The Technome - a predictive internal calibration approach for quantitative imaging biomarker research

The goal of radiomics is to convert medical images into a minable data space by extraction of quantitative imaging features for clinically relevant analyses, e.g. survival time prediction of a patient. One problem of radiomics from computed tomography is the impact of technical variation such as reconstruction kernel variation within a study. Additionally, what is often neglected is the impact of inter-patient technical variation, resulting from patient characteristics, even when scan and reconstruction parameters are constant. In our approach, measurements within 3D regions-of-interests (ROI) are calibrated by further ROIs such as air, adipose tissue, liver, etc. that are used as control regions (CR). Our goal is to derive general rules for an automated internal calibration that enhance prediction, based on the analysed features and a set of CRs. We define qualification criteria motivated by status-quo radiomics stability analysis techniques to only collect information from the CRs which is relevant given a respective task. These criteria are used in an optimisation to automatically derive a suitable internal calibration for prediction tasks based on the CRs. Our calibration enhanced the performance for centrilobular emphysema prediction in a COPD study and prediction of patients’ one-year-survival in an oncological study

Radiomic Analysis Reveals Prognostic Information in T1-Weighted Baseline Magnetic Resonance Imaging in Patients With Glioblastoma

Objectives The aim of this study was to investigate whether radiomic analysis with random survival forests (RSFs) can predict overall survival from T1-weighted contrast-enhanced baseline magnetic resonance imaging (MRI) scans in a cohort of glioblastoma multiforme (GBM) patients with uniform treatment. Materials and Methods This retrospective study was approved by the institutional review board and informed consent was waived. The MRI scans from 66 patients with newly diagnosed GBM from a previous prospective study were analyzed. Tumors were segmented manually on contrast-enhanced 3-dimensional T1-weighted images. Using these segmentations, P = 208 quantitative image features characterizing tumor shape, signal intensity, and texture were calculated in an automated fashion. On this data set, an RSF was trained using 10-fold cross validation to establish a link between image features and overall survival, and the individual risk for each patient was predicted. The mean concordance index was assessed as a measure of prediction accuracy. Association of individual risk with overall survival was assessed using Kaplan-Meier analysis and a univariate proportional hazards model. Results Mean overall survival was 14 months (range, 0.8-85 months). Mean concordance index of the 10-fold cross-validated RSF was 0.67. Kaplan-Meier analysis clearly distinguished 2 patient groups with high and low predicted individual risk (P = 5.5 x 10(-5)). Low predicted individual mortality was found to be a favorable prognostic factor for overall survival in a univariate Cox proportional hazards model (hazards ratio, 1.038;95% confidence interval, 1.015-1.062;P = 0.0059). Conclusions This study demonstrates that baseline MRI in GBM patients contains prognostic information, which can be accessed by radiomic analysis using RSFs

Optimized management of urolithiasis by coloured stent-stone contrast using dual-energy computed tomography (DECT)

Background We analysed in vitro the appearance of commonly used ureteral stents with dual-energy computed tomography (DECT) and we used these characteristics to optimize the differentiation between stents and adjacent stone. Methods We analysed in vitro a selection of 36 different stents from 7 manufacturers. They were placed in a self-build phantom model and measured using the SOMATOM® Force Dual Source CT-Scanner (Siemens, Forchheim, Germany). Each sample was scanned at various tube potentials of 80 and 150 peak kilovoltage (kVp), 90 and 150 kVp and 100 and 150 kVp. The syngo Post-Processing Suite software program (Siemens, Forchheim, Germany) was used for differentiation based on a 3–material decomposition algorithm (UA, calcium, urine) according to our standard stone protocol. Results Stents composed of polyurethane appeared blue and silicon-based stents were red on the image. The determined appearances were constant for various peak kilovoltage (kVp) values. The coloured stent-stone-contrast displayed on DECT improves monitoring, especially of small calculi adjacent to indwelling ureteral stents. Conclusion Both urinary calculi and ureteral stents can be accurately differentiated by a distinct appearance on DECT. For the management of urolithiasis patients can be monitored more easily and accurately using DECT if the stent shows a different colour than the adjacent stone

Dynamics in treatment response and disease progression of metastatic colorectal cancer (mCRC) patients with focus on BRAF status and primary tumor location: analysis of untreated RAS-wild-type mCRC patients receiving FOLFOXIRI either with or without panitumumab in the VOLFI trial (AIO KRK0109)

Purpose: In mCRC, disease dynamics may play a critical role in the understanding of long-term outcome. We evaluated depth of response (DpR), time to DpR, and post-DpR survival as relevant endpoints. Methods: We analyzed DpR by central review of computer tomography images (change from baseline to smallest tumor diameter), early tumor shrinkage (≥ 20% reduction in tumor diameter at first reassessment), time to DpR (study randomization to DpR-image), post-DpR progression-free survival (pPFS = DpR-image to tumor progression or death), and post-DpR overall survival (pOS = DpR-image to death) with special focus on BRAF status in 66 patients and primary tumor site in 86 patients treated within the VOLFI-trial, respectively. Results: BRAF wild-type (BRAF-WT) compared to BRAF mutant (BRAF-MT) patients had greater DpR (− 57.6% vs. − 40.8%, p = 0.013) with a comparable time to DpR [4.0 (95% CI 3.1–4.4) vs. 3.9 (95% CI 2.5–5.5) months; p = 0.8852]. pPFS was 6.5 (95% CI 4.9–8.0) versus 2.6 (95% CI 1.2–4.0) months in favor of BRAF-WT patients (HR 0.24 (95% CI 0.11–0.53); p < 0.001). This transferred into a significant difference in pOS [33.6 (95% CI 26.0–41.3) vs. 5.4 (95% CI 5.0–5.9) months; HR 0.27 (95% CI 0.13–0.55); p < 0.001]. Similar observations were made for patients stratified for primary tumor site. Conclusions: BRAF-MT patients derive a less profound treatment response compared to BRAF-WT patients. The difference in outcome according to BRAF status is evident after achievement of DpR with BRAF-MT patients hardly deriving any further disease control beyond DpR. Our observations hint towards an aggressive tumor evolution in BRAF-MT tumors, which may already be molecularly detectable at the time of DpR

