Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study.

BACKGROUND: To assess whether artificial intelligence (AI)-based decision support allows more reproducible and standardized assessment of treatment response on MRI in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden using the Response Assessment in Neuro-Oncology (RANO) criteria. METHODS: A series of 30 patients (15 lower-grade gliomas, 15 glioblastoma) with availability of consecutive MRI scans was selected. The time to progression (TTP) on MRI was separately evaluated for each patient by 15 investigators over two rounds. In the first round the TTP was evaluated based on the RANO criteria, whereas in the second round the TTP was evaluated by incorporating additional information from AI-enhanced MRI sequences depicting the longitudinal changes in tumor volumes. The agreement of the TTP measurements between investigators was evaluated using concordance correlation coefficients (CCC) with confidence intervals (CI) and P-values obtained using bootstrap resampling. RESULTS: The CCC of TTP-measurements between investigators was 0.77 (95% CI = 0.69,0.88) with RANO alone and increased to 0.91 (95% CI = 0.82,0.95) with AI-based decision support (P = .005). This effect was significantly greater (P = .008) for patients with lower-grade gliomas (CCC = 0.70 [95% CI = 0.56,0.85] without vs. 0.90 [95% CI = 0.76,0.95] with AI-based decision support) as compared to glioblastoma (CCC = 0.83 [95% CI = 0.75,0.92] without vs. 0.86 [95% CI = 0.78,0.93] with AI-based decision support). Investigators with less years of experience judged the AI-based decision as more helpful (P = .02). CONCLUSIONS: AI-based decision support has the potential to yield more reproducible and standardized assessment of treatment response in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden, particularly in patients with lower-grade gliomas. A fully-functional version of this AI-based processing pipeline is provided as open-source (https://github.com/NeuroAI-HD/HD-GLIO-XNAT).

Swiss QUality of life and healthcare impact Assessment in a Real-world Erenumab treated migraine population (SQUARE study): interim results.

BACKGROUND: The fully human monoclonal antibody erenumab, which targets the calcitonin gene-related peptide (CGRP) receptor, was licensed in Switzerland in July 2018 for the prophylactic treatment of migraine. To complement findings from the pivotal program, this observational study was designed to collect and evaluate clinical data on the impact of erenumab on several endpoints, such as quality of life, migraine-related impairment and treatment satisfaction in a real-world setting. METHODS: An interim analysis was conducted after all patients completed 6 months of erenumab treatment. Patients kept a headache diary and completed questionnaires at follow up visits. The overall study duration comprises 24 months. RESULTS: In total, 172 adults with chronic or episodic migraine from 19 different sites across Switzerland were enrolled to receive erenumab every 4 weeks. At baseline, patients had 16.6 ± 7.2 monthly migraine days (MMD) and 11.6 ± 7.0 acute migraine-specific medication days per month. After 6 months, erenumab treatment reduced Headache Impact Test (HIT-6™) scores by 7.7 ± 8.4 (p < 0.001), the modified Migraine Disability Assessment (mMIDAS) by 14.1 ± 17.8 (p < 0.001), MMD by 7.6 ± 7.0 (p < 0.001) and acute migraine-specific medication days per month by 6.6 ± 5.4 (p < 0.001). Erenumab also reduced the impact of migraine on social and family life, as evidenced by a reduction of Impact of Migraine on Partners and Adolescent Children (IMPAC) scores by 6.1 ± 6.7 (p < 0.001). Patients reported a mean effectiveness of 67.1, convenience of 82.4 and global satisfaction of 72.4 in the Treatment Satisfaction Questionnaire for Medication (TSQM-9). In total, 99 adverse events (AE) and 12 serious adverse events (SAE) were observed in 62 and 11 patients, respectively. All SAE were regarded as not related to the study medication. CONCLUSIONS: Overall quality of life improved and treatment satisfaction was rated high with erenumab treatment in real-world clinical practice. In addition, the reported impact of migraine on spouses and children of patients was reduced. TRIAL REGISTRATION: BASEC ID 2018-02,375 in the Register of All Projects in Switzerland (RAPS).

Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study.

