No description available.

The increase in diagnostic and therapeutic procedures in the treatment of oncological diseases, as well as the limited capacity of experts to provide information, necessitates the development of therapy decision support systems (TDSS). We have developed a treatment decision model that integrates available patient information as well as tumor characteristics. They are assessed according to their relevance in evaluating the optimal therapy option. Our treatment model is based on Bayesian networks (BN) which integrate patient-specific data with expert-based implemented causalities to suggest the optimal therapy option and therefore potentially support the decision-making process for treatment of laryngeal carcinoma. To test the reliability of our model, we compared the calculations of our model with the documented therapy from our data set, which contained information on 97 patients with laryngeal carcinoma. Information on 92 patients was used in our analyses and the model suggested the correct treatment in 419 out of 460 treatment modalities (accuracy of 91%). However, unequally distributed clinical data in the test sets revealed weak spots in the model that require revision for future utilization.

No description available.

New diagnostic methods and novel therapeutic agents spawn additional and heterogeneous information, leading to an increasingly complex decision-making process for optimal treatment of cancer. A great amount of information is collected in organ-specific multidisciplinary tumor boards (MDTBs). By considering the patient’s tumor properties, molecular pathological test results, and comorbidities, the MDTB has to consent an evidence-based treatment decision. Immunotherapies are increasingly important in today’s cancer treatment, resulting in detailed information that influences the decision-making process. Clinical decision support systems can facilitate a better understanding via processing of multiple datasets of oncological cases and molecular genetic information, potentially fostering transparency and comprehensibility of available information, eventually leading to an optimum treatment decision for the individual patient. We constructed a digital patient model based on Bayesian networks to combine the relevant patient-specific and molecular data with depended probabilities derived from pertinent studies and clinical guidelines to calculate treatment decisions in head and neck squamous cell carcinoma (HNSCC). In a validation analysis, the model can provide guidance within the growing subject of immunotherapy in HNSCC and, based on its ability to calculate reliable probabilities, facilitates estimation of suitable therapy options. We compared actual treatment decisions of 25 patients with the calculated recommendations of our model and found significant concordance (Cohen’s κ = 0.505, p = 0.009) and 84% accuracy.

Background: Standardized staging procedures and presentation of oral squamous cell carcinoma (OSCC) patients in multidisciplinary tumor boards (MDTB) before treatment and utilization of elective neck dissection (ND) are expected to improve the outcome, especially in local advanced LAOSCC (UICC stages III–IVB). As standardized diagnostics but also increased heterogeneity in treatment applied so far have not been demonstrated to improve outcome in LAOSCC, a retrospective study was initiated. Methods: As MDTB was introduced into clinical routine in 2007, 316 LAOSCC patients treated during 1991-2017 in our hospital were stratified into cohort 1 treated before (n=104) and cohort 2 since 2007 (n=212). Clinical characteristics, diagnostic procedures and treatment modality of patients were compared using Chi-square tests and outcome analyzed applying Kaplan-Meier plots and log-rank tests as well as Cox proportional hazard regression. Propensity scores (PS) were used to elucidate predictors for impaired distant metastasis-free survival (DMFS) in PS-matched patients. Results: Most patient characteristics and treatment modalities applied showed insignificant alteration. Surgical treatment included significantly more often resection of the primary tumor plus neck dissection, tracheostomy and percutaneous endoscopic gastrostomy tube use. Cisplatin-based chemo-radiotherapy was the most frequent. Only insignificant improved disease- (DFS), progression- (PFS) and event-free (EFS) as well as tumor-specific (TSS) and overall survival (OS) were found after 2006 as local (LC) and locoregional control (LRC) were significantly improved but DMFS significantly impaired. Cox regression applied to PS-matched patients elucidated N3, belonging to cohort 2 and cisplatin-based chemo-radiotherapy as independent predictors for shortened DMFS. The along chemo-radiotherapy increased dexamethasone use in cohort 2 correlates with increased DM. Conclusions: Despite standardized diagnostic procedures, decision-making considering clear indications and improved therapy algorithms leading to improved LC and LRC, shortened DMFS hypothetically linked to increased dexamethasone use had a detrimental effect on TSS and OS.

New diagnostic methods and novel therapeutic agents spawn additional and heterogeneous in-formation, leading to an increasingly complex decision-making process for optimal treatment of cancer. A great amount of information is collected in organ-specific multidisciplinary tumor boards (MDTBs). By considering the patient’s tumor properties, molecular pathological test re-sults, and comorbidities, the MDTB has to consent an evidence-based treatment decision. Im-munotherapies are increasingly important in today’s cancer treatment, resulting in detailed in-formation that influences the decision-making process. Clinical decision support systems can fa-cilitate a better understanding via processing of multiple datasets of oncological cases and mo-lecular genetic information, potentially fostering transparency and comprehensibility of available information, eventually leading to an optimum treatment decision for the individual patient. We constructed a digital patient model based on Bayesian networks to combine the relevant pa-tient-specific and molecular data with depended probabilities derived from pertinent studies and clinical guidelines to calculate treatment decisions in head and neck squamous cell carcinoma (HNSCC). In a validation analysis, the model can provide guidance within the growing subject of immunotherapy in HNSCC and, based on its ability to calculate reliable probabilities, facilitates estimation of suitable therapy options. We compared actual treatment decisions of 25 patients with the calculated recommendations of our model and found significant concordance (Cohen’s κ=0.505, p=0.009) and 84% accuracy.