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To compare the diagnostic performance between plain CT-based model and plain plus contrast CT-based modelin the classification of malignancy for solitary solid pulmonary nodules. Between January 2012 and July 2021, 527 patients with pathologically confirmed solitary solid pulmonary nodules were collected at dual centers with similar CT examinations and scanning parameters. Before surgery, all patients underwent both plain and contrast-enhanced chest CT scans. Two clinical characteristics, fifteen plain CT characteristics, and four enhanced characteristics were used to develop two logistic regression models: model 1 (plain CT only) and model 2 (plain + contrast CT). The diagnostic performance of the two models was assessed separately in the development and external validation cohorts using the AUC. 392 patients from Center A were included in the training cohort (median size, 20.0 [IQR, 15.0-24.0] mm; mean age, 55.8 [SD, 9.9] years; male, 53.3%). 135 patients from Center B were included in the external validation cohort (median size, 20.0 [IQR, 16.0-24.0] mm; mean age, 56.4 [SD, 9.6] years; male, 51.9%). Preoperative patients with 201 malignant (adenocarcinoma, 148 [73.6%]; squamous cell carcinoma, 35 [17.4%]; large cell carcinoma,18 [9.0%]) and 326 benign (pulmonary hamartoma, 118 [36.2%]; sclerosing pneumocytoma, 35 [10.7%]; tuberculosis, 104 [31.9%]; inflammatory pseudonodule, 69 [21.2%]) solitary solid pulmonary nodules were gathered from two independent centers. The mean sensitivity, specificity, accuracy, PPV, NPV, and AUC (95%CI) of model 1 (Plain CT only) were 0.79, 0.78, 0.79, 0.67, 0.87, and 0.88 (95%CI, 0.82-0.93), the model 2 (Plain + Contrast CT) were 0.88, 0.91, 0.90, 0.84, 0.93, 0.93 (95%CI, 0.88-0.98) in external validation cohort, respectively. A logistic regression model based on plain and contrast-enhanced CT characteristics showed exceptional performance in the evaluation of malignancy for solitary solid lung nodules. Utilizing this contrast-enhanced CT model would provide recommendations concerning follow-up or surgical intervention for preoperative patients presenting with solid lung nodules.

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Pulmonary Langerhans cell histiocytosis (PLCH) is a subtype of Langerhans cell histiocytosis, a rare neoplastic disease characterized by lung involvement. Here, we present a case involving a patient with multiple cavitary nodules who was diagnosed with PLCH during surveillance after lung cancer surgery. A 74-year-old woman underwent right upper lobe resection surgery for right upper lobe lung adenocarcinoma, pStage IIA, 5 years ago. The patient underwent surveillance without adjuvant chemotherapy. During the fifth year of follow-up, multiple nodules with cavitation were observed on computed tomography in both lung fields. Chemotherapy was considered to address the suspected recurrence of lung cancer; however, video-assisted thoracoscopic surgery was performed due to the need for biomarker testing. Pathological examination led to the diagnosis of PLCH. This case emphasizes the importance of a proactive histological diagnosis to determine the appropriate treatment strategy, even in situations where lung cancer recurrence is clinically suspected.

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Lemierre's syndrome primarily affects healthy adolescents and young adults as a complication of oropharyngeal infection, most commonly pharyngitis or peritonsillar abscess. Fusobacterium necrophorum is the principal pathogen, and the infection presents with classic symptoms including fever, sore throat, and neck tenderness. However, atypical presentations can pose diagnostic challenges. This report discusses a patient in her early 60s, contrary to the typical demographic, who presented with a one-week history of varied symptoms including sore throat, pleuritic chest pain, and haemoptysis. Examination revealed mild neck tenderness and lung crepitations. Laboratory tests indicated leucocytosis, thrombocytopenia, and elevated C-reactive protein (CRP). Imaging revealed pulmonary infiltrates with cavitation. F. necrophorum was detected in blood culture, promoting a CT scan of the neck, which confirmed soft tissue swelling and a small peritonsillar collection, leading to the diagnosis of Lemierre's syndrome. The classical feature of jugular vein thrombus was absent, further underscoring the atypical nature of this case. The patient received immediate initiation of intravenous antibiotics, piperacillin/tazobactam, followed by meropenem. This was complemented by a carefully tailored 21-day intravenous course, followed by an eight-week regimen of oral antibiotics consisting of amoxicillin and metronidazole. The patient demonstrated significant clinical improvement in pulmonary complications. Follow-up imaging showed minor residual changes, and the patient remained asymptomatic. Lemierre's syndrome presents a diagnostic challenge due to diverse clinical manifestations. Key diagnostic markers include deep neck infections, septicemia, and metastatic infections. Timely utilization of diagnostic tools, such as blood cultures and imaging, aid in confirmation. Early diagnosis is crucial for prompt treatment and prevention of complications. This case emphasizes the importance of maintaining a high index of suspicion for Lemierre's syndrome, especially in atypical presentations. Increased awareness among healthcare providers is vital for timely diagnosis and optimal patient outcomes.

