Applications of deep learning, optimization, and statistics in medical research

In the ever-evolving landscape of healthcare, the demand for sophisticated and efficient diagnostic tools has intensified. Medical imaging, serving as a cornerstone in disease detection and characterization, has witnessed a transformative shift with the advent of ar- tificial intelligence. In this thesis we explore the usage of both traditional image processing methods and advanced deep learning techniques to address medical questions. This thesis starts with a short overview of mathematical concepts, definitions, and proofs which we will use in later chapters. In Chapter 3, three image processing techniques are introduced, namely color decon- volution, background subtraction, and thresholding. Within Section 3.3, we propose a novel algorithm designed to efficiently compute multilevel thresholds. Depending on the input parameters, this algorithm is multiple orders of magnitude faster than currently used implementations. We achieve this by using an improving moves algorithm to find a local maximum and then repeat this process with different initial values to increase the likelihood of terminating in the global maximum. We show that this approach will find the best threshold in almost all test images, for both medical and images from the ImageNet (Deng et al. (2009)) dataset. In Chapter 4 we use a deep neural network to predict whether patients will develop a distant metastasis in five years after treatment based on images of the invasion front of their tumor. The early identification of metastatic risk can be a valuable tool for guiding treatment decisions, allowing for the implementation of more aggressive protocols when deemed necessary. The model takes binary images of tumors as input to emphasize the architecture of the tumor. These images were prepared from stained histological images for the invasion front of the tumor, which were subsequently transformed into binary black and white images. Based on the output of the neural network each patient is assigned to a high or low risk group. A drawback associated with the utilization of deep learning models is their tendency to function as black boxes, often making it challenging to decipher the factors influencing a particular prediction. Following the fitting and evaluation of the neural network model in Section 4.1, we employed two methodologies to delve into the reasoning behind the model’s predictions in Section 4.2. Firstly, we generated heatmaps to visually represent the areas of the input image exerting the most influence on the prediction. Subsequently, we crafted synthetic images with specific features to examine how these features, along with their intensity, impacted the prediction of the model. Our initial approach to create synthetic images involved using noise to generate random images. This enabled us to observe the influence of noise parameters on the model’s predictions. Further exploration involved modifying tumor borders of images previously used in the evaluation of the model and analyzing the effect of these alterations. In Chapter 5 we use deterministic image manipulation algorithms, as introduced in Chapter 3, to calculate a score for the expression of focal adhesion kinase (FAK) in tissue using microscope images. FAK expression is often used as a diagnostic marker, but is mostly evaluated subjectively by the individual pathologist. Our approach involves approximating the proportional stain concentration within lesions in comparison to the surrounding tissue, leveraging the Beer–Lambert Law (Beer (1852)). In Chapter 6, we present four supplementary studies, each of which culminated in publications resulting from collaborative efforts with research groups at the Augsburg University Hospital. Here the majority of the work was performed by the project partners at the Hospital, who performed the studies and aggregated the data. My involvement was planning and performing the statistical evaluation of said data. In Section 6.1, we present two studies scrutinizing the disparities in lymphocyte cell counts between distinct patient groups and a healthy control cohort. The initial study focuses on patients afflicted with COVID-19, while the subsequent investigation centers on patients diagnosed with colorectal cancer. In Section 6.2 two studies are outlined that investigate the potential advantages that patients can derive from the implementation of virtual reality-based interventions during their hospitalization and recovery periods

Alterations in peripheral lymphocyte subsets under immunochemotherapy in stage IV SCLC patients: Th17 cells as potential early predictive biomarker for response

UICC stage IV small-cell lung cancer (SCLC) is a highly aggressive malignancy without curative treatment options. Several randomized trials have demonstrated improved survival rates through the addition of checkpoint inhibitors to first-line platin-based chemotherapy. Consequently, a combination of chemo- and immunotherapy has become standard palliative treatment. However, no reliable predictive biomarkers for treatment response exist. Neither PD-L1 expression nor tumor mutational burden have proven to be effective predictive biomarkers. In this study, we compared the cellular immune statuses of SCLC patients to a healthy control cohort and investigated changes in peripheral blood B, T, and NK lymphocytes, as well as several of their respective subsets, during treatment with immunochemotherapy (ICT) using flow cytometry. Our findings revealed a significant decrease in B cells, while T cells showed a trend to increase throughout ICT. Notably, high levels of exhausted CD4+ and CD8+ cells, alongside NK subsets, increased significantly during treatment. Furthermore, we correlated decreases/increases in subsets after two cycles of ICT with survival. Specifically, a decrease in Th17 cells indicated a better overall survival. Based on these findings, we suggest conducting further investigation into Th17 cells as a potential early predictive biomarkers for response in patients receiving palliative ICT for stage IV SCLC

