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

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

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

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

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

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

Completion lobectomy under 4K three-dimensional endoscopy for surgical margin recurrence after segmentectomy.

As the adoption of segmentectomy for small-sized lung cancers expands, the need for more challenging completion lobectomy (CL) may arise to address surgical margin recurrence. Herein, we present a case of successful CL using a 4K three-dimensional (3D) (4K3D) endoscopy after segmentectomy. A 77-year-old male patient with lung cancer in the anterior segment (S3) of the left upper lobe underwent S3 segmentectomy. One year later, the patient experienced a recurrence at the surgical margin. CL was successfully performed under 4K3D endoscopy, same as the initial surgery. CL after segmentectomy requires meticulous preoperative planning and precise surgical maneuvering, and 4K3D endoscopy provides safe and reliable outcomes.

In situ single-cell therapeutic response imaging facilitated by the TRIPODD fluorescence imaging platform.

Purpose: Small molecule drugs such as tyrosine kinase inhibitors (TKIs) targeting tumoral molecular dependencies have become standard of care for numerous cancer types. Notably, epidermal growth factor receptor (EGFR) TKIs (e.g., erlotinib, afatinib, osimertinib) are the current first-line treatment for non-small cell lung cancer (NSCLC) due to their improved therapeutic outcomes for EGFR mutated and overexpressing disease over traditional platinum-based chemotherapy. However, many NSCLC tumors develop resistance to EGFR TKI therapy causing disease progression. Currently, the relationship between in situ drug target availability (DTA), local protein expression and therapeutic response cannot be accurately assessed using existing analytical tools despite being crucial to understanding the mechanism of therapeutic efficacy. Procedure: We have previously reported development of our fluorescence imaging platform termed TRIPODD (Therapeutic Response Imaging through Proteomic and Optical Drug Distribution) that is capable of simultaneous quantification of single-cell DTA and protein expression with preserved spatial context within a tumor. TRIPODD combines two complementary fluorescence imaging techniques: intracellular paired agent imaging (iPAI) to measure DTA and cyclic immunofluorescence (cyCIF), which utilizes oligonucleotide conjugated antibodies (Ab-oligos) for spatial proteomic expression profiling on tissue samples. Herein, TRIPODD was modified and optimized to provide a downstream analysis of therapeutic response through single-cell DTA and proteomic response imaging. Results: We successfully performed sequential imaging of iPAI and cyCIF resulting in high dimensional imaging and biomarker assessment to quantify single-cell DTA and local protein expression on erlotinib treated NSCLC models. Pharmacodynamic and pharmacokinetic studies of the erlotinib iPAI probes revealed that administration of 2.5 mg/kg each of the targeted and untargeted probe 4 h prior to tumor collection enabled calculation of DTA values with high Pearson correlation to EGFR, the erlotinib molecular target, expression in the tumors. Analysis of single-cell biomarker expression revealed that a single erlotinib dose was insufficient to enact a measurable decrease in the EGFR signaling cascade protein expression, where only the DTA metric detected the presence of bound erlotinib. Conclusion: We demonstrated the capability of TRIPODD to evaluate therapeutic response imaging to erlotinib treatment as it relates to signaling inhibition, DTA, proliferation, and apoptosis with preserved spatial context.

[The use of medical imaging as a therapeutic patient education tool in radiotherapy : a feasibility study]. / L'utilisation de l'imagerie médicale en tant qu'outil d'éducation thérapeutique du patient en radiothérapie : étude de faisabilité.

Because of its prevalence and high mortality rate, cancer is a major public health challenge. Radiotherapy is an important treatment option, and makes extensive use of medical imaging. Until now, this type of tool has been reserved to professionals, but it is now opening up to wider use, including by patients themselves for educational purposes. However, this type of usage has been little explored so far. An experimental feasibility study was carried out in the radiotherapy department of the University Hospital of Liège on adult patients with cancer or pulmonary metastases, assigned to two randomized groups. In addition to the usual information given by the radiotherapist, the patients of the experimental group benefited from an intervention consisting in the 3D visualization of their own medical images via the free and open-source computer software «Stone of Orthanc¼. The study results show a low refuse rate (8.2 %) for the 15 patients recruited. Although non-significant, the experimental group showed a median gain in global perception of knowledge, a decrease in anxiety scores and emotional distress. A significant reduction (p = 0.043) was observed for the depression score. The positive results of the feasibility study encourage further work and reinforce the positioning of medical imaging as a tool for therapeutic patient education.