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The current existing classifiers for distinguishing malignant from benign pulmonary nodules is limited by effectiveness or clinical practicality. In our study, we aimed to develop and validate a gene classifier for lung cancer diagnosis. To identify the genes involved in this process, we used the weighted gene co-expression network analysis to analyze gene expression datasets from Gene Expression Omnibus (GEO). We identified the three most relevant modules associated with malignant nodules and performed functional enrichment analysis on them. The results indicated significant involvement in metabolic, immune-related, cell cycle, and viral-related processes. All three modules showed enrichment in metabolic reprogramming pathways. Based on these genes, we intersected genes from the three modules with metabolic reprogramming-related genes and further intersected with differentially expressed genes to get 78 genes. After machine learning algorithms and manual selection, we finally got a nine-gene classifier consisting of SEC24D, RPSA, PSME3, PSMD8, PSMB7, NCOA1, MED12, LPCAT1, and AKR1C3. Our developed and validated classifier-based model demonstrated good discrimination, with an area under the curve (AUC) of 0.763 in the development cohort, 0.744 in the internal validation cohort, and 0.718 in the external validation cohort, and outperformed previous clinical models. Moreover, the addition of nodule size improved the predictive capability of the classifier. We further verify the expression of the gene in the classifier using TCGA lung cancer samples and found eight of the genes showed significant differential expression in lung adenocarcinoma while all nine genes showed significant differential expression in lung squamous carcinoma. Our findings underscore the significance of metabolic reprogramming pathways in patients with malignant pulmonary nodules, and our gene classifier can assist clinicians in differentiating benign from malignant pulmonary nodules in clinical settings.

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BACKGROUND: Radiation dose should be as low as reasonably achievable. With the invention of photon-counting detector computed tomography (PCD-CT), the radiation dose may be considerably reduced. PURPOSE: To evaluate the potential of PCD-CT for dose reduction in pulmonary nodule visualization for human readers as well as for computer-aided detection (CAD) studies. MATERIAL AND METHODS: A chest phantom containing pulmonary nodules of different sizes/densities (range 3-12 mm and -800-100 HU) was scanned on a PCD-CT with standard low-dose protocol as well as with half, quarter, and 1/40 dose (CTDIvol 0.4-0.03 mGy). Dose-matched scans were performed on a third-generation energy-integrating detector CT (EID-CT). Evaluation of nodule visualization and detectability was performed by two blinded radiologists. Subjective image quality was rated on a 5-point Likert scale. Artificial intelligence (AI)-based nodule detection was performed using commercially available software. RESULTS: Highest image noise was found at the lowest dose setting of 1/40 radiation dose (eff. dose = 0.01mSv) with 166.1 ± 18.5 HU for PCD-CT and 351.8 ± 53.0 HU for EID-CT. Overall sensitivity was 100% versus 93% at standard low-dose protocol (eff. dose = 0.2 mSv) for PCD-CT and EID-CT, respectively. At the half radiation dose, sensitivity remained 100% for human reader and CAD studies in PCD-CT. At the quarter radiation dose, PCD-CT achieved the same results as EID-CT at the standard radiation dose setting (93%, P = 1.00) in human reading studies. The AI-CAD system delivered a sensitivity of 93% at the lowest radiation dose level in PCD-CT. CONCLUSION: At half dose, PCD CT showed pulmonary nodules similar to full-dose PCD, and at quarter dose, PCD CT performed comparably to standard low-dose EID CT. The CAD algorithm is effective even at ultra-low doses.

