Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
Int J Mol Sci. 2023 Dec 20;25(1):88. doi: 10.3390/ijms25010088.
Accurate staging of bladder cancer assists in identifying optimal treatment (e.g., transurethral resection vs. radical cystectomy vs. bladder preservation). However, currently, about one-third of patients are over-staged and one-third are under-staged. There is a pressing need for a more accurate staging modality to evaluate patients with bladder cancer to assist clinical decision-making. We hypothesize that MRI/RNA-seq-based radiogenomics and artificial intelligence can more accurately stage bladder cancer. A total of 40 magnetic resonance imaging (MRI) and matched formalin-fixed paraffin-embedded (FFPE) tissues were available for analysis. Twenty-eight (28) MRI and their matched FFPE tissues were available for training analysis, and 12 matched MRI and FFPE tissues were used for validation. FFPE samples were subjected to bulk RNA-seq, followed by bioinformatics analysis. In the radiomics, several hundred image-based features from bladder tumors in MRI were extracted and analyzed. Overall, the model obtained mean sensitivity, specificity, and accuracy of 94%, 88%, and 92%, respectively, in differentiating intra- vs. extra-bladder cancer. The proposed model demonstrated improvement in the three matrices by 17%, 33%, and 25% and 17%, 16%, and 17% as compared to the genetic- and radiomic-based models alone, respectively. The radiogenomics of bladder cancer provides insight into discriminative features capable of more accurately staging bladder cancer. Additional studies are underway.
膀胱癌的准确分期有助于确定最佳治疗方法（例如，经尿道膀胱肿瘤切除术与根治性膀胱切除术与膀胱保留）。然而，目前约有三分之一的患者分期过高，三分之一的患者分期过低。迫切需要一种更准确的分期方式来评估膀胱癌患者，以协助临床决策。我们假设 MRI/RNA-seq 为基础的放射组学和人工智能可以更准确地分期膀胱癌。共获得 40 份磁共振成像（MRI）和匹配的福尔马林固定石蜡包埋（FFPE）组织进行分析。28 份 MRI 及其匹配的 FFPE 组织用于训练分析，12 份匹配的 MRI 和 FFPE 组织用于验证。FFPE 样本进行了批量 RNA-seq 分析，然后进行了生物信息学分析。在放射组学中，从 MRI 中的膀胱癌中提取并分析了数百个基于图像的特征。总的来说，该模型在区分膀胱内与膀胱外癌症方面，分别获得了 94%、88%和 92%的平均敏感性、特异性和准确性。与单独基于遗传和放射组学的模型相比，该模型在三个矩阵中分别提高了 17%、33%和 25%，以及 17%、16%和 17%。膀胱癌的放射组学提供了有鉴别能力的特征，可以更准确地分期膀胱癌。正在进行更多的研究。