BACKGROUND: Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after repeated GBCA administration with yet unknown clinical significance. We aimed to assess the feasibility and diagnostic value of synthetic post-contrast T1-weighted MRI generated from pre-contrast MRI sequences through deep convolutional neural networks (dCNN) for tumour response assessment in neuro-oncology. METHODS: In this multicentre, retrospective cohort study, we used MRI examinations to train and validate a dCNN for synthesising post-contrast T1-weighted sequences from pre-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery sequences. We used MRI scans with availability of these sequences from 775 patients with glioblastoma treated at Heidelberg University Hospital, Heidelberg, Germany (775 MRI examinations); 260 patients who participated in the phase 2 CORE trial (1083 MRI examinations, 59 institutions); and 505 patients who participated in the phase 3 CENTRIC trial (3147 MRI examinations, 149 institutions). Separate training runs to rank the importance of individual sequences and (for a subset) diffusion-weighted imaging were conducted. Independent testing was performed on MRI data from the phase 2 and phase 3 EORTC-26101 trial (521 patients, 1924 MRI examinations, 32 institutions). The similarity between synthetic and true contrast enhancement on post-contrast T1-weighted MRI was quantified using the structural similarity index measure (SSIM). Automated tumour segmentation and volumetric tumour response assessment based on synthetic versus true post-contrast T1-weighted sequences was performed in the EORTC-26101 trial and agreement was assessed with Kaplan-Meier plots. FINDINGS: The median SSIM score for predicting contrast enhancement on synthetic post-contrast T1-weighted sequences in the EORTC-26101 test set was 0·818 (95% CI 0·817-0·820). Segmentation of the contrast-enhancing tumour from synthetic post-contrast T1-weighted sequences yielded a median tumour volume of 6·31 cm3 (5·60 to 7·14), thereby underestimating the true tumour volume by a median of -0·48 cm3 (-0·37 to -0·76) with the concordance correlation coefficient suggesting a strong linear association between tumour volumes derived from synthetic versus true post-contrast T1-weighted sequences (0·782, 0·751-0·807, p<0·0001). Volumetric tumour response assessment in the EORTC-26101 trial showed a median time to progression of 4·2 months (95% CI 4·1-5·2) with synthetic post-contrast T1-weighted and 4·3 months (4·1-5·5) with true post-contrast T1-weighted sequences (p=0·33). The strength of the association between the time to progression as a surrogate endpoint for predicting the patients' overall survival in the EORTC-26101 cohort was similar when derived from synthetic post-contrast T1-weighted sequences (hazard ratio of 1·749, 95% CI 1·282-2·387, p=0·0004) and model C-index (0·667, 0·622-0·708) versus true post-contrast T1-weighted MRI (1·799, 95% CI 1·314-2·464, p=0·0003) and model C-index (0·673, 95% CI 0·626-0·711). INTERPRETATION: Generating synthetic post-contrast T1-weighted MRI from pre-contrast MRI using dCNN is feasible and quantification of the contrast-enhancing tumour burden from synthetic post-contrast T1-weighted MRI allows assessment of the patient's response to treatment with no significant difference by comparison with true post-contrast T1-weighted sequences with administration of GBCAs. This finding could guide the application of dCNN in radiology to potentially reduce the necessity of GBCA administration. FUNDING: Deutsche Forschungsgemeinschaft.

Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis.

OBJECTIVES: Patients with multiple sclerosis (MS) regularly undergo MRI for assessment of disease burden. However, interpretation may be time consuming and prone to intra- and interobserver variability. Here, we evaluate the potential of artificial neural networks (ANN) for automated volumetric assessment of MS disease burden and activity on MRI. METHODS: A single-institutional dataset with 334 MS patients (334 MRI exams) was used to develop and train an ANN for automated identification and volumetric segmentation of T2/FLAIR-hyperintense and contrast-enhancing (CE) lesions. Independent testing was performed in a single-institutional longitudinal dataset with 82 patients (266 MRI exams). We evaluated lesion detection performance (F1 scores), lesion segmentation agreement (DICE coefficients), and lesion volume agreement (concordance correlation coefficients [CCC]). Independent evaluation was performed on the public ISBI-2015 challenge dataset. RESULTS: The F1 score was maximized in the training set at a detection threshold of 7 mm3 for T2/FLAIR lesions and 14 mm3 for CE lesions. In the training set, mean F1 scores were 0.867 for T2/FLAIR lesions and 0.636 for CE lesions, as compared to 0.878 for T2/FLAIR lesions and 0.715 for CE lesions in the test set. Using these thresholds, the ANN yielded mean DICE coefficients of 0.834 and 0.878 for segmentation of T2/FLAIR and CE lesions in the training set (fivefold cross-validation). Corresponding DICE coefficients in the test set were 0.846 for T2/FLAIR lesions and 0.908 for CE lesions, and the CCC was ≥ 0.960 in each dataset. CONCLUSIONS: Our results highlight the capability of ANN for quantitative state-of-the-art assessment of volumetric lesion load on MRI and potentially enable a more accurate assessment of disease burden in patients with MS. KEY POINTS: • Artificial neural networks (ANN) can accurately detect and segment both T2/FLAIR and contrast-enhancing MS lesions in MRI data. • Performance of the ANN was consistent in a clinically derived dataset, with patients presenting all possible disease stages in MRI scans acquired from standard clinical routine rather than with high-quality research sequences. • Computer-aided evaluation of MS with ANN could streamline both clinical and research procedures in the volumetric assessment of MS disease burden as well as in lesion detection.

Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study.

BACKGROUND: The Response Assessment in Neuro-Oncology (RANO) criteria and requirements for a uniform protocol have been introduced to standardise assessment of MRI scans in both clinical trials and clinical practice. However, these criteria mainly rely on manual two-dimensional measurements of contrast-enhancing (CE) target lesions and thus restrict both reliability and accurate assessment of tumour burden and treatment response. We aimed to develop a framework relying on artificial neural networks (ANNs) for fully automated quantitative analysis of MRI in neuro-oncology to overcome the inherent limitations of manual assessment of tumour burden. METHODS: In this retrospective study, we compiled a single-institution dataset of MRI data from patients with brain tumours being treated at Heidelberg University Hospital (Heidelberg, Germany; Heidelberg training dataset) to develop and train an ANN for automated identification and volumetric segmentation of CE tumours and non-enhancing T2-signal abnormalities (NEs) on MRI. Independent testing and large-scale application of the ANN for tumour segmentation was done in a single-institution longitudinal testing dataset from the Heidelberg University Hospital and in a multi-institutional longitudinal testing dataset from the prospective randomised phase 2 and 3 European Organisation for Research and Treatment of Cancer (EORTC)-26101 trial (NCT01290939), acquired at 38 institutions across Europe. In both longitudinal datasets, spatial and temporal tumour volume dynamics were automatically quantified to calculate time to progression, which was compared with time to progression determined by RANO, both in terms of reliability and as a surrogate endpoint for predicting overall survival. We integrated this approach for fully automated quantitative analysis of MRI in neuro-oncology within an application-ready software infrastructure and applied it in a simulated clinical environment of patients with brain tumours from the Heidelberg University Hospital (Heidelberg simulation dataset). FINDINGS: For training of the ANN, MRI data were collected from 455 patients with brain tumours (one MRI per patient) being treated at Heidelberg hospital between July 29, 2009, and March 17, 2017 (Heidelberg training dataset). For independent testing of the ANN, an independent longitudinal dataset of 40 patients, with data from 239 MRI scans, was collected at Heidelberg University Hospital in parallel with the training dataset (Heidelberg test dataset), and 2034 MRI scans from 532 patients at 34 institutions collected between Oct 26, 2011, and Dec 3, 2015, in the EORTC-26101 study were of sufficient quality to be included in the EORTC-26101 test dataset. The ANN yielded excellent performance for accurate detection and segmentation of CE tumours and NE volumes in both longitudinal test datasets (median DICE coefficient for CE tumours 0·89 [95% CI 0·86-0·90], and for NEs 0·93 [0·92-0·94] in the Heidelberg test dataset; CE tumours 0·91 [0·90-0·92], NEs 0·93 [0·93-0·94] in the EORTC-26101 test dataset). Time to progression from quantitative ANN-based assessment of tumour response was a significantly better surrogate endpoint than central RANO assessment for predicting overall survival in the EORTC-26101 test dataset (hazard ratios ANN 2·59 [95% CI 1·86-3·60] vs central RANO 2·07 [1·46-2·92]; p<0·0001) and also yielded a 36% margin over RANO (p<0·0001) when comparing reliability values (ie, agreement in the quantitative volumetrically defined time to progression [based on radiologist ground truth vs automated assessment with ANN] of 87% [266 of 306 with sufficient data] compared with 51% [155 of 306] with local vs independent central RANO assessment). In the Heidelberg simulation dataset, which comprised 466 patients with brain tumours, with 595 MRI scans obtained between April 27, and Sept 17, 2018, automated on-demand processing of MRI scans and quantitative tumour response assessment within the simulated clinical environment required 10 min of computation time (average per scan). INTERPRETATION: Overall, we found that ANN enabled objective and automated assessment of tumour response in neuro-oncology at high throughput and could ultimately serve as a blueprint for the application of ANN in radiology to improve clinical decision making. Future research should focus on prospective validation within clinical trials and application for automated high-throughput imaging biomarker discovery and extension to other diseases. FUNDING: Medical Faculty Heidelberg Postdoc-Program, Else Kröner-Fresenius Foundation.