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OBJECTIVE: Develop a practical scoring system based on radiomics and imaging features, for predicting the malignant potential of incidental indeterminate small solid pulmonary nodules (IISSPNs) smaller than 20 mm. METHODS: A total of 360 patients with malignant IISSPNs (n = 213) and benign IISSPNs (n = 147) confirmed after surgery were retrospectively analyzed. The whole cohort was randomly divided into training and validation groups at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to debase the dimensions of radiomics features. Multivariate logistic analysis was performed to establish models. The receiver operating characteristic (ROC) curve, area under the curve (AUC), 95% confidence interval (CI), sensitivity and specificity of each model were recorded. Scoring system based on odds ratio was developed. RESULTS: Three radiomics features were selected for further model establishment. After multivariate logistic analysis, the combined model including Mean, age, emphysema, lobulated and size, reached highest AUC of 0.877 (95%CI: 0.830-0.915), accuracy rate of 83.3%, sensitivity of 85.3% and specificity of 80.2% in the training group, followed by radiomics model (AUC: 0.804) and imaging model (AUC: 0.773). A scoring system with a cutoff value greater than 4 points was developed. If the score was larger than 8 points, the possibility of diagnosing malignant IISSPNs could reach at least 92.7%. CONCLUSION: The combined model demonstrated good diagnostic performance in predicting the malignant potential of IISSPNs. A perfect accuracy rate of 100% can be achieved with a score exceeding 12 points in the user-friendly scoring system.

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Background: Radiomics has shown promise in improving malignancy risk stratification of indeterminate pulmonary nodules (IPNs) with many platforms available, but with no head-to-head comparisons. This study aimed to evaluate transportability of radiomic models across platforms by comparing performances of a commercial radiomic feature extractor (HealthMyne) with an open-source extractor (PyRadiomics) on diagnosis of lung cancer in IPNs. Methods: A commercial radiomic feature extractor was used to segment IPNs from computed tomography (CT) scans, and a previously validated radiomic model based on commercial features was used as baseline (ComRad). Using same segmentation masks, PyRadiomics, an open-source feature extractor was used to build three open-source radiomic models (OpenRad) using different methods: de novo open-source model derived using least absolute shrinkage and selection operator (LASSO) for feature selection, selecting open-source features matched to ComRad features based upon Imaging Biomarker Standardization Initiative (IBSI) nomenclature, and selecting open-source features most highly correlated to ComRad features. Radiomic models were trained on an internal cohort (n=161) and externally validated on 3 cohorts (n=278). We added Mayo clinical risk score to OpenRad and ComRad models, creating integrated clinical radiomic (ClinRad) models. All models were compared using area under the curve (AUC) and evaluated for clinical improvement using bias-corrected clinical net reclassification indices (cNRIs). Results: ComRad AUC was 0.76 [95% confidence interval (CI): 0.71-0.82], and OpenRad AUC was 0.75 (95% CI: 0.69-0.81) for LASSO model, 0.74 (95% CI: 0.68-0.79) for Spearman's correlation, and 0.71 (95% CI: 0.65-0.77) for IBSI. Mayo scores were added to OpenRad LASSO model, which performed best, forming open-source ClinRad model with AUC of 0.80 (95% CI: 0.74-0.86), identical to commercial ClinRad's AUC. Both ClinRad models showed clinical improvement compared to Mayo alone, with commercial ClinRad achieving cNRI of 0.09 (95% CI: 0.02-0.15) for benign and 0.07 (95% CI: 0.00-0.13) for malignant, and open-source ClinRad achieving cNRI of 0.09 (95% CI: 0.02-0.15) for benign and 0.06 (95% CI: 0.00-0.12) for malignant. Conclusions: Transportability of radiomic models across platforms directly does not conserve performance, but radiomic platforms can provide equivalent results when building de novo models allowing for flexibility in feature selection to maximize prediction accuracy.

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BACKGROUND: With the popularization of computed tomography, more and more pulmonary nodules (PNs) are being detected. Risk stratification of PNs is essential for detecting early-stage lung cancer while minimizing the overdiagnosis of benign nodules. This study aimed to develop a circulating tumor DNA (ctDNA) methylation-based, non-invasive model for the risk stratification of PNs. METHODS: A blood-based assay ("LUNG-TRAC") was designed to include novel lung cancer ctDNA methylation markers identified from in-house reduced representative bisulfite sequencing data and known markers from the literature. A stratification model was trained based on 183 ctDNA samples derived from patients with benign or malignant PNs and validated in 62 patients. LUNG-TRAC was further single-blindly tested in a single- and multi-center cohort. RESULTS: The LUNG-TRAC model achieved an area under the curve (AUC) of 0.810 (sensitivity = 74.4 % and specificity = 73.7 %) in the validation set. Two test sets were used to evaluate the performance of LUNG-TRAC, with an AUC of 0.815 in the single-center test (N = 61; sensitivity = 67.5 % and specificity = 76.2 %) and 0.761 in the multi-center test (N = 95; sensitivity = 50.7 % and specificity = 80.8 %). The clinical utility of LUNG-TRAC was further assessed by comparing it to two established risk stratification models: the Mayo Clinic and Veteran Administration models. It outperformed both in the validation and the single-center test sets. CONCLUSION: The LUNG-TRAC model demonstrated accuracy and consistency in stratifying PNs for the risk of malignancy, suggesting its utility as a non-invasive diagnostic aid for early-stage peripheral lung cancer. CLINICAL TRIAL REGISTRATION: www. CLINICALTRIALS: gov (NCT03989219).

