Owkin Lab, Owkin, Inc., 10003, New York, NY, USA.
Owkin Lab, Owkin, Inc., 10003, New York, NY, USA.
Eur J Cancer. 2022 Oct;174:90-98. doi: 10.1016/j.ejca.2022.06.055. Epub 2022 Aug 16.
The need for developing new biomarkers is increasing with the emergence of many targeted therapies. Artificial Intelligence (AI) algorithms have shown great promise in the medical imaging field to build predictive models. We developed a prognostic model for solid tumour patients using AI on multimodal data.
Our retrospective study included examinations of patients with seven different cancer types performed between 2003 and 2017 in 17 different hospitals. Radiologists annotated all metastases on baseline computed tomography (CT) and ultrasound (US) images. Imaging features were extracted using AI models and used along with the patients' and treatments' metadata. A Cox regression was fitted to predict prognosis. Performance was assessed on a left-out test set with 1000 bootstraps.
The model was built on 436 patients and tested on 196 patients (mean age 59, IQR: 51-6, 411 men out of 616 patients). On the whole, 1147 US images were annotated with lesions delineation, and 632 thorax-abdomen-pelvis CTs (total of 301,975 slices) were fully annotated with a total of 9516 lesions. The developed model reaches an average concordance index of 0.71 (0.67-0.76, 95% CI). Using the median predicted risk as a threshold value, the model is able to significantly (log-rank test P value < 0.001) isolate high-risk patients from low-risk patients (respective median OS of 11 and 31 months) with a hazard ratio of 3.5 (2.4-5.2, 95% CI).
AI was able to extract prognostic features from imaging data, and along with clinical data, allows an accurate stratification of patients' prognoses.
随着靶向治疗的出现，对新生物标志物的需求不断增加。人工智能 (AI) 算法在医学影像学领域显示出巨大的潜力，可以构建预测模型。我们使用 AI 对多模态数据为实体瘤患者开发了一种预后模型。
我们的回顾性研究纳入了 2003 年至 2017 年间在 17 家不同医院进行的七种不同癌症类型的患者检查。放射科医生对基线计算机断层扫描 (CT) 和超声 (US) 图像上的所有转移灶进行了注释。使用 AI 模型提取影像学特征，并与患者和治疗相关元数据一起使用。使用 Cox 回归对预后进行预测。在 1000 次自举测试集中评估性能。
该模型建立在 436 名患者身上，在 196 名患者身上进行了测试（平均年龄 59 岁，IQR：51-6，616 名患者中有 411 名男性）。总共对 1147 张 US 图像进行了病变勾画注释，对 632 张胸腹盆腔 CT（共 301975 张切片）进行了全面注释，共有 9516 个病变。开发的模型平均达到 0.71 的一致性指数（0.67-0.76，95%CI）。使用中位数预测风险作为阈值，该模型能够以显著的方式（对数秩检验 P 值 <0.001）将高危患者与低危患者区分开来（相应的中位 OS 分别为 11 个月和 31 个月），风险比为 3.5（2.4-5.2，95%CI）。
AI 能够从影像学数据中提取预后特征，并且与临床数据一起，可以对患者的预后进行准确分层。
