Standardised convolutional filtering for radiomics

The Image Biomarker Standardisation Initiative (IBSI) aims to improve reproducibility of radiomics studies by standardising the computational process of extracting image biomarkers (features) from images. We have previously established reference values for 169 commonly used features, created a standard radiomics image processing scheme, and developed reporting guidelines for radiomic studies. However, several aspects are not standardised. Here we present a preliminary version of a reference manual on the use of convolutional image filters in radiomics. Filters, such as wavelets or Laplacian of Gaussian filters, play an important part in emphasising specific image characteristics such as edges and blobs. Features derived from filter response maps have been found to be poorly reproducible. This reference manual forms the basis of ongoing work on standardising convolutional filters in radiomics, and will be updated as this work progresses.Comment: 62 pages. For additional information see https://theibsi.github.io

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics. Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.Comment: (1) Paper: 33 pages, 4 figures, 1 table; (2) SUPP info: 41 pages, 7 figures, 8 table

Sleep and quality of life in urban poverty : the effect of a slum housing upgrading program

Study Objectives : To evaluate the effect of a housing transition on sleep quality and quality of life in slum dwellers, participating in a slum housing upgrading program. Design : Observational before-and-after study with a convergent-parallel mixed method design. Setting : Five slums located in the metropolitan area of Buenos Aires, Argentina. Participants : A total of 150 slum dwellers benefited by a housing program of the nonprofit organization TECHO (spanish word for “roof”). Interventions : Participants moved from their very low-quality house to a basic prefabricated 18 m2 modular house provided by TECHO. Measurements and Results : The Pittsburgh Sleep Quality Index (PSQI) and World Health Organization Quality of Life brief scale (WHOQOL-BREF) were administered before and after housing upgrading. Data about housing conditions, income, education, sleeping conditions, and cardiovascular risk were also collected. Semistructured interviews were used to expand and nuance quantitative data obtained from a poorly educated sample. Results showed that sleep quality significantly increased after the housing program (z = -6.57, P < 0.001). Overall quality of life (z = -6.85, P < 0.001), physical health domain (z = -4.35, P < 0.001), psychological well-being domain (z = -3.72, P < 0.001) and environmental domain (z = -7.10, P < 0.001) of WHOQOL-BREF were also improved. Interviews demonstrated the importance of serenity for improving quality of life. Conclusions : A minimal improvement in the quality of basic housing can significantly increase sleep quality and quality of life among slum dwellers. Understanding sleep and daily life conditions in informal urban settlements could help to define what kind of low-cost intervention may improve sleep quality, quality of life, and reduce existent sleep disparity

Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics

Introduction There is a cumulative risk of 20-40% of developing brain metastases (BM) in solid cancers. Stereotactic radiotherapy (SRT) enables the application of high focal doses of radiation to a volume and is often used for BM treatment. However, SRT can cause adverse radiation effects (ARE), such as radiation necrosis, which sometimes cause irreversible damage to the brain. It is therefore of clinical interest to identify patients at a high risk of developing ARE. We hypothesized that models trained with radiomics features, deep learning (DL) features, and patient characteristics or their combination can predict ARE risk in patients with BM before SRT. Methods Gadolinium-enhanced T1-weighted MRIs and characteristics from patients treated with SRT for BM were collected for a training and testing cohort (N = 1,404) and a validation cohort (N = 237) from a separate institute. From each lesion in the training set, radiomics features were extracted and used to train an extreme gradient boosting (XGBoost) model. A DL model was trained on the same cohort to make a separate prediction and to extract the last layer of features. Different models using XGBoost were built using only radiomics features, DL features, and patient characteristics or a combination of them. Evaluation was performed using the area under the curve (AUC) of the receiver operating characteristic curve on the external dataset. Predictions for individual lesions and per patient developing ARE were investigated. Results The best-performing XGBoost model on a lesion level was trained on a combination of radiomics features and DL features (AUC of 0.71 and recall of 0.80). On a patient level, a combination of radiomics features, DL features, and patient characteristics obtained the best performance (AUC of 0.72 and recall of 0.84). The DL model achieved an AUC of 0.64 and recall of 0.85 per lesion and an AUC of 0.70 and recall of 0.60 per patient. Conclusion Machine learning models built on radiomics features and DL features extracted from BM combined with patient characteristics show potential to predict ARE at the patient and lesion levels. These models could be used in clinical decision making, informing patients on their risk of ARE and allowing physicians to opt for different therapies

FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort.: FDG-PET heterogeneity and volume

International audienceIntra-tumor uptake heterogeneity in 18F-FDG PET has been associated with patient treatment outcomes in several cancer types. Textural features (TF) analysis is a promising method for its quantification. An open issue associated with the use of TF for the quantification of intratumoral heterogeneity concerns its added contribution and dependence on the metabolically active tumor volume (MATV), which has already been shown as a significant predictive and prognostic parameter. Our objective was to address this question using a larger cohort of patients covering different cancer types.METHODS:A single database of 555 pre-treatment 18F-FDG PET images (breast, cervix, esophageal, head & neck and lung cancer tumors) was assembled. Four robust and reproducible TF-derived parameters were considered. The issues associated with the calculation of TF using co-occurrence matrices (such as the quantization and spatial directionality relationships) were also investigated. The relationship between these features and MATV, as well as among the features themselves was investigated using Spearman rank coefficients, for different volume ranges. The complementary prognostic value of MATV and TF was assessed through multivariate Cox analysis in the esophageal and NSCLC cohorts.RESULTS:A large range of MATVs was included in the population considered (3-415 cm3, mean = 35, median = 19, SD=50). The correlation between MATV and TF varied greatly depending on the MATVs, with reduced correlation for increasing volumes. These findings were reproducible across the different cancer types. The quantization and the calculation method both had an impact on the correlation. Volume and heterogeneity were independent prognostic factors (P = 0.0053 and 0.0093 respectively) along with stage (P = 0.002) in NSCLC, but in the esophageal tumors, volume and heterogeneity had less complementary value due to smaller overall volumes.CONCLUSION:Our results suggest that heterogeneity quantification and volume may provide valuable complementary information for volumes above 10cm3, although the complementary information increases substantially with larger volumes

Impact of parental emotional support and coercive control on adolescents' self-esteem and psychological distress : results of a four-year longitudinal study

This study aims at investigating the impact of parental practices on youths’ adjustment. In all, 605 adolescents completed questionnaires at ages 14, 16 and 18. Self-esteem, psychological distress as well as parental emotional support and coercive control were measured. Analyses based on individual growth models revealed that self-esteem increased with age, but psychological distress remained stable over time. Boys reported higher levels of self-esteem and lower levels of psychological distress than girls. Maternal and paternal emotional support reinforced self-esteem over time. Maternal coercive control undermined self-esteem, but only at ages 16 and 18. Psychological distress decreased with parental emotional support but increased with parental coercive control at ages 14, 16 and 18. Overall, these results indicate that positive parental practices are related to youths’ well-being. These findings support the importance of establishing intervention strategies designed to promote best practices among parents of teenagers to help them develop into well-adjusted adults

Stem cell treatment of degenerative eye disease

Stem cell therapies are being explored extensively as treatments for degenerative eye disease, either for replacing lost neurons, restoring neural circuits or, based on more recent evidence, as paracrine-mediated therapies in which stem cell-derived trophic factors protect compromised endogenous retinal neurons from death and induce the growth of new connections. Retinal progenitor phenotypes induced from embryonic stem cells/induced pluripotent stem cells (ESCs/iPSCs) and endogenous retinal stem cells may replace lost photoreceptors and retinal pigment epithelial (RPE) cells and restore vision in the diseased eye, whereas treatment of injured retinal ganglion cells (RGCs) has so far been reliant on mesenchymal stem cells (MSC). Here, we review the properties of non-retinal-derived adult stem cells, in particular neural stem cells (NSCs), MSC derived from bone marrow (BMSC), adipose tissues (ADSC) and dental pulp (DPSC), together with ESC/iPSC and discuss and compare their potential advantages as therapies designed to provide trophic support, repair and replacement of retinal neurons, RPE and glia in degenerative retinal diseases. We conclude that ESCs/iPSCs have the potential to replace lost retinal cells, whereas MSC may be a useful source of paracrine factors that protect RGC and stimulate regeneration of their axons in the optic nerve in degenerate eye disease. NSC may have potential as both a source of replacement cells and also as mediators of paracrine treatment

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing