Reith F H, Jarosch A, Albrecht J P, Ghoreschi F, Flörcken A, Dörr A, Roohani S, Schäfer F M, Öllinger R, Märdian S, Tielking K, Bischoff P, Frühauf N, Brandes F, Horst D, Sers C, Kainmüller D
Max Delbrück Center for Molecular Medicine in the Helmholtz Association.
Helmholtz Imaging, Berlin, Germany.
J Pathol Inform. 2025 May 9;18:100447. doi: 10.1016/j.jpi.2025.100447. eCollection 2025 Aug.
Tumoral PD-L1 expression is assessed to weigh immunotherapy options in the treatment of various types of cancer. To determine PD-L1 expression, each tumor cell needs to be assessed to calculate the percentage of PD-L1 positive tumor cells, called tumor proportion score (TPS). Pathologists cannot evaluate each cell individually due to time constraints and thus need to approximate TPS, which has been shown to result in low concordance rates. Decision quality could be improved by an AI-based TPS prediction tool which serves as a "second opinion". Establishing such a tool requires a certain amount of training data, which manifests a bottleneck for rare cancer types such as Angiosarcoma. To address this challenge, we developed and open sourced a pipeline that leverages pre-trained and generalist models to achieve strong TPS prediction performance on limited data. Pathologists were asked to reassess patients for which their TPS strongly disagreed with the AI's prediction. In many of these cases, pathologists updated their TPS score, improving their assessment, thus demonstrating the technical feasibility and practical value of AI-based TPS scoring assistance for rare cancers.
评估肿瘤性程序性死亡受体配体1（PD-L1）表达情况，以权衡免疫疗法在各类癌症治疗中的选择。为确定PD-L1表达情况，需要评估每个肿瘤细胞，以计算PD-L1阳性肿瘤细胞的百分比，即肿瘤比例评分（TPS）。由于时间限制，病理学家无法逐个评估每个细胞，因此需要估算TPS，而这已被证明会导致较低的一致性率。基于人工智能的TPS预测工具作为“第二意见”，可以提高决策质量。建立这样一个工具需要一定数量的训练数据，这对诸如血管肉瘤等罕见癌症类型来说是一个瓶颈。为应对这一挑战，我们开发并开源了一个流程，该流程利用预训练的通用模型，在有限的数据上实现强大的TPS预测性能。我们要求病理学家重新评估那些TPS与人工智能预测结果存在强烈分歧的患者。在许多此类病例中，病理学家更新了他们的TPS评分，改进了评估，从而证明了基于人工智能的TPS评分辅助对罕见癌症的技术可行性和实用价值。
