The IMRiS Trial: A Phase 2 Study of Intensity Modulated Radiation Therapy in Extremity Soft Tissue Sarcoma.

PURPOSE: Primary soft tissue sarcoma (STS) is rare, with many tumors occurring in extremities. Local management is limb-sparing surgery and preoperative/postoperative radiation therapy (RT) for patients at high risk of local recurrence. We prospectively investigated late normal tissue toxicity and limb function observed after intensity modulated RT (IMRT) in extremity STS. METHODS AND MATERIALS: Patients with extremity STS, age ≥16 years. Two treatment cohorts: IMRT 50 Gy in 25 × 2 Gy fractions (preoperative) or 60/66 Gy in 30/33 × 2 Gy fractions (postoperative). The primary endpoint was the rate of grade ≥2 late soft tissue fibrosis (subcutaneous tissue) at 24 months after IMRT (Radiation Therapy Oncology Group late radiation morbidity scoring). RESULTS: One hundred sixty-eight patients were registered between March 2016 and July 2017. Of those, 159 (95%) received IMRT (106, 67% preoperative RT; and 53, 33% postoperative RT) with a median follow-up of 35.2 months (IQR, 32.9-36.6); 62% men, median age 58 years. Of 111 patients assessable for the primary endpoint at 24 months, 12 (10.8%; 95% CI, 5.7%-18.1%) had grade ≥2 subcutaneous fibrosis. The overall rate at 24 months of Radiation Therapy Oncology Group late skin, bone, and joint toxicity was 7 of 112 (6.3%), 3 of 112 (2.7%), and 10 of 113 (8.8%), respectively, and for Stern's scale edema was 6 of 113 (5.3%). More wound complications were observed with preoperative than postoperative RT (29.2% vs 3.8%). Overall survival at 24 months was 84.6%, and the local recurrence event rate at 24 months was 10%. CONCLUSIONS: The rate of grade ≥2 subcutaneous fibrosis at 24 months after IMRT was 10.8%, consistent with other recent trials of IMRT and lower than historically reported rates in patients treated with 3-dimensional conformal RT. This trial provides further evidence for the benefits of IMRT in this patient population.

Evaluation of Deep Learning Clinical Target Volumes Auto-Contouring for Magnetic Resonance Imaging-Guided Online Adaptive Treatment of Rectal Cancer.

Purpose: Segmentation of clinical target volumes (CTV) on medical images can be time-consuming and is prone to interobserver variation (IOV). This is a problem for online adaptive radiation therapy, where CTV segmentation must be performed every treatment fraction, leading to longer treatment times and logistic challenges. Deep learning (DL)-based auto-contouring has the potential to speed up CTV contouring, but its current clinical use is limited. One reason for this is that it can be time-consuming to verify the accuracy of CTV contours produced using auto-contouring, and there is a risk of bias being introduced. To be accepted by clinicians, auto-contouring must be trustworthy. Therefore, there is a need for a comprehensive commissioning framework when introducing DL-based auto-contouring in clinical practice. We present such a framework and apply it to an in-house developed DL model for auto-contouring of the CTV in rectal cancer patients treated with MRI-guided online adaptive radiation therapy. Methods and Materials: The framework for evaluating DL-based auto-contouring consisted of 3 steps: (1) Quantitative evaluation of the model's performance and comparison with IOV; (2) Expert observations and corrections; and (3) Evaluation of the impact on expected volumetric target coverage. These steps were performed on independent data sets. The framework was applied to an in-house trained nnU-Net model, using the data of 44 rectal cancer patients treated at our institution. Results: The framework established that the model's performance after expert corrections was comparable to IOV, and although the model introduced a bias, this had no relevant impact on clinical practice. Additionally, we found a substantial time gain without reducing quality as determined by volumetric target coverage. Conclusions: Our framework provides a comprehensive evaluation of the performance and clinical usability of target auto-contouring models. Based on the results, we conclude that the model is eligible for clinical use.

