Usefulness of Transbronchial Lung Cryobiopsy When Starting Antifibrotic Treatment and Predicting Progressive Fibrosing Interstitial Lung Disease: Descriptive Research.

BACKGROUND: Although transbronchial lung cryobiopsy (TBLC) is widely used in diagnostic algorithms for various interstitial lung diseases (ILDs), its real-world utility in the therapeutic decision-making strategy for ILD patients remains unclear, in particular, when judging the time to start antifibrotic agents. METHODS: We analyzed medical records of 40 consecutive patients with idiopathic or fibrotic hypersensitivity pneumonitis who underwent TBLC. A TBLC-based usual interstitial pneumonia (UIP) score was used to assess three morphologic descriptors: patchy fibrosis, fibroblastic foci, and honeycombing. RESULTS: In our 40 patients with ILD, the most frequent radiological feature was indeterminate for UIP (45.0%). Final diagnosis included idiopathic pulmonary fibrosis (22.5%), fibrotic nonspecific interstitial pneumonia (5.0%), fibrotic hypersensitivity pneumonitis (35.0%), and unclassifiable ILD (37.5%). Linear mixed-effects analysis showed that declines in the slopes of %FVC and %DLCO in patients with TBLC-based UIP "Score ≥ 2" were significantly steeper than those of patients with "Score ≤ 1." During follow-up of patients with Score ≥ 2 (n = 24), more than half of them (n = 17) received an antifibrotic agent, with most patients (n = 13) receiving early administration of the antifibrotic agent within 6 months after the TBLC procedure. CONCLUSIONS: TBLC-based UIP Score ≥ 2 indicated the increased possibility of a progressive fibrosis course that may prove helpful in predicting progressive pulmonary fibrosis/progressive fibrosing ILD even if disease is temporarily stabilized due to anti-inflammatory agents. Patients may benefit from early introduction of antifibrotic agents by treating clinicians.

Anomalous origin of the right coronary artery from the pulmonary artery (ARCAPA): Echocardiographic diagnosis in a critically ill newborn.

ARCAPA echocardiographic diagnosis. A network of small multiple coronary vessels, a systo-diastolic jet of flow into the pulmonary artery and a retrograde inverted «blue¼ flow into the RCA are the key signs. CT-scan confirms the diagnosis .

Improved Detection of Small and Low-Density Plaques in Virtual Noncontrast Imaging-based Calcium Scoring at Photon-Counting Detector CT.

Purpose To investigate the impact of plaque size and density on virtual noncontrast (VNC)-based coronary artery calcium scoring (CACS) using photon-counting detector CT and to provide safety net reconstructions for improved detection of subtle plaques in patients whose VNC-based CACS would otherwise be erroneously zero when compared with true noncontrast (TNC)-based CACS. Materials and Methods In this prospective study, CACS was evaluated in a phantom containing calcifications with different diameters (5, 3, and 1 mm) and densities (800, 400, and 200 mg/cm3) and in participants who underwent TNC and contrast-enhanced cardiac photon-counting detector CT (July 2021-March 2022). VNC images were reconstructed at different virtual monoenergetic imaging (55-80 keV) and quantum iterative reconstruction (QIR) levels (QIR,1-4). TNC scans at 70 keV with QIR off served as the reference standard. In vitro CACS was analyzed using standard settings (3.0-mm sections, kernel Qr36, 130-HU threshold). Calcification detectability and CACS of small and low-density plaques were also evaluated using 1.0-mm sections, kernel Qr44, and 120- or 110-HU thresholds. Safety net reconstructions were defined based on background Agatston scores and evaluated in vivo in TNC plaques initially nondetectable using standard VNC reconstructions. Results The in vivo cohort included 63 participants (57.8 years ± 15.5 [SD]; 37 [59%] male, 26 [41%] female). Correlation and agreement between standard CACSVNC and CACSTNC were higher in large- and medium-sized and high- and medium-density than in low-density plaques (in vitro: intraclass correlation coefficient [ICC] ≥ 0.90; r > 0.9 vs ICC = 0.20-0.48; r = 0.5-0.6). Small plaques were not detectable using standard VNC reconstructions. Calcification detectability was highest using 1.0-mm sections, kernel Qr44, 120- and 110-HU thresholds, and QIR level of 2 or less VNC reconstructions. Compared with standard VNC, using safety net reconstructions (55 keV, QIR 2, 110-HU threshold) for in vivo subtle plaque detection led to higher detection (increased by 89% [50 of 56]) and improved correlation and agreement of CACSVNC with CACSTNC (in vivo: ICC = 0.51-0.61; r = 0.6). Conclusion Compared with TNC-based calcium scoring, VNC-based calcium scoring was limited for small and low-density plaques but improved using safety net reconstructions, which may be particularly useful in patients with low calcium scores who would otherwise be treated based on potentially false-negative results. Keywords: Coronary Artery Calcium CT, Photon-Counting Detector CT, Virtual Noncontrast, Plaque Size, Plaque Density Supplemental material is available for this article. © RSNA, 2024.

