Automated Imaging as an Adjunct to Serum and Clinical Biomarkers: A New Validated Prediction Tool for Metastatic Castration-Resistant Prostate Cancer.

We sought to develop an externally validated model that used novel quantification of soft-tissue and bone disease, integrated with standard clinical and serum biomarkers, at baseline and up to 6 months of treatment.

我们试图开发一个经过外部验证的模型，该模型在基线和治疗的前6个月内，结合了软组织和骨病变的新量化方法以及标准的临床和血清生物标志物。

experimental_design

Two randomized phase 3 trials, Cougar COU-AA-302 (NCT00887198; for derivation) and Alliance A031201 (NCT01949337; for validation), were used to evaluate the added value of early on-treatment bone imaging and more than 1,000 radiomics features on CT, used in conjunction with clinical and serum biomarkers in first-line metastatic castration-resistant prostate cancer.

使用了两项随机III期试验，Cougar COU-AA-302（NCT00887198；用于推导）和Alliance A031201（NCT01949337；用于验证），以评估早期治疗期间骨成像和超过1000个CT影像组学特征的附加价值，这些特征与临床和血清生物标志物结合使用，用于一线转移性去势抵抗性前列腺癌。

Predictive accuracy measures were computed to determine whether these early on-treatment biomarkers could reliably sort patients into risk groups that inform overall survival(OS) and whether the patient-specific biomarker risk score could precisely predict their OS time.

计算了预测准确性指标，以确定这些早期治疗期间的生物标志物是否能可靠地将患者分入能够预测总生存期（OS）的风险组，并且患者特定的生物标志物风险评分是否能精确预测他们的OS时间。

Results

Imaging improved patient risk stratification but did not improve individual survival predictions.

影像学改善了患者的危险分层，但并未改善个体生存预测。

The strongest risk prediction model was developed for patients with bone-only metastases.

为仅伴有骨转移的患者开发了最强的危险预测模型。

This model was also the least complex, relying on just 16 risk factors, whereas all other models were high-dimensional, incorporating approximately 1,100 intercept and 1,100 slope features from the early on-treatment biomarker trajectories.

该模型也是最不复杂的，仅依赖于16个风险因素，而其他所有模型都是高维的，早期治疗生物标志物轨迹中包含了大约1100个截距和1100个斜率特征。

Conclusions

Pretreatment and early on-treatment serum and automated quantitative imaging markers can well discriminate risk of death.

治疗前和早期治疗期间的血清和自动化定量成像标志物可以很好地辨别死亡风险。

Imaging improves this risk categorization relative to serum biomarkers alone.

与单独使用血清生物标志物相比，影像学改善了这种风险分类。

Such models can give early outcome predictions and can be used in future trials that involve imaging, even using traditional techniques such as bone scintigraphy.

此类模型可以提供早期的结果预测，并且可以在未来的涉及影像学的试验中使用，即使是使用传统技术如骨显像。