{
    "abstract": "Strategies for predicting the recurrence of nasopharyngeal carcinoma need to be further development and validation. We developed a recurrence prediction model based on the fusion of multi-omics features from pre-treatment conventional magnetic resonance sequences (CE-T1W) in nasopharyngeal carcinoma (NPC) patients to predict posttreatment recurrence.\nWe employed a deep unsupervised stacked denoising autoencoder (stacked denoising autoencoder, SDAE) and multi-omics feature fusion method to develop an NPC recurrence prediction model. Data and magnetic resonance images from 184 patients with newly diagnosed nasopharyngeal carcinoma (NPC), confirmed by pathological examination and who underwent radical comprehensive treatment, were collected. Propensity score matching (relapse: no recurrence = 1:1) was used to balance clinical factors that might influence recurrence, resulting in 136 matched cases. SDAE was utilized to extract deep features, combined with the fusion features of radiomics (Radiomics) features and clinical features, using support vector machine (SVM), multilayer perceptron (MLP), logistic regression (LR) and random forest (RF) machine learning methods to build models. The mean area under the curve (AUC), accuracy, sensitivity, and specificity of each model were compared to evaluate their performance in predicting recurrence.\nAfter parameter adjustment, 12 machine learning models based on different fusion features were developed. Model 1 (Radiomics+AutoEncoder+Clinical+SVM) achieved better prediction performance, with a mean AUC, accuracy, sensitivity, and specificity of 0.89 (95% CI: 0.84- 0.93), 81.5%, 67.3% and 97.9%, respectively. Model 2 (Radiomics+ AutoEncoder+SVM), Model 3 (Radiomics+SVM), Model 4 (Radiomics+ AutoEncoder+Clinical+MLP), Model 5 (Radiomics+Auto Encoder+MLP), Model 6 (Radiomics+MLP), Model 7 (Radiomics+AutoEncoder+Clinical+LR), Model 8 (Radiomics+AutoEncoder+LR), Model 9 (Radiomics+LR), Model 10 (Radiomics+Auto Encoder+Clinical+RF), Model 11 (Radiomics+AutoEncoder+RF), Model 12 (Radiomics+RF) achieved AUCs of 0.87, 0.87, 0.82, 0.80, 0.82, 0.82, 0.78, 0.80, 0.81, 0.80, and 0.82, respectively.\nThe SVM model for predicting the recurrence of nasopharyngeal carcinoma based on radiomics+autoEncoder+clinical fusion features established by CE-T1WI of nasopharyngeal carcinoma patients before treatment, achieved good predictive performance and is relatively reliable, which can provide more information and help for clinical diagnosis and treatment decisions and interventions.",
    "authors": [
        {
            "affiliation": "Department of Otolaryngology & Head and Neck Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.",
            "firstname": "Yibin",
            "initials": "Y",
            "lastname": "Liu"
        },
        {
            "affiliation": "Department of Otolaryngology & Head and Neck Surgery, Ganzhou People's Hospital, Ganzhou, 341000, China.",
            "firstname": "Xianwen",
            "initials": "X",
            "lastname": "Wang"
        },
        {
            "affiliation": "School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China.",
            "firstname": "Jiongyi",
            "initials": "J",
            "lastname": "Li"
        },
        {
            "affiliation": "Department of Otolaryngology & Head and Neck Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.",
            "firstname": "Junxiao",
            "initials": "J",
            "lastname": "Gao"
        },
        {
            "affiliation": "Department of Otolaryngology & Head and Neck Surgery, Wuzhou Red Cross Hospital, Wuzhou, 543002, China.",
            "firstname": "Bin",
            "initials": "B",
            "lastname": "He"
        },
        {
            "affiliation": "Department of Imaging Diagnosis, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.",
            "firstname": "Xianlong",
            "initials": "X",
            "lastname": "Wang"
        },
        {
            "affiliation": "School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China.",
            "firstname": "Lianfang",
            "initials": "L",
            "lastname": "Tian"
        },
        {
            "affiliation": "School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China.",
            "firstname": "Bin",
            "initials": "B",
            "lastname": "Li"
        },
        {
            "affiliation": "Department of Otolaryngology & Head and Neck Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.",
            "firstname": "Qianhui",
            "initials": "Q",
            "lastname": "Qiu"
        }
    ],
    "conclusions": "The SVM model for predicting the recurrence of nasopharyngeal carcinoma based on radiomics+autoEncoder+clinical fusion features established by CE-T1WI of nasopharyngeal carcinoma patients before treatment, achieved good predictive performance and is relatively reliable, which can provide more information and help for clinical diagnosis and treatment decisions and interventions.",
    "copyrights": "Copyright\u00a9 Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.",
    "doi": "10.2174/0118744710384129250327060846",
    "journal": "Current radiopharmaceuticals",
    "keywords": [
        "Nasopharyngeal carcinoma",
        "magnetic resonance imaging.",
        "prediction model",
        "recurrence",
        "stacked denoising autoencoder",
        "support vector machine"
    ],
    "methods": null,
    "publication_date": "2025-04-16",
    "pubmed_id": "40237056",
    "results": "After parameter adjustment, 12 machine learning models based on different fusion features were developed. Model 1 (Radiomics+AutoEncoder+Clinical+SVM) achieved better prediction performance, with a mean AUC, accuracy, sensitivity, and specificity of 0.89 (95% CI: 0.84- 0.93), 81.5%, 67.3% and 97.9%, respectively. Model 2 (Radiomics+ AutoEncoder+SVM), Model 3 (Radiomics+SVM), Model 4 (Radiomics+ AutoEncoder+Clinical+MLP), Model 5 (Radiomics+Auto Encoder+MLP), Model 6 (Radiomics+MLP), Model 7 (Radiomics+AutoEncoder+Clinical+LR), Model 8 (Radiomics+AutoEncoder+LR), Model 9 (Radiomics+LR), Model 10 (Radiomics+Auto Encoder+Clinical+RF), Model 11 (Radiomics+AutoEncoder+RF), Model 12 (Radiomics+RF) achieved AUCs of 0.87, 0.87, 0.82, 0.80, 0.82, 0.82, 0.78, 0.80, 0.81, 0.80, and 0.82, respectively.",
    "title": "Prediction of Recurrence using a Stacked Denoising Autoencoder and Multifaceted Feature Analysis of Pretreatment MRI in Patients with Nasopharyngeal Carcinoma.",
    "xml": "<Element 'PubmedArticle' at 0x788503f678d0>"
}