Spatial Data Mining in Histopathology

如果你被诊断出患有II期/IIIb期胃癌，并且生活在日本，你肯定会接受手术治疗，然后再进行辅助化疗。在许多国家，毫无疑问地接受这种治疗是一种常见的做法，但如果一小部分患者能够在没有额外化疗的情况下从手术中恢复过来，情况会怎样？作为经典试验的一部分，从II期/IIIb期胃癌患者身上提取了数百个含有肿瘤的组织样本，其中一半接受了手术，另一半接受了手术和化疗。使用深度学习和空间分析技术的组合，我们的目标是识别不会从增加化疗中受益的患者，并因此提供病理解释。

If you are diagnosed with stage II/stage IIIb gastric cancer and live Japan, you will most certainly be treated with surgery followed by adjuvant chemotherapy. In many countries, it is common practice to undergo this treatment without question, but what if a subset of patients were able to recover from surgery alone without the addition of chemotherapy? As part of the CLASSIC Trial, hundreds of tissue samples containing tumour were extracted from patients with stage II/stage IIIb gastric cancer, half of which received surgery and the other half received surgery and chemotherapy. Using a combination of deep learning and spatial analytical techniques, we aim to identify the patients who won't benefit from the addition of chemotherapy and provide a pathological explanation as a result.