Dataset related to article "Radiomics-based prognosis classification for high-risk prostate cancer treated with radiotherapy "

This record contains raw data related to article “Radiomics-based prognosis classification for high-risk prostate cancer treated with radiotherapy" Abstract: <strong>Objective: </strong> The present study aimed to investigate if CT-based radiomics features could correlate to the risk of metastatic progression in high-risk prostate cancer patients treated with radical RT and long-term androgen deprivation therapy (ADT). <strong>Materials and methods: </strong> A total of 157 patients were investigated and radiomics features extracted from the contrast-free treatment planning CT series. Three volumes were segmented: the prostate gland only (CTV_p), the prostate gland with seminal vesicles (CTV_psv), and the seminal vesicles only (CTV_sv). The patients were split into two subgroups of 100 and 57 patients for training and validation. Five clinical and 62 radiomics features were included in the analysis. Considering metastases-free survival (MFS) as an endpoint, the predictive model was used to identify the subgroups with favorable or unfavorable prognoses (separated by a threshold selected according to the Youden method). Pure clinical, pure radiomic, and combined predictive models were investigated. <strong>Results: </strong> With a median follow-up of 30.7 months, the MFS at 1 and 3 years was 97.2% ± 1.5 and 92.1% ± 2.0, respectively. Univariate analysis identified seven potential predictors for MFS in the CTV_p group, 11 in the CTV_psv group, and 9 in the CTV_sv group. After elastic net reduction, these were 4 predictors for MFS in the CTV_p group (positive lymph nodes, Gleason score, H_Skewness, and NGLDM_Contrast), 5 in the CTV_psv group (positive lymph nodes, Gleason score, H_Skewnesss, Shape_Surface, and NGLDM_Contrast), and 6 in the CTV_sv group (positive lymph nodes, Gleason score, H_Kurtosis, GLCM_Correlation, GLRLM_LRHGE, and GLZLM_SZLGE). The patients' group of the training and validation cohorts were stratified into favorable and unfavorable prognosis subgroups. For the combined model, for CTV_p, the mean MFS was 134 ± 14.5 vs. 96.9 ± 22.2 months for the favorable and unfavorable subgroups, respectively, and 136.5 ± 14.6 vs. 70.5 ± 4.3 months for CTV_psv and 150.0 ± 4.2 vs. 91.1 ± 8.6 months for CTV_sv, respectively. <strong>Conclusion: </strong> Radiomic features were able to predict the risk of metastatic progression in high-risk prostate cancer. Combining the radiomic features and clinical characteristics can classify high-risk patients into favorable and unfavorable prognostic groups.本数据集包含与论文《基于放射组学（Radiomics）的高危前列腺癌放疗预后分类》相关的原始数据。摘要：<strong>研究目的：</strong> 本研究旨在探讨基于CT的放射组学特征，是否与接受根治性放疗（radical RT）联合长期雄激素剥夺治疗（androgen deprivation therapy, ADT）的高危前列腺癌患者的转移进展风险相关。<strong>材料与方法：</strong> 本研究共纳入157例患者，从无对比剂治疗计划CT序列中提取放射组学特征。共分割三种靶区：仅前列腺腺体（CTV_p）、前列腺腺体联合精囊腺（CTV_psv）以及仅精囊腺（CTV_sv）。将患者按100例与57例分为训练组与验证组两个亚组。分析中共纳入5项临床特征与62项放射组学特征。以无转移生存期（metastases-free survival, MFS）作为终点指标，通过预测模型识别预后良好与预后不良的亚组（阈值根据尤登法（Youden method）选取）。本研究探究了纯临床模型、纯放射组学模型以及联合预测模型三种建模方案。<strong>结果：</strong> 本研究中位随访时间为30.7个月，1年与3年无转移生存率分别为97.2%±1.5与92.1%±2.0。单因素分析显示，在CTV_p组中识别出7项MFS潜在预测因子，CTV_psv组中为11项，CTV_sv组中为9项。经弹性网（elastic net）降维后，CTV_p组剩余4项预测因子（阳性淋巴结、格里森评分（Gleason score）、H_Skewness以及NGLDM_Contrast），CTV_psv组剩余5项（阳性淋巴结、格里森评分、H_Skewnesss、Shape_Surface以及NGLDM_Contrast），CTV_sv组剩余6项（阳性淋巴结、格里森评分、H_Kurtosis、GLCM_Correlation、GLRLM_LRHGE以及GLZLM_SZLGE）。将训练队列与验证队列的患者分为预后良好与预后不良亚组。对于联合模型，CTV_p组的良好与不良预后亚组平均MFS分别为134±14.5与96.9±22.2个月；CTV_psv组为136.5±14.6与70.5±4.3个月；CTV_sv组为150.0±4.2与91.1±8.6个月。<strong>结论：</strong> 放射组学特征能够预测高危前列腺癌患者的转移进展风险。将放射组学特征与临床特征相结合，可将高危患者分为预后良好与预后不良两个亚组。