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.

Background: Obtaining large amounts of real patient data involves great efforts and expenses, and processing this data is fraught with data protection concerns. Consequently, data sharing might not always be possible, particularly when large, open science datasets are needed, as for AI development. For such purposes, the generation of realistic synthetic data may be the solution. Our project aimed to generate realistic cancer data with the use case of laryngeal cancer. Methods: We used the open-source software Synthea and programmed an additional module for development, treatment and follow-up for laryngeal cancer by using external, real-world (RW) evidence from guidelines and cancer registries from Germany. To generate an incidence-based cohort view, we randomly drew laryngeal cancer cases from the simulated population and deceased persons, stratified by the real-world age and sex distributions at diagnosis. Results: A module with age- and stage-specific treatment and prognosis for laryngeal cancer was successfully implemented. The synthesized population reflects RW prevalence well, extracting a cohort of 50,000 laryngeal cancer patients. Descriptive data on stage-specific and 5-year overall survival were in accordance with published data. Conclusions: We developed a large cohort of realistic synthetic laryngeal cancer cases with Synthea. Such data can be shared and published open source without data protection issues.

Detection of p16 through immunohistochemistry (IHC) is the standard for determining the HPV status of the tumor according the TNM eighth edition released in 2017 and has become crucial for determining the HPV status of oropharyngeal squamous cell carcinomas (OPSCC) with direct impact on staging and prognostication. In recent years, detection of HPV DNA in mouthwashes has been proposed as a noninvasive alternative, both for OPSCCs and for other head and neck squamous cell carcinomas (HNSCCs). However, the prospect of using the mouthwashes to monitor the response to therapy is unclear. To evaluate the effect of curative therapy on the detection of HPV DNA, we performed a prospective study comparing the detection frequency of high-risk HPV DNA (HR-HPV-DNA) in pre- and post-therapy mouthwashes. We collected 137 mouthwashes from 88 pathologically confirmed HNSCC patients for DNA isolation and HPV genotyping with the Inno-LiPA assay. We show that HPV DNA in pretherapeutic mouthwashes can detect HPV-driven HNSCCs with a sensitivity of 50.0% and specificity of 85.4%, alongside a high negative predictive value of 79.5% and an accuracy of 74.5%. Furthermore, we observed a notable decrease in the detection frequency of HR-HPV-DNA after successful treatment (pre-therapy 50.0% (9/18) versus post-therapy 9.7% (3/28)). However, the comparatively low sensitivity regarding detection of HPV-driven OPSCC argues against its use in clinical routine.

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.