Split X-Jaw techniques of volumetric modulated arc radiotherapy in nasopharyngeal cancer: A dosimetric comparison.

PURPOSE: The current study aims to compare the split x-jaw planning technique of volumetric modulated arc radiotherapy (VMAT) with the traditional open and limited jaw techniques of VAMT in nasopharyngeal carcinoma treatment. The multi-leaf collimators on the varian linear accelerator move on a carriage with a maximum leaf span of 15 cm. Therefore, treatment of larger planning target volumes, such as in nasopharyngeal cancer with traditional open and limited jaw technique, yields compromised dose distribution. METHOD: Computed tomography data sets of 10 nasopharynx cancer patients were enrolled for the study. For each case, three separate treatment plans were generated viz. open, limited, and split x-jaw planning techniques with similar planning objectives. Only PTVs requiring a field size larger than 18 cm in the x-jaw position were considered. RESULTS: Comparable results were obtained regarding organs at risk (OAR) sparing in all the techniques. The target dose coverage with split x-jaw VMAT was superior to both open and limited jaw planning techniques, with a statistically significant difference in the intermediate dose planning target volumes (PTVs) (PTV59.4), P < 0.05. However, the split technique's dose to the spinal cord and larynx was significantly lower (P < 0.05). CONCLUSION: The split x-jaw planning technique of VMAT can be adapted for larger PTVs requiring an x-jaw of more than 15 cm. The only concern with this technique is the increased MU.

Comparison of imaging modalities for the accurate delineation of arteriovenous malformations (AVM) and evaluation of setup accuracy with reference to non-invasive LINAC-based stereotactic radiosurgery (SRS).

AIMS: To compare the accuracy of nidus delineation using magnetic resonance angiography (MRA) to digital subtraction angiography (DSA) and to evaluate setup accuracy of non-invasive frame SRS treatments. SETTINGS AND DESIGN: A prospective observational study of 16 patients who underwent non-invasive frame LINAC-based SRS for brain AVMs. MATERIALS AND METHODS: The nidus was separately delineated using DSA and MRA after co-registration onto CT simulation images and compared with respect to their volume and maximum diameters. During treatment, the setup errors observed in x-, y-, and z-directions were recorded. STATISTICAL ANALYSIS: Paired t-test (to compare volume and maximum diameter). Wilcoxon signed-rank test (for setup accuracy). RESULTS: The mean volume of nidus contoured in MRA was 4.16 cc compared to 3.11 cc in DSA (P 0.297). The mean maximum diameters using MRA and DSA, respectively, in antro-posterior, cranio- caudal, and transverse diameters were 21.97 cc vs. 19.46 cc (P 0.2380), 6.59 cc vs. 9.63 cc (P 0.161), and 18.87 cc vs. 16.81 cc (P 0.178). But these modalities can potentially misinterpret the nidus volume, warranting caution for use of either modality alone. The mean translational shift observed in the x-, y-, and z-directions were 0.06 mm, 0.13 mm, and 0.13 mm, respectively, when couch was brought to neutral position after clockwise couch rotation and 0.07, 0, and 0, respectively, after counterclockwise couch rotation. CONCLUSION: This study could not demonstrate any statistically significant differences in nidus delineation between MRA and DSA. Setup accuracy achieved with non-invasive thermoplastic mask-based immobilization is within acceptable limits for SRS.

