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INTRODUCTION: Scarce data exist for therapy regimens other than somatostatin analogues (SSA) and peptide receptor radiotherapy (PRRT) for siNET. We analyzed real world data for differences in survival according to therapy. PATIENTS AND METHODS: Analysis of 145 patients, diagnosed between 1993 and 2018 at a single institution, divided in treatment groups. Group (gr.) 0: no treatment (n = 10), gr 1: TACE and/or PRRT (n = 26), gr. 2: SSA (n = 32), gr. 3: SSA/PRRT (n = 8), gr. 4: chemotherapy (n = 8), gr. 5: not metastasized (at diagnosis), surgery only (n = 53), gr. 6 = metastasized (at diagnosis), surgery only (n = 10). RESULTS: 45.5% female, median age 60 years (range, 27-84). A total of 125/145 patients with a resection of the primary tumor. For all patients, 1-year OS (%) was 93.8 (95%-CI: 90-98), 3-year OS = 84.3 (CI: 78-90) and 5-year OS = 77.5 (CI: 70-85). For analysis of survival according to therapy, only stage IV patients (baseline) that received treatment were included. Compared with reference gr. 2 (SSA only), HR for OS was 1.49 (p = 0.47) for gr. 1, 0.72 (p = 0.69) for gr. 3, 2.34 (p = 0.19) for gr. 4. The 5 y OS rate of patients whose primary tumor was resected (n = 125) was 73.1%, and without PTR was 33.3% (HR: 4.31; p = 0.003). Individual patients are represented in swimmer plots. CONCLUSIONS: For stage IV patients in this analysis (limited by low patient numbers in co. 3/4), multimodal treatment did not significantly improve survival over SSA treatment alone. A resection of primary tumor significantly improves survival.
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In this chapter, different approaches for open-loop and closed-loop control applied in bioprocess automation are discussed. Although in recent years many contributions dealing with closed-loop control have been published, only a minority were actually applied in real bioprocesses, the majority being simulations. As a result of the diversity of bioprocess requirements, a single control algorithm cannot be applied in all cases; rather, different approaches are necessary. Most publications combine different closed-loop control techniques to construct hybrid systems. These systems are supposed to combine the advantages of each approach into a well-performing control strategy. The majority of applications are soft sensors in combination with a proportional-integral-derivative (PID) controller. The fact that soft sensors have become this importance for control purposes demonstrates the lack of direct measurements or their large additional expense for robust and reliable online measurement systems. The importance of model predictive control is increasing; however, reliable and robust process models are required, as well as very powerful computers to address the computational needs. The lack of theoretical bioprocess models is compensated by hybrid systems combining theoretical models, fuzzy logic, and/or artificial neural network methodology. Although many authors suggest a possible transfer of their presented control application to other bioprocesses, the algorithms are mostly specialized to certain organisms or certain cultivation conditions as well as to a specific measurement system.
