Leo Patrick, Nezami Behtash G, Akgul Mahmut, Tokuyama Naoto, Farré Xavier, Elliott Robin, Viswanathan Vidya S, Harper Holly, MacLennan Gregory, Madabhushi Anant
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH.
Northwestern University Feinberg School of Medicine, Chicago, IL.
JCO Clin Cancer Inform. 2025 Jul;9:e2400304. doi: 10.1200/CCI-24-00304. Epub 2025 Jul 22.
Current risk assessment tools for bladder cancer following transurethral resection of the bladder tumor (TURBT) depend on pathological examination of resected tissue, with the consequent intra- and inter-reviewer variability. Improved prognostic tools could enable increased monitoring and aggressive interventions for high-risk patients while reducing the frequency of invasive testing for low-risk patients.
We present an automated tumor risk assessment method based on quantitative features of nuclear pleomorphism and polarity extracted from digitized hematoxylin and eosin slides and compared this model with pathologist grading. Our model, incorporating six features, was trained to estimate overall survival risk on n = 189 patients and validated for recurrence prognosis on an independent validation set of n = 151 patients.
The model had an accuracy of 0.73 (95% CI, 0.66 to 0.81) in identifying patients who would have recurrence within 5 years of surgery. Within the validation set was a consensus set of patients (n = 94) on which three pathologists independently assigned the same grade and a nonconsensus set (n = 57) where they did not. The model had similar performance in the consensus and nonconsensus set, with accuracies of 0.70 (95% CI, 0.61 to 0.80) and 0.78 (95% CI, 0.67 to 0.89), respectively, and was able to recapitulate pathologist scoring on the consensus set (accuracy = 0.76).
The results of this study suggest the need to incorporate both computerized analysis and pathologist grading into post-TURBT treatment planning.
目前用于经尿道膀胱肿瘤切除术（TURBT）后膀胱癌的风险评估工具依赖于对切除组织的病理检查，这会导致审阅者内部和审阅者之间的差异。改进的预后工具可以加强对高危患者的监测并采取积极干预措施，同时减少对低危患者进行侵入性检查的频率。
我们提出了一种基于从数字化苏木精和伊红染色切片中提取的核多形性和极性定量特征的自动肿瘤风险评估方法，并将该模型与病理学家的分级进行了比较。我们的模型纳入了六个特征，在n = 189例患者中进行训练以估计总生存风险，并在n = 151例患者的独立验证集中对复发预后进行验证。
该模型在识别术后5年内会复发的患者方面的准确率为0.73（95%CI，0.66至0.81）。在验证集中有一组共识患者（n = 94），三位病理学家对其独立给出了相同的分级，还有一组非共识患者（n = 57），他们给出的分级不同。该模型在共识组和非共识组中的表现相似，准确率分别为0.70（95%CI，0.61至0.80）和0.78（95%CI，0.67至0.89），并且能够在共识组中重现病理学家的评分（准确率 = 0.76）。
本研究结果表明，在TURBT术后治疗规划中需要将计算机化分析和病理学家分级结合起来。
