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AbstractObjectiveMaterials and methodsResultsConclusionTo investigate the value and applicability of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomics in differentiating primary lung cancer (PLC) from solitary lung metastasis (SLM) in patients with colorectal cancer (CRC).This retrospective study included 103 patients with CRC and solitary pulmonary nodules (SPNs). The least absolute shrinkage and selection operator (LASSO) was used to screen for optimal radiomics features and establish a PET/CT radiomics model. PET/CT Visual and complex models (combining radiomics with PET/CT visual features) were developed. The area under the receiver operating characteristic (ROC) curve (AUC) was used to determine the predictive value and diagnostic efficiency of the models.The AUC of the PET/CT radiomics model for differentiating PLC from SLM was 0.872 (95% CI: 0.806–0.939), which was not different from that of the visual (0.829 [95% CI: 0.749–0.908; p = .352]). However, the AUC of the complex model (0.936 [95% CI:0.892–0.981]) was significantly higher than that of the PET/CT radiomics (p = .005) and visual model (p = .001). The sensitivity (SEN), specificity (SPE), accuracy (ACC), positive predictive value (PPV), and negative predictive value (NPV) of PET/CT radiomics for differentiating PLC from SLM were 0.720, 0.887, 0.806, 0.857, and 0.770, respectively.PET/CT radiomics can effectively distinguish PLC and SLM in patients with CRC and SPNs and guide the implementation of personalized treatment. [ABSTRACT FROM AUTHOR]

Background: The identification of benign and malignant pulmonary nodules (BPN and MPN) can significantly reduce mortality. However, a reliable and validated diagnostic model for clinical decision-making is still lacking. Methods: Enzyme-linked immunosorbent assay and electro chemiluminescent immunoassay were utilized to determine the serum concentrations of 7AABs (p53, GAGE7, PGP9.5, CAGE, MAGEA1, SOX2, GBU4-5), and 4TTMs (CYFR21, CEA, NSE and SCC) in 260 participants (72 BPNs and 188 early-stage MPNs), respectively. The malignancy probability was calculated using Artificial intelligence pulmonary nodule auxiliary diagnosis system, or Mayo model. Along with age, sex, smoking history and nodule size, 18 variables were enrolled for model development. Baseline comparison, univariate ROC analysis, variable correlation analysis, lasso regression, univariate and stepwise logistic regression, and decision curve analysis (DCA) was used to reduce and screen variables. A nomogram and DCA were built for model construction and clinical use. Training (60%) and validation (40%) cohorts were used to for model validation. Results: Age, CYFRA21_1, AI, PGP9.5, GAGE7, and GBU4_5 was screened out from 18 variables and utilized to establish the regression model for identifying BPN and early-stage MPN, as well as nomogram and DCA for clinical practical use. The AUC of the nomogram in the training and validation cohorts were 0.884 and 0.820, respectively. Moreover, the calibration curve showed high coherence between the predicted and actual probability. Conclusion: This diagnostic model and DCA could provide evidence for upgrading or maintaining the current clinical decision based on malignancy probability stratification. It enables low and moderate risk or ambiguous patients to benefit from more precise clinical decision stratification, more timely detection of malignant nodules, and early treatment. [ABSTRACT FROM AUTHOR]

Pulmonary nodules are common incidental findings requiring surveillance. Follow‐up recommendations vary depending on risk factors, size and solid or subsolid characteristics. This review aimed to evaluate the prevalence of clinically significant nodules detected on noncancer‐dedicated imaging and the prevalence of part‐solid and ground‐glass nodules. We conducted a systematic search of literature and screened texts for eligibility. Clinically significant nodules were noncalcified nodules >4–6 mm. Prevalence estimates were calculated for all studies and risk of bias was assessed by one reviewer. Twenty‐four studies were included, with a total of 30 887 participants, and 21 studies were cross‐sectional in design. Twenty‐two studies used computed tomography (CT) imaging with cardiac‐related CT being the most frequent. Prevalence of significant nodules was highest in studies with large field of view of the chest and low size thresholds for reporting nodules. The prevalence of part‐solid and ground‐glass nodules was only described in two cardiac‐related CT studies. The overall risk of bias was low in seven studies and moderate in 17 studies. While current literature frequently reports incidental nodules on cardiovascular‐related CT, there is minimal reporting of subsolid characteristics. Unclear quantification of smoking history and heterogeneity of imaging protocol also limits reliable evaluation of nodule prevalence in nonscreening cohorts. [ABSTRACT FROM AUTHOR]

Introduction: The study explored the effects of SHOX2 and RASSF1A DNA methylation in lung cancer (LC). Method: Bronchoalveolar lavage fluid (BALF) samples as well as LC and normal adjacent tissues were collected from 72 LC patients and 35 patients with benign pulmonary nodules. Quantitative analysis of SHOX2 and RASSF1A DNA methylation was performed in benign pulmonary nodules and different stages of LC. The diagnostic value of SHOX2 and RASSF1A DNA methylation in LC and benign pulmonary nodules was determined by receiver operating characteristics analysis. Gain/loss-of-function experiments were constructed in LC cells and mouse models of xenograft and pulmonary nodule metastasis. The levels of SHOX2 and transfer-associated genes were tested through quantitative reverse transcription polymerase chain reaction and Western blot. Malignant phenotype of LC cells was assessed by functional experiment. The tumor volume and weight of mice in xenograft models were measured. Pulmonary nodule metastasis was determined through HE staining assay. 5-azacytidine appeared as a positive control drug. Result: SHOX2 DNA methylation or RASSF1A DNA methylation had diagnostic efficiency in pulmonary nodules and early LC, with the two combined having better diagnostic value. SHOX2 expression was upregulated in LC. Similar to 5-azacytidine, SHOX2 knockdown inhibited LC cell viability, migration, and invasion in vitro as well as restrained LC tumorigenesis and pulmonary nodule metastasis in vivo, whereas overexpressed SHOX2 had the opposite effects. Conclusion: The combination of SHOX2 and RASSF1A DNA methylation had a diagnostic value in pulmonary nodules and early LC. SHOX2 positively modulated the tumorigenesis and metastasis of LC by regulating DNA methylation processes. [ABSTRACT FROM AUTHOR]

