0.595-0.761), Model_3D^LVI's 0.615 (0.528-0.703); Model_2D^pT's 0.840 (0.779-0.901), Model_3D^pT's 0.813 (0.747-0.879). Moreover, the auxiliary experiment indicated that Models_2D are statistically more advantageous than Models3D with different resampling spacings. Conclusion: Models constructed with 2D radiomic features revealed comparable performances with those constructed with 3D features in characterizing GC. Significance: Our work indicated that time-saving 2D annotation would be the better choice in GC, and provided a related reference to further radiomics-based researches., Comment: Published in IEEE Journal of Biomedical and Health Informatics

Published
2022
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5. From Signal to Knowledge: The Diagnostic Value of Raw Data in the Artificial Intelligence Prediction of Human Data for the First Time

Author

He, Bingxi, Guo, Yu, Zhu, Yongbei, Tong, Lixia, Kong, Boyu, Wang, Kun, Sun, Caixia, Li, Hailin, Huang, Feng, Wu, Liwei, Wang, Meng, Meng, Fanyang, Dou, Le, Sun, Kai, Tong, Tong, Liu, Zhenyu, Wei, Ziqi, Mu, Wei, Wang, Shuo, Tang, Zhenchao, Zhang, Shuaitong, Wei, Jingwei, Shao, Lizhi, Fang, Mengjie, Li, Juntao, Zhu, Shouping, Zhou, Lili, Dong, Di, Zhang, Huimao, and Tian, Jie

Published
2024
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22. Deep learning‐based radiomics model can predict extranodal soft tissue metastasis in gastric cancer

Author

Liu, Shengyuan, primary, Deng, Jingyu, additional, Dong, Di, additional, Fang, Mengjie, additional, Ye, Zhaoxiang, additional, Hu, Yanfeng, additional, Li, Hailin, additional, Zhong, Lianzhen, additional, Cao, Runnan, additional, Zhao, Xun, additional, Shang, Wenting, additional, Li, Guoxin, additional, Liang, Han, additional, and Tian, Jie, additional

Published
2023
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24. Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review.

Author

Wang, Zipei, Fang, Mengjie, Zhang, Jie, Tang, Linquan, Zhong, Lianzhen, Li, Hailin, Cao, Runnan, Zhao, Xun, Liu, Shengyuan, Zhang, Ruofan, Xie, Xuebin, Mai, Haiqiang, Qiu, Sufang, Tian, Jie, and Dong, Di

Abstract

Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical management compared to other types of cancer. Precision risk stratification and tailored therapeutic interventions are crucial to improving the survival outcomes. Artificial intelligence, including radiomics and deep learning, has exhibited considerable efficacy in various clinical tasks for nasopharyngeal carcinoma. These techniques leverage medical images and other clinical data to optimize clinical workflow and ultimately benefit patients. In this review, we provide an overview of the technical aspects and basic workflow of radiomics and deep learning in medical image analysis. We then conduct a detailed review of their applications to seven typical tasks in the clinical diagnosis and treatment of nasopharyngeal carcinoma, covering various aspects of image synthesis, lesion segmentation, diagnosis, and prognosis. The innovation and application effects of cutting-edge research are summarized. Recognizing the heterogeneity of the research field and the existing gap between research and clinical translation, potential avenues for improvement are discussed. We propose that these issues can be gradually addressed by establishing standardized large datasets, exploring the biological characteristics of features, and technological upgrades. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Deep learning‐based radiomics model can predict extranodal soft tissue metastasis in gastric cancer.

Author

Liu, Shengyuan, Deng, Jingyu, Dong, Di, Fang, Mengjie, Ye, Zhaoxiang, Hu, Yanfeng, Li, Hailin, Zhong, Lianzhen, Cao, Runnan, Zhao, Xun, Shang, Wenting, Li, Guoxin, Liang, Han, and Tian, Jie

Subjects
DEEP learning, RADIOMICS, STOMACH cancer, COMPUTED tomography, METASTASIS, PROGNOSIS
Abstract