RTT advanced practice and how it can change the future of radiotherapy.

The radiation therapy (RT) landscape is continuously evolving, necessitating adaptation in roles and responsibilities of radiation therapists (RTTs). Advanced Practice Radiation Therapists (APRTs) have taken on a proactive role in expanding services and assuming responsibilities within multi-professional teams. A European Society for Radiotherapy and Oncology (ESTRO) brought geographically diverse and experienced RTTs together, to discuss how advanced practice (AP) in the RTT profession should be future-proofed and create a global platform for collaboration. Challenges in achieving consensus and standardisation of APRT was identified across jurisdictions, emphasising the importance of international collaboration. Whilst highlighting the pivotal role of APRTs in driving innovation, improving patient care, and navigating the complexities of modern RT practice, this position paper presents outcomes and recommendations from the workshop. Discussions highlighted the need for standardised role definitions, education frameworks, regulatory support, and career development pathways to enable the advancement of APRT effectively. Increasing networks and collaboration is recommended to ensure APRTs can shape the future of RT.

Insights into cork weathering regarding colour, chemical and cellular changes in view of outdoor applications.

A comprehensive analysis of outdoor weathering and soil burial of cork during 1-year experiments was carried out with measurements of CIELAB color parameters, cellular observations by scanning electron microscopy, and surface chemical features analysed by ATR-FTIR and wet chemical analysis. Cork applied in outdoor conditions above and below ground retained its physical structure and integrity without signs of deterioration or fracturing. The cellular structure was maintained with some small changes at the one-cell layer at the surface, featuring cellular expansion and minute cell wall fractures. Surface color and chemistry showed distinct results for outdoor exposure and soil burial. The weathered cork surfaces acquired a lighter color while the soil buried cork surfaces became darker. With outdoor weathering, the cork polar solubles increased (13.0% vs. 7.6% o.d. mass) while a substantial decrease of lignin occurred (about 28% of the original lignin was removed) leading to a suberin-enriched cork surface. The chemical impact on lignin is therefore responsible for the surface change towards lighter colors. Soil-burial induced hydrolysis of ester bonds of suberin and xylan, and the lignin-enriched cork surface displayed a dark brown color. FTIR and wet chemical results were consistent. Overall cork showed a considerable structural and physical stability that allows its application in outdoor conditions, namely for building façades or other surfacing applications. Architects and designers should take into account the color dynamics of the cork surfaces.

Choosing the right questions - A systematic review of patient reported outcome measures used in radiotherapy and proton beam therapy.

The implementation of PROMs into clinical practice has been shown to improve quality of care. This systematic review aims to identify which PROMs are suitable for implementation within routine clinical practice in a radiotherapy or PBT service.The bibliographic databases MEDLINE, EMBASE and EMCARE were searched. Articles published between 1st January 2008 to 1st June 2023, that reported PROMs being utilised as an outcome measure were included. Inclusion criteria also included being written in English, involving human patients, aged 16 and above, receiving external beam radiotherapy or PBT for six defined tumour sites. PROMs identified within the included articles were subjected to quality assessment using the COSMIN reporting guidelines. Results are reported as per PRISMA guidelines. A total of 268 studies were identified in the search, of which 52 fulfilled the inclusion criteria. The use of 39 different PROMs was reported. The PROMs identified were mostly tumour or site-specific quality of life (n = 23) measures but also included generic cancer (n = 3), health-related quality-of-life (n = 6), and symptom specific (n = 7) measures.None of the PROMs identified received a high GRADE score for good content. There were 13 PROMs that received a moderate GRADE score. The remaining PROMs either had limited evidence of development and validation within the patient cohorts investigated, or lacked relevance or comprehensiveness needed for routine PROMs collection in a radiotherapy or PBT service.This review highlights that there are a wide variety of PROMs being utilised within radiotherapy research, but most lack specificity to radiotherapy side-effects. There is a risk that by using non-specific PROMs in clinical practice, patients might not receive the supportive care that they need.

Deep learning-based tools to distinguish plan-specific from generic deviations in EPID-based in vivo dosimetry.

BACKGROUND: Dose distributions calculated with electronic portal imaging device (EPID)-based in vivo dosimetry (EIVD) differ from planned dose distributions due to generic and plan-specific deviations. Generic deviations are characteristic to a class of plans. Examples include limitations in EIVD dose reconstruction, inaccuracies in treatment planning system (TPS) calculations and systematic machine deviations. Plan-specific deviations have an unpredictable character. Examples include discrepancies between the patient model used for dose calculation and the patient position or anatomy during delivery, random machine deviations, and data transfer, human or software errors. During the inspection work performed with traditional γ-evaluation statistical methods: (i) generic deviations raise alerts that need to be inspected but that rarely lead to action as their root cause is usually understood and (ii) the detection of relevant plan-specific deviations may be hindered by the presence of generic deviations. PURPOSE: To investigate whether deep learning-based tools can help in identifying γ-alerts raised by generic deviations and in improving the detectability of plan-specific deviations. METHODS: A 3D U-Net was trained as an autoencoder to reconstruct underlying patterns of generic deviations in γ-distributions. The network was trained for four treatment disease sites differently affected by generic deviations: volumetric modulated arc therapy (VMAT) lung (no known deviations), VMAT prostate (TPS inaccuracies), VMAT head-and-neck (EIVD limitations) and intensity modulated radiation therapy (IMRT) breast (large EIVD limitations). The network was trained with virtual non-transit γ-distributions: 60 train/10 validation for the VMAT sites and 30 train/10 validation for IMRT breast. It was hypothesized that in vivo γ-distributions obtained in the presence of plan-specific deviations would differ from those seen during training. For each disease site, the sensitivity of γ-analysis and the network to detect (synthetically introduced) patient-related deviations was compared by receiver operator characteristic analysis. The investigated deviations were patient positioning errors, weight gain or loss, and tumor volume changes. The clinical relevance was illustrated qualitatively with 793 in vivo clinical cases (141 lung, 136 head-and-neck, 209 prostate and 307 breast). RESULTS: Error detectability of patient-related deviations was better with the network than with γ-analysis. The average area under the curve values over all sites were 0.86 ± 0.12(1SD) and 0.69 ± 0.25(1SD), respectively. Regarding in vivo clinical results, the percentage of cases differently classified by γ-analysis and the network was 1%, 19%, 18% and 64% for lung, head-and-neck, prostate, and breast, respectively. In head-and-neck and breast cases, 45 γ-only alerts were examined, of which 43 were attributed to EPID dose reconstruction limitations. For prostate, all 15 investigated γ-only alerts were due to known TPS inaccuracies. All 59 investigated network alerts were explained by either patient-related deviations or EPID acquisition incidents. Some patient-related deviations detected by the network were not detected by γ-analysis. CONCLUSIONS: Deep learning-based tools trained to reconstruct underlying patterns of generic deviations in γ-distributions can be used to (i) automatically identify false positives within the set of γ-alerts and (ii) improve the detection of plan-specific deviations, hence minimizing the likelihood of false negatives. The presented method provides clear additional value to the γ-alert management process for large scale EIVD systems.

Cork cellular and chemical features underlying bark environmental protection in the miombo species Parinari curatellifolia.

