Details

Autor(en) / Beteiligte

• März, K.
• Hafezi, M.
• Weller, T.
• Saffari, A.
• Nolden, M.
• Fard, N.
• Majlesara, A.
• Zelzer, S.
• Maleshkova, M.
• Volovyk, M.
• Gharabaghi, N.
• Wagner, M.
• Emami, G.
• Engelhardt, S.
• Fetzer, A.
• Kenngott, H.
• Rezai, N.
• Rettinger, A.
• Studer, R.
• Mehrabi, A.
• Maier-Hein, L.

Titel

Toward knowledge-based liver surgery: holistic information processing for surgical decision support

Ist Teil von

• International journal for computer assisted radiology and surgery, 2015-06, Vol.10 (6), p.749-759

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2015

Quelle

MEDLINE

Beschreibungen/Notizen

• Purpose 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). Methods 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. Results 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 ± 15 assertions per patient. Conclusion The proposed concept paves the way for holistic treatment strategy planning by enabling joint storing and processing of heterogeneous data from various information sources.