Purpose: Predicting the malignancy of pure ground-glass nodules (GGNs) using CT is challenging. The optimal role of [18F]FDG PET/CT in this context has not been clarified. We compared the performance of [18F]FDG PET/CT in evaluating GGNs for predicting invasive adenocarcinomas (IACs) with CT. Methods: From June 2012 to December 2020, we retrospectively enrolled patients with pure GGNs on CT who underwent [18F]FDG PET/CT within 90 days. Overall, 38 patients with 40 ≥ 1–cm GGNs were pathologically confirmed. CT images were analyzed for size, attenuation, uniformity, shape, margin, tumor–lung interface, and internal/surrounding characteristics. Visual [18F]FDG positivity, maximum standardized uptake value (SUVmax), and tissue fraction-corrected SUVmax (SUVmaxTF) were evaluated on PET/CT. Results: The histopathology of the 40 GGNs were: 25 IACs (62.5%), 9 minimally invasive adenocarcinomas (MIA, 22.5%), and 6 adenocarcinomas in situ (AIS, 15.0%). No significant differences were found in CT findings according to histopathology, whereas visual [18F]FDG positivity, SUVmax, and SUVmaxTF were significantly different (P=0.001, 0.033, and 0.018, respectively). The size, visual [18F]FDG positivity, SUVmax, and SUVmaxTF showed significant diagnostic performance to predict IACs (area under the curve=0.693, 0.773, 0.717, and 0.723, respectively; P=0.029, 0.001, 0.018, and 0.013, respectively). In the multivariate logistic regression analysis, visual [18F]FDG positivity discriminated IACs among GGNs among various CT and PET findings (P=0.008). Conclusions: [18F]FDG PET/CT demonstrated superior diagnostic performance compared to CT in differentiating IAC from AIS/MIA among pure GGNs, thus it has the potential to guide the proper management of patients with pure GGNs. [ABSTRACT FROM AUTHOR]

Background: Video-assisted thoracic surgery (VATS) has become the gold-standard approach for lung resections. Given the impossibility of digital palpation, we witnessed the progressive development of peri-centimetric and deeply located pulmonary nodule alternative detection techniques. Intra-operative lung ultrasound is an increasingly effective diagnostic method, although only a few small studies have evaluated its accuracy. This study analyzed the effectiveness and sensitivity of uniportal VATS with intra-operative lung ultrasound (ILU), in comparison to multiportal VATS, for visualizing solitary and deep-sited pulmonary nodules. Methods: Patient data from October 2021 to October 2023, from a single center, were retrospectively gathered and analyzed. In total, 31 patients who received ILU-aided uniportal VATS (Group A) were matched for localization time, operative time, sensitivity, and post-operative complications, with 33 undergoing nodule detection with conventional techniques, such as manual or instrumental palpation, in multiportal VATS (Group B). Surgeries were carried out by the same team and ILU was performed by a certified operator. Results: Group A presented a significantly shorter time for nodule detection [median (IQR): 9 (8–10) vs. 14 (12.5–15) min; p < 0.001] and operative time [median (IQR): 33 (29–38) vs. 43 (39–47) min; p < 0.001]. All nodules were correctly localized and resected in Group A (sensitivity 100%), while three were missed in Group B (sensitivity 90.9%). Two patients in Group B presented with a prolonged air leak that was conservatively managed, compared to none in Group A, resulting in a post-operative morbidity rate of 6.1% vs. 0% (p = 0.16). Conclusions: ILU-aided uniportal VATS was faster and more effective than conventional techniques in multiportal VATS for nodule detection. [ABSTRACT FROM AUTHOR]

Summary of Key Points: Eligibility criteria for lung cancer screening increasingly need to consider family history of lung cancer, as well as age and smoking status.Lung cancer screening will reveal a multitude of incidental findings, of variable clinical significance, and with a need for clear pathways of management.Pulmonary nodule sampling is enhanced by intra‐procedural imaging and cutting‐edge robotic technology.Systematic thoracic lymph node sampling has implications for treatment efficacy.Bronchoscopic ablative techniques are feasible for peripheral lung cancers.Bronchoscopic sampling continues to have a high yield for lung cancer molecular characterization.Immunotherapy indications have expanded to include early stage and resectable lung cancer. [ABSTRACT FROM AUTHOR]

This study explores a multi-modal machine-learning-based approach to classify solitary pulmonary nodules (SPNs). Non-small cell lung cancer (NSCLC), presenting primarily as SPNs, is the leading cause of cancer-related deaths worldwide. Early detection and appropriate management of SPNs are critical to improving patient outcomes, necessitating efficient diagnostic methodologies. While CT and PET scans are pivotal in the diagnostic process, their interpretation remains prone to human error and delays in treatment implementation. This study proposes a machine-learning-based network to mitigate these concerns, integrating CT, PET, and manually extracted features in a multi-modal manner by integrating multiple image modalities and tabular features). CT and PET images are classified by a VGG19 network, while additional SPN features in combination with the outputs of VGG19 are processed by an XGBoost model to perform the ultimate diagnosis. The proposed methodology is evaluated using patient data from the Department of Nuclear Medicine of the University Hospital of Patras in Greece. We used 402 patient cases with human annotations to internally validate the model and 96 histopathological-confirmed cases for external evaluation. The model exhibited 97% agreement with the human readers and 85% diagnostic performance in the external set. It also identified the VGG19 predictions from CT and PET images, SUVmax, and diameter as key malignancy predictors. The study suggests that combining all available image modalities and SPN characteristics improves the agreement of the model with the human readers and the diagnostic efficiency. [ABSTRACT FROM AUTHOR]