Parinari curatellifolia is an important evergreen tree from the Miombo woodland of south-central and eastern Africa. The bark is corky, suggesting an increased protection against the ecosystem high temperatures and drought conditions as well as against wild fires. The cork in the bark rhytidome of P. curatellifolia was analyzed here for the first time with a focus on chemical and cellular features. P. curatellifolia cork has the cellular characteristics of cork tissues, with typical honeycomb structure in the tangential section and a brick-wall layer in the transverse and radial sections, without intercellular voids. Chemically P. curatellifolia cork has 8.4 % extractives, 33.9 % suberin, 31.9 % lignin and 25.2 % polysaccharides of the cork. The hemicelluloses are mostly xylans, with a substantial proportion of arabinose and galactose. Suberin showed a proportion of long chain lipids to glycerol (LCLip:Gly, mass ratio) of 8.5, and the long chain monomeric composition included a similar proportion of α,ω-diacids and ω-hydroxy acids (35.4 % and 31.5 % of long chain monomers) with a substantial proportion of monoacids (19.4 % of long chain monomers). Lignin is a guaiacyl-syringyl lignin with S/G of 0.32 and H:G:S of 1:14.1:4.5. The rhytidome composition and the cellular and chemical features of its cork are in line with environment-targeted protective features namely as a transpiration and insulation barrier, and as an increased fire protection.

A network score-based metric to optimize the quality assurance of automatic radiotherapy target segmentations.

Background and purpose: Existing methods for quality assurance of the radiotherapy auto-segmentations focus on the correlation between the average model entropy and the Dice Similarity Coefficient (DSC) only. We identified a metric directly derived from the output of the network and correlated it with clinically relevant metrics for contour accuracy. Materials and Methods: Magnetic Resonance Imaging auto-segmentations were available for the gross tumor volume for cervical cancer brachytherapy (106 segmentations) and for the clinical target volume for rectal cancer external-beam radiotherapy (77 segmentations). The nnU-Net's output before binarization was taken as a score map. We defined a metric as the mean of the voxels in the score map above a threshold (λ). Comparisons were made with the mean and standard deviation over the score map and with the mean over the entropy map. The DSC, the 95th Hausdorff distance, the mean surface distance (MSD) and the surface DSC were computed for segmentation quality. Correlations between the studied metrics and model quality were assessed with the Pearson correlation coefficient (r). The area under the curve (AUC) was determined for detecting segmentations that require reviewing. Results: For both tasks, our metric (λ = 0.30) correlated more strongly with the segmentation quality than the mean over the entropy map (for surface DSC, r > 0.65 vs. r < 0.60). The AUC was above 0.84 for detecting MSD values above 2 mm. Conclusions: Our metric correlated strongly with clinically relevant segmentation metrics and detected segmentations that required reviewing, indicating its potential for automatic quality assurance of radiotherapy target auto-segmentations.

Cuticular Waxes and Cutin in Terminalia catappa Leaves from the Equatorial São Tomé and Príncipe Islands.

This study presents for the first time an analysis of the content and chemical composition of the cuticular waxes and cutin in the leaves of the widespread and important tropical species Terminalia catappa. The leaves were collected in the equatorial Atlantic islands of São Tomé and Príncipe, in the Gulf of Guinea. The epicuticular and intracuticular waxes were determined via dichloromethane extraction and their chemical composition via GC-MS analysis, and the content and monomeric composition of cutin were determined after depolymerization via methanolysis. The leaves contained an epidermal cuticular coverage of 52.8 µg cm-2 of the cuticular waxes (1.4% of mass) and 63.3 µg cm-2 (1.5% of mass) of cutin. Cuticular waxes include mainly n-alkanols and fatty acids, with a substantial proportion of terpenes in the more easily solubilized fraction, and sterols in the more embedded waxes. Cutin is mostly constituted by C16 fatty acids and dihydroxyacids, also including aromatic monomers, suggesting a largely linear macromolecular arrangement. The high proportion of triacontanol, α-amyrin, ß-amyrin, germanicol, and lupeol in the easily solubilized cuticular fraction may explain the bioactive properties attributed to the T. catappa leaves via the popular medicine, which allows us to consider them as a potential source for the extraction of these compounds.

Handicap as a Measure of Perceived-Health Status in Parkinson's Disease.

