[Current status and research advances on the use of assisted traction technique in endoscopic full-thickness resection]

Endoscopic full-thickness resection (EFTR) allows completely resecting deep submucosal tumors (SMTs) in the gastrointestinal wall, which has a broad application prospect in clinic. However, its application and promotion are limited by complex surgical procedures and high surgical risk. Various auxiliary traction techniques are expected to reduce the operation difficulty and risk of EFTR and improve its operative success rate. To provide a reference for clinicians, we summarize various auxiliary traction techniques in EFTR in this article. The clip-with-line method is simple to operate and widely used, whereas its traction is limited and there is a risk of clip falling off. The snare traction method and the clip-snare traction method has advantage of large traction force, but its thrust is affected by the hardness of snare. The traction point of the grasping forceps traction method is flexible and easy to adjust. Nevertheless, it requires the use of a dual-channel upper endoscope, which is difficult to operate. The transparent cap traction method and the full-thickness resection device traction method takes a short time and is easy to promote, whereas the resectable lesion is limited, and the size of the lesion may affect the success rate. In contrast, the suture loop needle-T-tag tissue anchors assisted method has a large resection range, but the operation is complicated and the feasibility has not been verified. The robot-assisted method has flexible operation and excellent visualization, whereas it is expensive and difficult to operate. There is no report of the application of magnetic anchor technology in EFTR, but it may have good application prospects in the auxiliary traction of EFTR.

Deep learning for automatic calcium scoring in population based cardiovascular screening

Background

High volumes of standardized coronary artery calcium (CAC) scans are generated in screening that need to be scored accurately and efficiently to risk stratify individuals.

Purpose

To evaluate the performance of deep learning based software for automatic coronary calcium scoring in a screening setting.

Methods

Participants from the Robinsca trial that underwent low-dose ECG-triggered cardiac CT for calcium scoring were included. CAC was measured with fully automated deep learning prototype and compared to the original manual assessment of the Robinsca trial. Detection rate, positive Agatston score and risk categorization (0–99, 100–399, ≥400) were compared using McNemar test, ICC, and Cohen's kappa. False negative (FN), false positive (FP) rate and diagnostic accuracy were determined for preventive treatment initiation (cut-off ≥100 AU).

Results

In total, 997 participants were included between December 2015 and June 2016. Median age was 61.0 y (IQR: 11.0) and 54.4% was male. A high agreement for detection was found between deep learning based and manual scoring, κ=0.87 (95% CI 0.85–0.89). Median Agatston score was 58.4 (IQR: 12.3–200.2) and 61.2 (IQR: 13.9–212.9) for deep learning based and manual assessment respectively, ICC was 0.958 (95% CI 0.951–0.964). Reclassification rate was 2.0%, with a very high agreement with κ=0.960 (95% CI: 0.943–0.997), p<0.001. FN rate was 0.7% and FP rate was 0.1% and diagnostic accuracy was 99.2% for initiation of preventive treatment.

Conclusion

Deep learning based software for automatic CAC scoring can be used in a cardiovascular CT screening setting with high accuracy for risk categorization and initiation of preventive treatment.

Funding Acknowledgement

Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Robinsca trial was supported by advanced grant of European Research Council

Content-based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis with Chest CT

Background Evaluation of interstitial lung disease (ILD) at CT is a challenging task that requires experience and is subject to substantial interreader variability. Purpose To investigate whether a proposed content-based image retrieval (CBIR) of similar chest CT images by using deep learning can aid in the diagnosis of ILD by readers with different levels of experience. Materials and methods This retrospective study included patients with confirmed ILD after multidisciplinary discussion and available CT images identified between January 2000 and December 2015. Database was composed of four disease classes: usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), cryptogenic organizing pneumonia, and chronic hypersensitivity pneumonitis. Eighty patients were selected as queries from the database. The proposed CBIR retrieved the top three similar CT images with diagnosis from the database by comparing the extent and distribution of different regional disease patterns quantified by a deep learning algorithm. Eight readers with varying experience interpreted the query CT images and provided their most probable diagnosis in two reading sessions 2 weeks apart, before and after applying CBIR. Diagnostic accuracy was analyzed by using McNemar test and generalized estimating equation, and interreader agreement was analyzed by using Fleiss κ. Results A total of 288 patients were included (mean age, 58 years ± 11 [standard deviation]; 145 women). After applying CBIR, the overall diagnostic accuracy improved in all readers (before CBIR, 46.1% [95% CI: 37.1, 55.3]; after CBIR, 60.9% [95% CI: 51.8, 69.3]; P < .001). In terms of disease category, the diagnostic accuracy improved after applying CBIR in UIP (before vs after CBIR, 52.4% vs 72.8%, respectively; P < .001) and NSIP cases (before vs after CBIR, 42.9% vs 61.6%, respectively; P < .001). Interreader agreement improved after CBIR (before vs after CBIR Fleiss κ, 0.32 vs 0.47, respectively; P = .005). Conclusion The proposed content-based image retrieval system for chest CT images with deep learning improved the diagnostic accuracy of interstitial lung disease and interreader agreement in readers with different levels of experience. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Wielpütz in this issue.

