Sun Di, Hadjiiski Lubomir, Gormley John, Chan Heang-Ping, Caoili Elaine M, Cohan Richard H, Alva Ajjai, Gulani Vikas, Zhou Chuan
Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
Department of Internal Medicine-Hematology/Oncology, University of Michigan, Ann Arbor, MI 48109, USA.
Cancers (Basel). 2023 Sep 1;15(17):4372. doi: 10.3390/cancers15174372.
Accurate survival prediction for bladder cancer patients who have undergone radical cystectomy can improve their treatment management. However, the existing predictive models do not take advantage of both clinical and radiological imaging data. This study aimed to fill this gap by developing an approach that leverages the strengths of clinical (C), radiomics (R), and deep-learning (D) descriptors to improve survival prediction. The dataset comprised 163 patients, including clinical, histopathological information, and CT urography scans. The data were divided by patient into training, validation, and test sets. We analyzed the clinical data by a nomogram and the image data by radiomics and deep-learning models. The descriptors were input into a BPNN model for survival prediction. The AUCs on the test set were (C): 0.82 ± 0.06, (R): 0.73 ± 0.07, (D): 0.71 ± 0.07, (CR): 0.86 ± 0.05, (CD): 0.86 ± 0.05, and (CRD): 0.87 ± 0.05. The predictions based on D and CRD descriptors showed a significant difference (p = 0.007). For Kaplan-Meier survival analysis, the deceased and alive groups were stratified successfully by C (p < 0.001) and CRD (p < 0.001), with CRD predicting the alive group more accurately. The results highlight the potential of combining C, R, and D descriptors to accurately predict the survival of bladder cancer patients after cystectomy.
对接受根治性膀胱切除术的膀胱癌患者进行准确的生存预测可以改善其治疗管理。然而，现有的预测模型并未充分利用临床和放射影像学数据。本研究旨在通过开发一种利用临床（C）、放射组学（R）和深度学习（D）描述符的优势来改善生存预测的方法来填补这一空白。数据集包括163例患者，包含临床、组织病理学信息以及CT尿路造影扫描。数据按患者分为训练集、验证集和测试集。我们通过列线图分析临床数据，通过放射组学和深度学习模型分析图像数据。将描述符输入到BPNN模型中进行生存预测。测试集上的AUC分别为：（C）：0.82±0.06，（R）：0.73±0.07，（D）：0.71±0.07，（CR）：0.86±0.05，（CD）：0.86±0.05，以及（CRD）：0.87±0.05。基于D和CRD描述符的预测显示出显著差异（p = 0.007）。对于Kaplan-Meier生存分析，死亡组和存活组通过C（p < 0.001）和CRD（p < 0.001）成功分层，CRD对存活组的预测更准确。结果突出了结合C、R和D描述符准确预测膀胱癌患者膀胱切除术后生存情况的潜力。
