Sensitive Quantification of Cell-Free Tumor DNA for Early Detection of Recurrence in Colorectal Cancer

The detection of plasma cell–free tumor DNA (ctDNA) is prognostic in colorectal cancer (CRC) and has potential for early prediction of disease recurrence. In clinical routine, ctDNA-based diagnostics are limited by the low concentration of ctDNA and error rates of standard next-generation sequencing (NGS) approaches. We evaluated the potential to increase the stability and yield of plasma cell–free DNA (cfDNA) for routine diagnostic purposes using different blood collection tubes and various manual or automated cfDNA extraction protocols. Sensitivity for low-level ctDNA was measured in KRAS-mutant cfDNA using an error-reduced NGS procedure. To test the applicability of rapid evaluation of ctDNA persistence in clinical routine, we prospectively analyzed postoperative samples of 67 CRC (stage II) patients. ctDNA detection was linear between 0.0045 and 45%, with high sensitivity (94%) and specificity (100%) for mutations at 0.1% VAF. The stability and yield of cfDNA were superior when using Streck BCT tubes and a protocol by Zymo Research. Sensitivity for ctDNA increased 1.5-fold by the integration of variant reads from triplicate PCRs and with PCR template concentration. In clinical samples, ctDNA persistence was found in ∼9% of samples, drawn 2 weeks after surgery. Moreover, in a retrospective analysis of 14 CRC patients with relapse during adjuvant therapy, we successfully detected ctDNA (median 0.38% VAF; range 0.18–5.04% VAF) in 92.85% of patients significantly prior (median 112 days) to imaging-based surveillance. Using optimized pre-analytical conditions, the detection of postoperative ctDNA is feasible with excellent sensitivity and allows the prediction of CRC recurrence in routine oncology testing.

Canine and Feline Testicular Preservation

The increased interest in breeding dogs and cats and their use as models for other canids and felids demand research to improve reproductive techniques. Among them, testicular cryopreservation stands out. Testicular cryopreservation enables the maintenance of reproductive capacity and allows the establishment of germplasm banks for several species of commercial value or at risk of extinction. Furthermore, it enables the transport of genetic material among different regions. It is noteworthy that this biotechnology represents the only possibility of preserving the fertility of prepubertal animals that have died, so it has great importance in the propagation of the genetic material of animals. The spermatogonia present in the testes can be cultivated in vitro and the sperm obtained can be used in artificial reproduction programs. Although advances have been achieved with the use of testicular fragments to obtain viable and functional germ cells, the establishment of protocols that can be used in clinical routine have not been concluded yet. The testicular cryopreservation process can be carried out through techniques such as slow freezing, fast freezing and vitrification. However, the protocols used for the canine and feline species are still in the experimental phase. Given the importance of the topic, the aim of this review is to draw a profile of the subject approaching the main works on testicular cryopreservation in dogs and cats.

Long-term real-world effectiveness and safety of fingolimod over 5 years in Germany

Objective To evaluate the 5-year real-world benefit–risk profile of fingolimod in patients with relapsing–remitting MS (RRMS) in Germany. Methods Post-Authorization Non-interventional German sAfety study of GilEnyA (PANGAEA) is a non-interventional real-world study to prospectively assess the effectiveness and safety of fingolimod in routine clinical practice in Germany. The follow-up period comprised 5 years. Patients were included if they had been diagnosed with RRMS and had been prescribed fingolimod as part of clinical routine. There were no exclusion criteria except the contraindications for fingolimod as defined in the European label. The effectiveness and safety analysis set comprised 4032 and 4067 RRMS patients, respectively. Results At the time of the 5-year follow-up of PANGAEA, 66.57% of patients still continued fingolimod therapy. Annualized relapse rates decreased from baseline 1.5 ± 1.15 to 0.42 ± 0.734 at year 1 and 0.21 ± 0.483 at year 5, and the disability status remained stable, as demonstrated by the Expanded Disability Status Scale mean change from baseline (0.1 ± 2.51), the decrease of the Multiple Sclerosis Severity Score from 5.1 ± 2.59 at baseline to 3.9 ± 2.31 at the 60-months follow-up, and the percentage of patients with ‘no change’ in the Clinical Global Impression scale at the 60-months follow-up (78.11%). Adverse events (AE) occurring in 75.04% of patients were in line with the known safety profile of fingolimod and were mostly non-serious AE (33.62%) and non-serious adverse drug reactions (50.59%; serious AE 4.98%; serious ADR 10.82%). Conclusions PANGAEA demonstrated the sustained beneficial effectiveness and safety of fingolimod in the long-term real-world treatment of patients with RRMS.