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OBJECTIVES: There is currently no evidence documenting the clinical characteristics and prognosis of non-high-risk patients with incidental stage T1 lung cancer (LC). The aim of this study was to investigate the clinical characteristics and prognosis of non-high-risk patients with incidental stage T1 LC. METHODS: This prospective cohort study included patients with incidental stage T1 LC who were diagnosed pathologically at the First Affiliated Hospital of Chongqing Medical University between 1st Jan 2019 and 31st Dec 2023. The follow-up time for all participants concluded on 31st Jan 2024, or upon death. All included patients were divided into non-high-risk (observation) and high-risk (control) groups based on the 2021 US preventative services task force recommendations. The primary outcomes were overall survival probability and LC-specific survival probability. The secondary outcomes were clinical characteristics, including demographic variables, histological types and TNM staging. RESULTS: We studied 1876 patients with incidental stage T1 LC. Of these, 1491 (79.48%) non-high-risk patients were included in the observation group, and the remaining 385 (20.52%) high-risk patients composed the control group. The follow-up interval was between 0 and 248 months for all participants, with a median time of 41.64 ± 23.85 months. The patients in the observation group were younger and had smaller tumors, more adenocarcinomas, and earlier disease stages than those in the control group (p ≤ 0.001). The overall survival probability (HR = 0.23, [95% CI: 0.18, 0.31], p < 0.001) and the LC-specific survival probability (HR = 0.23, [95% CI: 0.17, 0.31], p < 0.001) for the patients in the observation group were also both higher than those in the control group. The results appeared to be consistent across important subgroups. CONCLUSION: In this study, non-high-risk patients with incidental stage T1 LC were younger, had smaller tumors, had more adenocarcinomas, had a lower probability of metastasis, and had longer survival than did high-risk patients.

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This report presents a unique case of Caplan syndrome that mimicked accelerated progressive massive fibrosis. The patient, a former coal miner, had been diagnosed with coal worker's pneumoconiosis 15 years prior and had been treated for rheumatoid arthritis for over 20 years. Accelerated progressive massive fibrosis and the development of multiple nodules with cavitation in the basal lungs were subsequently observed on serial CT scans. Here, the CT manifestations of Caplan syndrome are highlighted in a case in which Caplan syndrome mimicked accelerated progressive massive fibrosis.

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Objectives: Robotic bronchoscopy (RB) has emerged as a novel technique to address issues with the biopsy of small peripheral lung lesions. The objective of this study was to quantitatively assess the accuracy of a novel multisection robotic bronchoscope compared with current standards of care. Methods: This is a prospective, single-blind, comparative study where the accuracy of a multisection RB was compared against the accuracy of standard electromagnetic navigational bronchoscopy (EM-NB) during lesion localization and targeting. Five blinded subjects of varying bronchoscopy experience were recruited to use both RB and EM-NB in a swine lung model. Accuracy of localization and targeting success was measured as the distance from the center of pulmonary targets at each anatomic location. Subjects used both RB and EM-NB to navigate to 4 pulmonary targets assigned using 1:1 block randomization. Differences in accuracy and time between navigation systems were assessed using Wilcoxon rank-sum test. Results: Of the 40 total attempts per modality, successful targeting was achieved on 90% and 85% of attempts utilizing RB and EM-NB, respectively. Furthermore, RB demonstrated significantly lower median distance to the real-time EM target (1.1 mm; interquartile range [IQR], 0.6-2.0 mm) compared with EM-NB (2.6 mm; IQR, 1.6-3.8) (P < .001). Median target displacement resulting from lung and bronchus deformation during bronchoscopy was found to be significantly lower using RB (0.8 mm; IQR, 0.5-1.2 mm) compared with EM-NB (2.6 mm; IQR, 1.4-6.4 mm) (P < .001). Conclusions: The results of this study demonstrate that the multi-section RB prototype allows for improved localization and targeting of small peripheral lung nodules compared with current nonrobot bronchoscopy modalities.