Background: The diagnosis of solitary pulmonary nodules has always been a difficult and important point in clinical research, especially granulomatous nodules (GNs) with lobulation and spiculation signs, which are easily misdiagnosed as malignant tumors. Therefore, in this study, we utilised a CT deep learning (DL) model to distinguish GNs with lobulation and spiculation signs from solid lung adenocarcinomas (LADCs), to improve the diagnostic accuracy of preoperative diagnosis. Methods: 420 patients with pathologically confirmed GNs and LADCs from three medical institutions were retrospectively enrolled. The regions of interest in non-enhanced CT (NECT) and venous contrast-enhanced CT (VECT) were identified and labeled, and self-supervised labels were constructed. Cases from institution 1 were randomly divided into a training set (TS) and an internal validation set (IVS), and cases from institutions 2 and 3 were treated as an external validation set (EVS). Training and validation were performed using self-supervised transfer learning, and the results were compared with the radiologists' diagnoses. Results: The DL model achieved good performance in distinguishing GNs and LADCs, with area under curve (AUC) values of 0.917, 0.876, and 0.896 in the IVS and 0.889, 0.879, and 0.881 in the EVS for NECT, VECT, and non-enhanced with venous contrast-enhanced CT (NEVECT) images, respectively. The AUCs of radiologists 1, 2, 3, and 4 were, respectively, 0.739, 0.783, 0.883, and 0.901 in the (IVS) and 0.760, 0.760, 0.841, and 0.844 in the EVS. Conclusions: A CT DL model showed great value for preoperative differentiation of GNs with lobulation and spiculation signs from solid LADCs, and its predictive performance was higher than that of radiologists. [ABSTRACT FROM AUTHOR]

Introduction: Electromagnetic navigation bronchoscopy (ENB) and radial probe endobronchial ultrasound (RP‐EBUS) are essential bronchoscopic procedures for diagnosing peripheral lung lesions. Despite their individual advantages, the optimal circumstances for their combination remain uncertain. Methods: This single‐center retrospective study enrolled 473 patients with 529 pulmonary nodules who underwent ENB and/or RP‐EBUS biopsies between December 2021 and December 2022. Diagnostic yield was calculated using strict, intermediate, and liberal definitions. In the strict definition, only malignant and specific benign lesions were deemed diagnostic at the time of the index procedure. The intermediate and liberal definitions included additional results from the follow‐up period. Results: The diagnostic yield of the strict definition was not statistically different among the three groups (ENB/Combination/RP‐EBUS 63.8%/64.2%/62.6%, p = 0.944). However, the diagnostic yield was superior in the ENB + RP‐EBUS group for nodules with a bronchus type II or III and a solid part <20 mm (odds ratio 1.96, 95% confidence interval 1.09–3.53, p = 0.02). In terms of complications, bleeding was significantly higher in the ENB + RP‐EBUS group (ENB/Combination/RP‐EBUS 3.7% /6.2/0.6%, p = 0.002), but no major adverse event was observed. Conclusion: The combination of ENB and RP‐EBUS enhanced the diagnostic yield for nodules with bronchus type II or III and solid part <20 mm, despite a slightly elevated risk of bleeding. Careful patient selection based on nodule characteristics is important to benefit from this combined approach. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to explore the value of radiomics nomogram based on computed tomography (CT) on the diagnosis of benign and malignant solitary indeterminate smoothly marginated solid pulmonary nodules (SMSPNs). Methods: This study retrospectively reviewed 205 cases with solitary indeterminate SMSPNs on CT, including 112 cases of benign nodules and 93 cases of malignant nodules. They were divided into training (n=143) and validation (n=62) cohorts based on different CT scanners. Radiomics features of the nodules were extracted fromthe lung window CT images. The variance threshold method, SelectKBest, and least absolute shrinkage and selection operator were used to select the key radiomics features to construct the rad-score. Through multivariate logistic regression analysis, a nomogramwas built by combining rad-score, clinical factors, and CT features. The nomogram performance was evaluated by the area under the receiver operating characteristic curve (AUC). Results: A total of 19 radiomics features were selected to construct the rad-score, and the nomogram was constructed by the rad-score, one clinical factor (history of malignant tumor), and three CT features (including calcification, pleural retraction, and lobulation). The nomogram performed better than the radiomics model, clinical model, and experienced radiologists who specialized in thoracic radiology for nodule diagnosis. The AUC values of the nomogram were 0.942 in the training cohort and 0.933 in the validation cohort. The calibration curve and decision curve showed that the nomogram demonstrated good consistency and clinical applicability. Conclusion: The CT-based radiomics nomogram achieved high efficiency in the preoperative diagnosis of solitary indeterminate SMSPNs, and it is of great significance in guiding clinical decision-making. [ABSTRACT FROM AUTHOR]

Background: This study aimed to construct a clinical prediction model and nomogram to differentiate invasive from non-invasive pulmonary adenocarcinoma in solitary pulmonary nodules (SPNs). Method: We analyzed computed tomography and clinical features as well as preoperative biomarkers in 1,106 patients with SPN who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University between January 2020 and December 2021. Clinical parameters and imaging characteristics were analyzed using univariate and multivariate logistic regression analyses. Predictive models and nomograms were developed and their recognition abilities were evaluated using receiver operating characteristic (ROC) curves. The clinical utility of the nomogram was evaluated using decision curve analysis (DCA). Result: The final regression analysis selected age, carcinoembryonic antigen, bronchus sign, lobulation, pleural adhesion, maximum diameter, and the consolidation-to-tumor ratio as associated factors. The areas under the ROC curves were 0.844 (95% confidence interval [CI], 0.817-0.871) and 0.812 (95% CI, 0.766-0.857) for patients in the training and validation cohorts, respectively. The predictive model calibration curve revealed good calibration for both cohorts. The DCA results confirmed that the clinical prediction model was useful in clinical practice. Bias-corrected C-indices for the training and validation cohorts were 0.844 and 0.814, respectively. Conclusion: Our predictive model and nomogram might be useful for guiding clinical decisions regarding personalized surgical intervention and treatment options. [ABSTRACT FROM AUTHOR]