Deep Learning in Biomedical Optics

This article reviews deep learning applications in biomedical optics with a particular emphasis on image formation. The review is organized by imaging domains within biomedical optics and includes microscopy, fluorescence lifetime imaging, in vivo microscopy, widefield endoscopy, optical coherence tomography, photoacoustic imaging, diffuse tomography, and functional optical brain imaging. For each of these domains, we summarize how deep learning has been applied and highlight methods by which deep learning can enable new capabilities for optics in medicine. Challenges and opportunities to improve translation and adoption of deep learning in biomedical optics are also summarized. Lasers Surg. Med. © 2021 Wiley Periodicals LLC.

New Method for Combined Quantitative Assessment of Air-Trapping and Emphysema on Chest Computed Tomography in Chronic Obstructive Pulmonary Disease: Comparison with Parametric Response Mapping

Objective

Emphysema and small-airway disease are the two major components of chronic obstructive pulmonary disease (COPD). We propose a novel method of quantitative computed tomography (CT) emphysema air-trapping composite (EAtC) mapping to assess each COPD component. We analyzed the potential use of this method for assessing lung function in patients with COPD.

Materials and Methods

A total of 584 patients with COPD underwent inspiration and expiration CTs. Using pairwise analysis of inspiration and expiration CTs with non-rigid registration, EAtC mapping classified lung parenchyma into three areas: Normal, functional air trapping (fAT), and emphysema (Emph). We defined fAT as the area with a density change of less than 60 Hounsfield units (HU) between inspiration and expiration CTs among areas with a density less than −856 HU on inspiration CT. The volume fraction of each area was compared with clinical parameters and pulmonary function tests (PFTs). The results were compared with those of parametric response mapping (PRM) analysis.

Results

The relative volumes of the EAtC classes differed according to the Global Initiative for Chronic Obstructive Lung Disease stages (p < 0.001). Each class showed moderate correlations with forced expiratory volume in 1 second (FEV₁) and FEV₁/forced vital capacity (FVC) (r = −0.659–0.674, p < 0.001). Both fAT and Emph were significant predictors of FEV₁ and FEV₁/FVC (R² = 0.352 and 0.488, respectively; p < 0.001). fAT was a significant predictor of mean forced expiratory flow between 25% and 75% and residual volume/total vital capacity (R² = 0.264 and 0.233, respectively; p < 0.001), while Emph and age were significant predictors of carbon monoxide diffusing capacity (R² = 0.303; p < 0.001). fAT showed better correlations with PFTs than with small-airway disease on PRM.

Conclusion

The proposed quantitative CT EAtC mapping provides comprehensive lung functional information on each disease component of COPD, which may serve as an imaging biomarker of lung function.

Unusually thick shear-softening surface of micrometer-size metallic glasses

The surface of glass is crucial for understanding many fundamental processes in glassy solids. A common notion is that a glass surface is a thin layer with liquid-like atomic dynamics and a thickness of a few tens of nanometers. Here, we measured the shear modulus at the surface of both millimeter-size and micrometer-size metallic glasses (MGs) through high-sensitivity torsion techniques. We found a pronounced shear-modulus softening at the surface of MGs. Compared with the bulk, the maximum decrease in the surface shear modulus (G) for the micro-scale MGs reaches ~27%, which is close to the decrease in the G upon glass transition, yet it still behaves solid-like. Strikingly, the surface thickness estimated from the shear-modulus softening is at least 400 nm, which is approximately one order of magnitude larger than that revealed from the glass dynamics. The unusually thick surface is also confirmed by measurements using X-ray nano-computed tomography, and this may account for the brittle-to-ductile transition of the MGs with size reductions. The unique and unusual properties at the surface of the micrometer-size MGs are physically related to the negative pressure effect during the thermoplastic formation process, which can dramatically reduce the density of the proximate surface region in the supercooled liquid state.

