Development and Validation of an MRI-Based Radiomics Nomogram to Predict the Prognosis of De Novo Oligometastatic Prostate Cancer Patients

IF 2.9 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2024-12-20 DOI:10.1002/cam4.70481

Wen-Qi Liu, Yu-Ting Xue, Xu-Yun Huang, Bin Lin, Xiao-Dong Li, Zhi-Bin Ke, Dong-Ning Chen, Jia-Yin Chen, Yong Wei, Qing-Shui Zheng, Xue-Yi Xue, Ning Xu

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引用次数: 0

Abstract

Objective

We aimed to develop and validate a nomogram based on MRI radiomics to predict overall survival (OS) for patients with de novo oligometastatic prostate cancer (PCa).

Methods

A total of 165 patients with de novo oligometastatic PCa were included in the study (training cohort, n = 115; validating cohort, n = 50). Among them, MRI scans were conducted and T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences were collected for radiomics features along with their clinicopathological features. Radiological features were extracted from T2WI and ADC sequences for prostate tumors. Univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) combined with 10-fold cross-validation were used to select the optimal features on each sequence. Then, a weighted radiomics score (Rad-score) was generated and independent risk factors were obtained from univariate and multivariate Cox regressions to build the nomogram. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration, and decision curve analysis (DCA).

Results

Eastern Cooperative Oncology Group (ECOG) score, absolute neutrophil count (ANC) and Rad-score were included in the nomogram as independent risk factors for OS in de novo oligometastatic PCa patients. We found that the areas under the curves (AUCs) in the training cohort were 0.734, 0.851, and 0.773 for predicting OS at 1, 2, and 3 years, respectively. In the validating cohort, the AUCs were 0.703, 0.799, and 0.833 for predicting OS at 1, 2, and 3 years, respectively. Furthermore, the clinical relevance of the predictive nomogram was confirmed through the analysis of DCA and calibration curve analysis.

Conclusion

The MRI-based nomogram incorporating Rad-score and clinical data was developed to guide the OS assessment of oligometastatic PCa. This helps in understanding the prognosis and improves the shared decision-making process.

Abstract Image

查看原文本刊更多论文

基于mri的放射组学图预测新发少转移前列腺癌患者预后的发展和验证。

目的：我们旨在开发和验证基于MRI放射组学的nomogram放射图，以预测新发寡转移性前列腺癌（PCa）患者的总生存期（OS）。方法：本研究共纳入165例新发低转移性PCa患者(训练队列，n = 115；验证队列，n = 50)。其中行MRI扫描，收集T2WI和表观弥散系数（ADC）序列放射组学特征及临床病理特征。从T2WI和ADC序列中提取前列腺肿瘤的影像学特征。采用单变量Cox回归分析和最小绝对收缩选择算子（LASSO）结合10倍交叉验证对每个序列选择最优特征。然后，生成加权放射组学评分（Rad-score），并通过单因素和多因素Cox回归获得独立危险因素，构建正态图。采用受试者工作特征（ROC）曲线、校准和决策曲线分析（DCA）评估模型的性能。结果：东部合作肿瘤组（ECOG）评分、绝对中性粒细胞计数（ANC）和rad评分作为新发寡转移性PCa患者OS的独立危险因素纳入nomogram。我们发现，在训练队列中，预测1年、2年和3年OS的曲线下面积（auc）分别为0.734、0.851和0.773。在验证队列中，预测1年、2年和3年OS的auc分别为0.703、0.799和0.833。此外，通过DCA分析和校准曲线分析，证实了预测图的临床相关性。结论：结合rad评分和临床数据的基于mri的nomogram（影像学图）可用于指导低转移性PCa的OS评估。这有助于了解预后并改善共同决策过程。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.