The incidence of lung cancer brain metastasis combined with hemorrhagic cerebral venous sinus thrombosis (CVST) is very rare, and the understanding and treatment experience of this case is insufficient. We reported a case of lung cancer brain metastasis accompanied by venous sinus thrombosis, and describe the diagnosis and treatment plan for colleagues to learn from experience and lessons. Key points: 1. The incidence of lung cancer brain metastasis combined with hemorrhagic CVST is very rare, and the understanding and treatment experience of this case is insufficient. 2. The clinical manifestations of metastatic tumors and CVST in patients are not specific, and the evolution of the medical history should be inquired in detail, with careful physical examination and imaging review. 3. CVST should be considered in patients with risk factors. The incidence of hemorrhagic CVST is relatively rare, and due to atypical symptoms and imaging manifestations, it is easy to miss and misdiagnose. 4. The responsible lesion should be determined based on the evolution of the medical history, the treatment of the responsible lesion should be prioritized, symptoms should be relieved in a timely manner and the optimal treatment time should be strived for. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Our study analyzed the main manifestations of tuberculous pleurisy (TBP) in children under medical thoracoscopy (MT). This article aimed to explore the clinical application value of MT in the diagnosis and treatment of TBP in children. METHODS: In our study, we selected 23 TBP patients diagnosed in our hospital. We analyzed the clinical data and thoracoscopic morphology of these patients. At the same time, we also observed the pathological manifestations, acid-fast staining, and treatment effects of the patient's diseased tissue under MT. RESULTS: The MT clinical findings of TBP patients include pleural hyperemia and edema, miliary nodules, scattered or more white nodules, simple pleural adhesion, wrapped pleural effusion, massive cellulose exudation, yellow-white caseous necrosis, pleural hyperplasia and hyperplasia, and mixed pleural necrosis. The positive rate of pleural biopsy was 73.91% and that of acid-fast staining was 34.78%. The main pathologic types of these patients were tuberculous granulomatous lesions (16 cases), caseous necrosis (5 cases), and fibrinous exudative, multinucleated giant cell and other inflammatory cell infiltration lesions (13 cases). The average time of diagnosis of the 23 patients was 8.32 days (5.0-16.0 days), and they were transferred to specialized hospitals for treatment after diagnosis. The mean time of chest drainage was 3.0-5.0 days after treatment. The average time for their body temperature to return to normal was 3.31 days (2.0-5.0 days). CONCLUSION: Thoracoscopic lesions of TBP in children are varied. The use of MT is not only helpful for the early diagnosis and treatment of TBP. It also protects and improves lung function. Therefore, the use of MT has high clinical value. [ABSTRACT FROM AUTHOR]

Objectives: Lung cancer is one of the most common malignant tumors threatening human life and health. At present, low‐dose computed tomography (LDCT) screening for the high‐risk population to achieve early diagnosis and treatment of lung cancer has become the first choice recommended by many authoritative international medical organizations. To further optimize the lung cancer screening method, we conducted a real‐world study of LDCT lung cancer screening in a large sample of a healthy physical examination population, comparing differences in lung nodules and lung cancer detection between thin and thick‐slice LDCT scanning. Methods: A total of 29 296 subjects who underwent low‐dose thick‐slice CT scanning (5 mm thickness) from January 2015 to December 2015 and 28 058 subjects who underwent low‐dose thin‐slice CT scanning (1 mm thickness) from January 2018 to December 2018 in West China Hospital were included. The positive detection rate, detection rate of lung cancer, pathological stage of lung cancer, and mortality rate of lung cancer were analyzed and compared between the two groups. Results: The positive rate of LDCT screening in the thin‐slice scanning group was significantly higher than that in the thick‐slice scanning group (20.1% vs. 14.4%, p < 0.001). In addition, the lung cancer detection rate in the thin‐slice LDCT screening positive group was significantly higher than that in the thick‐slice scanning group (78.0% vs. 52.9%, p < 0.001). Conclusions: The screening positive rate of low‐dose thin‐slice CT scanning is higher and more early‐stage lung cancer (IA1 stage) can be detected in the screen‐positive group. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article presents a case study of a 41-year-old man with metastatic renal cell carcinoma (RCC) who underwent cytoreductive surgery after receiving immunotherapy. The article discusses the history and indications for cytoreductive nephrectomy (CN) in patients with metastatic RCC and ongoing clinical trials examining its efficacy in the era of immunotherapy. It also discusses the management of metastatic RCC with venous tumor thrombus and the subsequent treatment options for relapsed or stage IV clear cell RCC. The article highlights the promising results of lenvatinib plus pembrolizumab combination therapy and the management of adverse events associated with it. It also discusses the importance of genetic testing in RCC and the ongoing clinical trials evaluating HIF-2α inhibitors. The article emphasizes the importance of multidisciplinary management and personalized treatment plans, and includes the patient's perspective and challenges faced throughout the treatment process. [Extracted from the article]

Abstract Background This study aimed to quantitatively reveal contributing factors to airway navigation failure during radial probe endobronchial ultrasound (R-EBUS) by using geometric analysis in a three-dimensional (3D) space and to investigate the clinical feasibility of prediction models for airway navigation failure. Methods We retrospectively reviewed patients who underwent R-EBUS between January 2017 and December 2018. Geometric quantification was analyzed using in-house software built with open-source python libraries including the Vascular Modeling Toolkit ( http://www.vmtk.org ), simple insight toolkit ( https://sitk.org ), and sci-kit image ( https://scikit-image.org ). We used a machine learning-based approach to explore the utility of these significant factors. Results Of the 491 patients who were eligible for analysis (mean age, 65 years +/- 11 [standard deviation]; 274 men), the target lesion was reached in 434 and was not reached in 57. Twenty-seven patients in the failure group were matched with 27 patients in the success group based on propensity scores. Bifurcation angle at the target branch, the least diameter of the last section, and the curvature of the last section are the most significant and stable factors for airway navigation failure. The support vector machine can predict airway navigation failure with an average area under the curve of 0.803. Conclusions Geometric analysis in 3D space revealed that a large bifurcation angle and a narrow and tortuous structure of the closest bronchus from the lesion are associated with airway navigation failure during R-EBUS. The models developed using quantitative computer tomography scan imaging show the potential to predict airway navigation failure.