Validation of an automatic tool for the rapid measurement of brain atrophy and white matter hyperintensity: QyScore®

Objectives QyScore® is an imaging analysis tool certified in Europe (CE marked) and the US (FDA cleared) for the automatic volumetry of grey and white matter (GM and WM respectively), hippocampus (HP), amygdala (AM), and white matter hyperintensity (WMH). Here we compare QyScore® performances with the consensus of expert neuroradiologists. Methods Dice similarity coefficient (DSC) and the relative volume difference (RVD) for GM, WM volumes were calculated on 50 3DT1 images. DSC and the F1 metrics were calculated for WMH on 130 3DT1 and FLAIR images. For each index, we identified thresholds of reliability based on current literature review results. We hypothesized that DSC/F1 scores obtained using QyScore® markers would be higher than the threshold. In contrast, RVD scores would be lower. Regression analysis and Bland–Altman plots were obtained to evaluate QyScore® performance in comparison to the consensus of three expert neuroradiologists. Results The lower bound of the DSC/F1 confidence intervals was higher than the threshold for the GM, WM, HP, AM, and WMH, and the higher bounds of the RVD confidence interval were below the threshold for the WM, GM, HP, and AM. QyScore®, compared with the consensus of three expert neuroradiologists, provides reliable performance for the automatic segmentation of the GM and WM volumes, and HP and AM volumes, as well as WMH volumes. Conclusions QyScore® represents a reliable medical device in comparison with the consensus of expert neuroradiologists. Therefore, QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases. Key Points • QyScore® provides reliable automatic segmentation of brain structures in comparison with the consensus of three expert neuroradiologists. • QyScore® automatic segmentation could be performed on MRI images using different vendors and protocols of acquisition. In addition, the fast segmentation process saves time over manual and semi-automatic methods. • QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

Automated provision of clinical routine data for a complex clinical follow-up study: A data warehouse solution

A deep integration of routine care and research remains challenging in many respects. We aimed to show the feasibility of an automated transformation and transfer process feeding deeply structured data with a high level of granularity collected for a clinical prospective cohort study from our hospital information system to the study’s electronic data capture system, while accounting for study-specific data and visits. We developed a system integrating all necessary software and organizational processes then used in the study. The process and key system components are described together with descriptive statistics to show its feasibility in general and to identify individual challenges in particular. Data of 2051 patients enrolled between 2014 and 2020 was transferred. We were able to automate the transfer of approximately 11 million individual data values, representing 95% of all entered study data. These were recorded in n = 314 variables (28% of all variables), with some variables being used multiple times for follow-up visits. Our validation approach allowed for constant good data quality over the course of the study. In conclusion, the automated transfer of multi-dimensional routine medical data from HIS to study databases using specific study data and visit structures is complex, yet viable.

An Enhanced Image Segmentation From 3D to 2D by Using Modified Neural Network

Optical coherence tomography angiography (OCTA) is an imaging which can applied in ophthalmology to provide detailed visualization of the perfusion of vascular networks in the eye. compared to previous state of the art dye-based imaging, such as fluorescein angiography. OCTA is non-invasive, time efficient, and it allows for the examination of retinal vascular in 3D. These advantage of the technique combined with the good usability in commercial devices led to a quick adoption of the new modality in the clinical routine. However, the interpretation of OCTA data is not without problems commonly observed image artifacts and the quite involved algorithmic details of OCTA signal construction can make the clinical assessment of OCTA exams challenging. In this paper we describe the technical background of OCTA and discuss the data acquisition process, common image visualization techniques, as well as 3D to 2D projection using high pass filtering, relu function and convolution neural network (CNN) for more accuracy and segmentation results.

