[The development of real-time multispectral imaging for the diagnostics of bladder cancer]. / Entwicklung der Echtzeitmultispektralbildgebung für die Diagnostik des Harnblasenkarzinoms.

The performance of white light (WL) cystoscopy in the diagnostics of bladder cancer can be optimized by the use of modern imaging modalities, such as photodynamic diagnostics (PDD) and narrow band imaging (NBI). Real-time multispectral imaging (rMSI) enables simultaneous imaging of reflectance and fluorescence modalities in multiple spectral bands. We created a multiparametric cystoscopy image by digital overlapping of several modalities, e.g. WL, enhanced vascular contrast (EVC), raw fluorescence mode, protoporphyrin IX and autofluorescence (AF). The technical development and the subsequent clinical implementation of rMSI required a structured preclinical evaluation process, including both ex vivo and in vivo trials before the technology can be applied in patients. This review article presents the phases of testing, validation and the first clinical application of rMSI in urological endoscopy.

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

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 [Formula: see text] 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.