Background: The potential prognostic value of extranodal soft tissue metastasis (ESTM) has been confirmed by increasing studies about gastric cancer (GC). However, the gold standard of ESTM is determined by pathologic examination after surgery, and there are no preoperative methods for assessment of ESTM yet. Purpose: This multicenter study aimed to develop a deep learning‐based radiomics model to preoperatively identify ESTM and evaluate its prognostic value. Methods: A total of 959 GC patients were enrolled from two centers and split into a training cohort (N = 551) and a test cohort (N = 236) for ESTM evaluation. Additionally, an external survival cohort (N = 172) was included for prognostic analysis. Four models were established based on clinical characteristics and multiphase computed tomography (CT) images for preoperative identification of ESTM, including a deep learning model, a hand‐crafted radiomic model, a clinical model, and a combined model. C‐index, decision curve, and calibration curve were utilized to assess the model performances. Survival analysis was conducted to explore the ability of stratifying overall survival (OS). Results: The combined model showed good discrimination of the ESTM [C‐indices (95% confidence interval, CI): 0.770 (0.729–0.812) and 0.761 (0.718–0.805) in training and test cohorts respectively], which outperformed deep learning model, radiomics model, and clinical model. The stratified analysis showed this model was not affected by patient's tumor size, the presence of lymphovascular invasion, and Lauren classification (p < 0.05). Moreover, the model score showed strong consistency with the OS [C‐indices (95%CI): 0.723 (0.658–0.789, p < 0.0001) in the internal survival cohort and 0.715 (0.650–0.779, p < 0.0001) in the external survival cohort]. More interestingly, univariate analysis showed the model score was significantly associated with occult distant metastasis (p < 0.05) that was missed by preoperative diagnosis. Conclusions: The model combining CT images and clinical characteristics had an impressive predictive ability of both ESTM and prognosis, which has the potential to serve as an effective complement to the preoperative TNM staging system. [ABSTRACT FROM AUTHOR]

Published
2024
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30. From Signal to Knowledge: The Diagnostic Value of Raw Data in the Artificial Intelligence Prediction of Human Data for the First Time

Author

He, Bingxi, primary, Guo, Yu, additional, Zhu, Yongbei, additional, Tong, Lixia, additional, Kong, Boyu, additional, Wang, Kun, additional, Sun, Caixia, additional, Li, Hailin, additional, Huang, Feng, additional, Wu, Liwei, additional, Wang, Meng, additional, Meng, Fanyang, additional, Dou, Le, additional, Sun, Kai, additional, Tong, Tong, additional, Liu, Zhenyu, additional, Wei, Ziqi, additional, Mu, Wei, additional, Wang, Shuo, additional, Tang, Zhenchao, additional, Zhang, Shuaitong, additional, Wei, Jingwei, additional, Shao, Lizhi, additional, Fang, Mengjie, additional, Li, Juntao, additional, Zhu, Shouping, additional, Zhou, Lili, additional, Dong, Di, additional, Zhang, Huimao, additional, and Tian, Jie, additional

Published
2023
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31. Public Code from A New Approach to Predict Progression-free Survival in Stage IV EGFR-mutant NSCLC Patients with EGFR-TKI Therapy

Author

Song, Jiangdian, primary, Shi, Jingyun, primary, Dong, Di, primary, Fang, Mengjie, primary, Zhong, Wenzhao, primary, Wang, Kun, primary, Wu, Ning, primary, Huang, Yanqi, primary, Liu, Zhenyu, primary, Cheng, Yue, primary, Gan, Yuncui, primary, Zhou, Yongzhao, primary, Zhou, Ping, primary, Chen, Bojiang, primary, Liang, Changhong, primary, Liu, Zaiyi, primary, Li, Weimin, primary, and Tian, Jie, primary

Published
2023
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32. Data from A New Approach to Predict Progression-free Survival in Stage IV EGFR-mutant NSCLC Patients with EGFR-TKI Therapy

Author

Song, Jiangdian, primary, Shi, Jingyun, primary, Dong, Di, primary, Fang, Mengjie, primary, Zhong, Wenzhao, primary, Wang, Kun, primary, Wu, Ning, primary, Huang, Yanqi, primary, Liu, Zhenyu, primary, Cheng, Yue, primary, Gan, Yuncui, primary, Zhou, Yongzhao, primary, Zhou, Ping, primary, Chen, Bojiang, primary, Liang, Changhong, primary, Liu, Zaiyi, primary, Li, Weimin, primary, and Tian, Jie, primary