Background. Gamartoma occupies a special place among solitary lung masses not requiring active surgical tactics: structural heterogeneity due to inclusions of fat density and calcinates presents an opportunity to identify pathognomonic computed tomographic (CT) signs. However, their absence in conjunction with CT picture inherent in malignant neoplasms can cause biopsies and surgical interventions that are not necessary according to the results of histologic examination.Objective: to perform a cohort retrospective analysis of pulmonary hamartoma CT semiotics.Маterial and methods. We analyzed 142 cases of lung hamartomas detected at the Saint Petersburg Research Institute of Phthisiopulmonology from 2013 to 2023, confirmed histologically or with a follow-up period of more than 600 days, without endobronchial location and other foci/formations in the lungs, without contrast enhancement evaluation.Results. The results of data statistical analysis of patients with pulmonary hamartoma with distribution by gender and age were described. The occurrence rate was established for such hamartoma CT features as mass type, contour features, changes in the surrounding lung tissue, the largest diameter, density, calcination type with examples on CT images. Localizations of hamartomas in relation to the lung, its lobes and segments were considered. Four hamartoma clusters depending on fat and calcination combination in the structure were identified and illustrated. The size distribution of hamartomas from each cluster within the given ranges was also presented.Conclusion. Lung hamartomas are solid-type masses without preferential localization in lung segments, with the possibility of detection at any age. In a significant proportion of cases (43.7%) hamartomas did not have any structural features, which allow, according to CT data, to convincingly classify them as benign masses and avoid surgical resection. Only in 12% of cases hamartomas had structural changes considered highly pathognomonic for their classification as benign masses.

Background and Objective Methods Results Conclusion As the presentation of pulmonary nodules increases, the importance of a safe and accurate method of sampling peripheral pulmonary nodules is highlighted. First‐generation robotic bronchoscopy has successfully assisted navigation and improved peripheral reach during bronchoscopy. Integrating tool‐in‐lesion tomosynthesis (TiLT) may further improve yield.We performed a first‐in‐human clinical trial of a new robotic electromagnetic navigation bronchoscopy system with integrated digital tomosynthesis technology (Galaxy System, Noah Medical). Patients with moderate‐risk peripheral pulmonary nodules were enrolled in the study. Robotic bronchoscopy was performed using electromagnetic navigation with TiLT‐assisted lesion guidance. Non‐specific results were followed up until either a clear diagnosis was achieved or repeat radiology at 6 months demonstrated stability.Eighteen patients (19 nodules) were enrolled. The average lesion size was 20 mm, and the average distance from the pleura was 11.6 mm. The target was successfully reached in 100% of nodules, and the biopsy tool was visualized inside the target lesion in all cases. A confirmed specific diagnosis was achieved in 17 nodules, 13 of which were malignant. In one patient, radiological monitoring confirmed a true non‐malignant result. This translates to a yield of 89.5% (strict) to 94.7% (intermediate). Complications included one pneumothorax requiring observation only and another requiring an overnight chest drain. There was one case of severe pneumonia following the procedure.In this first‐in‐human study, second‐generation robotic bronchoscopy using electromagnetic navigation combined with integrated digital tomosynthesis was feasible with an acceptable safety profile and demonstrated a high diagnostic yield for small peripheral lung nodules. [ABSTRACT FROM AUTHOR]

Background: Human pulmonary dirofilariasis (HPD) is rare in Hungary, and it stems from Dirofilaria immitis, mainly transmitted through mosquito bites, with dogs as primary hosts. Despite its prevalence in veterinary settings, human cases are infrequent. Historically, Mediterranean countries report most HPD cases, but sporadic cases occur in temperate European regions. Radiologically, HPD often manifests in a non-specific manner, resembling pulmonary neoplasms, leading to unnecessary surgery and patient distress. Methods: This study presents a notable case series from Hungary, encompassing a 12-year period, documenting 5 instances of HPD with the aim to provide baseline estimate of occurrence for future comparison. Results: Among the patients studied, all were of middle age (median: 52 years, range: 37–69) and exhibited tumor-like lesions, primarily localized to the right lung, necessitating lobectomy or wedge resection. Histological examination consistently revealed a necrotizing granulomatous response characterized by remnants of helminths, without the presence of ovules. Furthermore, rigorous diagnostic procedures excluded other potential infectious agents through specialized staining techniques. Polymerase chain reaction analysis definitively confirmed the diagnosis of HPD in each case. Conclusions: This case series highlights HPD as a seldom zoonosis, with a probable escalation in its occurrence within temperate regions. Therefore, clinicians should maintain a heightened awareness of HPD in the differential diagnosis of pulmonary coin lesions. Early recognition and diagnosis are paramount for appropriate management and prevention of potential complications associated with this increasingly recognized infectious entity. [ABSTRACT FROM AUTHOR]

Simple Summary: Detecting lung cancer early and initiating treatment promptly can greatly enhance patient outcomes. While low-dose computed tomography (LDCT) screening aids in identifying lung cancer at an early stage, there is a risk of diagnostic delays as patients await follow-up scans. To mitigate this challenge, we developed a deep predictive model leveraging generative AI methods to forecast nodule growth patterns in follow-up LDCT scans based on baseline LDCT scans. Our findings illustrated that utilizing the predicted follow-up nodule images generated by our model during baseline screening improved diagnostic accuracy compared to using baseline nodules alone and achieved comparable performance with using real follow-up nodules. This demonstrated the potential of employing deep generative models to forecast nodule appearance in follow-up imaging from baseline LDCT scans, thereby enhancing risk assessment during initial screening. Early diagnosis of lung cancer can significantly improve patient outcomes. We developed a Growth Predictive model based on the Wasserstein Generative Adversarial Network framework (GP-WGAN) to predict the nodule growth patterns in the follow-up LDCT scans. The GP-WGAN was trained with a training set (N = 776) containing 1121 pairs of nodule images with about 1-year intervals and deployed to an independent test set of 450 nodules on baseline LDCT scans to predict nodule images (GP-nodules) in their 1-year follow-up scans. The 450 GP-nodules were finally classified as malignant or benign by a lung cancer risk prediction (LCRP) model, achieving a test AUC of 0.827 ± 0.028, which was comparable to the AUC of 0.862 ± 0.028 achieved by the same LCRP model classifying real follow-up nodule images (p = 0.071). The net reclassification index yielded consistent outcomes (NRI = 0.04; p = 0.62). Other baseline methods, including Lung-RADS and the Brock model, achieved significantly lower performance (p < 0.05). The results demonstrated that the GP-nodules predicted by our GP-WGAN model achieved comparable performance with the nodules in the real follow-up scans for lung cancer diagnosis, indicating the potential to detect lung cancer earlier when coupled with accelerated clinical management versus the current approach of waiting until the next screening exam. [ABSTRACT FROM AUTHOR]