•

The shear modulus and thickness of metallic glass (MG) surface is determined through torsion testing on micrometer-size wires

•

The surface region of MG wires has a significant shear-modulus softening close to the supercooled liquid, yet still behaves solid-like

•

The thickness of the soft surface of MG wires is at least 400 nm, which is about one order of magnitude larger than those revealed from surface dynamics

•

The unusually thick surface accounts for the brittle-to-ductile transition of the MGs with size reduction

Optimum diameter threshold for lung nodules at baseline lung cancer screening with low-dose chest CT: exploration of results from the Korean Lung Cancer Screening Project

OBJECTIVES

To explore the optimum diameter threshold for solid nodules to define positive results at baseline screening low-dose CT (LDCT) and to compare two-dimensional and volumetric measurement of lung nodules for the diagnosis of lung cancers.

METHODS

We included consecutive participants from the Korean Lung Cancer Screening project between 2017 and 2018. The average transverse diameter and effective diameter (diameter of a sphere with the same volume) of lung nodules were measured by semi-automated segmentation. Diagnostic performances for lung cancers diagnosed within 1 year after LDCT were evaluated using area under receiver-operating characteristic curves (AUCs), sensitivities, and specificities, with diameter thresholds for solid nodules ranging from 6 to 10 mm. The reduction of unnecessary follow-up LDCTs and the diagnostic delay of lung cancers were estimated for each threshold.

RESULTS

Fifty-two lung cancers were diagnosed among 10,424 (10,141 men; median age 62 years) participants within 1 year after LDCT. Average transverse (0.980) and effective diameters (0.981) showed similar AUCs (p = .739). Elevating the average transverse diameter threshold from 6 to 9 mm resulted in a significantly increased specificity (91.7 to 96.7%, p < .001), a modest reduction in sensitivity (96.2 to 94.2%, p = .317), a 60.2% estimated reduction of unnecessary follow-up LDCTs, and a diagnostic delay in 1.9% of lung cancers. Elevating the threshold to 10 mm led to a significant reduction in sensitivity (86.5%, p = .025).

CONCLUSIONS

Elevating the diameter threshold for solid nodules from 6 to 9 mm may lead to a substantial reduction in unnecessary follow-up LDCTs with a small proportion of diagnostic delay of lung cancers.

KEY POINTS

• Elevation of the diameter threshold for solid nodules from 6 to 9 mm can substantially reduce unnecessary follow-up LDCTs with a small proportion of diagnostic delay of lung cancers. • The average transverse and effective diameters of lung nodules showed similar performances for the prediction of a lung cancer diagnosis.

Variability in interpretation of low-dose chest CT using computerized assessment in a nationwide lung cancer screening program: comparison of prospective reading at individual institutions and retrospective central reading

OBJECTIVES

To evaluate the degree of variability in computer-assisted interpretation of low-dose chest CTs (LDCTs) among radiologists in a nationwide lung cancer screening (LCS) program, through comparison with a retrospective interpretation from a central laboratory.

MATERIALS AND METHODS

Consecutive baseline LDCTs (n = 3353) from a nationwide LCS program were investigated. In the institutional reading, 20 radiologists in 14 institutions interpreted LDCTs using computer-aided detection and semi-automated segmentation systems for lung nodules. In the retrospective central review, a single radiologist re-interpreted all LDCTs using the same system, recording any non-calcified nodules ≥ 3 mm without arbitrary rejection of semi-automated segmentation to minimize the intervention of radiologist's discretion. Positive results (requiring additional follow-up LDCTs or diagnostic procedures) were initially classified by the lung CT screening reporting and data system (Lung-RADS) during the interpretation, while the classifications based on the volumetric criteria from the Dutch-Belgian lung cancer screening trial (NELSON) were retrospectively applied. Variabilities in positive rates were assessed with coefficients of variation (CVs).

RESULTS

In the institutional reading, positive rates by the Lung-RADS ranged from 7.5 to 43.3%, and those by the NELSON ranged from 11.4 to 45.0% across radiologists. The central review exhibited higher positive rates by Lung-RADS (20.0% vs. 27.3%; p < .001) and the NELSON (23.1% vs. 37.0%; p < .001), and lower inter-institution variability (CV, 0.30 vs. 0.12, p = .003 by Lung-RADS; CV, 0.25 vs. 0.12, p = .014 by the NELSON) compared to the institutional reading.

CONCLUSION

Considerable inter-institution variability in the interpretation of LCS results is caused by different usage of the computer-assisted system.

KEY POINTS

• Considerable variability existed in the interpretation of screening LDCT among radiologists partly from the different usage of the computerized system. • A retrospective reading of low-dose chest CTs in the central laboratory resulted in reduced variability but an increased positive rate.