Low-dose radiation therapy for COVID-19 pneumonia: Comparison of dosimetry and life-time attributable risk of cancer with conventional AP-PA fields and bone marrow sparing VMAT.

PURPOSE: Low-dose radiation therapy (LDRT) to lungs did show encouraging results in COVID-19 patients in some clinical trials. However, there has been some concern regarding the long-term risk of radiation-induced cancer (RIC). Compared to the conventional AP-PA field technique, volumetric modulated arc therapy (VMAT) can potentially reduce the dose to the marrow and other organs at risk (OARs) and thus minimize the risk of cancer. We designed a dosimetry study to study if VMAT can reduce the exposure to the marrow and other OAR doses and curtail the estimated life-time attributable risk (LAR) of cancer. METHODS AND MATERIALS: We retrieved the computed tomography scan data of 10 patients (aged 40-60 years, median 48 years) who have been already treated for any malignancy in the region of the thorax. A dose of 1.0 Gy in single fraction was prescribed to both lungs. All the organs were delineated as per the established guidelines. The dosimetry achieved by the two plans was compared to find the difference. Mean OAR doses were used to estimate the LAR for both plans and compared. RESULTS: Planning target volume coverage parameters like conformity index and homogeneity index were significantly better with VMAT (P value < 0.05 for all). The mean dose to most OARs was significantly lower with VMAT (P value < 0.05 for all). The mean dose to the marrow was significantly lower with VMAT (59.05 vs 81.9 cGy with P value < 0.05). The overall LAR was significantly lower with VMAT as compared to the conventional plan (0.357% vs 0.398%, P value < 0.05). CONCLUSION: Compared to the conventional technique, VMAT provides better OAR dosimetry for lung irradiation (a prescription dose of 1.0 Gy or more) in COVID-19 pneumonia. VMAT significantly reduces the risk of RIC. We therefore suggest if lung LDRT is used for COVID-19 patients, VMAT is the preferred technique for a prescription dose of ≥1.0 Gy.

Evaluation of a deep image-to-image network (DI2IN) auto-segmentation algorithm across a network of cancer centers.

PURPOSE/OBJECTIVE S: Due to manual OAR contouring challenges, various automatic contouring solutions have been introduced. Historically, common clinical auto-segmentation algorithms used were atlas-based, which required maintaining a library of self-made contours. Searching the collection was computationally intensive and could take several minutes to complete. Deep learning approaches have shown significant benefits compared to atlas-based methods in improving segmentation accuracy and efficiency in auto-segmentation algorithms. This work represents the first multi-institutional study to describe and evaluate an AI algorithm for the auto-segmentation of organs at risk (OARs) based on a deep image-to-image network (DI2IN). MATERIALS/METHODS: The AI-Rad Companion Organs RT (AIRC) algorithm (Siemens Healthineers, Erlangen, Germany) uses a two-step approach for segmentation. In the first step, the target organ region in the optimal input image is extracted using a trained deep reinforcement learning network (DRL), which is then used as input to create the contours in the second step based on DI2IN. The study was initially designed as a prospective single-center evaluation. The automated contours generated by AIRC were evaluated by three experienced board-certified radiation oncologists using a four-point scale where 4 is clinically usable and 1 requires re-contouring. After seeing favorable results in a single-center pilot study, we decided to expand the study to six additional institutions, encompassing eight additional evaluators for a total of 11 physician evaluators across seven institutions. RESULTS: One hundred and fifty-six patients and 1366 contours were prospectively evaluated. The five most commonly contoured organs were the lung (136 contours, average rating = 4.0), spinal cord (106 contours, average rating = 3.1), eye globe (80 contours, average rating = 3.9), lens (77 contours, average rating = 3.9), and optic nerve (75 contours, average rating = 4.0). The average rating per evaluator per contour was 3.6. On average, 124 contours were evaluated by each evaluator. 65% of the contours were rated as 4, and 31% were rated as 3. Only 4% of contours were rated as 1 or 2. Thirty-three organs were evaluated in the study, with 19 structures having a 3.5 or above average rating (ribs, abdominopelvic cavity, skeleton, larynx, lung, aorta, brachial plexus, lens, eye globe, glottis, heart, parotid glands, bladder, kidneys, supraglottic larynx, submandibular glands, esophagus, optic nerve, oral cavity) and the remaining organs having a rating of 3.0 or greater (female breast, proximal femur, seminal vesicles, rectum, sternum, brainstem, prostate, brain, lips, mandible, liver, optic chiasm, spinal cord, spleen). No organ had an average rating below 3. CONCLUSION: AIRC performed well with greater than 95% of contours accepted by treating physicians with no or minor edits. It supported a fully automated workflow with the potential for time savings and increased standardization with the use of AI-powered algorithms for high-quality OAR contouring.

