Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT.

BACKGROUND: This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduced radiation doses. This is essential in the context of low-dose CT lung cancer screening where accurate volumetry and characterization of pulmonary nodules in repeated CT scanning are indispensable. MATERIALS AND METHODS: A standardized CT dataset was established using an anthropomorphic chest phantom (Lungman, Kyoto Kaguku Inc., Kyoto, Japan) containing a set of 3D-printed lung nodules including six diameters (4 to 9 mm) and three morphology classes (lobular, spiculated, smooth), with an established ground truth. Images were acquired at varying radiation doses (6.04, 3.03, 1.54, 0.77, 0.41 and 0.20 mGy) and reconstructed with combinations of reconstruction kernels (soft and hard kernel) and reconstruction algorithms (ASIR-V and DLIR at low, medium and high strength). Semi-automatic volumetry measurements and subjective image quality scores recorded by five radiologists were analyzed with multiple linear regression and mixed-effect ordinal logistic regression models. RESULTS: Volumetric errors of nodules imaged with DLIR are up to 50% lower compared to ASIR-V, especially at radiation doses below 1 mGy and when reconstructed with a hard kernel. Also, across all nodule diameters and morphologies, volumetric errors are commonly lower with DLIR. Furthermore, DLIR renders higher subjective IQ, especially at the sub-mGy doses. Radiologists were up to nine times more likely to score the highest IQ-score to these images compared to those reconstructed with ASIR-V. Lung nodules with irregular margins and small diameters also had an increased likelihood (up to five times more likely) to be ascribed the best IQ scores when reconstructed with DLIR. CONCLUSION: We observed that DLIR performs as good as or even outperforms conventionally used reconstruction algorithms in terms of volumetric accuracy and subjective IQ of nodules in an anthropomorphic chest phantom. As such, DLIR potentially allows to lower the radiation dose to participants of lung cancer screening without compromising accurate measurement and characterization of lung nodules.

Clinical usability of 3D gradient-echo-based ultrashort echo time imaging: Is it enough to facilitate diagnostic decision in real-world practice?

BACKGROUND: With recent advances in magnetic resonance imaging (MRI) technology, the practical role of lung MRI is expanding despite the inherent challenges of the thorax. The purpose of our study was to evaluate the current status of the concurrent dephasing and excitation (CODE) ultrashort echo-time sequence and the T1-weighted volumetric interpolated breath-hold examination (VIBE) sequence in the evaluation of thoracic disease by comparing it with the gold standard computed tomography (CT). METHODS: Twenty-four patients with lung cancer and mediastinal masses underwent both CT and MRI including T1-weighted VIBE and CODE. For CODE images, data were acquired in free breathing and end-expiratory images were reconstructed using retrospective respiratory gating. All images were evaluated through qualitative and quantitative approaches regarding various anatomical structures and lesions (nodule, mediastinal mass, emphysema, reticulation, honeycombing, bronchiectasis, pleural plaque and lymphadenopathy) inside the thorax in terms of diagnostic performance in making specific decisions. RESULTS: Depiction of the lung parenchyma, mediastinal and pleural lesion was not significant different among the three modalities (p > 0.05). Intra-tumoral and peritumoral features of lung nodules were not significant different in the CT, VIBE or CODE images (p > 0.05). However, VIBE and CODE had significantly lower image quality and poorer depiction of airway, great vessels, and emphysema compared to CT (p < 0.05). Image quality of central airways and depiction of bronchi were significantly better in CODE than in VIBE (p < 0.001 and p = 0.005). In contrast, the depiction of the vasculature was better for VIBE than CODE images (p = 0.003). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were significant greater in VIBE than CODE except for SNRlung and SNRnodule (p < 0.05). CONCLUSIONS: Our study showed the potential of CODE and VIBE sequences in the evaluation of localized thoracic abnormalities including solid pulmonary nodules.

An 18F-FDG-PET/CT-based radiomics signature for estimating malignance probability of solitary pulmonary nodule.