Published
2023
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33. Supplementary Appendix, Supplementary References, Supplementary Figures 1-5, Supplementary Tables 1-4 from A New Approach to Predict Progression-free Survival in Stage IV EGFR-mutant NSCLC Patients with EGFR-TKI Therapy

Author

Song, Jiangdian, primary, Shi, Jingyun, primary, Dong, Di, primary, Fang, Mengjie, primary, Zhong, Wenzhao, primary, Wang, Kun, primary, Wu, Ning, primary, Huang, Yanqi, primary, Liu, Zhenyu, primary, Cheng, Yue, primary, Gan, Yuncui, primary, Zhou, Yongzhao, primary, Zhou, Ping, primary, Chen, Bojiang, primary, Liang, Changhong, primary, Liu, Zaiyi, primary, Li, Weimin, primary, and Tian, Jie, primary

Published
2023
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35. Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review

Author

Wang, Zipei, primary, Fang, Mengjie, additional, Zhang, Jie, additional, Tang, Linquan, additional, Zhong, Lianzhen, additional, Li, Hailin, additional, Cao, Runnan, additional, Zhao, Xun, additional, Liu, Shengyuan, additional, Zhang, Ruofan, additional, Xie, Xuebin, additional, Mai, Haiqiang, additional, Qiu, Sufang, additional, Tian, Jie, additional, and Dong, Di, additional

Published
2023
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38. From signal to knowledge: The diagnostic value of rawdata in artificial intelligence prediction of human data for the first time

Author

He, Bingxi, primary, Guo, Yu, additional, Zhu, Yongbei, additional, Tong, Lixia, additional, Kong, Boyu, additional, Wang, Kun, additional, Sun, Caixia, additional, Li, Hailin, additional, Huang, Feng, additional, Wu, Liwei, additional, Wang, Meng, additional, Meng, Fanyang, additional, Dou, Le, additional, Sun, Kai, additional, Tong, Tong, additional, Liu, Zhenyu, additional, Wei, Ziqi, additional, Mu, Wei, additional, Wang, Shuo, additional, Tang, Zhenchao, additional, Zhang, Shuaitong, additional, Wei, Jingwei, additional, Shao, Lizhi, additional, Fang, Mengjie, additional, Li, Juntao, additional, Zhu, Shouping, additional, Zhou, Lili, additional, Dong, Di, additional, Zhang, Huimao, additional, and Tian, Jie, additional

Published
2022
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41. Deep learning‐based AI model for signet‐ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer

Author

Li, Cong, primary, Qin, Yun, additional, Zhang, Wei‐Han, additional, Jiang, Hanyu, additional, Song, Bin, additional, Bashir, Mustafa R., additional, Xu, Heng, additional, Duan, Ting, additional, Fang, Mengjie, additional, Zhong, Lianzhen, additional, Meng, Lingwei, additional, Dong, Di, additional, Hu, Zhenhua, additional, Tian, Jie, additional, and Hu, Jian‐Kun, additional

Published
2022
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42. Bi-Regional and Bi-Phasic Automated Machine Learning Radiomics for Defining Metastasis to Lesser Curvature and Supra-Pancreatic Lymph Node Stations in Gastric Cancer

Author

Huang, Huilin, primary, Wang, Siwen, additional, Deng, Jingyu, additional, Guo, Weihong, additional, Li, Hai-Lin, additional, He, Bingxi, additional, Fang, Mengjie, additional, Zhang, Nannan, additional, Liu, Jiaxin, additional, Dong, Di, additional, Liang, Han, additional, Li, Guoxin, additional, Tian, Jie, additional, and Hu, Yanfeng, additional

Published
2022
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44. A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study

Author

Zhong, Lianzhen, primary, Dong, Di, additional, Fang, Xueliang, additional, Zhang, Fan, additional, Zhang, Ning, additional, Zhang, Liwen, additional, Fang, Mengjie, additional, Jiang, Wei, additional, Liang, Shaobo, additional, Li, Cong, additional, Liu, Yujia, additional, Zhao, Xun, additional, Cao, Runnan, additional, Shan, Hong, additional, Hu, Zhenhua, additional, Ma, Jun, additional, Tang, Linglong, additional, and Tian, Jie, additional