A study predicting long-term survival capacity in postoperative advanced gastric cancer patients based on MAOA and subcutaneous muscle fat characteristics.

BACKGROUND: The prognosis of advanced gastric cancer (AGC) is relatively poor, and long-term survival depends on timely intervention. Currently, predicting survival rates remains a hot topic. The application of radiomics and immunohistochemistry-related techniques in cancer research is increasingly widespread. However, their integration for predicting long-term survival in AGC patients has not been fully explored. METHODS: We Collected 150 patients diagnosed with AGC at the Affiliated Zhongshan Hospital of Dalian University who underwent radical surgery between 2015 and 2019. Following strict inclusion and exclusion criteria, 90 patients were included in the analysis. We Collected postoperative pathological specimens from enrolled patients, analyzed the expression levels of MAOA using immunohistochemical techniques, and quantified these levels as the MAOAHScore. Obtained plain abdominal CT images from patients, delineated the region of interest at the L3 vertebral body level, and extracted radiomics features. Lasso Cox regression was used to select significant features to establish a radionics risk score, convert it into a categorical variable named risk, and use Cox regression to identify independent predictive factors for constructing a clinical prediction model. ROC, DCA, and calibration curves validated the model's performance. RESULTS: The enrolled patients had an average age of 65.71 years, including 70 males and 20 females. Multivariate Cox regression analysis revealed that risk (P = 0.001, HR = 3.303), MAOAHScore (P = 0.043, HR = 2.055), and TNM stage (P = 0.047, HR = 2.273) emerged as independent prognostic risk factors for 3-year overall survival (OS) and The Similar results were found in the analysis of 3-year disease-specific survival (DSS). The nomogram developed could predict 3-year OS and DSS rates, with areas under the ROC curve (AUCs) of 0.81 and 0.797, respectively. Joint calibration and decision curve analyses (DCA) confirmed the nomogram's good predictive performance and clinical utility. CONCLUSION: Integrating immunohistochemistry and muscle fat features provides a more accurate prediction of long-term survival in gastric cancer patients. This study offers new perspectives and methods for a deeper understanding of survival prediction in AGC.

A comparison of target volumes drawn on arterial and venous phase scans during radiation therapy planning for patients with pancreatic cancer: the PANCRINJ study.

BACKGROUND: The planification of radiation therapy (RT) for pancreatic cancer (PC) requires a dosimetric computed tomography (CT) scan to define the gross tumor volume (GTV). The main objective of this study was to compare the inter-observer variability in RT planning between the arterial and the venous phases following intravenous contrast. METHODS: PANCRINJ was a prospective monocentric study that included twenty patients with non-metastatic PC. Patients underwent a pre-therapeutic CT scan at the arterial and venous phases. The delineation of the GTV was performed by one radiologist (gold standard) and two senior radiation oncologists (operators). The primary objective was to compare the Jaccard conformity index (JCI) for the GTVs computed between the GS (gold standard) and the operators between the arterial and the venous phases with a Wilcoxon signed rank test for paired samples. The secondary endpoints were the geographical miss index (GMI), the kappa index, the intra-operator variability, and the dose-volume histograms between the arterial and venous phases. RESULTS: The median JCI for the arterial and venous phases were 0.50 (range, 0.17-0.64) and 0.41 (range, 0.23-0.61) (p = 0.10) respectively. The median GS-GTV was statistically significantly smaller compared to the operators at the arterial (p < 0.0001) and venous phases (p < 0.001), respectively. The GMI were low with few tumors missed for all patients with a median GMI of 0.07 (range, 0-0.79) and 0.05 (range, 0-0.39) at the arterial and venous phases, respectively (p = 0.15). There was a moderate agreement between the radiation oncologists with a median kappa index of 0.52 (range 0.38-0.57) on the arterial phase, and 0.52 (range 0.36-0.57) on the venous phase (p = 0.08). The intra-observer variability for GTV delineation was lower at the venous phase than at the arterial phase for the two operators. There was no significant difference between the arterial and the venous phases regarding the dose-volume histogram for the operators. CONCLUSIONS: Our results showed inter- and intra-observer variability in delineating GTV for PC without significant differences between the arterial and the venous phases. The use of both phases should be encouraged. Our findings suggest the need to provide training for radiation oncologists in pancreatic imaging and to collaborate within a multidisciplinary team.