Automated prognosis of renal function decline in ADPKD patients using deep learning

An accurate prognosis of renal function decline in Autosomal Dominant Polycystic Kidney Disease (ADPKD) is crucial for early intervention. Current biomarkers used are height-adjusted total kidney volume (HtTKV), estimated glomerular filtration rate (eGFR), and patient age. However, manually measuring kidney volume is time-consuming and subject to observer variability. Additionally, incorporating automatically generated features from kidney MRI images, along with conventional biomarkers, can enhance prognostic improvement. To address these issues, we developed two deep-learning algorithms. Firstly, an automated kidney volume segmentation model accurately calculates HtTKV. Secondly, we utilize segmented kidney volumes, predicted HtTKV, age, and baseline eGFR to predict chronic kidney disease (CKD) stages >=3A, >=3B, and a 30% decline in eGFR after 8 years from the baseline visit. Our approach combines a convolutional neural network (CNN) and a multi-layer perceptron (MLP). Our study included 135 subjects and the AUC scores obtained were 0.96, 0.96, and 0.95 for CKD stages >=3A, >=3B, and a 30% decline in eGFR, respectively. Furthermore, our algorithm achieved a Pearson correlation coefficient of 0.81 between predicted and measured eGFR decline. We extended our approach to predict distinct CKD stages after eight years with an AUC of 0.97. The proposed approach has the potential to enhance monitoring and facilitate prognosis in ADPKD patients, even in the early disease stages

The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization

Similar to the transformation towards personalized oncology treatment, emerging techniques for evaluating oncologic imaging are fostering a transition from traditional response assessment towards more comprehensive cancer characterization via imaging. This development can be seen as key to the achievement of truly personalized and optimized cancer diagnosis and treatment. This review gives a methodological introduction for clinicians interested in the potential of quantitative imaging biomarkers, treating of radiomics models, texture visualization, convolutional neural networks and automated segmentation, in particular. Based on an introduction to these methods, clinical evidence for the corresponding imaging biomarkers&mdash;(i) dignity and etiology assessment; (ii) tumoral heterogeneity; (iii) aggressiveness and response; and (iv) targeting for biopsy and therapy&mdash;is summarized. Further requirements for the clinical implementation of these imaging biomarkers and the synergistic potential of personalized molecular cancer diagnostics and liquid profiling are discussed

A Multiepitope Subunit Vaccine Conveys Protection against Extraintestinal Pathogenic Escherichia coli in Mice▿ †

Infections due to extraintestinal pathogenic Escherichia coli (ExPEC) are common in humans and animals and include urinary tract infections (from uropathogenic E. coli [UPEC]), septicemia, and wound infections. These infections result in significant morbidity and mortality and in high health care costs. In view of the increasing number of ExPEC infections and the ever-growing antibiotic resistance capability of ExPEC isolates, preventive measures such as an effective vaccine against ExPEC are desirable. An ExPEC vaccine may be cost-effective for select patient groups. Previous vaccine candidates consisted of single target proteins or whole ExPEC cells. Here we describe a subunit vaccine against ExPEC which is based on immunodominant epitopes of the virulence-associated ExPEC proteins FyuA, IroN, ChuA, IreA, Iha, and Usp. Using a novel approach of computer-aided design, two completely artificial genes were created, both encoding eight peptide domains derived from these ExPEC proteins. The recombinant expression of these two genes resulted in a protein vaccine directed against ExPEC but not against commensal E. coli of the gut flora. In mice, the vaccine was highly immunogenic, eliciting both strong humoral and cellular immune responses. Nasal application resulted in high secretory immunoglobulin A (sIgA) production, which was detectable on the mucosal surface of the urogenital tract. Finally, it conveyed protection, as shown by a significant reduction of bacterial load in a mouse model of ExPEC peritonitis. This study provides evidence that a novel vaccine design encompassing distinct epitopes of virulence-associated ExPEC proteins may represent a means for providing a protective and pathogen-specific vaccine

Deep Learning-Based Total Kidney Volume Segmentation in Autosomal Dominant Polycystic Kidney Disease Using Attention, Cosine Loss, and Sharpness Aware Minimization

Early detection of the autosomal dominant polycystic kidney disease (ADPKD) is crucial as it is one of the most common causes of end-stage renal disease (ESRD) and kidney failure. The total kidney volume (TKV) can be used as a biomarker to quantify disease progression. The TKV calculation requires accurate delineation of kidney volumes, which is usually performed manually by an expert physician. However, this is time-consuming and automated segmentation is warranted. Furthermore, the scarcity of large annotated datasets hinders the development of deep learning solutions. In this work, we address this problem by implementing three attention mechanisms into the U-Net to improve TKV estimation. Additionally, we implement a cosine loss function that works well on image classification tasks with small datasets. Lastly, we apply a technique called sharpness aware minimization (SAM) that helps improve the generalizability of networks. Our results show significant improvements (p-value 2 of 0.96 while using only 100 MRI datasets for training and testing. Furthermore, we tested four ensembles and obtained improvements over the best individual network, achieving a DSC and MSSD of 0.922 and 1.09 mm, respectively