The effects of graduate competency-based education and mastery learning on patient care and return on investment: a narrative review of basic anesthetic procedures.

BACKGROUND: Despite the widespread implementation of competency-based education, evidence of ensuing enhanced patient care and cost-benefit remains scarce. This narrative review uses the Kirkpatrick/Phillips model to investigate the patient-related and organizational effects of graduate competency-based medical education for five basic anesthetic procedures. METHODS: The MEDLINE, ERIC, CINAHL, and Embase databases were searched for papers reporting results in Kirkpatrick/Phillips levels 3-5 from graduate competency-based education for five basic anesthetic procedures. A gray literature search was conducted by reference search in Google Scholar. RESULTS: In all, 38 studies were included, predominantly concerning central venous catheterization. Three studies reported significant cost-effectiveness by reducing infection rates for central venous catheterization. Furthermore, the procedural competency, retention of skills and patient care as evaluated by fewer complications improved in 20 of the reported studies. CONCLUSION: Evidence suggests that competency-based education with procedural central venous catheterization courses have positive effects on patient care and are both cost-effective. However, more rigorously controlled and reproducible studies are needed. Specifically, future studies could focus on organizational effects and the possibility of transferability to other medical specialties and the broader healthcare system.

Visual assessment of early emphysema and interstitial abnormalities on CT is useful in lung cancer risk analysis.

OBJECTIVES: Screening for lung cancer should be limited to a high-risk-population, and abnormalities in low-dose computed tomography (CT) screening images may be relevant for predicting the risk of lung cancer. Our aims were to compare the occurrence of visually detected emphysema and interstitial abnormalities in subjects with and without lung cancer in a screening population of smokers. METHODS: Low-dose chest CT examinations (baseline and latest possible) of 1990 participants from The Danish Lung Cancer Screening Trial were independently evaluated by two observers who scored emphysema and interstitial abnormalities. Emphysema (lung density) was also measured quantitatively. RESULTS: Emphysema was seen more frequently and its extent was greater among participants with lung cancer on baseline (odds ratio (OR), 1.8, p = 0.017 and p = 0.002) and late examinations (OR 2.6, p < 0.001 and p < 0.001). No significant difference was found using quantitative measurements. Interstitial abnormalities were more common findings among participants with lung cancer (OR 5.1, p < 0.001 and OR 4.5, p < 0.001).There was no association between presence of emphysema and presence of interstitial abnormalities (OR 0.75, p = 0.499). CONCLUSIONS: Even early signs of emphysema and interstitial abnormalities are associated with lung cancer. Quantitative measurements of emphysema-regardless of type-do not show the same association. KEY POINTS: • Visually detected emphysema on CT is more frequent in individuals who develop lung cancer. • Emphysema grading is higher in those who develop lung cancer. • Interstitial abnormalities, including discrete changes, are associated with lung cancer. • Quantitative lung density measurements are not useful in lung cancer risk prediction. • Early CT signs of emphysema and interstitial abnormalities can predict future risk.

Mussels as a tool for mitigation of nutrients in the marine environment.

Long-line mussel farming has been proposed as a mitigation tool for removal of excess nutrients in eutrophic coastal waters. A full-scale mussel farm optimized for cost efficient nutrient removal was established in the eutrophic Skive Fjord, Denmark where biological and economic parameters related to nutrient removal was monitored throughout a full production cycle (1 yr). The results showed that it was possible to obtain a high area specific biomass of 60 t WW ha(-1) eqvivalent to a nitrogen and phosphorus removal of 0.6-0.9 and 0.03-0.04 t ha(-1)yr, respectively. The analysis of the costs related to establishment, maintenance and harvest revealed that mussel production optimized for mitigation can be carried out at a lower cost compared to mussel production for (human) consumption. The costs for nutrient removal was 14.8 € kg(-1)N making mitigation mussel production a cost-efficient measure compared to the most expensive land-based measures.