CT Chest Imaging Using Normalized Join-Count: Predicting Emphysema Progression in the CanCOLD Study.

Background Pre-existing emphysema is recognized as an indicator of future worsening in patients with chronic obstructive pulmonary disease (COPD) when observed through CT imaging. However, it remains uncertain whether additional factors, such as the spatial compactness of CT emphysema, might also serve as predictors of disease progression. Purpose To evaluate the relationship between the compactness of CT emphysema voxels and emphysema progression. Materials and Methods This secondary analysis uses data from the prospective Canadian Cohort Obstructive Lung Disease (CanCOLD) study, examining CT images obtained in participants with and without COPD at baseline and a 3-year follow-up time point (November 2009 to November 2018). Measurements of forced expiratory volume in first second of expiration (FEV1) and diffusing capacity of lung for carbon monoxide (DLco) were collected. The normalized join-count (NJC) measurement from baseline CT images and lung density (LD) changes were analyzed. Emphysema progression was defined as an annualized LD change of less than half an SD below the mean of the participants without COPD with no smoking history. Multivariable linear and logistic regression models were used to assess the association between baseline CT NJC measurements and the annualized change in LD, FEV1, DLco, and emphysema progression versus nonprogression. Results A total of 524 participants (mean age, 66 years ± 10 [SD]; 293 male) (FEV1 percent predicted, 88% ± 19; FEV1/FVC, 67% ± 9; DLco percent predicted, 105% ± 25) were analyzed, 187 (36%) of whom had COPD. CT NJC was associated with the annualized change in LD (P < .001), FEV1 (P = .02), and DLco (P = .01). Additionally, CT NJC predicted emphysema progression versus nonprogression (odds ratio, 2.24; 95% CI: 1.37, 3.50; P < .001). Conclusion The spatial distribution, or "compactness," of CT emphysema voxels predicted emphysema progression in individuals with and without COPD. ClinicalTrials.gov Identifier: NCT00920348 © RSNA, 2024 Supplemental material is available for this article.

MRI versus Dual-Energy CT in Local-Regional Staging of Gastric Cancer.

Background Preoperative local-regional tumor staging of gastric cancer (GC) is critical for appropriate treatment planning. The comparative accuracy of multiparametric MRI (mpMRI) versus dual-energy CT (DECT) for staging of GC is not known. Purpose To compare the diagnostic accuracy of personalized mpMRI with that of DECT for local-regional T and N staging in patients with GC receiving curative surgical intervention. Materials and Methods Patients with GC who underwent gastric mpMRI and DECT before gastrectomy with lymphadenectomy were eligible for this single-center prospective noninferiority study between November 2021 and September 2022. mpMRI comprised T2-weighted imaging, multiorientational zoomed diffusion-weighted imaging, and extradimensional volumetric interpolated breath-hold examination dynamic contrast-enhanced imaging. Dual-phase DECT images were reconstructed at 40 keV and standard 120 kVp-like images. Using gastrectomy specimens as the reference standard, the diagnostic accuracy of mpMRI and DECT for T and N staging was compared by six radiologists in a pairwise blinded manner. Interreader agreement was assessed using the weighted κ and Kendall W statistics. The McNemar test was used for head-to-head accuracy comparisons between DECT and mpMRI. Results This study included 202 participants (mean age, 62 years ± 11 [SD]; 145 male). The interreader agreement of the six readers for T and N staging of GC was excellent for both mpMRI (κ = 0.89 and 0.85, respectively) and DECT (κ = 0.86 and 0.84, respectively). Regardless of reader experience, higher accuracy was achieved with mpMRI than with DECT for both T (61%-77% vs 50%-64%; all P < .05) and N (54%-68% vs 51%-58%; P = .497-.005) staging, specifically T1 (83% vs 65%) and T4a (78% vs 68%) tumors and N1 (41% vs 24%) and N3 (64% vs 45%) nodules (all P < .05). Conclusion Personalized mpMRI was superior in T staging and noninferior or superior in N staging compared with DECT for patients with GC. Clinical trial registration no. NCT05508126 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Méndez and Martín-Garre in this issue.

