{
    "abstract": "Developing a high-value, convenient, and validated differential diagnosis model to differentiate alpha-fetoprotein (AFP) negative hepatic occupying lesions and assist clinicians in early identification and intervention.\nA total of 340 patients with AFP-negative hepatic occupying lesions who were admitted to the Guangxi Medical University Cancer Hospital between August 2021 and April 2023 were included in the final retrospective analysis. The data were randomly divided into training and validation sets in a 7:3 ratio after performing multiple interpolations. In the training set, laboratory variables and models were screened using least absolute shrinkage and selection operator regression analysis, comparison of five machine learning algorithms, and univariate, as well as multivariate logistic regression analysis. A diagnostic prediction nomogram model was developed. We evaluated and validated the model using the receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA).\nWe identified six significant predictive factors from the results of multivariate logistic analysis in the training set and incorporated them into the nomogram model for diagnosing AFP-negative hepatic malignant occupying lesions (HMOL). The diagnostic nomogram, including gender, age, des-gamma-carboxy prothrombin (DCP), serum ferritin (SF), AFP, and hepatitis B surface antigen (HBsAg), achieved an area under the curve of 0.905 discriminated patients with HMOL from those with benign occupying lesions. Additionally, calibration curves demonstrated the close alignment between the nomogram predictions and the ideal curve, along with the consistency between predictions and actual results. Moreover, the DCA curves illustrated indicated benefit for all patients. These finding were confirmed by the validation set.\nThe GADSAH model specifically targets the discrimination of malignant and benign liver lesions in AFP-negative patients. It offers a noninvasive, cost-effective, and efficient approach for diagnosing such cases.",
    "authors": [
        {
            "affiliation": "Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, 530021, People's Republic of China.",
            "firstname": "Xiaoling",
            "initials": "X",
            "lastname": "Long"
        },
        {
            "affiliation": "Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, 530021, People's Republic of China.",
            "firstname": "Huan",
            "initials": "H",
            "lastname": "Zeng"
        },
        {
            "affiliation": "Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, 530021, People's Republic of China.",
            "firstname": "Yun",
            "initials": "Y",
            "lastname": "Zhang"
        },
        {
            "affiliation": "Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, 530021, People's Republic of China.",
            "firstname": "Qiulong",
            "initials": "Q",
            "lastname": "Lu"
        },
        {
            "affiliation": "Department of Clinical Laboratory, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, People's Republic of China.",
            "firstname": "Zhao",
            "initials": "Z",
            "lastname": "Cao"
        },
        {
            "affiliation": "Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, 530021, People's Republic of China.",
            "firstname": "Hong",
            "initials": "H",
            "lastname": "Shu"
        }
    ],
    "conclusions": "The GADSAH model specifically targets the discrimination of malignant and benign liver lesions in AFP-negative patients. It offers a noninvasive, cost-effective, and efficient approach for diagnosing such cases.",
    "copyrights": "\u00a9 2024 Long et al.",
    "doi": "10.2147/JHC.S452628\n10.1038/ajg.2014.213\n10.1038/s41572-020-00240-3\n10.3322/caac.21660\n10.1016/j.cell.2020.05.038\n10.1245/s10434-007-9685-0\n10.1007/s00432-022-04169-w\n10.1002/hep.29086\n10.3233/dma-2011-0878\n10.1111/j.1572-0241.2006.00443.x\n10.1186/s12876-022-02279-w\n10.1053/j.gastro.2018.01.064\n10.1053/j.gastro.2022.03.024\n10.3748/wjg.15.1289\n10.1016/j.diii.2013.05.002\n10.1016/j.clinre.2020.11.013\n10.3389/fchem.2021.705341\n10.3350/cmh.2022.0212\n10.1016/j.jhep.2020.07.025\n10.1373/clinchem.2019.308965\n10.2147/jhc.S443273\n10.1111/joim.12822\n10.4251/wjgo.v15.i8.1486\n10.3390/diagnostics13081442\n10.3760/cma.j.cn501113-20220413-00193\n10.1016/j.jhep.2016.04.001\n10.1016/j.cjca.2020.11.010\n10.3389/fonc.2022.829761\n10.3390/cancers15041078\n10.1002/cncr.29936\n10.1097/00000478-199912000-00001\n10.1016/j.cld.2011.03.006\n10.1136/jcp.26.10.770\n10.1080/01635581.2019.1609053\n10.1111/liv.14223\n10.1016/j.jhep.2005.03.019\n10.1097/meg.0000000000001400\n10.5582/ddt.2014.01026\n10.2147/cmar.S167036\n10.1002/hep.27969\n10.1016/s0140-6736(22)01468-4\n10.31557/apjcp.2019.20.8.2239\n10.1053/j.gastro.2012.02.007\n10.1007/s00261-020-02564-w\n10.1148/radiol.2017170706\n10.1016/j.ultrasmedbio.2019.12.018",
    "journal": "Journal of hepatocellular carcinoma",
    "keywords": [
        "alpha-fetoprotein-negative",
        "diagnostic model",
        "hepatic occupying lesions",
        "nomogram"
    ],
    "methods": null,
    "publication_date": "2024-03-29",
    "pubmed_id": "38549786\n25135008\n33479224\n33538338\n32526205\n18236118\n35789428\n28130846\n22430193\n16542289\n35461226\n29425931\n35339460\n19294758\n23830777\n33756265\n34336790\n36710606\n32707225\n31672853\n38348099\n30102808\n37663947\n37189543\n35545562\n27085809\n33276049\n35719922\n36831423\n26959385\n10584697\n21689610\n4750458\n31044614\n1284788\n6205585\n31436873\n16136646\n30896550\n25031041\n30022853\n26146815\n36774930\n31450890\n22333950\n32681268\n29059036\n31954552",
    "results": "We identified six significant predictive factors from the results of multivariate logistic analysis in the training set and incorporated them into the nomogram model for diagnosing AFP-negative hepatic malignant occupying lesions (HMOL). The diagnostic nomogram, including gender, age, des-gamma-carboxy prothrombin (DCP), serum ferritin (SF), AFP, and hepatitis B surface antigen (HBsAg), achieved an area under the curve of 0.905 discriminated patients with HMOL from those with benign occupying lesions. Additionally, calibration curves demonstrated the close alignment between the nomogram predictions and the ideal curve, along with the consistency between predictions and actual results. Moreover, the DCA curves illustrated indicated benefit for all patients. These finding were confirmed by the validation set.",
    "title": "Development of a Reliable GADSAH Model for Differentiating AFP-negative Hepatic Benign and Malignant Occupying Lesions.",
    "xml": "<Element 'PubmedArticle' at 0x788503f76750>"
}