Bronchial thermoplasty : a new therapeutic option for the treatment of severe, uncontrolled asthma in adults

Bronchial thermoplasty is a young yet promising treatment for severe asthma whose benefit for long-term asthma control outweighs the short-term risk of deterioration and hospitalisation in the days following the treatment. It is an innovative treatment whose clinical efficacy and safety are beginning to be better understood. Since this is a device-based therapy, the overall evaluation of risk-benefit is unlike that of pharmaceutical products; safety aspects, regulatory requirements, study design and effect size assessment may be unfamiliar. The mechanisms of action and optimal patient selection need to be addressed in further rigorous clinical and scientific studies. Bronchial thermoplasty fits in perfectly with the movement to expand personalised medicine in the field of chronic airway disorders. This is a device-based complimentary asthma treatment that must be supported and developed in order to meet the unmet needs of modern severe asthma management. The mechanisms of action and the type of patients that benefit from bronchial thermoplasty are the most important challenges for bronchial thermoplasty in the future

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.Rudyanto, RD.; Kerkstra, S.; Van Rikxoort, EM.; Fetita, C.; Brillet, P.; Lefevre, C.; Xue, W.... (2014). Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study. Medical Image Analysis. 18(7):1217-1232. doi:10.1016/j.media.2014.07.003S1217123218

High-resolution CT phenotypes in pulmonary sarcoidosis: a multinational Delphi consensus study

One view of sarcoidosis is that the term covers many different diseases. However, no classification framework exists for the future exploration of pathogenetic pathways, genetic or trigger predilections, patterns of lung function impairment, or treatment separations, or for the development of diagnostic algorithms or relevant outcome measures. We aimed to establish agreement on high-resolution CT (HRCT) phenotypic separations in sarcoidosis to anchor future CT research through a multinational two-round Delphi consensus process. Delphi participants included members of the Fleischner Society and the World Association of Sarcoidosis and other Granulomatous Disorders, as well as members' nominees. 146 individuals (98 chest physicians, 48 thoracic radiologists) from 28 countries took part, 144 of whom completed both Delphi rounds. After rating of 35 Delphi statements on a five-point Likert scale, consensus was achieved for 22 (63%) statements. There was 97% agreement on the existence of distinct HRCT phenotypes, with seven HRCT phenotypes that were categorised by participants as non-fibrotic or likely to be fibrotic. The international consensus reached in this Delphi exercise justifies the formulation of a CT classification as a basis for the possible definition of separate diseases. Further refinement of phenotypes with rapidly achievable CT studies is now needed to underpin the development of a formal classification of sarcoidosis

IRM dynamique des pneumopathies infiltratives diffuses (Etude de faisabilité)

LE KREMLIN-B.- PARIS 11-BU Méd (940432101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Aspect tomodensitométrique haute résolution des fibroses pulmonaires idiopathiques atypiques biopsiées

PARIS7-Xavier Bichat (751182101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Aspect tomodensitométrique de la Pneumopathie Interstitielle Desquamative (PID)

PARIS7-Xavier Bichat (751182101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Sarcoïdose (étude en tomodensitométrie haute-résolution des lésions pulmonaires chez 500 patients)

PARIS6-Bibl.Pitié-Salpêtrie (751132101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Imagerie TDM de l'atteinte pulmonaire dans le syndrome de Gougerot Sjögren primitif et associé (Motifs lésionnels et évolution, A propos d'une étude rétrospective de 85 patients)

LE KREMLIN-B.- PARIS 11-BU Méd (940432101) / SudocSudocFranceF

Comparison of CNN architectures and training strategies for quantitative analysis of idiopathic interstitial pneumonia

International audienceFibrosing idiopathic interstitial pneumonia (IIP) is a subclass of interstitial lung diseases manifesting as progressive worsening of lung function. Such degradation is a continuous and irreversible process which requires quantitative follow-up of patients to assess the pathology occurrence and extent in the lung. The development of automated CAD tools for such purpose is oriented today towards machine learning approaches and in particular convolutional neural networks. The difficulty remains in the choice of the network architecture that best fit to the problem, in straight relationship with available databases for training. We follow-up our work on lung texture analysis and investigate different CNN architectures and training strategies in the context of a limited database, with high class imbalance and subjective and partial annotations. We show that increased performances are achieved using an end-to-end architecture versus patch-based, but also that naive implementation in the former case should be avoided. The proposed solution is able to leverage global information in the scan and shows a high improvement in the F1 scores of the predicted classes and visual results of predictions in better accordance with the radiologist expectations