BACKGROUND: Some solitary pulmonary nodules (SPNs) as early manifestations of lung cancer, it is difficult to determine its nature, which brings great trouble to clinical diagnosis and treatment. Radiomics can deeply explore the essence of images and provide clinical decision support for clinicians. The purpose of our study was to explore the effect of positron emission tomography (PET) with 2-deoxy-2-[fluorine-18] fluoro-d-glucose integrated with computed tomography (CT; 18F-FDG-PET/CT) combined with radiomics for predicting probability of malignancy of SPNs. METHODS: We retrospectively enrolled 190 patients with SPNs confirmed by pathology from January 2013 to December 2019 in our hospital. SPNs were benign in 69 patients and malignant in 121 patients. Patients were randomly divided into a training or testing group at a ratio of 7:3. Three-dimensional regions of interest (ROIs) were manually outlined on PET and CT images, and radiomics features were extracted. Synthetic minority oversampling technique (SMOTE) method was used to balance benign and malignant samples to a ratio of 1:1. In the training group, least absolute shrinkage and selection operator (LASSO) regression analyses and Spearman correlation analyses were used to select the strongest radiomics features. Three models including PET model, CT model, and joint model were constructed using multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were plotted to evaluate diagnostic efficiency, calibration degree, and clinical usefulness of all models in training and testing groups. RESULTS: The estimative effectiveness of the joint model was superior to the CT or PET model alone in the training and testing groups. For the joint model, CT model, and PET model, area under the ROC curve was 0.929, 0.819, 0.833 in the training group, and 0.844, 0.759, 0.748 in the testing group, respectively. Calibration and decision curves showed good fit and clinical usefulness for the joint model in both training and testing groups. CONCLUSION: Radiomics models constructed by combining PET and CT radiomics features are valuable for distinguishing benign and malignant SPNs. The combined effect is superior to qualitative diagnoses with CT or PET radiomics models alone.

Assessment of pulmonary vascular anatomy: comparing augmented reality by holograms versus standard CT images/reconstructions using surgical findings as reference standard.

BACKGROUND: We compared computed tomography (CT) images and holograms (HG) to assess the number of arteries of the lung lobes undergoing lobectomy and assessed easiness in interpretation by radiologists and thoracic surgeons with both techniques. METHODS: Patients scheduled for lobectomy for lung cancer were prospectively included and underwent CT for staging. A patient-specific three-dimensional model was generated and visualized in an augmented reality setting. One radiologist and one thoracic surgeon evaluated CT images and holograms to count lobar arteries, having as reference standard the number of arteries recorded at surgery. The easiness of vessel identification was graded according to a Likert scale. Wilcoxon signed-rank test and κ statistics were used. RESULTS: Fifty-two patients were prospectively included. The two doctors detected the same number of arteries in 44/52 images (85%) and in 51/52 holograms (98%). The mean difference between the number of artery branches detected by surgery and CT images was 0.31 ± 0.98, whereas it was 0.09 ± 0.37 between surgery and HGs (p = 0.433). In particular, the mean difference in the number of arteries detected in the upper lobes was 0.67 ± 1.08 between surgery and CT images and 0.17 ± 0.46 between surgery and holograms (p = 0.029). Both radiologist and surgeon showed a higher agreement for holograms (κ = 0.99) than for CT (κ = 0.81) and found holograms easier to evaluate than CTs (p < 0.001). CONCLUSIONS: Augmented reality by holograms is an effective tool for preoperative vascular anatomy assessment of lungs, especially when evaluating the upper lobes, more prone to anatomical variations. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04227444 RELEVANCE STATEMENT: Preoperative evaluation of the lung lobe arteries through augmented reality may help the thoracic surgeons to carefully plan a lobectomy, thus contributing to optimize patients' outcomes. KEY POINTS: • Preoperative assessment of the lung arteries may help surgical planning. • Lung artery detection by augmented reality was more accurate than that by CT images, particularly for the upper lobes. • The assessment of the lung arterial vessels was easier by using holograms than CT images.

The value of dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting EGFR mutations.