Background: Handicap is a patient-centered measure of health status that encompasses the impact of social and physical environment on daily living, having been assessed in advanced and late-stage Parkinson's Disease (PD). Objective: To characterize the handicap of a broader sample of patients. Methods: A cross-sectional study of 405 PD patients during the MDS-UPDRS Portuguese validation study, using the MDS-UPDRS, Unified Dyskinesias Rating Scale, Nonmotor symptoms questionnaire, PDQ-8 and EQ-5D-3L. Handicap was measured using the London Handicap Scale (LHS). Results: Mean age was 64.42 (±10.3) years, mean disease duration 11.30 (±6.5) years and median HY 2 (IQR, 2-3). Mean LHS was 0.652 (±0.204); "Mobility," "Occupation" and "Physical Independence" were the most affected domains. LHS was significantly worse in patients with longer disease duration, older age and increased disability. In contrast, PDQ-8 did not differentiate age groups. Handicap was significantly correlated with disease duration (r = -0.35), nonmotor experiences of daily living (EDL) (MDS-UPDRS-I) (r = -0.51), motor EDL (MDS-UPDRS-II) (r = -0.69), motor disability (MDS-UPDRS-III) (r = -0.49), axial signs of MDS-UPDRS-III (r = -0.55), HY (r = -0.44), presence of nonmotor symptoms (r = -0.51) and PDQ-8 index (r = -0.64) (all P < 0.05). Motor EDL, MDS-UPDRS-III and PDQ-8 independently predicted Handicap (adjusted R 2 = 0.582; P = 0.007). Conclusions: The LHS was easily completed by patients and caregivers. Patients were mild-moderately handicapped, which was strongly determined by motor disability and its impact on EDL, and poor QoL. Despite correlated, handicap and QoL seem to differ in what they measure, and handicap may have an added value to QoL. Handicap seems to be a good measure of perceived-health status in a broad sample of PD.

Toxicity, normal tissue and dose-volume planning parameters for radiotherapy in soft tissue sarcoma of the extremities: A systematic review of the literature.

BACKGROUND: Patients with soft tissue sarcoma of the extremities (STSE) are left with high incidence of toxicities after Radiotherapy (RT). Understanding the normal tissue dose relationship with the development of long-term toxicities may enable better RT planning in order to reduce treatment toxicities for STSE. This systematic review of the literature aims at reporting the incidence of acute and late toxicities and identifying RT delineation guidance the normal tissues structures and dose-volume parameters for STSE. METHODS: A literature search of PUBMED-MEDLINE for studies that reported data on RT toxicity outcomes, delineation guidelines and dose-volume parameters for STSE from 2000 to 2022. Data has been tabulated and reported. RESULTS: Thirty of 586 papers were selected after exclusion criteria. External beam RT prescriptions ranged from 30 to 72 Gy. The majority of studies reported the use of Intensity Modulated RT (IMRT) (27%). Neo-adjuvant RT was used in 40%. The highest long-term toxicities were subcutaneous and lymphoedema, reported when delivering 3DCRT. IMRT had a lower incidence of toxicities. Normal tissue outlining such as weight-bearing bones, skin and subcutaneous tissue, corridor and neurovascular bundle was recommended in 6 studies. Nine studies recommended the use of dose-volume constraints, but only one recommended evidence-based dose-volume constraints. CONCLUSION: Although the literature is replete with toxicity reports, there is a lack of evidence-based guidance on normal tissue and dose-volume parameters and strategies to reduce the normal tissues irradiation when optimising RT plans for STSE are poor compared to other tumour sites.

Deep learning for segmentation of the cervical cancer gross tumor volume on magnetic resonance imaging for brachytherapy.