Primary pulmonary meningioma is a rare benign tumor usually presenting as a solitary pulmonary nodule or mass. It can be easily misinterpreted as a primary lung tumor or metastases on imaging studies. We present a 54-year-old woman with an incidentally discovered solitary lung nodule, which was diagnosed as metaplastic primary pulmonary meningioma following resection. Metaplastic meningioma is a rare WHO grade 1 meningioma subtype with focal or global mesenchymal differentiation. To the authors' knowledge, primary pulmonary meningioma with mesenchymal differentiation has not been described previously in the English literature. [ABSTRACT FROM AUTHOR]

Simple Summary: Lung cancer (LC) is one of the most common and serious types of cancer. Unfortunately, it is not easy to detect in the early stages of the disease due to the absence of symptoms. Many patients have late-stage LC when they are diagnosed, and this is associated with limited treatment options and poor survival rates. To try to improve this, we have assessed which proteins in LC patients are recognised by the immune response and could be used to screen at-risk patients for LC before symptoms appear. We have shown that panels of blood and sputum biomarkers may offer the most effective way to improve early LC detection. Lung cancer (LC) is one of the leading causes of cancer-related deaths. Pulmonary nodules are one of the risk factors, and their discovery rate has been increasing due to enhanced performance of chest CT scans, but more than 90% are non-malignant, causing unnecessary stress to patients and costs to healthcare providers. Early diagnosis of LC is associated with a 5-year survival rate of up to 75% following surgical resection, but LC is often diagnosed late due to a lack of symptoms and poor 5-year survival rates as low as 10%. The cost of LC diagnosis is high, with 40% of it associated with benign lesions, which are difficult to differentiate from malignant lesions. Tumour-associated antigens (TAAs) may provide one way in which LC could be diagnosed early using minimally-invasive techniques, under their association with immune responses and specificity for disease. Here we discuss the potential of cancer-testis antigens (CTAs) to act as non-invasive biomarkers for the early detection of non-small cell lung cancer. [ABSTRACT FROM AUTHOR]

The study investigates the efficiency of integrating Machine Learning (ML) in clinical practice for diagnosing solitary pulmonary nodules' (SPN) malignancy. Patient data had been recorded in the Department of Nuclear Medicine, University Hospital of Patras, in Greece. A dataset comprising 456 SPN characteristics extracted from CT scans, the SUVmax score from the PET examination, and the ultimate outcome (benign/malignant), determined by patient follow-up or biopsy, was used to build the ML classifier. Two medical experts provided their malignancy likelihood scores, taking into account the patient's clinical condition and without prior knowledge of the true label of the SPN. Incorporating human assessments into ML model training improved diagnostic efficiency by approximately 3%, highlighting the synergistic role of human judgment alongside ML. Under the latter setup, the ML model had an accuracy score of 95.39% (CI 95%: 95.29–95.49%). While ML exhibited swings in probability scores, human readers excelled in discerning ambiguous cases. ML outperformed the best human reader in challenging instances, particularly in SPNs with ambiguous probability grades, showcasing its utility in diagnostic grey zones. The best human reader reached an accuracy of 80% in the grey zone, whilst ML exhibited 89%. The findings underline the collaborative potential of ML and human expertise in enhancing SPN characterization accuracy and confidence, especially in cases where diagnostic certainty is elusive. This study contributes to understanding how integrating ML and human judgement can optimize SPN diagnostic outcomes, ultimately advancing clinical decision-making in PET/CT screenings. [ABSTRACT FROM AUTHOR]

Purpose: To investigate clinical and radiological differences between kidney metastases to the lung (RCCM +) and metachronous lung cancer (LC) detected during follow-up in patients surgically treated for Renal Cell Carcinoma (RCC). Methods: cM0 surgically-treated RCC who harbored a pulmonary mass during follow-up were retrospectively scrutinized. Univariate logistic regression assessed predictive features for differentiating between LC and RCCM +. Multivariable analyses (MVA) were fitted to predict factors that could influence time between detection and histological diagnosis of the pulmonary mass, and how this interval could impact on survivals. Results: 87% had RCCM + and 13% had LC. LC were more likely to have smoking history (75% vs. 29%, p < 0.001) and less aggressive RCC features (cT1-2: 94% vs. 65%, p = 0.01; pT1-2: 88% vs. 41%, p = 0.02; G1-2: 88% vs. 37%, p < 0.001). The median interval between RCC surgery and lung mass detection was longer between LC (55 months [32.8–107.2] vs. 20 months [9.0–45.0], p = 0.01). RCCM + had a higher likelihood of multiple (3[1–4] vs. 1[1–1], p < 0.001) and bilateral (51% vs. 6%, p = 0.002) pulmonary nodules, whereas LC usually presented with a solitary pulmonary nodule, less than 20 mm. Univariate analyses revealed that smoking history (OR:0.79; 95% CI 0.70–0.89; p < 0.001) and interval between RCC surgery and lung mass detection (OR:0.99; 95% CI 0.97–1.00; p = 0.002) predicted a higher risk of LC. Conversely, size (OR:1.02; 95% CI 1.01–1.04; p = 0.003), clinical stage (OR:1.14; 95% CI 1.06–1.23; p < 0.001), pathological stage (OR:1.14; 95% CI 1.07–1.22; p < 0.001), grade (OR:1.15; 95% CI 1.07–1.23; p < 0.001), presence of necrosis (OR:1.17; 95% CI 1.04–1.32; p = 0.01), and lymphovascular invasion (OR:1.18; 95% CI 1.01–1.37; p = 0.03) of primary RCC predicted a higher risk of RCCM +. Furthermore, number (OR:1.08; 95% CI 1.04–1.12; p < 0.001) and bilaterality (OR:1.23; 95% CI 1.09–1.38; p < 0.001) of pulmonary lesions predicted a higher risk of RCCM +. Survival analysis showed a median second PFS of 10.9 years (95% CI 3.3-not reached) for LC and a 3.8 years (95% CI 3.2–8.4) for RCCM +. The median OS time was 6.5 years (95% CI 4.4-not reached) for LC and 6 years (95% CI 4.3–11.6) for RCCM +. Conclusions: Smoking history, primary grade and stage of RCC, interval between RCC surgery and lung mass detection, and number of pulmonary lesions appear to be the most valuable predictors for differentiating new primary lung cancer from RCC progression. [ABSTRACT FROM AUTHOR]