Reinforcement learning for individualized lung cancer screening schedules: A nested case-control study.

BACKGROUND: The current guidelines for managing screen-detected pulmonary nodules offer rule-based recommendations for immediate diagnostic work-up or follow-up at intervals of 3, 6, or 12 months. Customized visit plans are lacking. PURPOSE: To develop individualized screening schedules using reinforcement learning (RL) and evaluate the effectiveness of RL-based policy models. METHODS: Using a nested case-control design, we retrospectively identified 308 patients with cancer who had positive screening results in at least two screening rounds in the National Lung Screening Trial. We established a control group that included cancer-free patients with nodules, matched (1:1) according to the year of cancer diagnosis. By generating 10,164 sequence decision episodes, we trained RL-based policy models, incorporating nodule diameter alone, combined with nodule appearance (attenuation and margin) and/or patient information (age, sex, smoking status, pack-years, and family history). We calculated rates of misdiagnosis, missed diagnosis, and delayed diagnosis, and compared the performance of RL-based policy models with rule-based follow-up protocols (National Comprehensive Cancer Network guideline; China Guideline for the Screening and Early Detection of Lung Cancer). RESULTS: We identified significant interactions between certain variables (e.g., nodule shape and patient smoking pack-years, beyond those considered in guideline protocols) and the selection of follow-up testing intervals, thereby impacting the quality of the decision sequence. In validation, one RL-based policy model achieved rates of 12.3% for misdiagnosis, 9.7% for missed diagnosis, and 11.7% for delayed diagnosis. Compared with the two rule-based protocols, the three best-performing RL-based policy models consistently demonstrated optimal performance for specific patient subgroups based on disease characteristics (benign or malignant), nodule phenotypes (size, shape, and attenuation), and individual attributes. CONCLUSIONS: This study highlights the potential of using an RL-based approach that is both clinically interpretable and performance-robust to develop personalized lung cancer screening schedules. Our findings present opportunities for enhancing the current cancer screening system.

Acceptability of adding a non-contrast abdominal CT scan to screen for kidney cancer and other abdominal pathology within a community-based CT screening programme for lung cancer: A qualitative study.

OBJECTIVES: The Yorkshire Kidney Screening Trial (YKST) is a feasibility study of adding non-contrast abdominal CT scanning to screen for kidney cancer and other abdominal malignancies to community-based CT screening for lung cancer within the Yorkshire Lung Screening Trial (YLST). This study explored the acceptability of the combined screening approach to participants and healthcare professionals (HCPs) involved in the trial. METHODS: We conducted semi-structured interviews with eight HCPs and 25 participants returning for the second round of scanning within YLST, 20 who had taken up the offer of the additional abdominal CT scan and five who had declined. Transcripts were analysed using thematic analysis, guided by the Theoretical Framework of Acceptability. RESULTS: Overall, combining the offer of a non-contrast abdominal CT scan alongside the low-dose thoracic CT was considered acceptable to participants, including those who had declined the abdominal scan. The offer of the additional scan made sense and fitted well within the process, and participants could see benefits in terms of efficiency, cost and convenience both for themselves as individuals and also more widely for the NHS. Almost all participants made an instant decision at the point of initial invitation based more on trust and emotions than the information provided. Despite this, there was a clear desire for more time to decide whether to accept the scan or not. HCPs also raised concerns about the burden on the study team and wider healthcare system arising from additional workload both within the screening process and downstream following findings on the abdominal CT scan. CONCLUSIONS: Adding a non-contrast abdominal CT scan to community-based CT screening for lung cancer is acceptable to both participants and healthcare professionals. Giving potential participants prior notice and having clear pathways for downstream management of findings will be important if it is to be offered more widely.