BACKGROUND: Segmentation of the Gross Tumor Volume (GTV) is a crucial step in the brachytherapy (BT) treatment planning workflow. Currently, radiation oncologists segment the GTV manually, which is time-consuming. The time pressure is particularly critical for BT because during the segmentation process the patient waits immobilized in bed with the applicator in place. Automatic segmentation algorithms can potentially reduce both the clinical workload and the patient burden. Although deep learning based automatic segmentation algorithms have been extensively developed for organs at risk, automatic segmentation of the targets is less common. The aim of this study was to automatically segment the cervical cancer GTV on BT MRI images using a state-of-the-art automatic segmentation framework and assess its performance. METHODS: A cohort of 195 cervical cancer patients treated between August 2012 and December 2021 was retrospectively collected. A total of 524 separate BT fractions were included and the axial T2-weighted (T2w) MRI sequence was used for this project. The 3D nnU-Net was used as the automatic segmentation framework. The automatic segmentations were compared with the manual segmentations used for clinical practice with Sørensen-Dice coefficient (Dice), 95th Hausdorff distance (95th HD) and mean surface distance (MSD). The dosimetric impact was defined as the difference in D98 (ΔD98) and D90 (ΔD90) between the manual segmentations and the automatic segmentations, evaluated using the clinical dose distribution. The performance of the network was also compared separately depending on FIGO stage and on GTV volume. RESULTS: The network achieved a median Dice of 0.73 (interquartile range (IQR) = 0.50-0.80), median 95th HD of 6.8 mm (IQR = 4.2-12.5 mm) and median MSD of 1.4 mm (IQR = 0.90-2.8 mm). The median ΔD90 and ΔD98 were 0.18 Gy (IQR = -1.38-1.19 Gy) and 0.20 Gy (IQR =-1.10-0.95 Gy) respectively. No significant differences in geometric or dosimetric performance were observed between tumors with different FIGO stages, however significantly improved Dice and dosimetric performance was found for larger tumors. CONCLUSIONS: The nnU-Net framework achieved state-of-the-art performance in the segmentation of the cervical cancer GTV on BT MRI images. Reasonable median performance was achieved geometrically and dosimetrically but with high variability among patients.

CT radiomics models are unable to predict new liver metastasis after successful thermal ablation of colorectal liver metastases.

BACKGROUND: Patients with colorectal liver metastases (CRLM) who undergo thermal ablation are at risk of developing new CRLM after ablation. Identification of these patients might enable individualized treatment. PURPOSE: To investigate whether an existing machine-learning model with radiomics features based on pre-ablation computed tomography (CT) images of patients with colorectal cancer can predict development of new CRLM. MATERIAL AND METHODS: In total, 94 patients with CRLM who were treated with thermal ablation were analyzed. Radiomics features were extracted from the healthy liver parenchyma of CT images in the portal venous phase, before thermal ablation. First, a previously developed radiomics model (Original model) was applied to the entire cohort to predict new CRLM after 6 and 24 months of follow-up. Next, new machine-learning models were developed (Radiomics, Clinical, and Combined), based on radiomics features, clinical features, or a combination of both. RESULTS: The external validation of the Original model reached an area under the curve (AUC) of 0.57 (95% confidence interval [CI]=0.56-0.58) and 0.52 (95% CI=0.51-0.53) for 6 and 24 months of follow-up. The new predictive radiomics models yielded a higher performance at 6 months compared to 24 months. For the prediction of CRLM at 6 months, the Combined model had slightly better performance (AUC=0.60; 95% CI=0.59-0.61) compared to the Radiomics and Clinical models (AUC=0.55-0.57), while all three models had a low performance for the prediction at 24 months (AUC=0.52-0.53). CONCLUSION: Both the Original and newly developed radiomics models were unable to predict new CLRM based on healthy liver parenchyma in patients who will undergo ablation for CRLM.

TORPEdO: A phase III trial of intensity-modulated proton beam therapy versus intensity-modulated radiotherapy for multi-toxicity reduction in oropharyngeal cancer.

•There is a lack of prospective level I evidence for the use of PBT for most adult cancers including oropharyngeal squamous cell carcinoma (OPSCC).•TORPEdO is the UK's first PBT clinical trial and aims to determine the benefits of PBT for OPSCC.•Training and support has been provided before and during the trial to reduce variations of contouring and radiotherapy planning.•There is a strong translational component within TORPEdO. Imaging and physics data along with blood, tissue collection will inform future studies in refining patient selection for IMPT.