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Background: Preoperative computed tomography (CT)-guided localization of small pulmonary nodules (SPNs) is the major approach for accurate intraoperative visualization in video-assisted thoracoscopic surgery (VATS). However, this interventional procedure has certain risks and may challenge to less experienced junior doctors. This study aims to evaluate the feasibility and efficacy of robotic-assisted CT-guided preoperative pulmonary nodules localization with the modified hook-wire needles before VATS. Methods: A total of 599 patients with 654 SPNs who preoperatively accepted robotic-assisted CT-guided percutaneous pulmonary localization were respectively enrolled and compared to 90 patients with 94 SPNs who underwent the conventional CT-guided manual localization. The clinical and imaging data including patients' basic information, pulmonary nodule features, location procedure findings, and operation time were analyzed. Results: The localization success rate was 96.64% (632/654). The mean time required for marking was 22.85±10.27 min. Anchor of dislodgement occurred in 2 cases (0.31%). Localization-related complications included pneumothorax in 163 cases (27.21%), parenchymal hemorrhage in 222 cases (33.94%), pleural reaction in 3 cases (0.50%), and intercostal vascular hemorrhage in 5 cases (0.83%). Localization and VATS were performed within 24 hours. All devices were successfully retrieved in VATS. Histopathological examination revealed 166 (25.38%) benign nodules and 488 (74.62%) malignant nodules. For patients who received localizations, VATS spent a significantly shorter time, especially the segmentectomy group (93.61±35.72 vs. 167.50±40.70 min, P<0.001). The proportion of pneumothorax in the robotic-assisted group significantly decreased compared with the conventional manual group (27.21% vs. 43.33%, P=0.002). Conclusions: Robotic-assisted CT-guided percutaneous pulmonary nodules hook-wire localization could be effectively helpful for junior less experienced interventional physicians to master the procedure and potentially increase precision.

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Background: Pulmonary nodules (PNs) are commonly considered too small to cause respiratory symptoms. However, many PN patients present with respiratory symptoms of unknown origin. This study aims to explore these symptoms and identify the associated factors. Methods: Demographic and clinical information were retrospectively collected from 1,633 patients with incidental PNs who visited the thoracic outpatient clinic of Guangdong Provincial People's Hospital. Hospital Anxiety and Depression Scale was used to assess their anxiety and depression level. Logistic regression analyzes were employed to assess the independent risk factors for respiratory symptoms and the psychological impact on patients. Results: Among the 1,633 patients, 37.2% reported at least one respiratory symptom. The most common symptoms in patients with PNs were cough (23.6%), followed by chest pain (14.0%), expectoration (13.8%) and hemoptysis (1.3%). Patients with large PNs (>20 mm) showed significantly higher odds of having cough [odds ratio (OR) =2.5; P=0.011] and expectoration (OR =3.6; P=0.001). Patients with multiple PNs were more susceptible to chest pain compared to those with solitary PNs (OR =1.5; P=0.007). Environmental factors such as passive smoking, kitchen fume pollution, environmental dust were the consistent risk contributors to the presence of these respiratory symptoms. Comparable findings were observed among the subgroup of individuals who undergo chest computed tomography scans as a part of their routine health check-up. Presence of respiratory symptoms, especially chest pain, was associated with increased the odds of anxiety (OR =2.2; P<0.001) and depression (OR =2.5; P<0.001) in patients. Conclusions: Respiratory symptoms are common in PN patients, exhibiting a higher prevalence in patients with larger and multiple PNs and there is a strong association with exposure to environmental risk factors. These symptoms might exacerbate the anxiety and depression level in patients.