Comprehensive immunohistochemical study of the SWI/SNF complex expression status in gastric cancer reveals an adverse prognosis of SWI/SNF deficiency in genomically stable gastric carcinomas

SIMPLE SUMMARY: This study aimed to investigate the clinical relevance of immunohistochemical expression of proteins of the SWI/SNF complex, SMARCA2, SMARCA4 SMARCB1, ARID1A, ARID1B, and PBRM1 in 477 adenocarcinomas of the stomach and gastroesophageal junction. Additionally, the tumors were classified immunohistochemically in analogy to The Cancer Genome Atlas (TCGA) classification. Overall, 32% of cases demonstrated aberrant expression of the SWI/SNF complex. SWI/SNF aberration emerged as an independent negative prognostic factor for overall survival in all patients and in genomically stable patients in analogy to TCGA. In conclusion, determination of SWI/SNF status could be suggested in routine diagnostics in genomically stable tumors to identify patients who might benefit from new therapeutic options. ABSTRACT: The SWI/SNF complex has important functions in the mobilization of nucleosomes and consequently influences gene expression. Numerous studies have demonstrated that mutations or deficiency of one or more subunits can have an oncogenic effect and influence the development, progression, and eventual therapy resistance of tumor diseases. Genes encoding subunits of the SWI/SNF complex are mutated in approximately 20% of all human tumors. This study aimed to investigate the frequency, association with clinicopathological characteristics, and prognosis of immunohistochemical expression of proteins of the SWI/SNF complexes, SMARCA2, SMARCA4 SMARCB1, ARID1A, ARID1B, and PBRM1 in 477 adenocarcinomas of the stomach and gastroesophageal junction. Additionally, the tumors were classified immunohistochemically in analogy to The Cancer Genome Atlas (TCGA) classification. Overall, 32% of cases demonstrated aberrant expression of the SWI/SNF complex. Complete loss of SMARCA4 was detected in three cases (0.6%) and was associated with adverse clinical characteristics. SWI/SNF aberration emerged as an independent negative prognostic factor for overall survival in genomically stable patients in analogy to TCGA. In conclusion, determination of SWI/SNF status could be suggested in routine diagnostics in genomically stable tumors to identify patients who might benefit from new therapeutic options

Influence of perioperative step volume on complication rate and length of hospital stay after colorectal cancer surgery (IPOS trial): study protocol for a randomised controlled single-centre trial at a German university hospital

Background Perioperative mobilisation and physical activity are critical components of postoperative rehabilitation. Physical inactivity is a significant risk factor for complications and prolonged hospitalisation. However, specific recommendations for preoperative and postoperative physical activity levels are currently lacking. Evidence suggests that daily step count before and after surgery may impact the length of hospital stay and complication rate. The goal of this study is to determine the effectiveness of perioperative step volume recommendations, measured by pedometers, in reducing the length of hospital stay and complication rate for patients undergoing colorectal cancer surgery. Methods This study is a single-centre randomised controlled trial with two arms, allocated at a 1:1 ratio. The trial includes individuals undergoing colorectal surgery for either suspected or confirmed colorectal malignancy. A total of 222 patients will be randomly assigned to either an intervention or a control group. Step counts will be measured using a pedometer. Patients assigned to the intervention group will be given a predetermined preoperative and postoperative step count goal. The analysis will be conducted on preoperative and postoperative physical activity, quality of life, health, duration of hospitalisation, complication rate and bowel function, among other factors. Ethics and dissemination The trial was approved by the ethics committee of the Ludwig-Maximilians-University of Munich, Germany (reference number: 22-0758, protocol version 2022.02). Results will be published in peer-reviewed journals and shared at academic conferences. After the publication of the results, a fully anonymised data set and the statistical code can be made available on justified scientific request and after ethical approval has been granted. Trial registration number DRKS00030017