Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge (preprint)

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway trees remains prohibitively time-consuming. While significant efforts have been made towards enhancing airway modelling, current public-available datasets concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for prognosis, a strong airway-derived biomarker (Hazard ratio>1.5, p<0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.

Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation (preprint)

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.

A correlation study ofCT and clinical features of different clinical types of 2019 novel coronavirus pneumonia / 中华放射学杂志

Objective@#To investigate the CT and clinical features of 2019 novel coronavirus (NCP) pneumonia.@*Methods@#Chest CT and clinical data of confirmed 103 patients with 2019 novel coronavirus pneumonia in January 2020, retrospectively. According to diagnosis and treatment of NCP infected pneumonia (trial version 5), all the patients were classified into mild(n=58), severe (n=36) and very severe (n=9) type, and their clinical findings, laboratory examination and CT finding were analyzed. CT features included lesions’ distribution, location, size, shape, edge, number, density, percentage of pneumonia lesions of the whole lung and extra-pulmonary manifestations. The CT features of different clinical subtypes were compared using χ2test or Fisher's exact probability. Comparisons between the percentage of pneumonic lesions to total lung volume were computed by using analysis of variance (normal distribution) or Kruskal-Wallis rank sum test (non-normal distribution).@*Results@#In terms of clinical manifestations, the patients with severe NCP were more common in elderly men, with a median age of 65 years. Fever was the first symptom in 49 (84%) of 58 patients with NCP, and fever was the first symptom in both severe and critical NCP patients. The incidence of cough in severe (25 / 36, 69%) and critical (6 /9, 67%) NCP patients was higher than that in general (20 /58, 34%). All critical patients have dyspnea. In terms of CT findings, common NCP showed double lung (40/58,71%) multiple (40 / 58,69%) ground glass (31/58,52%) or mixed (25 / 58,43%) lesions (56 / 58,97%); severe and critical NCP showed double lung lesions, heavy NCP mainly showed multiple (34 / 36,96%) patches (33 / 36,92%) mixed density lesions (26 / 36,72%); 9 severe NCP lesions were more than 3 cm Mixed density lesions. The percentage of pneumonia focus in the whole lung volume: the common type (12.5% ± 6.1%) was significantly lower than the severe type (25.9% ± 10.7%) and the critical type (47.2% ± 19.2%) NCP, the difference was statistically significant (P values were < 0.001 and 0.002 respectively), and the severe type NCP was also significantly lower than the critical type (P = 0.032).@*Conclusions@#CT and clinical features of different clinical types of NCP pneumonia are different. Chest CT findings have unique characteristic, which can not only make early diagnosis, but also evaluate its clinical course and severity.