Giving With a Warm Hand. Evidence on Estate Planning and Bequests.

Hervorming Sociale Regelgevin

Comparison of computed tomography image features extracted by radiomics, self-supervised learning and end-to-end deep learning for outcome prediction of oropharyngeal cancer

Background and purpose: To compare the prediction performance of image features of computed tomography (CT) images extracted by radiomics, self-supervised learning and end-to-end deep learning for local control (LC), regional control (RC), locoregional control (LRC), distant metastasis-free survival (DMFS), tumor-specific survival (TSS), overall survival (OS) and disease-free survival (DFS) of oropharyngeal squamous cell carcinoma (OPSCC) patients after (chemo)radiotherapy.Methods and materials: The OPC-Radiomics dataset was used for model development and independent internal testing and the UMCG-OPC set for external testing. Image features were extracted from the Gross Tumor Volume contours of the primary tumor (GTVt) regions in CT scans when using radiomics or a self-supervised learning-based method (autoencoder). Clinical and combined (radiomics, autoencoder or end-to-end) models were built using multivariable Cox proportional-hazard analysis with clinical features only and both clinical and image features for LC, RC, LRC, DMFS, TSS, OS and DFS prediction, respectively.Results: In the internal test set, combined autoencoder models performed better than clinical models and combined radiomics models for LC, RC, LRC, DMFS, TSS and DFS prediction (largest improvements in C-index: 0.91 vs. 0.76 in RC and 0.74 vs. 0.60 in DMFS). In the external test set, combined radiomics models performed better than clinical and combined autoencoder models for all endpoints (largest improvements in LC, 0.82 vs. 0.71). Furthermore, combined models performed better in risk stratification than clinical models and showed good calibration for most endpoints.Conclusions: Image features extracted using self-supervised learning showed best internal prediction performance while radiomics features have better external generalizability.</p

Real-time laser speckle contrast imaging measurement during normothermic machine perfusion in pretransplant kidney assessment

Objectives: Normothermic machine perfusion (NMP) provides a platform for pre-transplant kidney quality assessment that is essential for the use of marginal donor kidneys. Laser speckle contrast imaging (LSCI) presents distinct advantages as a real-time and noncontact imaging technique for measuring microcirculation. In this study, we aimed to assess the value of LSCI in visualizing renal cortical perfusion and investigate the additional value of dual-side LSCI measurements compared to single aspect measurement during NMP. Methods: Porcine kidneys were obtained from a slaughterhouse and then underwent NMP. LSCI was used to measure one-sided cortical perfusion in the first 100 min of NMP. Thereafter, the inferior renal artery branch was occluded to induce partial ischemia and LSCI measurements on both ventral and dorsal sides were performed. Results: LSCI fluxes correlated linearly with the renal blood flow (R2 = 0.90, p &lt; 0.001). After renal artery branch occlusion, absence of renal cortical perfusion could be visualized and semiquantified by LSCI. The overall ischemic area percentage of the ventral and dorsal sides was comparable (median interquartile range [IQR], 38 [24−43]% vs. 29 [17−46]%, p = 0.43), but heterogenous patterns between the two aspects were observed. There was a significant difference in oxygen consumption (mean ± standard deviation [SD], 2.57 ± 0.63 vs. 1.83 ± 0.49 mLO2/min/100 g, p &lt; 0.001), urine output (median [IQR], 1.3 [1.1−1.7] vs. 0.8 [0.6−1.3] mL/min, p &lt; 0.05), lactate dehydrogenase (mean ± SD, 768 ± 370 vs. 905 ± 401 U/L, p &lt; 0.05) and AST (mean ± SD, 352 ± 285 vs. 462 ± 383 U/L, p &lt; 0.01) before and after renal artery occlusion, while no significant difference was found in creatinine clearance, fractional excretion of sodium, total sodium reabsorption and histological damage. Conclusions: LSCI fluxes correlated linearly with renal blood flow during NMP. Renal cortical microcirculation and absent perfusion can be visualized and semiquantified by LSCI. It provides a relative understanding of perfusion levels, allowing for a qualitative comparison between regions in the kidney. Dual-side LSCI measurements are of added value compared to single aspect measurement and renal function markers.Medical Instruments & Bio-Inspired Technolog

Machine learning based natural language processing of radiology reports in orthopaedic trauma