OBJECTIVES: 18F-fluorodeoxyglucose (FDG) PET/CT has been widely used for the differential diagnosis of cancer. Semi-quantitative standardized uptake value (SUV) is known to be affected by multiple factors and may make it difficult to differentiate between benign and malignant lesions. It is crucial to find reliable quantitative metabolic parameters to further support the diagnosis. This study aims to evaluate the value of the quantitative metabolic parameters derived from dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting epidermal growth factor receptor (EGFR) mutation status. METHODS: We included 147 patients with lung lesions to perform FDG PET/CT dynamic plus static imaging with informed consent. Based on the results of the postoperative pathology, the patients were divided into benign/malignant groups, adenocarcinoma (AC)/squamous carcinoma (SCC) groups, and EGFR-positive (EGFR+)/EGFR-negative (EGFR-) groups. Quantitative parameters including K1, k2, k3, and Ki of each lesion were obtained by applying the irreversible two-tissue compartmental modeling using an in-house Matlab software. The SUV analysis was performed based on conventional static scan data. Differences in each metabolic parameter among the group were analyzed. Wilcoxon rank-sum test, independent-samples T-test, and receiver-operating characteristic (ROC) analysis were performed to compare the diagnostic effects among the differentiated groups. P < 0.05 were considered statistically significant for all statistical tests. RESULTS: In the malignant group (N = 124), the SUVmax, k2, k3, and Ki were higher than the benign group (N = 23), and all had-better performance in the differential diagnosis (P < 0.05, respectively). In the AC group (N = 88), the SUVmax, k3, and Ki were lower than in the SCC group, and such differences were statistically significant (P < 0.05, respectively). For ROC analysis, Ki with cut-off value of 0.0250 ml/g/min has better diagnostic specificity than SUVmax (AUC = 0.999 vs. 0.70). In AC group, 48 patients further underwent EGFR testing. In the EGFR (+) group (N = 31), the average Ki (0.0279 ± 0.0153 ml/g/min) was lower than EGFR (-) group (N = 17, 0.0405 ± 0.0199 ml/g/min), and the difference was significant (P < 0.05). However, SUVmax and k3 did not show such a difference between EGFR (+) and EGFR (-) groups (P>0.05, respectively). For ROC analysis, the Ki had a cut-off value of 0.0350 ml/g/min when predicting EGFR status, with a sensitivity of 0.710, a specificity of 0.588, and an AUC of 0.674 [0.523-0.802]. CONCLUSION: Although both techniques were specific, Ki had a greater specificity than SUVmax when the cut-off value was set at 0.0250 ml/g/min for the differential diagnosis of lung cancer. At a cut-off value of 0.0350 ml/g/min, there was a 0.710 sensitivity for EGFR status prediction. If EGFR testing is not available for a patient, dynamic imaging could be a valuable non-invasive screening method.

Complications due to ultrasound transthoracic cutting biopsy of peripheral pulmonary lesions and lesions in the chest wall and mediastinum.

INTRODUCTION: Evaluation of patients with peripheral lung lesions and lesions of the chest wall and mediastinum is challenging. The nature of the lesion identified by imaging studies can be determined by histological evaluation of biopsies. An important place in this direction is the ever-increasing popularity among thoracic surgeons of the transthoracic biopsy with a cutting needle under ultrasound control (US-TTCNB).

A giant synovial sarcoma of the left lung.

Primary pulmonary synovial sarcoma is an extremely rare and aggressive neoplasm that primarily affects young people and has a poor prognosis. Establishing this diagnosis requires the exclusion of a wide number of other neoplasms with multimodal clinical, imaging, histological, immunohistochemical, and cytogenetic assessment. We present a case of synovial sarcoma of the left lung in a 44-year-old man, diagnosed immunohistochemically after left lower lobectomy with atypical resection of the 5th segment. Imaging, diagnostic workup, histological and immunohistochemical characteristics, surgical treatment, and prognosis are discussed.

Engineering PD-1-targeted small protein variants for in vitro diagnostics and in vivo PET imaging.