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Background: Pulmonary nodules are small, focal lesions often identified via computed tomography (CT) scans. Although the majority are benign, a small percentage of them may be malignant or potentially become malignant, underscoring the importance of early detection and effective management. This study systematically reviews the epidemiology, risk factors, and management strategies for pulmonary nodules, comparing findings across Chinese and non-Chinese populations to better inform the actuarial calculations for predicting the demand of medical services for patients with pulmonary nodules. Methods: We performed a systematic analysis of the PubMed and China Knowledge Infrastructure (CNKI) databases for studies reporting the detection rate of pulmonary nodules through CT scans. Both cross-sectional studies and the baseline data from longitudinal studies were included. A modified version of the Newcastle-Ottawa Scale was used to assess the risk of bias and random effect models were used to estimate the overall prevalence. Results: We identified 32 studies and included 24 of them in our meta-analysis. Pooled analysis showed that the overall prevalence of pulmonary nodules was 0.27 (95% confidence interval: 0.25-0.29) after outliers removal. Subgroup analysis showed that there was no significant difference for prevalence between Chinese and non-Chinese populations. Males (0.38) were shown to have slightly higher prevalence compared to females (0.36), but not significant (P=0.88). Age and smoking are the most frequently reported risk factors by studies. Conclusions: Overall, 27% of participants were positive for pulmonary nodules. Advancing age and smoking were consistently identified as a key risk factor for the incidence of pulmonary nodules. Although the management strategies are different across studies, recent guidelines recommend personalized management strategies, prioritizing nodule size, characteristics, and individual risk factors to optimize outcomes.

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Objective: Inflammation plays an important role in the transformation of pulmonary nodules (PNs) from benign to malignant. Prediction of benignancy and malignancy of PNs is still lacking efficacy methods. Although Mayo or Brock model have been widely applied in clinical practices, their application conditions are limited. This study aims to construct a diagnostic model of PNs by machine learning using inflammation-related biological markers (IRBMs). Methods: Inflammatory related genes (IRGs) were first extracted from GSE135304 chip data. Then, differentially expressed genes (DEGs) and infiltrating immune cells were screened between malignant pulmonary nodules (MN) and benign pulmonary nodule (BN). Correlation analysis was performed on DEGs and infiltrating immune cells. Molecular modules of IRGs were identified through Consistency cluster analysis. Subsequently, IRBMs in IRGs modules were filtered through Weighted gene co-expression network analysis (WGCNA). An optimal diagnostic model was established using machine learning methods. Finally, external dataset GSE108375 was used to verify this result. Results: 4 hub IRGs and 3 immune cells showed significantly difference between MN and BN, C1 and C2 module, namely PRTN3, ELANE, NFKB1 and CTLA4, T cells CD4 naïve, NK cells activated and Monocytes. IRBMs were screened from black module and yellowgreen module through WGCNA analysis. The Support vector machines (SVM) was identified as the optimal model with the Area Under Curve (AUC) was 0.753. A nomogram was established based on 5 hub IRBMs, namely HS.137078, KLC3, C13ORF15, STOM and KCTD13. Finally, external dataset GSE108375 verified this result, with the AUC was 0.718. Conclusion: SVM model established by 5 hub IRBMs was able to effectively identify MN or BN. Accumulating inflammation and immune dysfunction were important to the transformation from BN to MN.

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Intravascular large B-cell lymphoma (IVLBCL) is a rare form of extranodal large B-cell lymphoma characterized by the growth of lymphoma cells within lumina of blood vessels, especially capillaries, which aggregate to form clots, resulting in organ ischemia. In Caucasians, it predominantly involves the central nervous system (CNS) and the skin, with the cutaneous variant carrying a better prognosis. Whereas in Asians it preferentially involves the bone marrow, liver, and spleen and is associated with hemophagocytic syndrome. We report a case of a young Asian male with neurological, pulmonary, and hepatosplenic involvement. He presented with recurrent strokes, chronic cough, and unintentional weight loss. The chest radiograph (CXR) on admission was clear. Magnetic resonance imaging (MRI) of the brain showed acute multifocal infarcts, and a whole-body computed tomography (CT) scan revealed upper-lobe predominant pulmonary ground glass opacities (GGOs) with mediastinal lymphadenopathy. Interestingly, a CXR performed one week after the CT scan remained clear. The positron emission tomography-computed tomography (PET-CT) showed hepatosplenic and adrenal involvement. The diagnosis was confirmed via a bronchoscopic approach. The patient received chemotherapy consisting of MR-CHOP (methotrexate, rituximab, cyclophosphamide, adriamycin, vincristine, and prednisolone), high-dose methotrexate, and intrathecal cytarabine, which led to complete remission. Subsequently, he underwent an autologous peripheral blood stem cell transplant. At the time of writing this case report, the patient is still in complete remission for three years after the initial diagnosis. As IVLBCL has a non-specific clinicoradiological presentation, it is important to suspect IVLBCL in patients with an atypical neurological and pulmonary presentation in the presence of raised serum lactate dehydrogenase (LDH) and to consider a CT scan of the thorax if CXR is normal.