OBJECTIVES: To compare different Machine Learning (ML) Natural Language Processing (NLP) methods to classify radiology reports in orthopaedic trauma for the presence of injuries. Assessing NLP performance is a prerequisite for downstream tasks and therefore of importance from a clinical perspective (avoiding missed injuries, quality check, insight in diagnostic yield) as well as from a research perspective (identification of patient cohorts, annotation of radiographs). METHODS: Datasets of Dutch radiology reports of injured extremities (n = 2469, 33% fractures) and chest radiographs (n = 799, 20% pneumothorax) were collected in two different hospitals and labeled by radiologists and trauma surgeons for the presence or absence of injuries. NLP classification was applied and optimized by testing different preprocessing steps and different classifiers (Rule-based, ML, and Bidirectional Encoder Representations from Transformers (BERT)). Performance was assessed by F1-score, AUC, sensitivity, specificity and accuracy. RESULTS: The deep learning based BERT model outperforms all other classification methods which were assessed. The model achieved an F1-score of (95 ± 2)% and accuracy of (96 ± 1)% on a dataset of simple reports (n= 2469), and an F1 of (83 ± 7)% with accuracy (93 ± 2)% on a dataset of complex reports (n= 799). CONCLUSION: BERT NLP outperforms traditional ML and rule-base classifiers when applied to Dutch radiology reports in orthopaedic trauma

Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients

Aim: The development and evaluation of deep learning (DL) and radiomics based models for recurrence-free survival (RFS) prediction in oropharyngeal squamous cell carcinoma (OPSCC) patients based on clinical features, positron emission tomography (PET) and computed tomography (CT) scans and GTV (Gross Tumor Volume) contours of primary tumors and pathological lymph nodes. Methods: A DL auto-segmentation algorithm generated the GTV contours (task 1) that were used for imaging biomarkers (IBMs) extraction and as input for the DL model. Multivariable cox regression analysis was used to develop radiomics models based on clinical and IBMs features. Clinical features with a significant correlation with the endpoint in a univariable analysis were selected. The most promising IBMs were selected by forward selection in 1000 times bootstrap resampling in five-fold cross validation. To optimize the DL models, different combinations of clinical features, PET/CT imaging, GTV contours, the selected radiomics features and the radiomics model predictions were used as input. The combination with the best average performance in five-fold cross validation was taken as the final input for the DL model. The final prediction in the test set, was an ensemble average of the predictions from the five models for the different folds. Results: The average C-index in the five-fold cross validation of the radiomics model and the DL model were 0.7069 and 0.7575, respectively. The radiomics and final DL models showed C-indexes of 0.6683 and 0.6455, respectively in the test set. Conclusion: The radiomics model for recurrence free survival prediction based on clinical, GTV and CT image features showed the best predictive performance in the test set with a C-index of 0.6683.</p

Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging:A Transfer Learning Approach

Delineation of Gross Tumor Volume (GTV) is essential for the treatment of cancer with radiotherapy. GTV contouring is a time-consuming specialized manual task performed by radiation oncologists. Deep Learning (DL) algorithms have shown potential in creating automatic segmentations, reducing delineation time and inter-observer variation. The aim of this work was to create automatic segmentations of primary tumors (GTVp) and pathological lymph nodes (GTVn) in oropharyngeal cancer patients using DL. The organizers of the HECKTOR 2022 challenge provided 3D Computed Tomography (CT) and Positron Emission Tomography (PET) scans with ground-truth GTV segmentations acquired from nine different centers. Bounding box cropping was applied to obtain an anatomic based region of interest. We used the Swin UNETR model in combination with transfer learning. The Swin UNETR encoder weights were initialized by pre-trained weights of a self-supervised Swin UNETR model. An average Dice score of 0.656 was achieved on a test set of 359 patients from the HECKTOR 2022 challenge. Code is available at: https://github.com/HC94/swin_unetr_hecktor_2022.</p

A genome scan for quantitative trait loci affecting cyanogenic potential of cassava root in an outbred population