Background: Although electromagnetic navigation bronchoscopy (ENB) is highly sensitive in the diagnosis of peripheral pulmonary nodules (PPNs), its diagnostic yield for subgroups of smaller PPNs is under evaluation. Objectives: Diagnostic yield evaluation of biopsy using ENB for PPNs <2 cm. Design: The diagnostic yield, sensitivity, specificity, positive predictive value, and negative predictive value of the ENB-mediated biopsy for PPNs were evaluated. Methods: Patients who had PPNs with diameters <2 cm and underwent ENB-mediated biopsy between May 2015 and February 2020 were consecutively enrolled. The final diagnosis was made via pathological examination after surgery. Results: A total of 82 lesions from 65 patients were analyzed. The median tumor size was 11 mm. All lesions were subjected to ENB-mediated biopsy, of which 29 and 53 were classified as malignant and benign, respectively. Subsequent segmentectomy, lobectomy, or wedge resection, following pathological examinations were performed on 64 nodules from 57 patients. The overall sensitivity, specificity, positive predictive value, and negative predictive value for nodules <2 cm were 53.3%, 91.7%, 92.3%, and 51.2%, respectively. The receiver operating curve showed an area under the curve of 0.721 (p < 0.001). Additionally, the sensitivity, specificity, positive predictive value, and negative predictive value were 62.5%, 100%, 100%, and 42.9%, respectively, for nodules with diameters equal to or larger than 1 cm; and 30.8%, 86.7%, 66.7%, and 59.1%, respectively, for nodules less than 1 cm. In the subgroup analysis, neither the lobar location nor the distance of the PPNs to the pleura affected the accuracy of the ENB diagnosis. However, the spiculated sign had a negative impact on the accuracy of the ENB biopsy (p = 0.010). Conclusion: ENB has good specificity and positive predictive value for diagnosing PPNs <2 cm; however, the spiculated sign may negatively affect ENB diagnostic accuracy. In addition, the diagnostic reliability may only be limited to PPNs equal to or larger than 1 cm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

OBJECTIVES Robotic-assisted thoracoscopic surgery (RATS) facilitates complex pulmonary segmentectomy which offers one-stage diagnostic and therapeutic management of small pulmonary nodules. We aimed to explore the potential advantages of a faster, simplified pathway and earlier diagnosis against the disadvantages of unnecessary morbidity in benign cases. METHODS In an observational study, patients with small, solitary pulmonary nodules deemed suspicious of malignancy by a multidisciplinary team were offered surgery without a pre or intraoperative biopsy. We report our initial experience with RATS complex segmentectomy (using >1 parenchymal staple line) to preserve as much functioning lung tissue as possible. RESULTS Over a 4-year period, 245 RATS complex segmentectomies were performed; 140 right: 105 left. A median of 2 (1–4) segments was removed. There was no in-hospital mortality and no requirement for postoperative ventilation. Complications were reported in 63 (25.7%) cases, of which 36 (57.1%) were hospital-acquired pneumonia. A malignant diagnosis was found in 198 (81%) patients and a benign diagnosis in 47 (19%). The malignant diagnoses included: adenocarcinoma in 136, squamous carcinoma in 31 and carcinoid tumour in 15. The most frequent benign diagnosis was granulomatous inflammation in 18 cases. CONCLUSIONS RATS complex segmentectomy offers a precise, safe and effective one-stop therapeutic biopsy in incidental and screen-detected pulmonary nodules. [ABSTRACT FROM AUTHOR]

Introduction: The use of cryobiopsy in conjunction with robotic assisted bronchoscopy is on the rise due to the safety and increased diagnostic yield of cryobiopsy. The incorporation of 3D fluoroscopy in the procedure improves the workflow and helps confirm the accuracy of sampling of peripheral pulmonary nodules. Methods: We describe an observational series of 12 patients comprising 14 nodules where cryobiopsy was performed during shape-sensing robot-assisted bronchoscopy cryobiopsy under general anesthesia. 3D fluoroscopy was used to confirm accurate placement of the cryoprobe. All these patients underwent a second spin with the 3D fluoroscopy either to sample a second lesion intraoperatively or to investigate suspected pneumothorax. Results: The development of a pneumatocele was noted after cryobiopsy in each of the cases. The majority of these were in the upper lobe with the median size of a sampled nodule being 14 mm. The majority of patients were asymptomatic with 1 patient developing mild hemoptysis and 4 patients developing chest tightness or dyspnea. None of the patients required an intervention for the pneumatocele. Conclusion: The development of pneumatoceles appears to be a fairly frequent and benign occurrence following cryobiopsy, likely due to increased tissue destruction. The increased use of intraoperative 3D fluoroscopy is likely to highlight changes to the pulmonary parenchyma that were previously not known. The occurrence of pneumatoceles does not appear to adversely impact the safety or tolerability profile of cryobiopsy. [ABSTRACT FROM AUTHOR]