BACKGROUND: Programmed cell death 1 (PD-1) belongs to immune checkpoint proteins ensuring negative regulation of the immune response. In non-small cell lung cancer (NSCLC), the sensitivity to treatment with anti-PD-1 therapeutics, and its efficacy, mostly correlated with the increase of tumor infiltrating PD-1+ lymphocytes. Due to solid tumor heterogeneity of PD-1+ populations, novel low molecular weight anti-PD-1 high-affinity diagnostic probes can increase the reliability of expression profiling of PD-1+ tumor infiltrating lymphocytes (TILs) in tumor tissue biopsies and in vivo mapping efficiency using immune-PET imaging. METHODS: We designed a 13 kDa ß-sheet Myomedin scaffold combinatorial library by randomization of 12 mutable residues, and in combination with ribosome display, we identified anti-PD-1 Myomedin variants (MBA ligands) that specifically bound to human and murine PD-1-transfected HEK293T cells and human SUP-T1 cells spontaneously overexpressing cell surface PD-1. RESULTS: Binding affinity to cell-surface expressed human and murine PD-1 on transfected HEK293T cells was measured by fluorescence with LigandTracer and resulted in the selection of most promising variants MBA066 (hPD-1 KD = 6.9 nM; mPD-1 KD = 40.5 nM), MBA197 (hPD-1 KD = 29.7 nM; mPD-1 KD = 21.4 nM) and MBA414 (hPD-1 KD = 8.6 nM; mPD-1 KD = 2.4 nM). The potential of MBA proteins for imaging of PD-1+ populations in vivo was demonstrated using deferoxamine-conjugated MBA labeled with 68Galium isotope. Radiochemical purity of 68Ga-MBA proteins reached values 94.7-99.3% and in vitro stability in human serum after 120 min was in the range 94.6-98.2%. The distribution of 68Ga-MBA proteins in mice was monitored using whole-body positron emission tomography combined with computerized tomography (PET/CT) imaging up to 90 min post-injection and post mortem examined in 12 mouse organs. The specificity of MBA proteins was proven by co-staining frozen sections of human tonsils and NSCLC tissue biopsies with anti-PD-1 antibody, and demonstrated their potential for mapping PD-1+ populations in solid tumors. CONCLUSIONS: Using directed evolution, we developed a unique set of small binding proteins that can improve PD-1 diagnostics in vitro as well as in vivo using PET/CT imaging.

A Case of Organizing Pneumonia Resembling Lung Cancer.

BACKGROUND: Organizing pneumonia (OP) is a pathologic diagnosis with clinical and imaging manifestations that often resemble other diseases, such as infections and cancers, which can lead to delays in diagnosis and inappropriate management of the underlying disease. In this article, we present a case of organized pneumonia that resembles lung cancer. METHODS: We report a case of initial suspicion of pulmonary malignancy, treated with anti-inflammatory medication and then reviewed with CT suggesting no improvement, and finally confirmed to be OP by pathological biopsy taken via transbronchoscopy. A joint literature analysis was performed to raise clinicians' awareness of the diagnosis and treatment of OP. RESULTS: Initially, because of the atypical auxiliary findings, we thought that the disease turned out to be a lung tumor, which was eventually confirmed as OP by pathological diagnosis. CONCLUSIONS: The diagnosis and treatment of OP requires a combination of clinical information and radiological expertise, as well as biopsy to obtain histopathological evidence. That is, clinical-imaging-pathological tripartite cooperation and comprehensive analysis.

Establishment and validation of multiclassification prediction models for pulmonary nodules based on machine learning.

BACKGROUND: Lung cancer is the leading cause of cancer-related death worldwide. This study aimed to establish novel multiclassification prediction models based on machine learning (ML) to predict the probability of malignancy in pulmonary nodules (PNs) and to compare with three published models. METHODS: Nine hundred fourteen patients with PNs were collected from four medical institutions (A, B, C and D), which were organized into tables containing clinical features, radiologic features and laboratory test features. Patients were divided into benign lesion (BL), precursor lesion (PL) and malignant lesion (ML) groups according to pathological diagnosis. Approximately 80% of patients in A (total/male: 632/269, age: 57.73 ± 11.06) were randomly selected as a training set; the remaining 20% were used as an internal test set; and the patients in B (total/male: 94/53, age: 60.04 ± 11.22), C (total/male: 94/47, age: 59.30 ± 9.86) and D (total/male: 94/61, age: 62.0 ± 11.09) were used as an external validation set. Logical regression (LR), decision tree (DT), random forest (RF) and support vector machine (SVM) were used to establish prediction models. Finally, the Mayo model, Peking University People's Hospital (PKUPH) model and Brock model were externally validated in our patients. RESULTS: The AUC values of RF model for MLs, PLs and BLs were 0.80 (95% CI: 0.73-0.88), 0.90 (95% CI: 0.82-0.99) and 0.75 (95% CI: 0.67-0.88), respectively. The weighted average AUC value of the RF model for the external validation set was 0.71 (95% CI: 0.67-0.73), and its AUC values for MLs, PLs and BLs were 0.71 (95% CI: 0.68-0.79), 0.98 (95% CI: 0.88-1.07) and 0.68 (95% CI: 0.61-0.74), respectively. The AUC values of the Mayo model, PKUPH model and Brock model were 0.68 (95% CI: 0.62-0.74), 0.64 (95% CI: 0.58-0.70) and 0.57 (95% CI: 0.49-0.65), respectively. CONCLUSIONS: The RF model performed best, and its predictive performance was better than that of the three published models, which may provide a new noninvasive method for the risk assessment of PNs.