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OBJECTIVES: Evaluating the diagnostic feasibility of accelerated pulmonary MR imaging for detection and characterisation of pulmonary nodules with artificial intelligence-aided compressed sensing. MATERIALS AND METHODS: In this prospective trial, patients with benign and malignant lung nodules admitted between December 2021 and December 2022 underwent chest CT and pulmonary MRI. Pulmonary MRI used a respiratory-gated 3D gradient echo sequence, accelerated with a combination of parallel imaging, compressed sensing, and deep learning image reconstruction with three different acceleration factors (CS-AI-7, CS-AI-10, and CS-AI-15). Two readers evaluated image quality (5-point Likert scale), nodule detection and characterisation (size and morphology) of all sequences compared to CT in a blinded setting. Reader agreement was determined using the intraclass correlation coefficient (ICC). RESULTS: Thirty-seven patients with 64 pulmonary nodules (solid n = 57 [3-107 mm] part-solid n = 6 [ground glass/solid 8 mm/4-28 mm/16 mm] ground glass nodule n = 1 [20 mm]) were analysed. Nominal scan times were CS-AI-7 3:53 min; CS-AI-10 2:34 min; CS-AI-15 1:50 min. CS-AI-7 showed higher image quality, while quality remained diagnostic even for CS-AI-15. Detection rates of pulmonary nodules were 100%, 98.4%, and 96.8% for CS-AI factors 7, 10, and 15, respectively. Nodule morphology was best at the lowest acceleration and was inferior to CT in only 5% of cases, compared to 10% for CS-AI-10 and 23% for CS-AI-15. The nodule size was comparable for all sequences and deviated on average < 1 mm from the CT size. CONCLUSION: The combination of compressed sensing and AI enables a substantial reduction in the scan time of lung MRI while maintaining a high detection rate of pulmonary nodules. CLINICAL RELEVANCE STATEMENT: Incorporating compressed sensing and AI in pulmonary MRI achieves significant time savings without compromising nodule detection or characteristics. This advancement holds clinical promise, enhancing efficiency in lung cancer screening without sacrificing diagnostic quality. KEY POINTS: Lung cancer screening by MRI may be possible but would benefit from scan time optimisation. Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors. Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality.

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Background: In order to address the low compliance and dissatisfied specificity of low-dose computed tomography (LDCT), efficient and non-invasive approaches are needed to complement its limitations for lung cancer screening and management. The ASCEND-LUNG study is a prospective two-stage case-control study designed to evaluate the performance of a liquid biopsy-based comprehensive lung cancer screening and post-screening pulmonary nodules management system. Methods: We aimed to develop a comprehensive lung cancer system called Peking University Lung Cancer Screening and Management System (PKU-LCSMS) which comprises a lung cancer screening model to identify specific populations requiring LDCT and an artificial intelligence-aided (AI-aided) pulmonary nodules diagnostic model to classify pulmonary nodules following LDCT. A dataset of 465 participants (216 cancer, 47 benign, 202 non-cancer control) were used for the two models' development phase. For the lung cancer screening model development, cancer participants were randomly split at a ratio of 1:1 into the train and validation cohorts, and then non-cancer controls were age-matched to the cancer cases in a 1:1 ratio. Similarly, for the AI-aided pulmonary nodules model, cancer and benign participants were also randomly divided at a ratio of 2:1 into the train and validation cohorts. Subsequently, during the model validation phase, sensitivity and specificity were validated using an independent validation cohort consisting of 291 participants (140 cancer, 25 benign, 126 non-cancer control). Prospectively collected blood samples were analyzed for multi-omics including cell-free DNA (cfDNA) methylation, mutation, and serum protein. Computerized tomography (CT) images data was also obtained. Paired tissue samples were additionally analyzed for DNA methylation, DNA mutation, and messenger RNA (mRNA) expression to further explore the potential biological mechanisms. This study is registered with ClinicalTrials.gov, NCT04817046. Findings: Baseline blood samples were evaluated for the whole screening and diagnostic process. The cfDNA methylation-based lung cancer screening model exhibited the highest area under the curve (AUC) of 0.910 (95% CI, 0.869-0.950), followed by the protein model (0.891 [95% CI, 0.845-0.938]) and lastly the mutation model (0.577 [95% CI, 0.482-0.672]). Further, the final screening model, which incorporated cfDNA methylation and protein features, achieved an AUC of 0.963 (95% CI, 0.942-0.984). In the independent validation cohort, the multi-omics screening model showed a sensitivity of 99.2% (95% CI, 0.957-1.000) at a specificity of 56.3% (95% CI, 0.472-0.652). For the AI-aided pulmonary nodules diagnostic model, which incorporated cfDNA methylation and CT images features, it yielded a sensitivity of 81.1% (95% CI, 0.732-0.875), a specificity of 76.0% (95% CI, 0.549-0.906) in the independent validation cohort. Furthermore, four differentially methylated regions (DMRs) were shared in the lung cancer screening model and the AI-aided pulmonary nodules diagnostic model. Interpretation: We developed and validated a liquid biopsy-based comprehensive lung cancer screening and management system called PKU-LCSMS which combined a blood multi-omics based lung cancer screening model incorporating cfDNA methylation and protein features and an AI-aided pulmonary nodules diagnostic model integrating CT images and cfDNA methylation features in sequence to streamline the entire process of lung cancer screening and post-screening pulmonary nodules management. It might provide a promising applicable solution for lung cancer screening and management. Funding: This work was supported by Science, Science, Technology & Innovation Project of Xiongan New Area, Beijing Natural Science Foundation, CAMS Innovation Fund for Medical Sciences (CIFMS), Clinical Medicine Plus X-Young Scholars Project of Peking University, the Fundamental Research Funds for the Central Universities, Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, Peking University People's Hospital Research and Development Funds, National Key Research and Development Program of China, and the fundamental research funds for the central universities.

