Malignant neoplasms of the liver are among the most frequent cancers worldwide. Given the diversity of options for liver cancer therapy, the choice of treatment depends on various parameters including patient condition, tumor size and location, liver function, and previous interventions. To address this issue, we present the first approach to treatment strategy planning based on holistic processing of patient-individual data, practical knowledge (i.e., case knowledge), and factual knowledge (e.g., clinical guidelines and studies).
The contributions of this paper are as follows: (1) a formalized dynamic patient model that incorporates all the heterogeneous data acquired for a specific patient in the whole course of disease treatment; (2) a concept for formalizing factual knowledge; and (3) a technical infrastructure that enables storing, accessing, and processing of heterogeneous data to support clinical decision making.
Our patient model, which currently covers 602 patient-individual parameters, was successfully instantiated for 184 patients. It was sufficiently comprehensive to serve as the basis for the formalization of a total of 72 rules extracted from studies on patients with colorectal liver metastases or hepatocellular carcinoma. For a subset of 70 patients with these diagnoses, the system derived an average of \(37 \pm 15\) assertions per patient.
The proposed concept paves the way for holistic treatment strategy planning by enabling joint storing and processing of heterogeneous data from various information sources.
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This work was carried out with the support of the German Research Foundation (DFG) as part of project A02, I01, and S01, SFB/TRR 125 Cognition-Guided Surgery, with additional support from the projects A01 and B01. All of the authors state no conflict of interests. All studies have been approved and performed in accordance with ethical standards. Patient data were gathered and evaluated under informed consent only.
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März, K., Hafezi, M., Weller, T. et al. Toward knowledge-based liver surgery: holistic information processing for surgical decision support. Int J CARS 10, 749–759 (2015). https://doi.org/10.1007/s11548-015-1187-0
- Decision support
- Liver cancer
- Computer-assisted intervention
- Treatment planning