[Primary Pulmonary Diffuse Large B-cell Lymphoma Mimics Advanced Lung Cancer:Report of a Case].

Primary pulmonary diffuse large B-cell lymphoma( DLBCL) is rare, accounting for 0.4% to 1.0% of all malignant lymphomas and 0.45% of all lung malignancies. We report a case of primary pulmonary DLBCL caused by methotrexate-associated lymphoproliferative disorder (MTX-LPD). A 73-year-old man was referred to our hospital due to a growing lung nodule. Transbronchoscopic biopsy did not confirm the diagnosis, but positron emission tomography-computed tomography (PET-CT) showed an accumulation of SUVmax 28.7 in the same area and SUVmax 40.5 in the contralateral mediastinum, suggesting an advanced primary lung cancer. A partial thoracoscopic left lower lobe resection was performed in our department. Histopathological examination revealed AE1/AE3 negative, CD20 and 79a positive, bcl-2 positive, and a diagnosis of primary lung DLBCL. MTX-LPD was suspected, and discontinuation of the drug resulted in subsequent shrinkage of the residual tumor. If the diagnosis cannot be made by transbronchoscopic biopsy of an expanding nodule shadow, aggressive surgical diagnosis should be considered.

Quantitative analysis of lung lesions using unenhanced chest computed tomography images.

INTRODUCTION: Chest radiograph and computed tomography (CT) scans can accidentally reveal pulmonary nodules. Malignant and benign pulmonary nodules can be difficult to distinguish without specific imaging features, such as calcification, necrosis, and contrast enhancement. However, these lesions may exhibit different image texture characteristics which cannot be assessed visually. Thus, a computer-assisted quantitative method like histogram analysis (HA) of Hounsfield unit (HU) values can improve diagnostic accuracy, reducing the need for invasive biopsy. METHODS: In this exploratory control study, nonenhanced chest CT images of 20 patients with benign (10) and cancerous (10) lesion were selected retrospectively. The appearances of benign and malignant lesions were very similar in chest CT images, and only pathology report was used to discriminate them. Free hand region of interest (ROI) was inserted inside the lesion for all slices of each lesion. Mean, minimum, maximum, and standard deviations of HU values were recorded and used to make HA. RESULTS: HA showed that the most malignant lesions have a mean HU value between 30 and 50, a maximum HU less than 150, and a minimum HU between -30 and 20. Lesions outside these ranges were mostly benign. CONCLUSION: Quantitative CT analysis may differentiate malignant from benign lesions without specific malignancy patterns on unenhanced chest CT image.

Value of turbo spin echo-based diffusion-weighted imaging in the differential diagnosis of benign and malignant solitary pulmonary lesions.

To quantitatively assess the diagnostic efficacy of multiple parameters derived from multi-b-value diffusion-weighted imaging (DWI) using turbo spin echo (TSE)-based acquisition techniques in patients with solitary pulmonary lesions (SPLs). A total of 105 patients with SPLs underwent lung DWI using single-shot TSE-based acquisition techniques and multiple b values. The apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) parameters, and lesion-to-spinal cord signal intensity ratio (LSR), were analyzed to compare the benign and malignant groups using the Mann-Whitney U test and receiver operating characteristic analysis. The Dstar values observed in lung cancer were slightly lower than those observed in pulmonary benign lesions (28.164 ± 31.950 versus 32.917 ± 34.184; Z = -2.239, p = 0.025). The LSR values were significantly higher in lung cancer than in benign lesions (1.137 ± 0.581 versus 0.614 ± 0.442; Z = - 4.522, p < 0.001). Additionally, the ADC800, ADCtotal, and D values were all significantly lower in lung cancer than in the benign lesions (Z = - 5.054, -5.370, and -6.047, respectively, all p < 0.001), whereas the f values did not exhibit any statistically significant difference between the two groups. D had the highest area under the curve (AUC = 0.887), followed by ADCtotal (AUC = 0.844), ADC800 (AUC = 0.824), and LSR (AUC = 0.789). The LSR, ADC800, ADCtotal, and D values did not differ statistically significantly in diagnostic effectiveness. Lung DWI using TSE is feasible for differentiating SPLs. The LSR method, conventional DWI, and IVIM have comparable diagnostic efficacy for assessing SPLs.