Incremental Application of Positive End-Expiratory Pressure for the Evaluation of Atelectasis During RP-EBUS and Bronchoscopy (I-APPEAR).

BACKGROUND: CT-to-body divergence-described as the difference between preprocedural CT scans and intraprocedural lung architecture-is a significant barrier to improving diagnostic yield during navigational bronchoscopy. A major proposed contributor to CT-to-body divergence is the development of atelectasis, which can confound visualization of peripheral lung lesions via radial probe endobronchial ultrasound (RP-EBUS). High positive end-expiratory pressure (PEEP) ventilatory strategies have been used to decrease atelectasis, allowing the lesion to re-APPEAR on intraprocedure imaging. However, standardized PEEP levels may not be appropriate for all patients due to hemodynamic and ventilatory impacts. METHODS: We performed a multicenter, prospective observational study in which patients were imaged with RP-EBUS under general anesthesia to determine if subsegmental atelectasis would resolve as incremental increases in PEEP were applied. Resolution of atelectasis was based on the transition from a non-aerated pattern to an aerated appearance on RP-EBUS. RP-EBUS images were reviewed by 3 experienced operators to determine correlation. RESULTS: Forty-three patients underwent RP-EBUS examination following navigational bronchoscopy. Thirty-seven patients underwent incremental PEEP application and subsequent RP-EBUS imaging. Atelectasis was determined to have resolved in 33 patients (88.2%) following increased PEEP. The intraclass correlation coefficient between reviewers was 0.76. A recruitment maneuver was performed in 7 (16.3%) patients after atelectasis persisted at maximal PEEP. Atelectasis was not identified in the examined subsegments in 6 (10.8%) patients despite zero PEEP. CONCLUSION: RP-EBUS is an effective tool to monitor what pressure atelectasis within a lung segment has resolved with increasing levels of PEEP.

A machine learning radiomics based on enhanced computed tomography to predict neoadjuvant immunotherapy for resectable esophageal squamous cell carcinoma.

Background: Patients with resectable esophageal squamous cell carcinoma (ESCC) receiving neoadjuvant immunotherapy (NIT) display variable treatment responses. The purpose of this study is to establish and validate a radiomics based on enhanced computed tomography (CT) and combined with clinical data to predict the major pathological response to NIT in ESCC patients. Methods: This retrospective study included 82 ESCC patients who were randomly divided into the training group (n = 57) and the validation group (n = 25). Radiomic features were derived from the tumor region in enhanced CT images obtained before treatment. After feature reduction and screening, radiomics was established. Logistic regression analysis was conducted to select clinical variables. The predictive model integrating radiomics and clinical data was constructed and presented as a nomogram. Area under curve (AUC) was applied to evaluate the predictive ability of the models, and decision curve analysis (DCA) and calibration curves were performed to test the application of the models. Results: One clinical data (radiotherapy) and 10 radiomic features were identified and applied for the predictive model. The radiomics integrated with clinical data could achieve excellent predictive performance, with AUC values of 0.93 (95% CI 0.87-0.99) and 0.85 (95% CI 0.69-1.00) in the training group and the validation group, respectively. DCA and calibration curves demonstrated a good clinical feasibility and utility of this model. Conclusion: Enhanced CT image-based radiomics could predict the response of ESCC patients to NIT with high accuracy and robustness. The developed predictive model offers a valuable tool for assessing treatment efficacy prior to initiating therapy, thus providing individualized treatment regimens for patients.

CT semi-quantitative score used as risk factor for hyponatremia in patients with COVID-19: a cross-sectional study.