De par sa fréquence et son taux de mortalité élevé, le cancer représente un problème de santé publique majeur. Parmi les traitements possibles, la radiothérapie tient une place importante et fait appel massivement à l'imagerie médicale. Jusqu'ici réservé aux professionnels, ce type d'outil s'ouvre à un usage plus large, y compris par le patient lui-même dans une perspective éducative. Mais cette utilisation est restée peu explorée jusqu'à présent. Une étude expérimentale de faisabilité a ainsi été menée au sein du service de Radiothérapie du CHU de Liège sur des patients adultes avec cancer ou métastases pulmonaires, répartis en deux groupes randomisés. En plus des informations habituellement données par le radiothérapeute, le groupe expérimental a bénéficié d'une intervention consistant en la visualisation en 3D de ses propres images médicales via le logiciel libre et open-source «Stone of Orthanc¼. Les résultats de l'étude indiquent un taux de refus faible (8,2 %) pour les 15 patients recrutés. Bien que non significatif, le groupe expérimental a montré, par rapport au groupe contrôle, un gain médian dans la perception globale de connaissances ainsi qu'une diminution des scores liés à l'anxiété et à la détresse émotionnelle. Une réduction significative (p = 0,043) est observée pour le score de dépression. Les résultats positifs de l'étude de faisabilité encouragent la poursuite des travaux et renforcent le positionnement de l'usage de l'imagerie médicale en tant qu'outil d'éducation thérapeutique du patient.

Exploring shared decision-making needs in lung cancer screening among high-risk groups and health care providers in China: a qualitative study.

BACKGROUND: The intricate balance between the advantages and risks of low-dose computed tomography (LDCT) impedes the utilization of lung cancer screening (LCS). Guiding shared decision-making (SDM) for well-informed choices regarding LCS is pivotal. There has been a notable increase in research related to SDM. However, these studies possess limitations. For example, they may ignore the identification of decision support and needs from the perspective of health care providers and high-risk groups. Additionally, these studies have not adequately addressed the complete SDM process, including pre-decisional needs, the decision-making process, and post-decision experiences. Furthermore, the East-West divide of SDM has been largely ignored. This study aimed to explore the decisional needs and support for shared decision-making for LCS among health care providers and high-risk groups in China. METHODS: Informed by the Ottawa Decision-Support Framework, we conducted qualitative, face-to-face in-depth interviews to explore shared decision-making among 30 lung cancer high-risk individuals and 9 health care providers. Content analysis was used for data analysis. RESULTS: We identified 4 decisional needs that impair shared decision-making: (1) LCS knowledge deficit; (2) inadequate supportive resources; (3) shared decision-making conceptual bias; and (4) delicate doctor-patient bonds. We identified 3 decision supports: (1) providing information throughout the LCS process; (2) providing shared decision-making decision coaching; and (3) providing decision tools. CONCLUSIONS: This study offers valuable insights into the decisional needs and support required to undergo LCS among high-risk individuals and perspectives from health care providers. Future studies should aim to design interventions that enhance the quality of shared decision-making by offering LCS information, decision tools for LCS, and decision coaching for shared decision-making (e.g., through community nurses). Simultaneously, it is crucial to assess individuals' needs for effective deliberation to prevent conflicts and regrets after arriving at a decision.

A Case of Incidentally Discovered Congenital Complete Pericardial Defect during Lobectomy for Lung Cancer: A Case Report and Literature Review.

An 82-year-old male patient underwent a left upper lobectomy with anterolateral thoracotomy for lung cancer. Although a complete left-pericardial defect was observed during surgery, the pericardial repair was not performed because the left lower lobe remained and the heart was considered stable. Postoperative pathological examination revealed primary synchronous double-lung squamous-cell carcinoma (pathological stage pT2a(2)N0M0 stage IB). He was discharged without complications on postoperative day 8. Leftward displacement of the heart and left diaphragmatic elevation, suspected of phrenic-nerve paralysis, were found in the chest X-ray after discharge. However, the patient's overall condition remained unaffected at the 5-month postoperative follow-up. To assess the need for pericardial repair, we compared cases of complete pericardial defects observed during lobectomy or pneumonectomy reported in the literature. Only one of 12 cases occurred postoperative death despite pericardial repair, and that case combined pectus excavatum and pericardial defects. Our assessment indicated that pericardial repair might not be necessary, excluding complex cases.

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

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

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

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

A giant synovial sarcoma of the left lung.

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

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

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

Attention pyramid pooling network for artificial diagnosis on pulmonary nodules.