FDG PET/CT in a Case of Solitary Mixed Squamous Cell and Glandular Papilloma of the Lung With High CEA Level.

ABSTRACT: Solitary mixed squamous cell and glandular papilloma of the lung is an extremely rare benign neoplasm. We describe FDG PET/CT findings in a case of solitary mixed squamous cell and glandular papilloma of the lung with high serum carcinoembryonic antigen level (63.3 ng/mL; reference, <5 ng/mL). The tumor showed intense FDG uptake with SUVmax of 23.8 mimicking lung cancer.

Primary lung cancer in children and adolescents.

PURPOSE: Primary lung cancer is extremely rare in children and adolescents. The aim of this study is to clarify clinical features and outcomes of primary lung cancer in children and adolescents. METHODS: Young patients (aged ≤ 20 years) diagnosed as primary lung cancer between 2012 and 2023 were retrospective reviewed. According to radiological appearance of the nodules, they were divided into solid nodule (SN) group and ground glass opacity (GGO) group. RESULTS: A total of 74 patients were identified, with a median age at diagnosis of 18 years old (range: 11-20), including 7 patients in SN group and 67 patients in GGO group. In the GGO group, none of the nodules enlarged or changed during an average surveillance period of 10.8 months before surgery, except one. Wedge resection was the most common procedure (82.1%), followed by segmentectomy (16.4%) and lobectomy (1.5%). Histopathological analysis revealed that 64.2% of GGO nodules were adenocarcinoma in situ and minimally invasive adenocarcinomas, while the remaining 35.8% were invasive adenocarcinomas. Mutational analysis was performed in nine patients, with mutations identified in all cases. After a mean follow-up period of 1.73 ± 1.62 years, two patients in the SN group died due to multiple distant metastases, while all patients in the GGO group survived without recurrence. The overall survival (100%) of the GGO group was significantly higher than SN group (66.7%). CONCLUSIONS: Primary lung cancer in children and adolescents are rare and histopathological heterogeneous. Persistent GGO nodules may indicate early-stage lung adenocarcinoma in children and adolescents.

Improving image quality of sparse-view lung tumor CT images with U-Net.

BACKGROUND: We aimed to improve the image quality (IQ) of sparse-view computed tomography (CT) images using a U-Net for lung metastasis detection and determine the best tradeoff between number of views, IQ, and diagnostic confidence. METHODS: CT images from 41 subjects aged 62.8 ± 10.6 years (mean ± standard deviation, 23 men), 34 with lung metastasis, 7 healthy, were retrospectively selected (2016-2018) and forward projected onto 2,048-view sinograms. Six corresponding sparse-view CT data subsets at varying levels of undersampling were reconstructed from sinograms using filtered backprojection with 16, 32, 64, 128, 256, and 512 views. A dual-frame U-Net was trained and evaluated for each subsampling level on 8,658 images from 22 diseased subjects. A representative image per scan was selected from 19 subjects (12 diseased, 7 healthy) for a single-blinded multireader study. These slices, for all levels of subsampling, with and without U-Net postprocessing, were presented to three readers. IQ and diagnostic confidence were ranked using predefined scales. Subjective nodule segmentation was evaluated using sensitivity and Dice similarity coefficient (DSC); clustered Wilcoxon signed-rank test was used. RESULTS: The 64-projection sparse-view images resulted in 0.89 sensitivity and 0.81 DSC, while their counterparts, postprocessed with the U-Net, had improved metrics (0.94 sensitivity and 0.85 DSC) (p = 0.400). Fewer views led to insufficient IQ for diagnosis. For increased views, no substantial discrepancies were noted between sparse-view and postprocessed images. CONCLUSIONS: Projection views can be reduced from 2,048 to 64 while maintaining IQ and the confidence of the radiologists on a satisfactory level. RELEVANCE STATEMENT: Our reader study demonstrates the benefit of U-Net postprocessing for regular CT screenings of patients with lung metastasis to increase the IQ and diagnostic confidence while reducing the dose. KEY POINTS: • Sparse-projection-view streak artifacts reduce the quality and usability of sparse-view CT images. • U-Net-based postprocessing removes sparse-view artifacts while maintaining diagnostically accurate IQ. • Postprocessed sparse-view CTs drastically increase radiologists' confidence in diagnosing lung metastasis.

