Cancer is one of the leading death causes globally with about 8.2 million deaths per year and an increase in numbers in recent years. About 90% of cancer deaths do not occur due to primary tumors but due to metastases, of which most are not clinically identifiable because of their relatively small size at primary diagnosis and limited technical possibilities. However, therapeutic decisions are formed depending on the existence of metastases and their properties. Therefore non-identified metastases might have huge influence in the treatment outcome. The quantification of clinically visible and invisible metastases is important for the choice of an optimal treatment of the individual patient as it could clarify the burden of non-identifiable tumors as well as the future behavior of the cancerous disease. The mathematical model presented in this study gives insights in how this could be achieved, taking into account different treatment possibilities and therefore being able to compare therapy schedules for individual patients with different clinical parameters. The framework was tested on three patients with non-small cell lung cancer, one of the deadliest types of cancer worldwide, and clinical history including platinum-based chemotherapy and PD-L1-targeted immunotherapy. Results yield promising insights into the framework to establish methods to quantify effects of different therapy methods and prognostic features for individual patients already at stage of primary diagnosis.

中文翻译：

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数学模型如何有助于量化治疗中的转移性肿瘤负荷：非小细胞肺癌免疫治疗的见解

癌症是全球主要死亡原因之一，每年约有 820 万人死亡，而且近年来死亡人数还在增加。大约 90% 的癌症死亡不是由原发性肿瘤引起，而是由转移引起，其中大多数转移灶由于初次诊断时的尺寸相对较小且技术可能性有限，因此无法在临床上识别。然而，治疗决策的形成取决于转移的存在及其特性。因此，未识别的转移可能对治疗结果产生巨大影响。临床可见和不可见转移的量化对于选择个体患者的最佳治疗非常重要，因为它可以澄清不可识别肿瘤的负担以及癌症疾病的未来行为。本研究中提出的数学模型给出了如何实现这一目标的见解，考虑到不同的治疗可能性，因此能够比较具有不同临床参数的个体患者的治疗方案。该框架在三名患有非小细胞肺癌（全世界最致命的癌症类型之一）的患者身上进行了测试，其临床病史包括铂类化疗和 PD-L1 靶向免疫治疗。结果为建立量化不同治疗方法的效果的方法和已经处于初步诊断阶段的个体患者的预后特征的框架提供了有希望的见解。