Introduction: Lung cancer remains the leading cause of cancer death worldwide. Subsolid nodules (SSN), including ground-glass nodules (GGNs) and part-solid nodules (PSNs), are slow-growing but have a higher risk for malignancy. Therefore, timely diagnosis is imperative. Shape-sensing robotic-assisted bronchoscopy (ssRAB) has emerged as reliable diagnostic procedure, but data on SSN and how ssRAB compares to other diagnostic interventions such as CT-guided transthoracic biopsy (CTTB) are scarce. In this study, we compared diagnostic yield of ssRAB versus CTTB for evaluating SSN. Methods: A retrospective study of consecutive patients who underwent either ssRAB or CTTB for evaluating GGN and PSN with a solid component less than 6 mm from February 2020 to April 2023 at Mayo Clinic Florida and Rochester. Clinicodemographic information, nodule characteristics, diagnostic yield, and complications were compared between ssRAB and CTTB. Results: A total of 66 nodules from 65 patients were evaluated: 37 PSN and 29 GGN. Median size of PSN solid component was 5 mm (IQR: 4.5, 6). Patients were divided into two groups: 27 in the ssRAB group and 38 in the CTTB group. Diagnostic yield was 85.7% for ssRAB and 89.5% for CTTB (p = 0.646). Sensitivity for malignancy was similar between ssRAB and CTTB (86.4% vs. 88.5%; p = 0.828), with no statistical difference. Complications were more frequent in CTTB with no significant difference (8 vs. 2; p = 0.135). Conclusion: Diagnostic yield for SSN was similarly high for ssRAB and CTTB, with ssRAB presenting less complications and allowing mediastinal staging within the same procedure. [ABSTRACT FROM AUTHOR]

Background: Routine concordance evaluation between pathology and imaging findings was introduced for CT-guided biopsies. Purpose: To analyze malignancy rate in concordant, discordant, and indeterminate non-malignant results of CT-guided lung biopsies. Methods: Concordance between pathology results and imaging findings of consecutive patients undergoing CT-guided lung biopsy between 7/1/2016 and 9/30/2021 was assessed during routine meetings by procedural radiologists. Concordant was defined as pathology consistent with imaging findings; discordant was used when pathology could not explain imaging findings; indeterminate when pathology could explain imaging findings but there was concern for malignancy. Recommendations for discordant and indeterminate were provided. All the malignant results were concordant. Pathology of repeated biopsy, surgical sample, or follow-up was considered reference standard. Results: Consecutive 828 CT-guided lung biopsies were performed on 795 patients (median age 70 years, IQR 61–77), 423/828 (51%) women. On pathology, 224/828 (27%) were non-malignant. Among the non-malignant, radiology-pathology concordance determined 138/224 (62%) to be concordant with imaging findings, 54/224 (24%) discordant, and 32/224 (14%) indeterminate. When compared to the reference standard, 33/54 (61%) discordant results, 6/30 (20%) indeterminate, and 3/133 (2%) concordant were malignant. The prevalence of malignancy in the three groups was significantly different (p < 0.001). Time to diagnosis was significantly different between patients who reached the diagnosis with imaging follow-up (median 114 days, IQR 69–206) compared to repeat biopsy (33 days, IQR 18–133) (p = 0.01). Conclusion: Routine radiology-pathology concordance evaluation of CT-guided lung biopsy correctly identifies patients at high risk for missed diagnosis of malignancy. Repeat biopsy is the fastest method to reach diagnosis. Clinical relevance statement: A routine radiology-pathology concordance assessment identifies patients with non-malignant CT-guided lung biopsy result who are at greater risk of missed diagnosis of malignancy. Key Points: • A routine radiology-pathology concordance evaluation of CT-guided lung biopsies classified 224 non-malignant results as concordant, discordant, or indeterminate. • The percentage of malignancy on follow-up was significantly different in concordant (2%), discordant (61%), and indeterminate (20%) (p < 0.001). • Time to definitive diagnosis was significantly shorter with repeat biopsy (33 days), compared to imaging follow-up (114 days), p = 0.01. [ABSTRACT FROM AUTHOR]

Objective: Mistake in diagnosis Background: Marginal zone lymphoma is a low-grade, B-cell, non-Hodgkin lymphoma. Bone marrow involvement (BMI) of leukemia or lymphoma can usually be displayed in fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) with high standardized uptake values (SUV), while diffuse homogeneous 18F-FDG bone marrow uptake (BMU) in PET/CT primarily reflects hyperplastic bone marrow status. This report is of a 64-year-old man presenting with anemia and a diagnosis of recurrent marginal zone lymphoma with bone marrow involvement identified with 18F-FDG PET/CT imaging and biopsy. Case Report: A 64-year-old man with severe anemia and body weight loss of 7 kg in 1 month was diagnosed with marginal zone lymphoma, stage III, in July 2011. He went into complete remission in April 2012 after 6 cycles of chemotherapy, with Hb restored. Anemia and diffuse homogeneous 18F-FDG BMU in PET/CT were then noted during a routine check-up in October 2021, and recurrent disease was established through positive biopsy of subcutaneous nodules and bone marrow. Subsequent complete remission after 6 cycles of combination therapy was validated with pathologically negative BMI, the resolution of the slightly enhanced 18F-FDG BMU in PET/CT, and restored hemoglobin. Conclusions: This report has highlighted the importance of follow-up for patients with lymphoma and supports the diagnostic role of 18F-FDG PET/CT imaging and the pathological verification in identifying malignant involvement in bone marrow. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to compare breast cancer patients with pulmonary oligometastases (POM) and primary lung cancer (PLC) and to assess whether there were differences in clinical features, CT features, and survival outcomes between the two groups. Methods: From January 2010 to December 2021, the clinical records of 437 with malignant pulmonary nodules who had breast cancer patients were reviewed. POM was identified in 45 patients and PLC in 43 patients after the initial detection of pulmonary nodules. The clinicopathological characteristics, CT appearance of pulmonary nodules, and survival of the two groups were compared. Results: Stage II to IV breast tumors (p < 0.001), high pathological grade of breast cancer (p = 0.001), low proportion of luminal‐type breast cancer (p = 0.003), and the higher serum CYFRA 21‐1 level (p = 0.046) were the clinical characteristics of pulmonary nodules suggestive of POM rather than PLC. The CT features of lung nodules indicative of PLC rather than POM were the subsolid component (p < 0.001), lobulation (p = 0.010), air bronchogram (p < 0.001) and pleural indentation (p = 0.004). Ten‐year survival rate for PLC was 93.2%, which was higher compared with 57.8% in those with POM (p = 0.001). Conclusions: Elevated serum CYFRA 21‐1 levels and late‐stage breast cancer may be beneficial for the diagnosis of POM. CT imaging appearances of the subsolid component, lobulation, air bronchogram, and pleural indentation increase the likelihood of PLC. Breast cancer patients with PLC presented better survival with attentive monitoring than those with POM. [ABSTRACT FROM AUTHOR]