Preoperative CT-based radiomic prognostic index to predict the benefit of postoperative radiotherapy in patients with non-small cell lung cancer: a multicenter study.

BACKGROUND: The value of postoperative radiotherapy (PORT) for patients with non-small cell lung cancer (NSCLC) remains controversial. A subset of patients may benefit from PORT. We aimed to identify patients with NSCLC who could benefit from PORT. METHODS: Patients from cohorts 1 and 2 with pathological Tany N2 M0 NSCLC were included, as well as patients with non-metastatic NSCLC from cohorts 3 to 6. The radiomic prognostic index (RPI) was developed using radiomic texture features extracted from the primary lung nodule in preoperative chest CT scans in cohort 1 and validated in other cohorts. We employed a least absolute shrinkage and selection operator-Cox regularisation model for data dimension reduction, feature selection, and the construction of the RPI. We created a lymph-radiomic prognostic index (LRPI) by combining RPI and positive lymph node number (PLN). We compared the outcomes of patients who received PORT against those who did not in the subgroups determined by the LRPI. RESULTS: In total, 228, 1003, 144, 422, 19, and 21 patients were eligible in cohorts 1-6. RPI predicted overall survival (OS) in all six cohorts: cohort 1 (HR = 2.31, 95% CI: 1.18-4.52), cohort 2 (HR = 1.64, 95% CI: 1.26-2.14), cohort 3 (HR = 2.53, 95% CI: 1.45-4.3), cohort 4 (HR = 1.24, 95% CI: 1.01-1.52), cohort 5 (HR = 2.56, 95% CI: 0.73-9.02), cohort 6 (HR = 2.30, 95% CI: 0.53-10.03). LRPI predicted OS (C-index: 0.68, 95% CI: 0.60-0.75) better than the pT stage (C-index: 0.57, 95% CI: 0.50-0.63), pT + PLN (C-index: 0.58, 95% CI: 0.46-0.70), and RPI (C-index: 0.65, 95% CI: 0.54-0.75). The LRPI was used to categorize individuals into three risk groups; patients in the moderate-risk group benefited from PORT (HR = 0.60, 95% CI: 0.40-0.91; p = 0.02), while patients in the low-risk and high-risk groups did not. CONCLUSIONS: We developed preoperative CT-based radiomic and lymph-radiomic prognostic indexes capable of predicting OS and the benefits of PORT for patients with NSCLC.

Thyroid abnormalities identified on CT screening for lung cancer.

PURPOSE: Because incidental thyroid nodules (ITNs) are common extrapulmonary findings in low-dose computed tomography (LDCT) scans for lung cancer screening, we aimed to investigate the frequency of ITNs on LDCT scans separately on baseline and annual repeat scans, the frequency of malignancy among the ITNs, and any association with demographic, clinical, CT characteristics. METHODS: Retrospective case series of all 2309 participants having baseline and annual repeat screening in an Early Lung and Cardiac Action Program (MS-ELCAP) LDCT lung screening program from January 2010 to December 2016 was performed. Frequency of ITNs in baseline and annual repeat rounds were determined. Multivariable regression analysis was performed to identify significant predictors. RESULTS: Dominant ITNs were seen in 2.5 % of 2309 participants on baseline and in 0.15 % of participants among 4792 annual repeat LDCTs. The low incidence of new ITNs suggests slow growth as it would take approximately an average of 16.8 years for a new ITN to be detected on annual rounds of screening. Newly detected ITNs on annual repeat LDCT were all smaller than 15 mm. Regression analysis showed that the increasing of age, coronary artery calcifications score and breast density grade were significant predictors for females having an ITN. No significant predictors were found for ITNs in males. CONCLUSION: ITNs are detected at LDCT however, no malignancy was found. Certain predictors for ITNs in females have been identified including breast density, which may point towards a common causal pathway.