High frequency of Depressive Disorders and Suicidal Phenomena in Late-Stage Parkinson´s Disease - A Cross-Sectional Study.

BACKGROUND: Depressive disorders (DD) are widely recognized as one of the most frequent neuropsychiatric disorders in Parkinson´s disease. Patients with late-stage Parkinson´s disease (LSPD) continue to be a neglected population, and little is known about DD frequency in LSPD. OBJECTIVES: To determine the frequency of DD in LSPD patients through a clinical diagnostic interview (CDI) and according to diagnostic DSM- 5 criteria. Secondary objectives were to determine the predictive ability of depressive scales to detect DD, to identify potential predictors of DD in LSPD and, to evaluate suicidal phenomena in LSPD. METHODS: A cross-sectional study including LSPD patients (≥7 years from symptom onset and Hoehn and Yahr scale score >3 or a Schwab and England scale score <50% in the ON condition) was conducted. Patients were subjected to psychiatric, neurological, and neuropsychological evaluations. Six depression scales were applied. RESULTS: 92 LSPD patients were included. 59.78% of LSPD patients had a current diagnosis of DD according to CDI, 38.04% patients had a diagnosis of major depressive disorder, and 21.72% non-major depressive disorder. Suicidal ideation was present in 36.96% of patients. All applied scales were able to detect depressive disorders. CONCLUSIONS: More than half of LSPD patients met DD diagnostic criteria and over one-third were diagnosed with major depressive disorder. Overall, the LSPD population seem to have a unique clinical phenotype regarding the frequency and features of DD, whose early identification and treatment could improve the quality of life of patients and caregivers.

Enterotoxigenic Escherichia coli heat-labile toxin drives enteropathic changes in small intestinal epithelia.

Enterotoxigenic E. coli (ETEC) produce heat-labile (LT) and/or heat-stable (ST) enterotoxins, and commonly cause diarrhea in resource-poor regions. ETEC have been linked repeatedly to sequelae in children including enteropathy, malnutrition, and growth impairment. Although cellular actions of ETEC enterotoxins leading to diarrhea are well-established, their contributions to sequelae remain unclear. LT increases cellular cAMP to activate protein kinase A (PKA) that phosphorylates ion channels driving intestinal export of salt and water resulting in diarrhea. As PKA also modulates transcription of many genes, we interrogated transcriptional profiles of LT-treated intestinal epithelia. Here we show that LT significantly alters intestinal epithelial gene expression directing biogenesis of the brush border, the major site for nutrient absorption, suppresses transcription factors HNF4 and SMAD4 critical to enterocyte differentiation, and profoundly disrupts microvillus architecture and essential nutrient transport. In addition, ETEC-challenged neonatal mice exhibit substantial brush border derangement that is prevented by maternal vaccination with LT. Finally, mice repeatedly challenged with toxigenic ETEC exhibit impaired growth recapitulating the multiplicative impact of recurring ETEC infections in children. These findings highlight impacts of ETEC enterotoxins beyond acute diarrheal illness and may inform approaches to prevent major sequelae of these common infections including malnutrition that impact millions of children.

Strategies for tackling the class imbalance problem of oropharyngeal primary tumor segmentation on magnetic resonance imaging.

Background and purpose: Contouring oropharyngeal primary tumors in radiotherapy is currently done manually which is time-consuming. Autocontouring techniques based on deep learning methods are a desirable alternative, but these methods can render suboptimal results when the structure to segment is considerably smaller than the rest of the image. The purpose of this work was to investigate different strategies to tackle the class imbalance problem in this tumor site. Materials and methods: A cohort of 230 oropharyngeal cancer patients treated between 2010 and 2018 was retrospectively collected. The following magnetic resonance imaging (MRI) sequences were available: T1-weighted, T2-weighted, 3D T1-weighted after gadolinium injection. Two strategies to tackle the class imbalance problem were studied: training with different loss functions (namely: Dice loss, Generalized Dice loss, Focal Tversky loss and Unified Focal loss) and implementing a two-stage approach (i.e. splitting the task in detection and segmentation). Segmentation performance was measured with Sørensen-Dice coefficient (Dice), 95th Hausdorff distance (HD) and Mean Surface Distance (MSD). Results: The network trained with the Generalized Dice Loss yielded a median Dice of 0.54, median 95th HD of 10.6 mm and median MSD of 2.4 mm but no significant differences were observed among the different loss functions (p-value > 0.7). The two-stage approach resulted in a median Dice of 0.64, median HD of 8.7 mm and median MSD of 2.1 mm, significantly outperforming the end-to-end 3D U-Net (p-value < 0.05). Conclusion: No significant differences were observed when training with different loss functions. The two-stage approach outperformed the end-to-end 3D U-Net.