Published
2021
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45. Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study

Author

Hu, Hao, primary, Gong, Lixin, additional, Dong, Di, additional, Zhu, Liang, additional, Wang, Min, additional, He, Jie, additional, Shu, Lei, additional, Cai, Yiling, additional, Cai, Shilun, additional, Su, Wei, additional, Zhong, Yunshi, additional, Li, Cong, additional, Zhu, Yongbei, additional, Fang, Mengjie, additional, Zhong, Lianzhen, additional, Yang, Xin, additional, Zhou, Pinghong, additional, and Tian, Jie, additional

Published
2021
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46. Study on automated machine learning application

Author

Chen, Kani, Zhang, Jiheng, Fang, Mengjie, Chen, Kani, Zhang, Jiheng, and Fang, Mengjie

Abstract

With the development of science and technology, there is an increasing number of tasks which use machine learning algorithms for analysis. The use of machine learning needs professional experience, and some of the processes are time consuming. Automated machine learning (AutoML) is designed for automating the machine learning tasks, while it aims to save time for other important procedures such as data collection and model evaluation. In this thesis, we will introduce some open source AutoML frameworks and test them with the S&P 500 Index. The innovation of this study is the use of ensemble learning to optimize the AutoML frameworks.

Published
2021

49. MR‐Based Radiomics Nomogram of Cervical Cancer in Prediction of the Lymph‐Vascular Space Invasion preoperatively

Author

Li, Zhicong, Li, Hailin, Wang, Shiyu, Dong, Di, Yin, Fangfang, Chen, An, Wang, Siwen, Zhao, Guangming, Fang, Mengjie, Tian, Jie, Wu, Sufang, and Wang, Han

Subjects
cervical cancer, lymph‐vascular space invasion, Contrast Media, Reproducibility of Results, Uterine Cervical Neoplasms, Cervix Uteri, radiomics nomogram, Middle Aged, Image Enhancement, Magnetic Resonance Imaging, Sensitivity and Specificity, Pelvis, prediction model, Cohort Studies, Nomograms, Lymphatic Metastasis, Humans, Female, Original Research, MRI, Retrospective Studies
Abstract

Background Lymph‐vascular space invasion (LVSI) is an unfavorable prognostic factor in cervical cancer. Unfortunately, there are no current clinical tools for the preoperative prediction of LVSI. Purpose To develop and validate an axial T1 contrast‐enhanced (CE) MR‐based radiomics nomogram that incorporated a radiomics signature and some clinical parameters for predicting LVSI of cervical cancer preoperatively. Study Type Retrospective. Population In all, 105 patients were randomly divided into two cohorts at a 2:1 ratio. Field Strength/Sequence T1 CE MRI sequences at 1.5T. Assessment Univariate analysis was performed on the radiomics features and clinical parameters. Multivariate analysis was performed to determine the optimal feature subset. The receiver operating characteristic (ROC) analysis was performed to evaluate the performance of prediction model and radiomics nomogram. Statistical Tests The Mann–Whitney U‐test and the chi‐square test were used to evaluate the performance of clinical characteristics and LVSI status by pathology. The minimum‐redundancy/maximum‐relevance and recursive feature elimination methods were applied to select the features. The radiomics model was constructed using logistic regression. Results Three radiomics features and one clinical characteristic were selected. The radiomics nomogram showed favorable discrimination between LVSI and non‐LVSI groups. The AUC was 0.754 (95% confidence interval [CI], 0.6326–0.8745) in the training cohort and 0.727 (95% CI, 0.5449–0.9097) in the validation cohort. The specificity and sensitivity were 0.756 and 0.828 in the training cohort and 0.773 and 0.692 in the validation cohort. Data Conclusion T1 CE MR‐based radiomics nomogram serves as a noninvasive biomarker in the prediction of LVSI in patients with cervical cancer preoperatively. Level of Evidence: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1420–1426.

Published
2018

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