<p>Abstract</p> <p>Background</p> <p>Cassava (<it>Manihot esculenta </it>Crantz) can produce cyanide, a toxic compound, without self-injury. That ability was called the cyanogenic potential (CN). This project aimed to identify quantitative trait loci (QTL) associated with the CN in an outbred population derived from 'Hanatee' × 'Huay Bong 60', two contrasting cultivars. CN was evaluated in 2008 and in 2009 at Rayong province, and in 2009 at Lop Buri province, Thailand. CN was measured using a picrate paper kit. QTL analysis affecting CN was performed with 303 SSR markers.</p> <p>Results</p> <p>The phenotypic values showed continuous variation with transgressive segregation events with more (115 ppm) and less CN (15 ppm) than either parent ('Hanatee' had 33 ppm and 'Huay Bong 60' had 95 ppm). The linkage map consisted of 303 SSR markers, on 27 linkage groups with a map that encompassed 1,328 cM. The average marker interval was 5.8 cM. Five QTL underlying CN were detected. <it>CN08R1</it>from 2008 at Rayong, <it>CN09R1</it>and <it>CN09R2 </it>from 2009 at Rayong, and <it>CN09L1 </it>and <it>CN09L2 </it>from 2009 at Lop Buri were mapped on linkage group 2, 5, 10 and 11, respectively. Among all the identified QTL, <it>CN09R1 </it>was the most significantly associated with the CN trait with LOD score 5.75 and explained the greatest percentage of phenotypic variation (%Expl.) of 26%.</p> <p>Conclusions</p> <p>Five new QTL affecting CN were successfully identified from 4 linkage groups. Discovery of these QTL can provide useful markers to assist in cassava breeding and studying genes affecting the trait.</p

CT-based deep multi-label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma

Background: Personalized treatment is increasingly required for oropharyngeal squamous cell carcinoma (OPSCC) patients due to emerging new cancer subtypes and treatment options. Outcome prediction model can help identify low or high-risk patients who may be suitable to receive de-escalation or intensified treatment approaches.Purpose: To develop a deep learning (DL)-based model for predicting multiple and associated efficacy endpoints in OPSCC patients based on computed tomography (CT).Methods: Two patient cohorts were used in this study: a development cohort consisting of 524 OPSCC patients (70% for training and 30% for independent testing) and an external test cohort of 396 patients. Pre-treatment CT-scans with the gross primary tumor volume contours (GTVt) and clinical parameters were available to predict endpoints, including 2-year local control (LC), regional control (RC), locoregional control (LRC), distant metastasis-free survival (DMFS), disease-specific survival (DSS), overall survival (OS), and disease-free survival (DFS). We proposed DL outcome prediction models with the multi-label learning (MLL) strategy that integrates the associations of different endpoints based on clinical factors and CT-scans.Results: The multi-label learning models outperformed the models that were developed based on a single endpoint for all endpoints especially with high AUCs ≥ 0.80 for 2-year RC, DMFS, DSS, OS, and DFS in the internal independent test set and for all endpoints except 2-year LRC in the external test set. Furthermore, with the models developed, patients could be stratified into high and low-risk groups that were significantly different for all endpoints in the internal test set and for all endpoints except DMFS in the external test set. Conclusion: MLL models demonstrated better discriminative ability for all 2-year efficacy endpoints than single outcome models in the internal test and for all endpoints except LRC in the external set.</p

Natural Variation in Arabidopsis thaliana Revealed a Genetic Network Controlling Germination Under Salt Stress

Plant responses to environmental stresses are polygenic and complex traits. In this study quantitative genetics using natural variation in Arabidopsis thaliana was used to investigate the genetic architecture of plant responses to salt stress. Eighty seven A. thaliana accessions were screened and showed a large variation for root development and seed germination under 125 and 200 mM NaCl, respectively. Twenty two quantitative trait loci for these traits have been detected by phenotyping two recombinants inbred line populations, Sha x Col and Sha x Ler. Four QTLs controlling germination under salt were detected in the Sha x Col population. Interestingly, only one allelic combination at these four QTLs inhibits germination under salt stress, implying strong epistatic interactions between them. In this interacting context, we confirmed the effect of one QTL by phenotyping selected heterozygous inbred families. We also showed that this QTL is involved in the control of germination under other stress conditions such as KCl, mannitol, cold, glucose and ABA. Our data highlights the presence of a genetic network which consists of four interacting QTLs and controls germination under limiting environmental conditions

Towards the genetic architecture of seed lipid biosynthesis and accumulation in Arabidopsis thaliana

We report the quantitative genetic analysis of seed oil quality and quantity in six Arabidopsis thaliana recombinant inbred populations, in which the parent accessions were from diverse geographical origins, and were selected on the basis of variation for seed oil content and lipid composition. Although most of the biochemical steps involved in lipid biosynthesis are known and the key genes have been identified, the regulation of the processes that results in the final oil composition and total amount is not understood. By using physically anchored markers it was possible to compare results across populations. A total of 219 quantitative trait loci (QTLs) were identified, of which 81 were significant at P<0.001. Some of these colocalise with QTLs identified previously, but many novel QTLs were also identified. The results highlight the importance of studying traits in multiple populations, which will lead to a better understanding of the contribution that natural variation makes to the genetic architecture of a phenotype