Department of Urology, Austin Health, Heidelberg, Victoria, Australia.
Young Urology Researchers Organisation (YURO), Melbourne, Victoria, Australia.
BJU Int. 2024 Apr;133 Suppl 4:44-52. doi: 10.1111/bju.16226. Epub 2024 Jan 18.
To evaluate near-infrared (NIR) spectroscopy in differentiating between benign and malignant bladder pathologies ex vivo immediately after resection, including the grade and stage of malignancy.
A total of 355 spectra were measured on 71 bladder specimens from patients undergoing transurethral resection of bladder tumour (TURBT) between April and August 2022. Scan time was 5 s, undertaken using a portable NIR spectrometer within 10 min from excision. Specimens were then sent for routine histopathological correlation. Machine learning models were applied to the spectral dataset to construct diagnostic algorithms; these were then tested for their ability to predict the histological diagnosis of each sample using its NIR spectrum.
A two-group algorithm comparing low- vs high-grade urothelial cancer demonstrated 97% sensitivity, 99% specificity, and the area under the receiver operating characteristic curve (AUC) was 0.997. A three-group algorithm predicting stages Ta vs T1 vs T2 achieved 97% sensitivity, 92% specificity, and the AUC was 0.996.
This first study evaluating the diagnostic potential of NIR spectroscopy in urothelial cancer shows that it can be accurately used to assess tissue in an ex vivo setting immediately after TURBT. This offers point-of-care assessment of bladder pathology, with potential to influence the extent of resection, reducing both the need for re-resection where invasive disease may be suspected, and also the potential for complications where extent of diagnostic resection can be limited. Further studies utilising fibre-optic probes offer the potential for in vivo assessment.
评估近红外（NIR）光谱技术在离体即刻切除后区分良性和恶性膀胱病变中的应用，包括恶性程度和分期。
在 2022 年 4 月至 8 月期间，对接受经尿道膀胱肿瘤切除术（TURBT）的 71 例患者的 355 个膀胱标本进行了光谱测量。扫描时间为 5 秒，使用便携式近红外光谱仪在切除后 10 分钟内进行。然后将标本送检进行常规组织病理学相关性检查。将机器学习模型应用于光谱数据集以构建诊断算法；然后使用其近红外光谱测试这些算法预测每个样本组织学诊断的能力。
比较低级别和高级别尿路上皮癌的两组算法显示出 97%的敏感性、99%的特异性，受试者工作特征曲线下面积（AUC）为 0.997。预测 Ta 期、T1 期和 T2 期的三组算法实现了 97%的敏感性、92%的特异性，AUC 为 0.996。
这是第一项评估近红外光谱技术在尿路上皮癌诊断潜力的研究，表明它可以准确地用于评估 TURBT 后离体即刻的组织情况。这为膀胱病理提供了即时的床边评估，有可能影响切除范围，减少因怀疑侵袭性疾病而需要再次切除的可能性，也有可能减少因诊断性切除范围受限而导致的并发症。进一步利用光纤探头的研究提供了体内评估的潜力。