Explaining the dosimetric impact of contouring errors in head and neck radiotherapy.

Objective. Auto-contouring of organs at risk (OAR) is becoming more common in radiotherapy. An important issue in clinical decision making is judging the quality of the auto-contours. While recent studies considered contour quality by looking at geometric errors only, this does not capture the dosimetric impact of the errors. In this work, we studied the relationship between geometrical errors, the local dose and the dosimetric impact of the geometrical errors.Approach. For 94 head and neck patients, unmodified atlas-based auto-contours and clinically used delineations of the parotid glands and brainstem were retrieved. VMAT plans were automatically optimized on the auto-contours and evaluated on both contours. We defined the dosimetric impact on evaluation (DIE) as the difference in the dosimetric parameter of interest between the two contours. We developed three linear regression models to predict the DIE using: (1) global geometric metrics, (2) global dosimetric metrics, (3) combined local geometric and dosimetric metrics. For model (3), we next determined the minimal amount of editing information required to produce a reliable prediction. Performance was assessed by the root mean squared error (RMSE) of the predicted DIE using 5-fold cross-validation.Main results. In model (3), the median RMSE of the left parotid was 0.4 Gy using 5% of the largest editing vectors. For the right parotid and brainstem the results were 0.5 Gy using 10% and 0.4 Gy using 1% respectively. The median RMS of the DIE was 0.6 Gy, 0.7 Gy and 0.9 Gy for the left parotid, the right parotid and the brainstem, respectively. Model (3), combining local dosimetric and geometric quantities, outperformed the models that used only geometric or dosimetric information.Significance. We showed that the largest local errors plus the local dose suffice to accurately predict the dosimetric impact, opening the door to automated dosimetric QA of auto-contours.

Profile of cognitive impairment in late-stage Parkinson's disease.

INTRODUCTION: The profile of cognitive impairment associated with the late stages of Parkinson's disease (LSPD) is rarely reported. Its characterization is necessary to better understand the cognitive changes that occur as the disease progresses and to better contribute to its management. METHODS: In this cross-sectional study, we characterized the cognitive profile of LSPD patients using the comprehensive assessment methodology proposed by the International Parkinson and Movement Disorders Society Task Force. The association of clinical and demographic variables with dementia diagnosis was also investigated using binary logistic regression analysis. RESULTS: Eighty-four LSPD patients were included (age 75.4 ± 6.9; disease duration 16.9 ± 7.5). Fifty-four (64.3%) were classified as demented and presented a global impairment cognitive profile. In the nondemented group (N = 30), 25 (83.3%) LSPD patients met the diagnostic criteria for mild cognitive impairment, mostly with multiple domain impairment (96.0%) and a heterogeneous profile. Memory was the most frequent and severely impaired cognitive domain in both groups. Disease disability, orientation, complex order comprehension, verbal learning, and visuoconstructive abilities were significantly associated with dementia diagnosis (p < .05). CONCLUSIONS: Cognitive impairment in multiple domains was common in LSPD patients. The most frequent and prominent deficits were in the memory domain, with a strong interference from attention impairment. Disease disability, orientation, complex order comprehension, verbal learning, and visuoconstructive abilities proved to be important determinants for dementia diagnosis.