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BACKGROUND: Accurately identifying the branches of pulmonary segmental vessels and bronchi, as well as adjacent structures, and determining the spatial location of lesions within pulmonary segments, are major challenges for thoracic surgeons. The application of three-dimensional reconstruction technology holds promise in addressing this issue. OBJECTIVE: To evaluate the clinical value of three-dimensional reconstruction in thoracoscopic segmental surgery. METHODS: Seventy-seven patients who underwent thoracoscopic segmental surgery combined with three-dimensional reconstruction at our hospital from January 1, 2020, to August 31, 2023, were retrospectively analyzed. Preoperative chest enhanced CT scans were conducted, and MIMICS software aided in reconstructing DICOM format original data for patients with pulmonary nodules to facilitate intraoperative nodule localization. Accurate segmental pneumonectomy was performed by comparing preoperative anatomical identification of target segmental arteries, veins, and bronchi, with surgical details and postoperative outcomes recorded, including intraoperative pulmonary resection distribution, operation time, blood loss, chest tube drainage, extubation time, hospital stay, and complications. RESULTS: Following preoperative three-dimensional reconstruction, successful segmental lung surgeries were performed, predominantly with single segmental resection (92.2%), and a minority with combined segmentectomy (7.8%). Median operation time was 130225 minutes, with intraoperative blood loss at 70100 mL and postoperative chest tube drainage at 347 mL (159690 mL). Median extubation time and hospital stay were 4 days and 7 days, respectively. Complications within the 3-month follow-up affected 11.7% of cases, including persistent pulmonary leakage (7.1%), pulmonary infection (4.3%), atelectasis (4.3%), and pleural effusion (1.4%), with no fatalities. CONCLUSION: Preoperative 3D reconstruction can help the operator to perform safe, efficient and accurate thoracoscopic segmental pneumonectomy, which is worth popularizing in clinic.

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This study investigated the relationship between mediastinal fat and pulmonary nodule status, aiming to develop a deep learning-based radiomics model for diagnosing benign and malignant pulmonary nodules. We proposed a combined model using CT images of both pulmonary nodules and the fat around the chest (mediastinal fat). Patients from three centers were divided into training, validation, internal testing, and external testing sets. Quantitative radiomics and deep learning features from CT images served as predictive factors. A logistic regression model was used to combine data from both pulmonary nodules and mediastinal adipose regions, and personalized nomograms were created to evaluate the predictive performance. The model incorporating mediastinal fat outperformed the nodule-only model, with C-indexes of 0.917 (training), 0.903 (internal testing), 0.942 (external testing set 1), and 0.880 (external testing set 2). The inclusion of mediastinal fat significantly improved predictive performance (NRI = 0.243, p < 0.05). A decision curve analysis indicated that incorporating mediastinal fat features provided greater patient benefits. Mediastinal fat offered complementary information for distinguishing benign from malignant nodules, enhancing the diagnostic capability of this deep learning-based radiomics model. This model demonstrated strong diagnostic ability for benign and malignant pulmonary nodules, providing a more accurate and beneficial approach for patient care.