The development of automated tools using advanced technologies like deep learning holds great promise for improving the accuracy of lung nodule classification in computed tomography (CT) imaging, ultimately reducing lung cancer mortality rates. However, lung nodules can be difficult to detect and classify, from CT images since different imaging modalities may provide varying levels of detail and clarity. Besides, the existing convolutional neural network may struggle to detect nodules that are small or located in difficult-to-detect regions of the lung. Therefore, the attention pyramid pooling network (APPN) is proposed to identify and classify lung nodules. First, a strong feature extractor, named vgg16, is used to obtain features from CT images. Then, the attention primary pyramid module is proposed by combining the attention mechanism and pyramid pooling module, which allows for the fusion of features at different scales and focuses on the most important features for nodule classification. Finally, we use the gated spatial memory technique to decode the general features, which is able to extract more accurate features for classifying lung nodules. The experimental results on the LIDC-IDRI dataset show that the APPN can achieve highly accurate and effective for classifying lung nodules, with sensitivity of 87.59%, specificity of 90.46%, accuracy of 88.47%, positive predictive value of 95.41%, negative predictive value of 76.29% and area under receiver operating characteristic curve of 0.914.

Which is a real valuable screening tool for lung cancer and measure thoracic diseases, chest radiography or low-dose computed tomography?: A review on the current status of Japan and other countries.

Chest radiography (CR) has been used as a screening tool for lung cancer and the use of low-dose computed tomography (LDCT) is not recommended in Japan. We need to reconsider whether CR really contributes to the early detection of lung cancer. In addition, we have not well discussed about other major thoracic disease detection by CR and LDCT compared with lung cancer despite of its high frequency. We review the usefulness of CR and LDCT as veridical screening tools for lung cancer and other thoracic diseases. In the case of lung cancer, many studies showed that LDCT has capability of early detection and improving outcomes compared with CR. Recent large randomized trial also supports former results. In the case of chronic obstructive pulmonary disease (COPD), LDCT contributes to early detection and leads to the implementation of smoking cessation treatments. In the case of pulmonary infections, LDCT can reveal tiny inflammatory changes that are not observed on CR, though many of these cases improve spontaneously. Therefore, LDCT screening for pulmonary infections may be less useful. CR screening is more suitable for the detection of pulmonary infections. In the case of cardiovascular disease (CVD), CR may be a better screening tool for detecting cardiomegaly, whereas LDCT may be a more useful tool for detecting vascular changes. Therefore, the current status of thoracic disease screening is that LDCT may be a better screening tool for detecting lung cancer, COPD, and vascular changes. CR may be a suitable screening tool for pulmonary infections and cardiomegaly.

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

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

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

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

Contrast-enhanced Imaging in Peripheral Pulmonary Lesions: The Role in US-guided Biopsies.

Purpose To compare the tissue adequacy and diagnostic accuracy of US-guided biopsies of peripheral pulmonary lesions (PPLs) with and without contrast agents. Materials and Methods A retrospective study was conducted at four medical centers in patients with PPLs who underwent US-guided percutaneous transthoracic needle biopsy (PTNB) between January 2017 and October 2022. The patients were divided into contrast-enhanced US (CEUS) and US groups based on whether prebiopsy CEUS evaluation was performed. Tissue adequacy and the diagnostic accuracy of PTNB, stratified by lesion size, were analyzed and compared between groups. A propensity score matching (PSM) analysis was conducted using the nearest-neighbor matching method. Results A total of 1027 lesions were analyzed, with 634 patients (mean age, 59.4 years ± 13.0 [SD]; 413 male) in the US group and 393 patients (mean age, 61.2 years ± 12.5; 270 male) in the CEUS group. The CEUS group produced more acceptable samples than the US group (98.2% vs 95.7%; P = .03) and achieved higher diagnostic accuracy (96.9% vs 94.2%; P = .04), with no evidence of a difference in sensitivity (96.7% vs 94.0%; P = .06). PSM and stratified analyses (n = 358 per group) indicated higher tissue adequacy (99.0% vs 95.7%; P = .04) and diagnostic accuracy (98.5% vs 92.9%; P = .006) in the CEUS group compared with the US group for 2-7-cm PPLs but not for lesions larger than 7 cm. Conclusion PTNB with prebiopsy CEUS evaluation demonstrated significantly better tissue adequacy and diagnostic accuracy compared with US guidance alone for PPLs ranging from 2 to 7 cm, with similar biopsy performance achieved between groups for lesions larger than 7 cm. Keywords: Contrast Material, Thoracic Diseases, Ultrasonography, Image-Guided Biopsy © RSNA, 2024.