Robust, Primitive, and Unsupervised Quality Estimation for Segmentation Ensembles

A multitude of image-based machine learning segmentation and classification algorithms has recently been proposed, offering diagnostic decision support for the identification and characterization of glioma, Covid-19 and many other diseases. Even though these algorithms often outperform human experts in segmentation tasks, their limited reliability, and in particular the inability to detect failure cases, has hindered translation into clinical practice. To address this major shortcoming, we propose an unsupervised quality estimation method for segmentation ensembles. Our primitive solution examines discord in binary segmentation maps to automatically flag segmentation results that are particularly error-prone and therefore require special assessment by human readers. We validate our method both on segmentation of brain glioma in multi-modal magnetic resonance - and of lung lesions in computer tomography images. Additionally, our method provides an adaptive prioritization mechanism to maximize efficacy in use of human expert time by enabling radiologists to focus on the most difficult, yet important cases while maintaining full diagnostic autonomy. Our method offers an intuitive and reliable uncertainty estimation from segmentation ensembles and thereby closes an important gap toward successful translation of automatic segmentation into clinical routine.

Do clinimetric properties of LCI change after correction of signal processing?

Background: The recently described sensor-crosstalk error in the multiple-breath washout (MBW) device (Exhalyzer D, Eco Medics AG, Duernten, Switzerland) could highly influence clinimetric properties and the current interpretation of MBW results. This study reanalyzes MBW data from clinical routine in the corrected software version Spiroware® 3.3.1 and evaluates the effect on outcomes. Methods: We included nitrogen-MBW data from healthy children and children with CF from previously published trials and ongoing cohort studies. We specifically compared LCI analyzed in Spiroware 3.2.1 and 3.3.1 with regards to i) feasibility, ii) repeatability and iii) validity as outcome parameters in children with CF. Results: (i) All previously collected measurements could be reanalyzed and resulted in unchanged feasibility in Spiroware 3.3.1. (ii) Short- and midterm repeatability of LCI was similar in both software versions. (iii) Clinical validity of LCI remained similar in Spiroware 3.3.1, however, resulted in lower values. Discrimination between health and disease was comparable between both software versions. The increase in LCI over time was less pronounced with 0.16 LCI units/year (95% CI 0.08; 0.24) vs. 0.30 LCI units/year (95% CI 0.21; 0.38) in 3.2.1. Response to intervention in children receiving CFTR-modulator therapy resulted in a comparable improvement in LCI in both Spiroware versions. Conclusion: Our study confirms that clinimetric properties of LCI remain unaffected after correction for the cross-sensitivity error in Spiroware software.

Commercial AI solutions in detecting COVID-19 pneumonia in chest CT: not yet ready for clinical implementation?

Objectives In response to the COVID-19 pandemic, many researchers have developed artificial intelligence (AI) tools to differentiate COVID-19 pneumonia from other conditions in chest CT. However, in many cases, performance has not been clinically validated. The aim of this study was to evaluate the performance of commercial AI solutions in differentiating COVID-19 pneumonia from other lung conditions. Methods Four commercial AI solutions were evaluated on a dual-center clinical dataset consisting of 500 CT studies; COVID-19 pneumonia was microbiologically proven in 50 of these. Sensitivity, specificity, positive and negative predictive values, and AUC were calculated. In a subgroup analysis, the performance of the AI solutions in differentiating COVID-19 pneumonia from other conditions was evaluated in CT studies with ground-glass opacities (GGOs). Results Sensitivity and specificity ranges were 62–96% and 31–80%, respectively. Negative and positive predictive values ranged between 82–99% and 19–25%, respectively. AUC was in the range 0.54–0.79. In CT studies with GGO, sensitivity remained unchanged. However, specificity was lower, and ranged between 15 and 53%. AUC for studies with GGO was in the range 0.54–0.69. Conclusions This study highlights the variable specificity and low positive predictive value of AI solutions in diagnosing COVID-19 pneumonia in chest CT. However, one solution yielded acceptable values for sensitivity. Thus, with further improvement, commercial AI solutions currently under development have the potential to be integrated as alert tools in clinical routine workflow. Randomized trials are needed to assess the true benefits and also potential harms of the use of AI in image analysis. Key Points • Commercial AI solutions achieved a sensitivity and specificity ranging from 62 to 96% and from 31 to 80%, respectively, in identifying patients suspicious for COVID-19 in a clinical dataset. • Sensitivity remained within the same range, while specificity was even lower in subgroup analysis of CT studies with ground-glass opacities, and interrater agreement between the commercial AI solutions was minimal to nonexistent. • Thus, commercial AI solutions have the potential to be integrated as alert tools for the detection of patients with lung changes suspicious for COVID-19 pneumonia in a clinical routine workflow, if further improvement is made.