{
    "created": "2021-07-05 01:10:16",
    "updated": "2026-06-20 12:38:28",
    "id": "e7281ad3-3543-4736-a507-f4b6ed981e46",
    "version": 3,
    "ds_topic": null,
    "title_cn": "使用深度学习方法自动生成病人特异性放疗剂量分布可行性研究",
    "title_en": "A feasibility study on an automated method to generate patient-specific dose distributions for radiotherapy using deep learning",
    "ds_abstract": "<p>该文章基于深度学习建立一种病人特异性放疗计划3D剂量分布预测方法，可以自动生成病人特异性放疗剂量分布，预测结果可用于基于肿瘤百分剂量覆盖度、正常组织和危及器官的容积剂量等为参数的评判模型中，评估剂量分布的优劣性。</p>",
    "ds_source": "<p>在给定规划图像的情况下，开发一种预测最佳剂量分布的方法和分段解剖，通过将深度学习技术应用于先前优化的数据库和批准的调强放射治疗计划。本研究共纳入 80 例早期鼻咽癌 (NPC) 病例。随机选取 70 个案例作为训练集，其余作为测试集。输入是具有结构的图像，每个目标和处于危险中的器官 (OAR) 都被分配了一个唯一的标签。输出是剂量图，包括粗剂量图和转换后的精细剂量图 (FDM)卷积。在模型构建中使用了两种类型的具有结构的输入图像。一种输入包括未经处理的图像（带有相关结构）。第二种类型输入涉及使用来自辐射束几何的信息修改图像灰色标签。ResNet101 被选为两者的深度学习网络。根据临床使用的相应剂量评估预测剂量分布的准确性。</p>",
    "ds_process_way": "<p>输入具有结构的图像，每个目标和处于危险中的器官 (OAR) 都被分配了一个唯一的标签。输出剂量图，包括粗剂量图和转换后的精细剂量图 (FDM)卷积。</p>",
    "ds_quality": "<p>数据质量良好</p>",
    "ds_acq_start_time": "2017-07-01 00:00:00",
    "ds_acq_end_time": "2019-12-31 00:00:00",
    "ds_acq_place": "甘肃兰州",
    "ds_acq_lon_east": null,
    "ds_acq_lat_south": null,
    "ds_acq_lon_west": null,
    "ds_acq_lat_north": null,
    "ds_acq_alt_low": null,
    "ds_acq_alt_high": null,
    "ds_share_type": "apply-access",
    "ds_total_size": 947568,
    "ds_files_count": 2,
    "ds_format": "pdf",
    "ds_space_res": null,
    "ds_time_res": "",
    "ds_coordinate": "无",
    "ds_projection": "",
    "ds_thumbnail": "e7281ad3-3543-4736-a507-f4b6ed981e46.png",
    "ds_thumb_from": 0,
    "ds_ref_way": "",
    "paper_ref_way": "",
    "ds_ref_instruction": "",
    "ds_from_station": null,
    "organization_id": "9971252d-7beb-4464-bc08-bdcc5a1d7dd1",
    "ds_serv_man": "敏玉芳",
    "ds_serv_phone": "0931-4967596",
    "ds_serv_mail": "ncdc@lzb.ac.cn",
    "doi_value": "",
    "subject_codes": [],
    "quality_level": 3,
    "publish_time": "2023-08-25 09:38:55",
    "last_updated": "2023-08-28 15:45:04",
    "protected": false,
    "protected_to": null,
    "lang": "zh",
    "cstr": "11738.11.ncdc.IMP.db2510.2022",
    "i18n": {
        "en": {
            "title": "A feasibility study on an automated method to generate patient-specific dose distributions for radiotherapy using deep learning",
            "ds_format": "",
            "ds_source": "<p>In the case of given planning image, a method for predicting the optimal dose distribution and segmented anatomy are developed, and the deep learning technology is applied to the previously optimized database and approved IMRT plan. A total of 80 cases of early nasopharyngeal carcinoma (NPC) were included in this study. Randomly selected 70 cases as the training set, the rest as the test set. The input is a structured image, and each target and organ at risk (OAR) is assigned a unique tag. The output is dose map, including coarse dose map and converted fine dose map (FDM) convolution. Two types of structured input images are used in the model construction. One input includes an unprocessed image (with a correlation structure). The second type of input involves modifying the gray label of the image with information from the geometry of the radiation beam. Resnet101 was chosen as the deep learning network of both. The accuracy of predicting dose distribution was evaluated according to the corresponding dose used in clinic.</p>",
            "ds_quality": "<p>Good data quality</p>",
            "ds_ref_way": "",
            "ds_abstract": "<p>Based on deep learning, a 3D dose distribution prediction method for patient specific radiotherapy plan is established in this paper, which can automatically generate the patient specific radiotherapy dose distribution. The prediction results can be used in the evaluation model based on tumor percentage dose coverage, volume dose of normal tissues and organs at risk as parameters to evaluate the advantages and disadvantages of dose distribution.</p>",
            "ds_time_res": "",
            "ds_acq_place": "Lanzhou, Gansu",
            "ds_space_res": "",
            "ds_projection": "",
            "ds_process_way": "<p>Input structured images, and each target and organ at risk (OAR) is assigned a unique label. The output dose map includes coarse dose map and converted fine dose map (FDM).</p>",
            "ds_ref_instruction": ""
        }
    },
    "submit_center_id": "ncdc",
    "data_level": 0,
    "recommendation_value": 0,
    "license_type": "https://creativecommons.org/licenses/by/4.0/",
    "doi_reg_from": "reg_local",
    "cstr_reg_from": "reg_local",
    "doi_not_reg_reason": null,
    "cstr_not_reg_reason": null,
    "is_paper_in_submitting": false,
    "belong_to_nieer": false,
    "ds_topic_tags": [
        "深度学习",
        "放疗剂量",
        "病人特异性"
    ],
    "ds_subject_tags": [],
    "ds_class_tags": [],
    "ds_locus_tags": [
        "兰州",
        "甘肃省"
    ],
    "ds_time_tags": [
        2017,
        2018,
        2019
    ],
    "ds_contributors": [
        {
            "true_name": "肖国青",
            "email": "xiaogq@impcas.ac.cn",
            "work_for": "中国科学院近代物理研究所",
            "country": "中国"
        }
    ],
    "ds_meta_authors": [
        {
            "true_name": "肖国青",
            "email": "xiaogq@impcas.ac.cn",
            "work_for": "中国科学院近代物理研究所",
            "country": "中国"
        }
    ],
    "ds_managers": [
        {
            "true_name": "肖国青",
            "email": "xiaogq@impcas.ac.cn",
            "work_for": "中国科学院近代物理研究所",
            "country": "中国"
        }
    ],
    "category": "其他"
}