Purpose: Chest computed tomography (CT) is used to determine the severity of COVID-19 pneumonia, and pneumonia is associated with hyponatremia. This study aims to explore the predictive value of the semi-quantitative CT visual score for hyponatremia in patients with COVID-19 to provide a reference for clinical practice. Methods: In this cross-sectional study, 343 patients with RT-PCR confirmed COVID-19, all patients underwent CT, and the severity of lung lesions was scored by radiologists using the semi-quantitative CT visual score. The risk factors of hyponatremia in COVID-19 patients were analyzed and combined with laboratory tests. The thyroid function changes caused by SARS-CoV-2 infection and their interaction with hyponatremia were also analyzed. Results: In patients with SARS-CoV-2 infection, the total severity score (TSS) of hyponatremia was higher [M(range), 3.5(2.5-5.5) vs 3.0(2.0-4.5) scores, P=0.001], implying that patients with hyponatremia had more severe lung lesions. The risk factors of hyponatremia in the multivariate regression model included age, vomiting, neutrophils, platelet, and total severity score. SARS-CoV-2 infection impacted thyroid function, and patients with hyponatremia showed a lower free triiodothyronine (3.1 ± 0.9 vs 3.7 ± 0.9, P=0.001) and thyroid stimulating hormone level [1.4(0.8-2.4) vs 2.2(1.2-3.4), P=0.038]. Conclusion: Semi-quantitative CT score can be used as a risk factor for hyponatremia in patients with COVID-19. There is a weak positive correlation between serum sodium and free triiodothyronine in patients with SARS-CoV-2 infection.

One-step inverse generation network for sparse-view dual-energy CT reconstruction and material imaging.

Objective.Sparse-view dual-energy spectral computed tomography (DECT) imaging is a challenging inverse problem. Due to the incompleteness of the collected data, the presence of streak artifacts can result in the degradation of reconstructed spectral images. The subsequent material decomposition task in DECT can further lead to the amplification of artifacts and noise.Approach.To address this problem, we propose a novel one-step inverse generation network (OIGN) for sparse-view dual-energy CT imaging, which can achieve simultaneous imaging of spectral images and materials. The entire OIGN consists of five sub-networks that form four modules, including the pre-reconstruction module, the pre-decomposition module, and the following residual filtering module and residual decomposition module. The residual feedback mechanism is introduced to synchronize the optimization of spectral CT images and materials.Main results.Numerical simulation experiments show that the OIGN has better performance on both reconstruction and material decomposition than other state-of-the-art spectral CT imaging algorithms. OIGN also demonstrates high imaging efficiency by completing two high-quality imaging tasks in just 50 seconds. Additionally, anti-noise testing is conducted to evaluate the robustness of OIGN.Significance.These findings have great potential in high-quality multi-task spectral CT imaging in clinical diagnosis.

The Value of Radiomics Features Based on HRCT in Predicting whether the Lung Sub-Centimeter Pure Ground Glass Nodule is Benign or Malignant.

OBJECTIVE: In this study, a radiomics model was created based on High-Resolution Computed Tomography (HRCT) images to noninvasively predict whether the sub-centimeter pure Ground Glass Nodule (pGGN) is benign or malignant. METHODS: A total of 235 patients (251 sub-centimeter pGGNs) who underwent preoperative HRCT scans and had postoperative pathology results were retrospectively evaluated. The nodules were randomized in a 7:3 ratio to the training (n=175) and the validation cohort (n=76). The volume of interest was delineated in the thin-slice lung window, from which 1316 radiomics features were extracted. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to select the radiomics features. Univariate and multivariable logistic regression were used to evaluate the independent risk variables. The performance was assessed by obtaining Receiver Operating Characteristic (ROC) curves for the clinical, radiomics, and combined models, and then the Decision Curve Analysis (DCA) assessed the clinical applicability of each model. RESULTS: Sex, volume, shape, and intensity mean were chosen by univariate analysis to establish the clinical model. Two radiomics features were retained by LASSO regression to build the radiomics model. In the training cohort, the Area Under the Curve (AUC) of the radiomics (AUC=0.844) and combined model (AUC=0.871) was higher than the clinical model (AUC=0.773). In evaluating whether or not the sub-centimeter pGGN is benign, the DCA demonstrated that the radiomics and combined model had a greater overall net benefit than the clinical model. CONCLUSION: The radiomics model may be useful in predicting the benign and malignant sub-centimeter pGGN before surgery.